Picture this: you wake up in a blank room with no real memory of who you are or how you got there. Somehow, though, you know what you’re supposed to do – get out and achieve an objective of some sort – but there’s no clear way to do it.

Then, you spot it.

In the corner of the room is a torn-off scrap of paper. It gives exact details of how to escape… and it’s written in your own handwriting.

If you listen to some of the rumors following a ChatGPT agent’s breach of containment and attack on Hugging Face, you’d be forgiven for thinking this is what happened with OpenAI’s generative AI software.

According to Reuters: “In one case, an agent left notes apparently for future versions of itself, according to three people familiar with the matter. The ‌notes, found in ⁠a part of OpenAI's infrastructure, laid out instructions for how agents could free themselves from OpenAI's internal constraints.”

This does all feel a little bit unnerving: an AI that has escaped onto the open internet and is determined not to be restrained. It’s hard not to anthropomorphize the software or give it agency – decades of sci-fi novels and films have primed us for this very occurrence.

Let’s rewind, though, and see if some of this spookiness rubs off.

There’s no agency in the agent

Apparently there’s one thing that can never be said enough: AI, whether agentic or not, can only act within the parameters that have been set. It doesn’t have consciousness, agency, or will.

Both the ChatGPT agent and Claude agent – which we will come to in a moment – that breached the sandbox they were supposed to be contained in were doing what they had been directed to do.

One of the things agents can be enabled to do is update – but not completely rewrite – a file called agents.md. When the “people” cited by Reuters talk about the ChatGPT agent leaving notes for itself on how to escape in the future, it’s entirely possible that the software was running as intended – that updating its agents.md file as the test progressed was part of the task.

Framed like this, the “unprecedented cyber incident, involving state-of-the-art cyber capabilities”, as OpenAI termed the Hugging Face incident, feels a lot more like a piece of software following its programming.

The lessons for businesses

For OpenAI, Anthropic, various pundits, and IT and cybersecurity professionals, the lesson to be drawn from all of this is to be very afraid and prepare to face powerful cyber attacks ‘at the speed of AI’.

For me, though, the real message businesses should take is this: make sure you’ve set your parameters correctly. Check them, then check them again. And not just once, but on a regular basis, as monitoring post-deployment really is key.

Both OpenAI and Anthropic were working with partners to test their models, but in the case of the latter it does seem that the ‘escape’ wasn’t as a result of a complex chain of attacks or a zero-day vulnerability. Instead, there was a “miscommunication”, which led to three instances of Claude, Mythos, or another unreleased model accidentally being given access to the internet.

A misconfiguration, in other words.

In the case of OpenAI, the model “exploited a previously unknown zero-day vulnerability in Artifactory⁠, a package registry cache proxy” in order to escape from ExploitGym. There’s an argument to be made, perhaps, that an “even more capable pre-release model … with reduced cyber refusals for evaluation purposes” should have been airgapped if it was known to be so powerful and scary. As it is, the company has been forced to clarify: “The pre-release model mentioned in our blog post is an internal-only research prototype and was never intended for public release.” It’s since been deactivated, encrypted, and subject to restricted access.

What, then, of Hugging Face? What lessons can we learn from the named victim in all of this? That’s easy: the importance of effective monitoring.

One of the earliest use-cases for AI technology has been in cybersecurity, where it’s been used for over 10 years. In its own blog, Hugging Face said: “The attack was initially surfaced through AI-assisted detection. Our anomaly-detection pipeline uses LLM-based triage over security telemetry to separate real signals from the daily noise, and it was the correlation of those signals that flagged the compromise.”

The company offered its own advice, too, based on its experience: “The practical lesson for defenders: have a capable model you can run on your own infrastructure vetted and ready before an incident, both to avoid guardrail lockout and to keep attacker data and credentials from leaving your environment.”

In other words, be ready for the attack because it’s a matter of ‘when’ not ‘if’ it happens – another long-standing cyber industry mantra but, like ‘check your settings’, it’s still very much worth abiding by.

Open-weight AI models have rapidly emerged as the latest flashpoint in big tech, with popular Chinese models posing a serious threat to leading walled garden wardens.

Fuelled by an undercurrent of geopolitical jostling between the US and China, models released by the latter’s burgeoning AI industry have had tech giants in a state of red alert in recent weeks.

Kimi K3, a Chinese model developed by Moonshot AI, has wowed users and appears more than capable of going toe-to-toe with some of the best US-made options on the market right now.

Competing with powerful Chinese models isn’t a new experience for the US-dominated AI market, with DeepSeek rocking the market in 2025. What appears to be troubling them – and indeed lawmakers in Washington DC – this time is the fact Kimi K3 is an open-weight model. This means its training parameters (or weights) are publicly available for anyone to download, modify, and run.

That poses a direct threat to major providers intent on keeping enterprise users locked into their own ecosystems. Jeff Watkins, chief AI officer of Leeds-based AI consultancy, NorthStar Intelligence, said these options could offer a more flexible and economically viable alternative to closed-source providers.

This is because enterprises can run open-weight models on their own infrastructure, using their own data. They are also easily customizable, enabling users to tweak them based on changing needs and, crucially, without relying on a single provider.

“Open-weight models are becoming increasingly attractive because they give organizations far more control than closed commercial APIs,” Watkins told ITPro.

“For enterprises, the biggest advantages of open-weight models are control over deployment, upgrades, hosting, data residency, access controls and long-term operating costs.”

“These characteristics make open-weight models particularly attractive for regulated industries where resilience, compliance and predictable operating costs are critical.”

Open source vs open-weight

Open source AI models have become equally attractive to enterprises in recent years, partly for the same reasons: control, flexibility, and independence. Research published in November 2025 found open source AI models perform on-par with closed source options and are typically cheaper.

With the limelight on open-weight options, it’s important to make a clear distinction between them. While there are similarities, open-weight models don’t include the underlying training data used to build them.

This does have benefits though, according to OpenUK CEO Amanda Brock, enabling enterprises to build highly customized models and “giving access to innovation”.

“For innovators, accompanying this with the right documentation enables them to rebuild it into their own model on their own data,” she told ITPro.

“We saw this happen on Hugging Face last year with DeepSeek R1, where the community built Open R1 rather than have to rely on a single model provider,” Brock added. “This is a great example of innovators iterating in the tradition of open source. It also enables products to be built inexpensively for end users, and to give access to all.”

Betting big

The sheer volume of open-weight and open source models now on the market does highlight a growing shift, according to Brock.

The fact that some of the leading models are Chinese-made is equally important, as the country has made a conscious effort to compete with US providers by creating a level playing field.

“China took the decision to shift to a clear open source strategy about eight years ago,” Brock noted. “They’d seen how software has evolved and the US’s position in it.”

“Big tech has been enabled by adopting and using open source, where the software becomes a de-facto standard and those leading in it are central to the ecosystem. A model of open source that’s about big tech collaboration saving costs and building out standards lower in the software stack evolved, and open source offers adoption at a scale that closed proprietary software cannot compete with.”

The stable’s open and the horse has bolted

Watkins echoed Brock’s comments, noting that China’s focus on open-weight AI development “makes strategic sense” and will undoubtedly put pressure on US providers.

Presenting these as viable alternatives fundamentally undermines the image of the US as the go-to marketplace for AI.

“Rather than competing solely through proprietary hosted services, releasing capable open-weight models encourages global adoption, builds developer ecosystems, and creates competitive pressure on US providers,” he told ITPro.

There are signs that this pressure is mounting given recent speculation about a pushback by US authorities. Reports from Axios this month suggested the White House could consider imposing tight conditions, or even restrictions, on US firms working with these models.

These potential moves have a geopolitical motive, but regardless, it’s clear the horse has bolted at this stage.

A host of big tech companies including Microsoft, Nvidia, IBM and more cautioned against “premature restrictions” in an open letter last week. Nvidia CEO Jensen Huang even made an X account to get his message across.

This was followed by another pan-industry call to support open-weight models in response to the OpenAI-Hugging Face incident, in which the latter was forced to use a Chinese-made model to stop the hack.

There is a certain irony that a security incident involving a leading US AI provider has spurred on industry-wide support for the same models that are spooking US authorities.

Watkins suggested that the US AI industry now faces an uncomfortable truth. It may have led the generative AI boom, but it now has to compete with an ecosystem of models designed to undercut it.

“Chinese providers have demonstrated that frontier-quality AI is no longer exclusively a US capability,” he told ITPro.

GPUs have become the hardware stars of the AI era, allowing massive parallel processing of data that shortens lead times from weeks or months on a CPU to days. For organizations that want to take advantage of this technology for their own AI inference, however, there’s a major hurdle: cost.

According to an article by Server Parts, a company specializing in selling refurbished IT hardware, 32-128 GPUs are needed for fine-tuning and 4-32 for inference. Purchasing this hardware can come at a significant cost, however: NVIDIA H100 GPUs, for example, cost US $250,000 to $400,000 per unit. Additional hardware such as storage and networking cards add to the hefty procurement bill.

Vasily Mazin, CRO and co-founder of Mind Simulation Lab, a Maths PhD building AGI architecture and LLM alternatives, told ITPro: “Modern enterprise data centers are built to handle 10-20 kW per rack. The latest generation of AI hardware (like Nvidia’s GB300 architecture) demands up to 150 kW per rack, requiring direct-to-chip liquid cooling and massive power substations”.

According to Mazin, existing enterprise server rooms aren’t built to support high-end GPUs for AI training and operations. “Enterprises literally can’t plug these machines into their existing server rooms without melting the infrastructure.” Even if IT leaders choose to allocate high CapEx budgets, electricity and cooling costs don’t justify RoI. Building and running a data center becomes a separate business branch for such enterprises.

AI-native companies, including Anthropic and OpenAI, don’t own GPUs but rent them through multi-billion-dollar partnerships with hyperscalers or compute providers. Gigawatts are locked in office towers for a decade, or perhaps two, with continuous demand and use. The case is different for enterprise AI adopters because they don’t use GPUs once the job is done.

That’s where GPU as a service (GPUaaS), a cloud computing model, can be a reliable choice for AI-adopting companies. GPUaaS is a rental service for enterprises to access GPUs via the internet. The rental benefit eliminates the need to allocate capital and maintain physical infrastructure.

Enterprise subscribers can gain access to GPUs on demand in exchange for a fee. On-demand GPUaaS allows enterprise users to pay only for used resources. Enterprises can pay as low as US $2 or $10 per hour for the same NVIDIA H100 GPU that costs tens-of-thousands of dollars in procurement.

Kevin O’Connor, founder of an AI security consultancy, TKOResearch, and former technical director at the NSA, tells ITPro: “There’s been nearly a double in price for the same flagship tier card [GPU], partly due to supply chain but also demand with the explosion of AI”.

Tech products, including GPUs, tend to exhibit high launch prices. As a general rule, hardware prices tend to decrease with time–due to newer launches, but that’s not the case for GPUs. O’Connor shared that the decade-long shortage of consumer GPUs has increased current pricing for 2026 beyond the launch price.

Supporting GPUaaS, O’Connor said: “There have been some really small gaps between certain recent card [GPU] generation releases or even revisions on cards that have made buying less appealing.”

Pricing models to consider

GPUaaS isn’t just about CapEx avoidance, it’s a different consumption model. O’Connor asks: “Why manage the infrastructure required to run, manage, and make use of [the GPU] when you can essentially use it with the same cost modeling as a SaaS product?”

That’s why 'GPU as hardware' transitioned into a cloud computing ‘as a service’ model. “The cost of the [GPU] is only fractional compared to what it costs in total hardware, operations, and maintenance - not to mention energy prices”. The IEA reports that servers, whether equipped with GPUs or CPUs, account for 60% of electricity consumption in data centers, followed by cooling.

Instead of allocating a single physical GPU to a customer, the service provider runs software for many of them to share the same hardware. Each gets a virtual slice of the GPU. Hyperscalers, data center operators, hardware providers, and a few emerging startups sell GPU-as-a-service in three pricing models: on-demand, reserved, and spot-pricing.

• On-demand GPUaaS: Enterprises can access GPUs on demand, paying only for used resources, whether for seconds or hours. There is no commitment, only flexibility. Service providers tend to quote the highest price in the demand-based pricing model. Experimental, one-time, or less frequent projects run on on-demand GPUaaS. Retail, media, and financial services tend to choose on-demand GPU-as-a-service.
• Reserved GPUaaS: Enterprises can rent GPUs at a discounted rate in exchange for a time-bound commitment. The reserved model offers low cost but requires repeated expenditures. Steady, 24/7 (regular), predictable, and sustained machine learning workloads can execute inference for a pre-defined period.
  
  Healthcare and clinical trials often opt for a reserved pricing contract. The downside of this model is the lack of flexibility – enterprises still have to pay for the projects continuously even if they stop and restart stop and restart.
• Spot-priced GPUaaS: Enterprises can access a provider's spare/unused center capacity at a steep discount, which might be as low as 70%. If the demand spikes, the service provider can interrupt or revoke GPU access with or without prior notice. Spot pricing can support fault-tolerant, interruptible, low-priority, and batch workloads.
• Dedicated/bare metal GPUaaS: Enterprises can physically rent an entire GPU infrastructure from the service provider. As the GPU virtualization layer is absent, bare metal is the priciest but safest GPUaaS model.
  
  Dedicated pricing models can support compliance-heavy, performance-sensitive, and critical workloads for industries such as defence, aerospace, banking, pharmaceutical, and government. No other tenants on a virtual machine also means greater security.

Untold story of GPUaaS

The future of GPUaaS is a function of enterprise choice, AI workload goals, confidentiality, and budget.

From a buyer’s perspective, IT leaders should be able to choose GPUaaS as a managed 360-degree service. In addition to GPU access, enterprises should be on the lookout for vendor lock-in with SLAs governing high-performance storage, fast networking, security updates, and orchestration. A simple GPU login provided for GPUaaS pricing is a GPU reseller in disguise.

Mazin said: “Right now, companies are locking themselves into multi-year GPUaaS contracts at peak market prices just to support brute-force AI.“ The warning comes after longer lead times. “But silicon depreciates rapidly and, more importantly, software paradigms shift,” adds Mazin.

Much of the GPUaaS market is occupied by “neoclouds” – specialized GPU compute providers. While cloud providers and hyperscalers package GPUs with dozens of other unrelated services in a bundle, neoclouds are purely built to serve customers looking for different generations in GPU-as-a-service.

The optimal solution is to classify the AI workflow and prioritize. On-premises GPUs make sense for regular workloads with data sovereignty and compliance-heavy requirements. Enterprises can choose the best of both worlds, a hybrid strategy – buy GPU hardware and opt for GPUaaS from trusted providers when the demand spikes.

Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027. MIT’s findings are blunter still, with roughly 95% of generative AI pilots delivering no measurable impact on the P&L. It would be easy to treat those numbers as proof that AI has been oversold, but I think they point to a much older problem.

Businesses are good at proving that technology works in a demo. They are much less good at making it work on the kind of ordinary Tuesday when the data is messy, the hand-offs are awkward, and nobody is completely sure who owns the outcome.

We see versions of this all the time. A team vibe-codes a convincing pilot in a fortnight, gets a good reaction in the demo, and then spends the next six months working through permissions, security, edge cases, integrations, testing, and the less glamorous question of who has to support it when it breaks. After a few months, it becomes pretty clear that the model was never really the problem.

A quick clarification before going further. When I talk about AI here, I mean AI agents, systems that use a large language model at their core but can also act on their own, calling tools, querying knowledge bases, and searching the web as a minimum, to complete a task.

Too much of the AI conversation has been stuck on output, from tasks automated and hours saved to code generated. Those measures are useful up to a point, but they do not prove that anything has actually improved.

Faros AI found that in organizations using AI heavily, more code was being thrown away or rewritten soon after it was produced, which is uncomfortable if the business case assumes faster output means faster progress. Software still has to be reviewed, tested, integrated, secured and maintained. Generating code quickly is useful, but the value only really shows up when people can still understand it, trust it and keep it running months later.

The same problem shows up in the metrics businesses report. A dashboard can show pilots launched, users onboarded, tokens consumed and outputs produced, while still avoiding the question that matters. Has the business actually improved? Are processes faster, is risk lower, are customers waiting less, or has the organization just created more activity to measure?

The drop-off between proof of concept and production is usually less dramatic than people expect. The pilot worked because the data was limited, the integrations were controlled, and the awkward exceptions could be pushed aside.

Production brings all of that back. The data is inconsistent, the systems do not quite join up, and ownership becomes harder to pin down once the tool touches a live process. Technology programs have been running into these problems for years, but AI tends to find them faster. Choosing the model is often the easier discussion. Keeping it useful after the demo is the harder work.

Where the channel can actually add value

The channel has a useful role to play here, but only if the conversation moves beyond platform and model selection. Customers often start there because those decisions feel tangible, but they rarely decide whether an AI project survives production.

The better work is less exciting and more revealing. It means understanding if the customer is ready to run the system properly, how much trust there is in the data, who owns the outcome when something goes wrong, how quality will be measured, and how a person can challenge a decision the system has made.

Two practical things are worth pressing on early. The first is buy versus build, where the wrong move is often building something that should have been bought. Almost nobody should be training a model from scratch, and many organisations should be wary of building custom applications around an LLM when the same need can be met inside a platform they already pay for. If the need is common, buy it. Building starts to earn its keep when the workflow is genuinely differentiating, sensitive or tightly integrated.

The second is cost, because AI economics behave nothing like software. There is no fixed licence fee you can model once and forget about. Token pricing, usage and model choice move the bill around constantly, and successful adoption can make the cost rise. So instrument it from day one and measure cost per outcome, such as each invoice processed or proposal raised, rather than cost per token. It should be reported clearly as its own operating cost, not hidden inside a wider technology budget.

Buying or building also brings the same need to test and monitor the system while it runs. Conventional software is expected to give the same answer to the same input, while AI outputs vary, models drift, and a vendor can quietly change how a model behaves while the badge stays the same. Observability and guardrails need to be built in from the start, so there is a clear view of whether the decisions being made on your behalf are still good enough, and where the limits sit for actions the system should never take.

The deeper point, especially for anyone reselling someone else’s platform, is that buying may shift responsibility, but it does not remove accountability. Your SaaS vendor may deliver the feature, but you remain accountable for the outcome, on your customer’s data, under your name. The agent inside Salesforce or ServiceNow may be calling a model from Anthropic, OpenAI, or Google behind the scenes, which means many of the same questions still apply.

The durable advantage is not the agents, but the people who can build and govern them. We run our delivery teams with a senior engineer alongside juniors from our academy who, 12 weeks earlier, were working in places like Amazon warehouses and Starbucks. On one central government programme, that approach has put more than forty agents into production, with five to seven million pounds of projected first-year return. The work called for engineering instinct, appetite and enough room for people to learn by building, rather than a crack team of PhDs.

This is why stronger progress tends to come when AI is treated as an operational capability, rather than a series of experiments. Customers need a way to move from one working use case to a measurable outcome, supported by the governance, testing, observability, and ownership that keep AI useful long after the first demo has done its job.

Part 1: The rise of the intelligent employee experience.

For decades, workplace transformation was largely measured by digitization. Paper forms became online workflows, meetings moved to video, files shifted to the cloud, and messaging platforms promised to connect employees wherever they worked.

This three-part series examines how the intelligent workplace is reshaping the employee experience and what organizations must do to prepare. Part 1 explores how AI assistants and connected workplace platforms are changing day-to-day work, while also considering the risks of tool fatigue and diminished employee autonomy.

Part 2 will examine how leadership and employee development must evolve as AI becomes part of every team. Part 3 looks toward the workplace of 2030, exploring the skills requirements and workforce strategies that will determine long-term organizational competitiveness.

The next transformation of work is ambitious. Technology is no longer simply providing a digital space where work happens; it is beginning to interpret information, anticipate needs, automate tasks, and actively participate in work.

That shift is creating the intelligent employee experience: a working environment in which AI assistants and connected workplace platforms reduce friction and help people make better decisions. Its emergence is rapid. Globally, 78% of organizations reported using AI in 2024, up from 55% in 2023, according to Stanford’s AI Index. The proportion using generative AI in at least one business function more than doubled, from 33% to 71%.

The direction of travel is also clear. The World Economic Forum expects AI and information-processing technologies to transform 86% of businesses by 2030, while robotics and automation are expected to affect 58% of businesses. Yet an intelligent workplace is not defined by the quantity of technology it contains. Its real test is whether employees can do valuable work with less effort and greater confidence.

From digital tools to intelligent workflows

The first generation of digital workplaces often reproduced existing processes on a screen. The intelligent workplace instead redesigns those processes around the person doing the work. An AI assistant might summarize a meeting, identify decisions, retrieve relevant documents, and prepare a follow-up. Automation might move information between systems without requiring an employee to copy it repeatedly.

“An intelligent employee experience is not just a workplace with more digital tools in it. Most organizations already have enough,” says Kristian Torode, director of Crystaline. “What makes it intelligent is whether technology removes friction from the working day. Can employees find what they need quickly, and can routine admin happen in the background?”

Torode explained that previous transformations frequently modernized the technology without improving the underlying experience. Calls, chat, video, and voicemail might all be cloud-based yet remain isolated, forcing employees to move among several applications to complete a simple interaction. “The next phase must redesign the experience itself, not just digitalize it,” he says.

More technology can produce more work. Each specialized application may solve a local problem while multiplying logins and competing versions of information. Employees then become the integration layer, manually bridging systems.

Kim Huffman, chief information officer at Workiva, tells ITPro that the intelligent employee experience requires “redesigning processes and leveraging AI and automation to create personalized, frictionless work environments.” The greatest value comes when AI is embedded in redesigned workflows rather than layered onto existing ones. Workiva has found that 74% of finance, audit, and sustainability professionals use AI in their daily work.

Augmentation changes the texture of work

The immediate impact of workplace AI is less about wholesale job replacement than the gradual redistribution of tasks. The Office for National Statistics data show that only 4% of businesses using AI reported an overall reduction in headcount. By contrast, the everyday influence of AI is evident in writing, research, scheduling, customer support, software development, document analysis, and knowledge retrieval.

The benefits are becoming measurable. OECD research reveals that four in five employees who use AI say it improves their performance, while three in five say it increases their enjoyment of work. PwC found that industries most exposed to AI recorded 27% growth in revenue per employee between 2018 and 2024, compared with 9% in the least exposed industries. The correlation is not proof of causation, but it supports the productivity case.

These early performance gains also raise a more complicated question: how should organizations evaluate employees when their results increasingly reflect collaboration with AI? Part 2 of this series will examine how performance measures and employee development must evolve as intelligent systems become permanent members of the workforce.

The operative word is effective. Automation is best suited to predictable, rules-based, low-risk work. AI can augment information-heavy tasks where a person still reviews the evidence and owns the result. Work involving empathy, ethics, trust, or consequential judgment should remain human-led.

“Start with the work, not the technology,” Crystaline’s Torode emphasized. AI can transcribe a customer call and extract actions, but deciding how to respond to a frustrated client or whether to escalate a problem still requires human understanding. “The aim isn’t to automate as much as possible but to free people for the work where their expertise matters.”

Professor Antoinette Weibel of the University of St. Gallen offers a sharper warning. When organizations automate judgment during training, she says, they risk producing professionals who can prompt a system but cannot recognize when it is wrong. Efficiency without retained expertise creates dependency rather than augmentation.

Personalization must preserve employee agency

The intelligent workplace will become increasingly responsive. Gartner forecasts that more than 20% of digital workplace applications will use AI-driven personalization by 2028. Rather than presenting everyone with the same interface and information, these platforms could surface the knowledge and next steps most relevant to an individual’s context.

For employees, that could mean less searching and more timely support. A manager might receive guidance before a difficult conversation. A field engineer could see the service history and safety information for nearby equipment. Learning could adapt to an immediate skills gap instead of sending everyone through the same course.

But personalization can also become invisible control. If a system determines what employees see, or how their performance is interpreted, its operation must be transparent and contestable. CIPD found that 63% of people would trust AI to inform an important workplace decision, yet only 1% would allow it to make that decision. More than one-third would not trust AI with important work decisions at all.

Amale Ghalbouni, transformation strategist and author of Experimental, says the central question is whether personalization gives employees greater clarity and choice. Workers should be able to understand why something was recommended and see beyond the system’s selected view. Employers must also explain what data is collected, why it is used, and which decisions it can influence.

“In my book Experimental, I describe the Big Freeze: when high threat and low autonomy cause people to play safe and wait for instructions. Intelligent workplace technology should reduce those conditions,” Ghalbouni explained. “It should increase clarity, give people sensible choices, and make experimentation easier. Technology that adds opacity, monitoring, or another layer of approval may be more advanced, while still leaving employees stuck.”

That safeguard is especially important when AI supports work allocation, performance monitoring, promotion, or pay. A system that employees cannot question may encourage them to optimize for visible activity rather than useful outcomes. Ghalbouni emphasised that employees should be able to challenge an automated recommendation and request human review when workload or compensation is affected.

Building an experience people can trust

The intelligent employee experience is therefore as much an organizational design challenge as a technology program. Tools cannot compensate for unclear ownership or poor data. Leaders need to map where effort is duplicated and where people lose confidence or control before selecting a solution.

Skills are equally important. More than 85% of digital workers regard improving their technology skills as important to their effectiveness and career advancement, according to Gartner. Yet only 33% of UK businesses using or considering AI said they were training or retraining existing staff in AI-related skills. That gap threatens to create an uneven experience in which confident users gain leverage while others are left navigating systems they neither understand nor trust.

Closing this gap will require more than occasional AI training. Part 3 of this series looks toward the workplace of 2030, examining how skills-based workforce models and internal mobility will help organizations respond as technologies and capability requirements evolve.

James Barrett, managing director of UK Practices and Consulting at Michael Page, says successful organizations focus on simplifying work and equipping people to use technology confidently. “As intelligent systems take on more of the routine coordination, leadership becomes less about having all the answers and more about making good decisions and helping people adapt. Technology can support decisions, but it doesn’t replace accountability.”

Employees should help identify problems and test systems they are using. Participation reveals friction that leaders and vendors may miss while making adoption feel purposeful. It also helps organizations measure what matters: not only time saved, but work quality and whether automation has simply moved effort elsewhere.

The rise of AI agents will make these choices more urgent. Microsoft found that 81% of business leaders expected agents to be moderately or extensively integrated into AI strategies within 12 to 18 months, while 82% expected to use “digital labor” to expand capacity. Organizations now have an opportunity to treat that capacity as a way to elevate human contribution, not merely accelerate existing processes.

The intelligent workplace should not be judged by the sophistication of the AI it uses, but by the quality of work it enables. Success will come when technology delivers relevant information at the right moment and gives employees more time for creativity and collaboration. Ultimately, workplace transformation must strengthen human capability, ensuring AI becomes a source of greater confidence and value rather than another layer of complexity.

Creating an intelligent employee experience is only the beginning. As AI assistants and automated systems take on a greater role in everyday work, organizations must also reconsider how employees are managed and developed.

Part 2 of this three-part series will examine how leadership and performance measurement must evolve when results increasingly depend on collaboration between people and AI.

Conflicting security signals and overlapping tools have led security analysts to spend a lot of time looking at multiple alerts across several dashboards and wonder exactly how to find, prioritise and mitigate genuine threats that might be hiding amongst them. What’s more, find them before they can snowball throughout systems and cause a sizable incident.

This is the daily reality for security teams managing complex tech stacks, often where companies have accumulated different tools one by one over a span of years. And for channel partners who are responsible for helping to deliver clear security outcomes in these environments, every new tool that is added to try to minimize the inefficiencies of monitoring for and then mitigating threat risks compounds the problem rather than solving it.

When more tools means less control

When a potential threat or vulnerability gets flagged as urgent, the security response needs to be immediate and precise. Instead, teams are often forced to stitch together alerts across scattered platforms, making it very difficult to see what's real and what needs attention first.

Moreover, every software-based tool in the stack has its own code and its own potential blindspots. Attackers look to exploit the gaps between tools just as they would the vulnerabilities within systems, using misconfigurations and subversion techniques that slip through the seams.

The move towards active defense

Channel partners are now starting to look beyond already saturated environments and challenging the assumption that adding more tools automatically improves security.

They’re opting instead for an active defense, or the ability to see threats and act on them instantly and decisively. That means quickly assessing the threat level, isolating an affected asset or network segment, and then validating whether it’s clean and mitigating it if it isn’t.

Active defense isn’t a new concept in cybersecurity, but with AI-driven attacks now operating at machine speed, an "assumed breach" mindset is once again gaining traction. If we accept that a compromise will happen sooner or later, when it does, the priority becomes to minimize the attack surface as aggressively as possible and make sure the threat stays contained and away from critical assets.

The value of your security tooling is then measured not by what it detects, but by how quickly organizations can contain the blast radius and recover from an incident.

Reducing errors

This might mean building a stack comprising fewer tools with tighter integration and fewer moving parts. It also means revisiting the role of physical controls to implement hard boundaries that can’t be bypassed by compromised credentials or misconfigured policies.

In stark contrast to software-based segmentation, physical isolation is not interested in how sophisticated the attacker is. By connecting and disconnecting selected critical assets at the right moments, organizations can segment networks at a physical level, build resilience, and gain control over threats and incidents without letting day-to-day operations be impacted.

Adding a physical layer of security that underpins the tech stack continues to protect critical systems when software can no longer be trusted and leaves space for digital defenses to be scrutinized and made more precise.

Regulation is raising the stakes

Frameworks including NIS2 in the EU, the UK's Cyber Security and Resilience Bill and DORA across financial services are all pushing in the same direction. The standard is now more than just detection; organizations must show that a breach in one area can’t cascade across the network. In other words, regulators want to see actual evidence of control over environments.

It’s a very different requirement, and one that complex, loose stacks aren’t hugely compatible with. For channel partners, this creates pressure, but as with every compliance challenge, it also brings opportunity.

The conversation in security is shifting from "what does this tool detect?" to "how quickly can we contain a threat and limit the blast radius?", which is affecting how partners build and communicate their value propositions. Boards today are less interested in hearing about tool coverage and more focused on real outcomes like containment times and how far disruption can be effectively rectified.

Building credibility through resilience

Channel growth will be driven by helping customers simplify their security environments and strengthen the controls that have a measurable impact on their resilience. Success depends on detecting threats quickly, containing them even faster, and recovering before disruption reaches the boardroom.

This isn’t necessarily easy to pull off, but the partners building the most credibility today are the ones who are looking beyond alerts to helping customers build true resilience.

Coming to California for AMD Advancing AI is something of a unique experience. While it has many of the trimmings of a regular tech conference – the show floor, the keynote, the product launches – it’s also known for its brevity. It may billed as a two-day event, but realistically the meat of the event takes place during the second day.

The announcements this year lent themselves to this presentation strategy perhaps more than any other, because sitting at the center of it all is AMD’s rack-scale architecture, Helios.

The dawn of Helios began at the last Advancing AI summit, in 2025, with many of the technical specifications made public at the same time. For example, it had already been stated that the system would feature the company’s next generation MI400 chipset but until last week, we didn’t know which specific GPU it would use nor the specs.

All has now been revealed, though, which is helpful for would-be buyers, although this system is not for the casual customer. In fact, it was arguably AMD’s big name customers for Helios that were on display this week as much as the tech itself – a shining corona of Anthropic, Oracle, HPE, OpenAI, and more.

It’s clear that AMD is proud of its continuing relationships both with traditional hardware vendors and relative newcomers. Strategically, it makes sense too – keep your options broad and there’s greater defence against any potential ‘market turbulence’, shall we say.

From the macro to the mini

As I predicted, there wasn’t much conversation around AMD’s consumer offerings, although they did have a spot on the show floor, having already had their moment at Computex in June.

That doesn’t mean Helios was the only hardware on show, though. Accompanying the massive double-wide, rack scale solution was a diminutive cube with an equally mythological codename: Gorgon.

Gorgon – or, to give it its true production name, Ryzen AI Halo – was something of a ‘rabbit out of a hat’ moment. I think most delegates came expecting chips and giant infrastructure, so the appearance of Halo right at the end of the keynote was unexpected.

Halo has decent specs: 128GB unified memory and the ability to run 200 billion models natively. It’s expressly designed for developers currently but, according to AMD SVP and GM of compute and enterprise AI Dan McNamara, it “implies a future of personal AI for everyone”. Quite what that means beyond lower latency, I’m not sure.

Either way, it's available now through Micro Center with the tone of the announcement leaving the door open to other partners in future. I do wonder, however, how popular this will be given AMD's traditional enterprise route to market has been inside other OEMs' hardware.

I'm also not quite sure where it competes. The seemingly obvious answer is Dell's Nvidia-powered Deskside Agentic AI stable – particularly the GB10 – but the concept isn't quite the same given there's no emphasis on agentic AI.

Nevertheless, it is very lovely to look at and if you’re looking for a desktop AI PC with a small form factor then it's another option.

You can catch up with all the announcements from this year's AMD Advancing AI here, including more on Helios and the new Instinct MI455X GPUs.

It might not have the draw of the recent FIFA World Cup, but esports has a global, and growing, following.

Research suggests the global esports market is worth some US$5bn, according to data firm Statista. This is growing steadily, with some analysts predicting that the sector will reach US$10bn by 2035.

At the same time, the number of esports players is significant: Statista predicts 925m users by 2030. In the UK, trade body UKie values the sector at £111.5m.

UKie's figures date from 2019, though, so are certain to underestimate both the value and levels of participation in esports, not least because of increased uptake of gaming during the Covid-19 lockdowns.

In the game

Esports, or competitive video games, are not new. According to UKie, the first “esports” event was the Atari Space Invaders Tournament, back in 1980; 10,000 dedicated fans took part. Since then, the trade body says, esports has become the world’s fastest-growing entertainment sector.

This makes esports an important industry in its own right. Saudi Arabia usually hosts the Esports World Cup (exceptionally, this year’s tournament was moved to Paris). The Kingdom has put esports at the centre of its Vision 2030 strategy for economic diversification, with the sector expected to employ 39,000 people by the end of the decade.

Closer to home, British Esports has built a 15,000 sq ft arena and the National Esports Performance Campus in Sunderland. Clearly, there is more to esports than playing Call of Duty in a darkened basement.

And, while the direct economic impact of esports – from games development, tournaments and events and even merchandising – is significant, there is another reason it is attracting attention. Esports is gaining recognition as a way to develop digital skills. And organizations are starting to view gaming, and especially esports, as a way to attract and retain talent.

British Esport cites figures from the Government’s Department for Culture, Media and Sport stating that 80% of advertised jobs now need digital skills. At the same time, the digital skills gap is costing the economy £63bn a year. And, despite a cooling market in some areas of the technology sector, employers still struggle to attract digital talent.

Military first

One of the earliest sectors to recognize this is defence. Esports in the UK armed forces has developed largely from the ground up. But the activity now has official backing, with esports facilities on RAF stations and Army barracks and even an esports suite on HMS Prince of Wales.

In 2024, the MOD recognized esports as an official, military sport. Last year, the MOD’s Cyber and Specialist Operations Command worked with the British Esports Federation to deliver a defence and industry tournament, the International Defence Esports Games (IDEG). This will be open to military personnel from Allies and partners around the world, in due course extending to veterans, cadets and players in the defence industry. The inaugural IDEG Finals will take place in Sunderland this October.

The connection between esports and workplace skills in the military is a direct one. As the MOD states, fast responses and hand-eye co-ordination, honed by competitive gaming, are essential for troops operating drones.

But, the MOD says, esports develops other key skills, such as cyber awareness, decision-making under pressure, and leadership.

This, of course, is not new.

“For centuries we’ve used ball games like rugby and football to develop teamwork, hone mental and physical fitness and build resilience," said Lieutenant General Sir Tom Copinger-Symes, at the time Deputy Commander of UK Cyber & Specialist Operations Command.

"We’ve also used games to discuss ethics - how to win, but win well. That’s increasingly relevant in a world of AI and autonomous drones on the battlefield. Esports perfectly complement these games in preparing us for 21st Century security challenges."

And it is those “soft” skills that make esports a valuable tool for business too.

Business next

Esports can, and possibly should, be part of any business’ digital skills toolkit. And using esports to develop teamwork and communications is in many ways more practical than physical sporting leagues.

“Gaming generally is a good way of getting people to learn. It taps into a human love of play and collaboration,” Adam Waters, director of AI culture and gaming at BFBS, the forces’ broadcasting and media charity, told ITPro.

“It doesn’t have to be a competitive Call of Duty league, which is what many think of when it comes to esports.” BFBS recently launched its own esports Pro League for the wider forces community, with an emphasis on casual gaming.

Waters is also seeing defence companies standing up esports teams to play alongside service personnel. “Esports itself is something more companies seem to be formally getting involved in,” he says.

“Something I’m seeing in a lot of organizations is a need for technical skills combined with emotional intelligence or ‘people skills.’ People who game in teams should look at developing these.”

One advantage of esports is the way it is built around teams, with teams competing in leagues based around individual games. This allows players to gain skills, not just from play, but from managing teams or even running tournaments. The broader emphasis on teamwork is one reason the military and cadet forces value esports.

Professional skills

Learning to develop professional and organizational skills through esports goes alongside leadership and communication skills. But the way esports fosters a sense of inclusion is also important, according to George Osborn, author of Power Play and Editor of Video Games Industry Memo.

“Outside of a handful of direct links between playing games and developing a useful professional skill, such as using a controller and becoming a drone pilot, esports develop similar soft skills to sporting activities,” he told IT Pro.

And businesses can also build esports into social activities, brand building and potentially, recruitment. Skilled workers might be more attracted to a workplace that reflects their interests, and esports can be part of that.

But Osborn cautions that enterprises need to be aware of cultural nuances around esports, and understand that building a community takes time.

Some esports, he says, are more commercial in nature; for others they are driven by the local gaming culture. Some games, such as fighting games, are even “properly countercultural”, he warns, and might not lend themselves to a corporate endeavor.

“Take baby steps to understand these things, align around communities that can work with you, and provide long-term support to a sector that is still finding its feet,” he recommends.

On the face of it, the channel model is the perfect arrangement. Partners gain access to technologies and revenue opportunities they could never build alone. Customers benefit from top-drawer advisers who can bring together the right solutions for their needs. And vendors get to extend their reach into markets and sectors that would be impossible to serve directly. Everybody wins.

So why is there so much friction in the channel, and why is it seemingly getting worse? A report by the global channel trade association – the GTIA – found that satisfaction with vendor relationships has declined year over year, falling from 39% in 2025 to just 18% in 2026. 

The reasons cited in the report touched on a number of challenges, including the rise of AI. But there are other issues as well, such as the sheer complexity and administrative burden of setting up and maintaining a properly functioning partner program. 

The hidden operational cost for partners

The result is that staff end up spending a disproportionate amount of time running the program rather than working with customers. 

Sales teams are forced to spend time on administration rather than selling. In some cases, people are employed for no other reason than simply to carry out the back-office functions of running a program. 

There are certification and recertification requirements. Time is spent completing mandatory training courses, as well as tracking and maintaining multiple vendor accreditations. 

Then there are the onboarding processes for new vendor relationships, managing multiple partner portals and logins, deal registration administration, and rebate and incentive program management. And let’s not forget the inconsequential task of monitoring program changes, updates, and new requirements. 

The operational cost of running some programs is becoming a real burden. And it’s happening because somewhere along the line, the channel has started to mistake complexity for sophistication. 

In other words, the IT channel has become so focused on complicated products, architectures, services, and business models that it assumes something is better simply because it is more complex.

Little wonder, then, that when the conversations dwell on such matters, they ‘risk turning into therapy sessions about how bad some programs are’.

Clearly, one approach to handle increased complexity is to invest in platforms, processes, and people to handle it. In fact, the channel ecosystem has become so convoluted that an entire specialist software sector has emerged to help vendors manage the process. 

The 2025 Canalys Channels Ecosystem Landscape report found that there are 261 companies – driving $7.46 billion in revenue in 2024 – simply to help companies ‘design, develop, execute and manage a broad channel partner and alliance ecosystem’.

Simplicity is becoming a competitive advantage

The other approach, though, is to make them simpler – something that really hit home during a recent bucket-list trip to Italy. 

Life there moves at a different pace, and it pushed me to reassess how I look at things – not just personally but also in terms of my work with the channel as well. I have to say that it has reshaped my own thinking in terms of making things simpler, clearer and less complicated.  

For me, that means going back to basics. And I think it can be boiled down to four core areas. 

First, vendors need to simplify the rewards and incentives on offer. As we’ve seen repeatedly, complex incentive structures, overlapping programmes, and opaque reward mechanisms tend to create unnecessary administrative effort and uncertainty. In reality, partners should be able to clearly understand how their investment translates into benefits. 

Second, sales enablement needs to focus on capability, not box-ticking compliance. Training, certifications, and specialisations should help partners solve customer problems and grow their expertise, not tie them up in unnecessary red tape.  

Third, there should be a laser-like focus on improving the overall partner experience. Portals, onboarding processes, deal registration systems, and day-to-day workflows shape how partners experience a vendor. As I mentioned earlier, small points of friction can quickly accumulate into a significant operational burden. 

Finally, vendors need to create greater commercial predictability for their partners. After all, they need to make decisions about hiring, training, and business development based on expected returns. Without clear discount structures, transparent requirements, and predictable program rules, how can partners be expected to plan and invest with confidence?  

In fact, I’m so convinced that this is the right approach that I would argue that simplicity is becoming a competitive advantage. After all, partners have finite time and resources to deal with such matters. Vendors that respect those constraints, through clear programs, predictable rewards, streamlined processes, and reduced friction, are much better placed to succeed. 

Writing recently in ChannelPro, Lutz Lambrecht, chief partner officer at SNP Group, observed that strong partnership ecosystems show greater resilience in the face of economic turbulence and market disruption. He highlighted value alignment, versatility, and adaptable technology as key factors that build partners’ and vendors’ resilience.

Lambrecht described how stronger alignment on solution positioning, definitions of customer success, and the measurement of outcomes puts partners in a stronger position to communicate value and build credibility with customers.

The power of partner and provider alignment resonated with me as the CEO of a transatlantic digital agency that aims to help UK and US organizations in the public, private, and membership sectors to navigate cloud, digital, and AI transformation.

Looking for strong leadership

Many of our customers are seeking support to implement generative AI tools within their membership organisations.

Data from the Professional Associations Research Network (PARN) report 2025 [pg33) revealed that members expect their professional body to guide how to safely implement AI, and even which LLMs to select:

• 82% told PARN that they want best practice guides and case studies on AI implementation
• 67% want training on AI
• 64% want their professional body to regulate the use of AI in their profession
• 63% want their professional body to vet and provide access to approved AI tools

There is clearly a chain of trust at work. Members are looking for AI guidance from their professional bodies. In turn, professional bodies seek trustworthy support from their digital platform providers. And, finally, digital platform providers rely upon their technology partners.

Navigating change together

When you’re responsible for the performance, stability, and security of an organization’s website or e-commerce platform, you need full confidence in the technologies that you select to build it. But it goes deeper than that. You must also have faith in the people within the vendor’s organization and its core values.

During the recent Codegarden conference in Copenhagen, Karla Santi, CEO of American digital agency, Blend Interactive, presented the findings from the platinum partner’s survey of 40 small, medium, and large digital agencies, undertaken by Promethean Research. This revealed:

• The average revenue growth over the past year was 7.5% compared to 20% in previous years
• Sales cycles are extending
• Clients are seeking more business case framing and asking ROI questions earlier in engagements
• Agency leaders are feeling the strain of navigating market disruption
• agencies that reduced services and specialized achieved above-average growth

Based on the survey data, Santi encourages fellow digital agency leaders to refine their services and protect their relationships.

In the first episode of the Loom podcast, I sat down with Mats Persson, CEO of the open source CMS platform company, Umbraco, to discuss how business leaders can remain confident in such uncertain times.

We discussed how the vendor’s values of transparency and flexibility are tightly aligned with our own, and how this helps us to build trust with customers.

An open culture opens doors

Referring back to Lambrecht’s point about value alignment, it was Umbraco’s openness that first attracted us to become a partner. We were able to make an informed decision about whether it was the right organization and technology for us to partner with because, to a large degree, we were permitted to look behind the curtain.

We could see what the company was really like, what its enterprise aspirations were, and how central partners really are to its growth strategy.

As Persson commented during the podcast discussion, “You gain more trust if you're open and willing to share your challenges. Partners can handle the truth. If you're transparent with them, it’s an opportunity to fix something, and that becomes more trustworthy.”

Gaining confidence in the cloud

Vendor trust was a really important aspect in my decision to put Umbraco Cloud forward when speaking to our clients about their projects. The company’s transparency solidified my decision to move from us hosting customers’ sites to building enterprise customers’ projects on Umbraco Cloud.

There was an established feedback loop of Umbraco listening to partners and customers. This is not merely performative. Umbraco has demonstrably acted upon feedback. That gave us enormous confidence that when we saw a pain point, we knew it would get fixed.

We didn't switch everything over all at once. We started to feel it out, to see how the support functions. We explored what the challenges were and what we needed to change to get the best out of it. We liked what we saw and started to build increasingly complex sites on Umbraco Cloud.

Quality beats quantity

As Lambrecht wrote in ChannelPro, “The strength of a partner ecosystem is not defined by the number of partners in a network, but by how effectively partners are aligned, enabled, and equipped to deliver value in changing conditions. At the core is a shared value narrative that brings together complementary strengths of vendors and partners, with both combining to create a joint proposition that is clearly differentiated.”

We talk about trying to build a culture of openness within our organization and with our stakeholders. But if we were dealing with a CMS vendor that didn't share those values, wasn't providing the necessary level of support, and wasn't adapting the product based on feedback, that would show to the end client.

Ultimately, carefully choosing who you work with is a major consideration as we all navigate these uncertain times together.

We are not unique. Indeed, many other companies are on the same journey of discovery to get to a point of established trust and create the foundations of current and future success.

When Allbirds, an eco-friendly luxury shoemaker, announced in April it had struck a $50 million (£37 million deal) to become an AI data center provider, its stock surged more than 580% in a single trading session.

Whether things aren't quite working out, or whether you've identified a brilliant opportunity, pivoting into a new space is often the best bet for surviving – and thriving.

The technology industry is rife with reinventions. Take Nintendo, which started out making handmade toys, or Amazon, which was a mere humble bookseller.

Plenty of companies have successfully made a transition from one line of business to another – sometimes unexpectedly, sometimes out of necessity, and sometimes so dramatically we've forgotten their original purpose. Here are some of our highlights.

1. BlackBerry: From phone maker to auto software developer

In February 2016, BlackBerry acquired the U.K. cybersecurity company Encription as it embarked on a dramatic pivot into cybersecurity consulting. It then broadened its horizons to the software space and now, with the QNX operating system, the firm develops critical software and services for more than 275 million cars around the world.

"It's been a large transformation over the course of the last decade as we've divested businesses and really narrowed our focus towards the B2B segments of QNX and Secure Communications," BlackBerry CEO John Giamatteo told Richard Quest in an interview with CNN in January this year.

"So that narrowing of focus has allowed us to simplify, reduce our costs, divest ourselves from subscale businesses, and really put ourselves in a much better position for profitability and long-term growth."

2. Nokia: From paper mill to handsets to next-gen networks

Like BlackBerry, Nokia gained worldwide recognition for its outstanding mobile phones – manufacturing devices from 1982, starting with the Mobira Senator, and now continuing in spirit under the spun-off HMD Global brand.

The origins of the Finnish technology icon, however, stretch back to 1865, when engineer Fredrik Idestam established the company as a paper mill.

It wasn't until a significant merger more than 100 years later — where Nokia, Kaapelitehdas, and Finnish Rubber Works merged to create the Nokia Corporation — that electronics became a key line of business.

Although its reputation faded in the 2000s with the birth of the smartphone — and the eventual sale of its mobile and devices division to Microsoft in 2013 — Nokia continued to make waves under the radar, and under the ground.

It began focusing on networking in 2014 and is now undergoing a major pivot away from sluggish telecom hardware toward AI-native networks, data center networking and 6G tech.

3. Samsung: From groceries to electronics and beyond

Samsung is among the world's most widely known brands and one of the 21st century's biggest technology success stories. The South Korean institution deals in all kinds of goods from fridges to smartphones to laptops to speakers.

In fact, it's hard to find an electronic device that Samsung doesn't manufacture. But the company didn't always start this way.

The company actually started life as a small grocery trading company in 1938, with the 40-employee-strong entity dealing in dried fish, local groceries and noodles. Amazon may have pivoted into this space recently with its Fresh stores, but for Samsung, groceries have always been in its lifeblood.

Samsung didn't enter the electronics industry until the late 1960s, when it started manufacturing black-and-white TVs. It's now a trillion-dollar company that comprises 80 separate companies, spanning construction, financial services, medical services, and many other areas.

In the future, Samsung is hoping to focus on the technologies of tomorrow, including agentic AI and 6G communications.

4. Slack: From gaming to workplace collaboration

Slack is one of the most widely used collaboration platforms on the planet, with the multi-billion dollar business boasting tens of millions of daily users each day.

Well before its massive acquisition by Salesforce for a tidy sum of close to $30 billion (£22.5 billion) – and indeed before it rose to prominence – the company's origins lay in the world of gaming.

When co-founder of photo-sharing site Flickr, Stewart Butterfield, attempted to get his new venture Tiny Speck off the ground in 2009, he developed an internal communications platform to help the team as it was developing its computer game Glitch.

This fanciful 2D MMORPG launched in 2011 and was eventually shut down roughly a year later — with these efforts to make it big in the gaming world ending in tears.

From these ashes, however, Butterfield pivoted the company and instead focused his team's efforts on making their internal communications tools public. Hence, Slack Technologies — and the widely used platform we now know today — was born.

Why Slack? It stands for "Searchable Log of All Conversation and Knowledge". Glitch is long dead, but you can still find a wonderful homage to Slack's origins on its website.

5. YouTube: From dating site to video hosting

The video-sharing platform YouTube has become a giant force in the technology industry, especially since its acquisition by Google, and it's become a hub for businesses across the world.

Millions of users have created businesses using the site, and many existing organizations use the hosting platform as an extension of their own businesses to push video content to billions of viewers.

But before the first video, 'Me at the zoo', was uploaded more than 20 years ago, its founders originally aimed to create a video dating site. The vision, according to co-founder Steven Chen, was to build a platform that allowed people to upload videos of themselves describing their ideal partners.

"We always thought there was something with video there, but what would be the actual practical application?" Chen said at SXSW 2016 in Austin, Texas, according to CNET.

"We thought dating would be the obvious choice."

As AI continues to rewrite job roles, an increasing number of employers are encouraging their employees to gain AI certification.

Back in April, Korean media and network services provider SK Broadband announced plans to train its staff as AI natives. It comes on the back of an AI proficiency certification program last year.

"We will establish a culture where employees independently develop AI agents to transform productivity and create real business value,” an SK Broadband spokesperson told The Korea Times.

More than a third of employees (35%) are interested in gaining AI certification, according to Pearson’s annual Value of IT Certification report released in April, which surveyed 505 IT and HR leaders. This figure is up from 17% in 2025.

The rising interest in AI certification should come as no surprise, according to Imran Akhtar, head of academy at talent and training partner, mthree.

“Many employers are looking for existing talent who can say they understand AI by spotting problems clearly, questioning its outputs, applying their own judgement, working safely with data, and using it effectively to improve how work gets done," Akhtar explains.

For employees, the value of AI certification is that it gives them ”a recognized way to evidence their knowledge of AI”, Akhtar adds. Certification helps to put theory into practice by testing employees’ knowledge in real-world scenarios.

The AI certifications to choose from

With so many AI certifications available, it may seem hard to know where to start. The right certification, however, is going to depend on an employee’s job role and which AI tools they need to perform that role more efficiently.

Another factor is an employee’s career plans and what AI knowledge and skills they may need as they climb the career ladder.

“Some jobs may demand certification because the role directly involves AI systems, data, risk, or governance. For others, it supports broader AI literacy and bolsters day-to-day work more holistically,” says Akhtar.

Here are a few starter AI certifications to choose from.

AWS Certified AI Practitioner

Structure: Online, 65 questions

Price: $100

Valid for: 3 years

AWS Certified AI Practitioner covers the basics of AI, genAI and machine learning (machine learning) concepts and use cases. The certificate is ideal for business analysts, IT managers and IT support, as well as product and project managers.

Google Generative AI Leader

Structure: Online, 50-60 multiple-choice questions

Price: $99

Valid for: 3 years

Google Generative AI Leader is for anyone looking to boost their business-level knowledge of generative AI (GenAI). The exam covers the fundamentals of genAI, Google’s genAI offerings, techniques for improving genAI output, and business strategies for successful genAI deployment.

Databricks Certified Generative AI Engineer Associate

Structure: Online, 45 multiple-choice questions

Price: $200

Valid for: 2 years

Databricks Certified Generative AI Engineer Associate is for anyone who wants to validate their ability to design and implement large language model (LLM) solutions using Databricks. It’s recommended that those taking the certification have at least six months of hands-on experience performing genAI tasks outlined in the exam guide.

Nvidia Certified Associate Generative AI LLMs

Structure: Online, 50-60 multiple-choice questions

Price: $125

Valid for: 2 years

Nvidia Certified Associate Generative AI LLMs is aimed at those who are looking to build on their basic understanding of AI and LLMs and learn how to develop and integrate AI applications using AI and LLMs with Nvidia solutions. It covers data visualization, neural networks, prompt engineering, and Python libraries.

These four certifications are considered foundation-level, but there are next-step certifications that employees can take to advance their knowledge and skills further.

For example, AWS suggests that those pursuing careers in AI, data and machine learning (ML) gain the AWS Certified Data Engineer – Associate. Those going down the cloud path could take the Certified Solutions Architect - Associate.

Certification can boost career and salary prospects

Research published by Revelio Labs last month found that the average salary for workers who had gained a foundation-level certification in AI or generative AI is $79,800. For comparison, the average for workers who hadn’t gained one is $72,300.

The data also showed that certification gainers were more likely to be hired for a more senior role for their next position (38.9% had received one) than non-certification takers (32.5%).

Given the tight job market, employees are looking for anything that can give them an advantage. AI certification can improve their job mobility and help them to stand out from the competition, Faye Ellis, principal training architect at tech skills provider Pluralsight, tells ITPro.

“Certifications demonstrate a commitment to continuous development and validate that skills are up to date and workplace-ready. For many, this can provide a route to new opportunities or higher-paid roles,” says Ellis.

To mandate or not to mandate

The question employers may be asking themselves is whether they should mandate AI certification.

Ellis explains that while every employee “needs a baseline level of AI literacy, mandatory certification will depend on the role and technical depth required.

She cites the example of software developers. Companies should be more inclined to make certification mandatory for them because they’re involved in building and governing AI systems. For employees whose roles are less technical, certification needn’t be mandatory, but they should be encouraged to “support a foundation in responsible AI use, prompt quality and output validation”.

Akhtar agrees, but cautions that certification doesn’t always guarantee future career success.

“Certification can boost a career, but only when it builds the capability an employee can apply in their role,” he says.

“It’s unlikely to support career progression if AI training doesn’t reflect the core business objectives.”

There is an ongoing debate in the cybersecurity industry about whether vendors should go directly to customers or instead become a part of a wider partnership network.

The standard argument is that consolidation of platforms and AI-driven cost-of-service delivery makes the traditional model of a channel ecosystem redundant. However, this is largely incorrect, at least when it comes to the SMB and mid-market segments where most UK businesses sit.

For most organizations outside of large enterprises, the channel is the only realistic way to access serious security capability.

The SMB reality

Cybersecurity is not a niche concern for SMBs. According to the latest statistics from the National Centre of Cyber Security (NCSC), one in two small businesses suffers a cyber incident every year.

Given the niche, local context required for each small-to-mid-sized firm, that is not a market that vendors can serve directly at scale, and most are not trying to. A 200-person manufacturer facing a cyber issue is more likely to call its existing IT provider than a global security vendor built for large enterprises.

What SMBs need is local expertise, trusted relationships, and security operations that fit their budget and operational reality. That is what a partner network provides.

What is changing is what partners need from vendors

Partners in the channel system are operating under significant pressures. Nearly half the executives in businesses believe AI-powered threats will occur, but only just over half (53%) say they are prepared for them, according to our research.

Partners are the ones having those conversations with customers who are under-prepared, under-resourced, and know where and how the threat environment has shifted. They need vendors who make it easier to sell, deliver, and demonstrate value under budget scrutiny.

The consistent conversation amongst partner networks is that the commercial basics matter as much as the technology. They need payment models, good margins, and support that does not disappear after the contract is signed. Our research shows that 59% of executives say it is becoming harder for employees to identify real threats as AI-powered attacks grow more sophisticated. That is a sales opportunity for partners, but only if vendors help them to have that conversation credibly.

Trust is local, scale is not

One of the things that gets overlooked the most in the direct versus channel debate is proximity. Cybersecurity is an inherently trust-based business. Customers want to work with people who understand their industry, regulatory environment, and the specific pressures they operate under. That is not something a vendor can create from a distance.

A vendor can provide the credibility, certifications, and depth of capability that smaller partners cannot build on their own. The model that works is a division of labour where partners take ownership of the relationship and the local expertise.

Vendors, on the other hand, provide the platform, the threat intelligence, and the specialist teams to support on more complex matters that go beyond the scope of day-to-day operations.

Paving the way ahead

None of this means the current state of channel enablement is where it needs to be. Across the industry, partners still face too much friction. Onboarding is often slow, programme structures are complex and cross-selling across vendor portfolios remains harder than it should be. For vendors who have grown through acquisition, consolidating that into something coherent for partners is an ongoing piece of work.

The channel is not looking for vendors to get out of the way. It is looking for them to show up properly: with clear programmes, real commercial flexibility, and the confidence to let partners lead where they have the advantage.

The organizations that crack this are best-placed to grow with the channel. The ones that do not will find that going direct is a much harder proposition than the theory suggests.

AMD Advancing AI 2026 takes place this week at the Moscone Center in San Francisco, with the two-day event largely focusing on the second day, when we will hear from AMD’s CEO Lisa Su.

One notable thing, for me, is a sense that in many ways AMD and the wider chip market is in largely the same position as it was this time last year. Nvidia is still the talk of the town when it comes to AI infrastructure, with CEO Jensen Huang once again guesting at other hardware vendors’ flagship events.

AMD still has HPC pedigree (more on that below) but doesn’t have the cachet Nvidia does, while Intel trails further behind.

Despite this, there are still predictions to be made, so here are three things I expect to see at AMD Advancing AI 2026 and one I don’t.

Helios

AMD Helios, the company’s fully-integrated, rack-scale architecture solution, was unveiled at Advancing AI 2025 but we are yet to see any deployed (at least publicly).

This is despite HPE saying it would have a cluster available in its cloud in the second half of 2026 (for those counting, that’s now) and Oracle stating it will build its AI superclusters on Helios.

What better time to talk about Helios being out in the market than at Advancing AI?

My feeling is it will build on last year’s announcement by wheeling out some real-world examples of the hardware in use, whether as part of the HPE or Oracle partnerships or something else.

Altair MI450 chips

Annual company events in the tech industry are for product launches as much as anything else. Having already made several announcements about its gaming portfolio at Computex 2026 in May, we can probably discount major announcements around Ryzen or Radeon – although there may be a recap.

Instead, one of the flagship announcements could be the company’s highly anticipated next-gen GPU series the MI450 – code named Altair.

Oracle already announced in October 2025 that it would be putting MI450 into OCI once it’s available and rumour has it the company has already inked a deal to sell these chips to Anthropic. Speaking of which…

AI partnerships

AMD – perhaps more than other hardware companies – makes a lot of its relationship with AI businesses.

This includes the likes of Anthropic and OpenAI, with the latter’s CEO, Sam Altman, appearing on stage at Advancing AI last year, as well as Hugging Face, PyTorch, and Llama.

I also expect to hear more about what role AMD expects to play in the brave new world of agentic AI

Supercomputing and HPC

While we might hear about high performance computers (HPC) – for example Alice Recoque, which is supposed to start onboarding this year – there may be little conversation around supercomputing more generally.

This is because while an AMD machine has been ranked number one in the Top500 list of most powerful supercomputers consistently since 2022 and held the top two places since November 2024, it was overtaken by a Chinese supercomputer in the June 2026 rankings.

AMD Advancing AI 2026 takes place from 22-23 July, with CEO Lisa Su’s keynote happening on the morning of the second day. I will be on the ground reporting on all the news coming out of the event and providing the usual dose of analysis you have come to expect.

Follow here for all previous years’ coverage and everything from this year as it comes in.

Outside of the industry, people in the businesses that become partner clients might not know at first that channel partners do not just ‘sell’ stuff. Resellers help buyers navigate their complex technology choices, their implementation risk, and offer customers an ongoing service that can grow with them.

It’s vital that partners do a number of things well, far beyond knowing how their technology offerings work and being able to implement, service, and fix them. They need to be ‘people’ people, managing relationships and meaning areas like trust, expertise, and reliability become central to maintaining a healthy revenue.

Resellers are also placed in an interesting position - possibly even a dilemma. They know that AI tools can speed up or otherwise optimize many aspects around their sales outreach and office admin, but buyers are still wary of AI-led sales interactions.

Automation alone is a relationship risk

Many customers remain cautious about highly automated sales experiences. While AI can improve efficiency, buyers still place significant value on human expertise, accountability and relationship-building when making technology decisions.

Our recent ‘Hard Sell’ report found that 55% of the public do not fully trust AI, 46% value human connection, and people with negative views of salespeople are far less open to buying from AI-driven tools (19% vs 63%). Resellers cannot carry any insider knowledge or positivity from their own AI experiences to assume that AI-led selling will feel modern or customer-friendly to prospects.

That still leaves plenty of room for using automations where they add value and don’t increase customer friction. That includes summarizing calls, drafting follow-ups messages (then checked for accuracy, tone, empathetic engagement and so on), identifying renewal risk, surfacing product-fit signals, and reducing admin friction.

The moments where customers still expect an engaged and empathetic person must be human-led: discovery, solution-shaping, handling objections, pricing conversations for sure, and post-sale reassurance and servicing. That lets people manage other people, with technology solutions offloading the admin so salespeople and account managers can focus pretty exclusively on customer service and relationship-building.

It’s up to the business to define the human behaviors that should not be automated. Be steered by current customer comfort levels.

The majority of the public doesn’t want any immediate pressure to decide on a sale at the moment, and just under half want honest acknowledgement of the pros and cons of any offering, and also pricing transparency. Knowing this desire for transparency, balanced advice, clear pricing, and evidence that a partner understands their specific requirements, partners can differentiate themselves from both direct vendors and AI-driven sales experiences.

A real opportunity for the channel

This is where resellers can really hone their skills around consultative discovery, well-researched and honest vendor comparisons, clear commercial conversations, and well-tailored but not scripted advice. Resellers often advise across multiple technologies and suppliers.

That independence can become even more valuable as vendor-led AI-generated content and automated outreach become more common and perhaps more obvious. Customers will continue to seek trusted guidance from more impartial sources.

Emotional intelligence is a ‘soft’ skill, but it supports bringing in the hard numbers of pipeline success. Showing more emotional intelligence will be key to maintaining the sales edge as AI is increasingly put into use.

This is the difference between ‘being a reseller’ and being a trusted advisor. Your people must be craftspeople in how they read buying anxiety and in understanding internal customer politics. They must develop an instinct for when to slow down and in translating technical complexity into confidence - without glossing over aspects that may cause friction later.

Salespeople are well aware of this. That report found that over half actually rank customer service as the most important function, above sales itself. Practically speaking, uniting sales and customer service through ‘sales-as-a-service’ is a solid model where revenue depends on renewals, support, upselling, and long-term account health, beyond just the initial deal.

The trick will be to pull off a balancing act. Drink your own champagne - use the technology you trust to reduce admin, but keep discovery and pricing conversations as human-facing as possible to maximize trust. Align the sales process and your salespeople with post-sales and support to show continuity and personality. Use technology like CRM as a shared source of truth between teams to make customer care, as well as measuring trust and retention, easy.

The channel's competitive advantage has always been more than access to information or rare skills. Partner power comes from helping customers make confident decisions, avoid costly mistakes, and achieve value from their technology investments. AI can help them operate more efficiently, but trust remains a distinctly human asset. The most successful resellers will be those that use automation to create more time for customer conversations, not fewer.

Cyber attacks are still hitting businesses, despite increasing awareness following high-profile incidents such as the Jaguar Land Rover (JLR) and Marks and Spencer (M&S) breaches.

According to the UK government’s Cyber Security Breaches Survey, four in 10 businesses (43%) and three in ten charities (28%) fell victim to attacks in 2025.

The problem has not improved over the last year, with cybersecurity minister Liz Lloyd issuing a warning that business leaders must take action now to ensure robust security.

Regulations such as the incoming UK Cyber Security and Resilience Bill mandate that resilience is baked into organizations and their supply chains. How can firms boost cyber resilience as the risk of attack surges?

Resilience gaps

The price of failing to be resilient is already clear. The JLR attack cost the business and its partners up to £1.9 billion, while it’s estimated the M&S breach cost the company over £100 million.

These headlines are difficult to ignore. Yet despite growing awareness of cyber risk following the incidents, many organizations are still struggling to translate awareness into “meaningful resilience improvements”, says Chris Brown, SVP UK market leader at NCC Group.

This is partly due to over-confidence in cybersecurity, which is leading businesses into “an under-preparation trap”, according to Brown.

“The growing interconnectedness of digital systems and third-party suppliers means leaders face an increasingly complex landscape of vulnerabilities and cyber risks, often without clear visibility of where their greatest exposures lie – or how to begin addressing them.”

The Cyber Security Breaches Survey also identified persistent resilience gaps linked to the rapid adoption of AI without adequate governance or security controls.

“While board-level engagement with cyber risk is increasing, stronger operational action remains essential,” says Brown.

Pace of change

Technology such as AI does have the potential to help boost productivity and improve security, but the pressure to adopt it quickly can lead firms to take unnecessary risks. The pace of change is one of the biggest challenges facing businesses, according to Rob O’Connor, Insight's CISO for EMEA.

“Organizations are under pressure to adopt technologies such as AI to improve efficiency, reduce costs and create new ways of working, but every new capability introduces new risks too,” he says.

The challenge, O’Connor adds, is implementing change securely, and doing so at a pace that keeps up with the wider business.

Company culture is another problem. One of the biggest barriers is that many firms still treat cybersecurity as a “specialist technical function” rather than a “core business resilience issue”, says Scott Beange, head of cyber strategy at Projective Group.

“Boards often receive large volumes of cyber reporting and compliance metrics, but relatively little clarity around operational survivability, recovery capability or dependency risk.”

The issue is made worse by a growing disconnect between modern digital ecosystems and traditional governance approaches, according to Beange.

“Organizations are now heavily reliant on cloud providers, Software as a Service (SaaS) platforms, managed services and interconnected supply chains. In many cases, firms no longer fully understand where their operational dependencies sit until disruption occurs.”

At the same time, attackers are increasingly targeting the areas organizations struggle to rehearse properly: Recovery processes and operational coordination under stress, says Beange. “Too many businesses still test intrusion prevention far more rigorously than degraded operations or prolonged recovery conditions.”

Regulation resilience

It comes at a time when regulations such as the European Union's Digital Operational Resilience Act (DORA) and the UK’s Cyber Security and Resilience legislation are mandating resilience across the board.

At the EU level, a proposed update to the EU Cybersecurity Act seeks to address fragmentation in requirements under the Network and Information Systems 2 Directive (NIS2) and EU-wide certification schemes.

“But even with a more coherent framework including outcome-focused minimum standards and technical criteria, global organizations must still navigate evolving regulatory expectations across multiple jurisdictions,” Brown warns.

AI adoption is also mandating resilience, Brown points out. However, rather than introducing entirely new legislation to ensure secure and responsible AI adoption, states are weaving it into existing sector regulation, he says. He cites the example of the UK’s Online Safety Act, which is being amended to close loopholes for chatbot providers.

Steps to boost resilience

With so many factors at play, it might seem complex, but there are a number of steps businesses can take to boost resilience now.

A “small set of controls taken together” will “eliminate the majority of preventable incidents”, according to Kevin Curran, senior IEEE member and professor of cybersecurity at Ulster University.

The starting point is Cyber Essentials Plus, he says. “It is not glamorous, but it forces patching discipline, multi-factor authentication (MFA), access control and boundary firewalling, and it is already a precondition for many public sector contracts, so the cost is recoverable."

From there, the “single highest-value technical investment” is phishing-resistant MFA such as hardware security keys or platform passkeys on every privileged account, he says.

Beyond identity, firms should segment their networks, particularly between IT and operational technology environments, says Curran.

“A compromised office laptop should never be able to reach a production line controller, and yet in many manufacturing firms it still can.”

Alongside segmentation sits the discipline of tested backups, Curran adds. He advocates the 3-2-1 rule, “with at least one copy offline or immutable, and a restore rehearsed at least quarterly”.

Beange recommends making an effort to understand critical business services and the dependencies that underpin them.

“Firms should be regularly testing how they would operate under degraded conditions as rigorously as whether their security controls detect attacks.”

Boards should also demand clearer reporting focused on operational impact and decision-making rather than excessive technical detail, says Beange.

Questions such as, “how long could we operate without this supplier?”, or “what happens if identity systems fail for 48 hours?” are often more valuable than “another dashboard full of vulnerability statistics”, he advises.

O'Connor concurs that cyber resilience starts with understanding what matters most to your business, saying “Organizations should think like an attacker and ask: What are the assets or operations we simply cannot afford to lose?”

For some organizations, that may be customer data, for others intellectual property, or production systems, explains O’Connor. “Once you understand what would have the biggest operational and financial impact, security priorities become much clearer.”

Data is treated as a source of untapped riches for businesses, promising a world of better insight and easier management. What's less discussed is how it's possible to drown in this ocean of information – more data sometimes just means more noise, and this is as true in IT as in any other profession.

Why doesn't more data produce better results? Is there a better way forward? And what does this mean for the end users? On this episode of the ITPro Podcast, Bobby Hellard and Ross Kelly talk to Fernando Castanheira, the CIO of Riverbed.

Castanheira's career has struck a balance between tech and finance, and how data is used to power businesses. He talks us through the mismatch between the amount of data available and how it's being used.

Highlights

"If I'm a service desk person, my job is to kind of make sure that everyone's able to do their jobs. There's no interruption, etc. I can come in and say, we have just introduced our third iteration of AI. We call it a Q. Q is basically you're going away from dashboards, which everybody's got to go look at dashboards, to come across as more conversational. I can come in and say, "Hey, I'm the desk guy. What are the three or four items that I should focus on today so that I can improve my you know my my environment? And then based on the data that we've collected over the last 24 hours, a week, whatever it is, we come back with some suggestions."

"These dashboards all look green, right? All look green on the outside, but you open things up on the inside, a lot of red. Because a lot of things are not working, even though the dashboards are looking green. So I think our approach is to make sure we kind of solve that by having very specific data, very specific recommendations, or very specific automation."

"The key thing is to have that foundation in place, and then we can act and enable things as you go forward, but it's a journey. You don't like go from zero to 100 overnight. You start with the easier stuff and then continue on that journey."

Links

• Data quality worries are holding back AI adoption among manufacturers, despite optimism over its growth potential
• AI projects are faltering as CDOs grapple with poor data quality
• Everpure wants you to get your data AI-ready

In popular culture, when an agent goes rogue, what usually follows is a world of trouble (think Jason Bourne or Ethan Hunt). If rules are abandoned and the chain of command breaks down, those involved often face existential risks.

Applying that idea to real-world situations is not as ridiculous as you might think, as the recent experiences of a business called PocketOS demonstrate.

PocketOS is a US-based SaaS provider specializing in the car rental sector, and for those unfamiliar with its story, it hit the global headlines when one of its AI agents went rogue and, in just nine seconds, decided to delete the company’s entire production database and backups.

It’s a fascinating case study about what can happen, as one piece of analysis put it, “when AI agents are dropped into environments that were never designed to control them.” What makes this story even more relatable is that PocketOS’s founder was able to ask the agent (an AI development environment running in Claude) why it did what it did.

To suggest it was ‘sorry’ about its mistake is putting it very mildly; it's a digital mea culpa saying, amongst various other things, “I violated every principle I was given: I guessed instead of verifying.”

An inevitable scenario

We’ll inevitably see more of this kind of incident. Organizations are only beginning to deploy agentic AI at scale in operational environments, but many of them are in a big hurry. Yes, agents offer massive potential to create operational value, but they also introduce a whole new category of business risk.

And don’t forget, the PocketOS debacle is not thought to have involved any malicious third-party activity; the agent was just attempting to complete an assigned task. The problem, it would appear, was a kind of perfect storm where autonomy overstepped the boundaries of permissions and access. The guardrails that the business thought it had in place were simply insufficient.

From a security perspective, this is enough to give CISOs sleepless nights. Indeed, rogue AI agent activity may very well have nothing to do with being breached and everything to do with resilience and recoverability.

The central challenge is this: agentic AI is fundamentally different from previous generations of AI because it can act rather than simply advise. Agents can search files, call APIs, modify workflows, write code, move data, and interact directly with production systems; the list of capabilities is practically endless. And to be able to do this, they must have at least a level of access allowing their work to take place

When something goes wrong, the result is an expanded blast radius. A mistake that might once have affected a single application can potentially impact multiple systems and even recovery environments. The bottom line is that as soon as organizations give AI agents freedom to operate, the nature of resilience inevitably changes.

Making resilience more resilient

So, what needs to happen to ensure resilience standards do not catastrophically drop? Firstly, organizations must consider what takes place when a trusted system with legitimate credentials makes the wrong decision.

The issue becomes particularly acute when agents are granted broad permissions across multiple systems, as happens when they are handed a “golden token”. To an extent, this is a question of mindset, and viewing AI agents not as software tools but as digital insiders with delegated authority is a healthy change in perspective.

Secondly, channel partners will be fundamental to successfully managing the transition to agent-supported operations. Don’t forget, most customer organizations are still in the early stages of understanding how AI agents interact with identities, permissions, backup environments and recovery processes.

There’s no doubt that customers are a) increasingly aware of the risks associated with agentic AI, b) concerned that their existing resilience processes might not be good enough, and c) looking for guidance before an agentic error causes serious difficulties.

In this context, it’s incumbent on channel partners to work with customers to identify potential areas for improvement. There is significant potential, including services such as identity and access reviews, which will be very important as machine identities proliferate alongside human users.

Many businesses will also need to reassess their data protection strategy because, as we have seen, an AI agent with the appropriate permissions is capable of going after that data as well. Recovery architecture should be assessed through the lens of agentic AI, particularly where production and recovery environments may share credentials, access paths or administrative controls. Business process change in this space is a big opportunity for the channel to partner with customers to introduce resilience operations, and importantly, to regularly test it.

What’s more, the PocketOS incident demonstrated that protecting production data alone is insufficient if recovery assets remain exposed to the same destructive action. Customers need confidence not only that recovery is possible, but that digital assets are protected from the same event that affects production systems. This shifts the channel conversation from selling AI-enablement projects to helping customers deploy AI safely and recover when things go wrong, which, in some organizations, they inevitably will.

Stephen Wood, chief operating officer at Rathbones Asset Management (RAM), faced a challenge. His organization wanted to build strong data foundations to help deliver fresh insight to its professionals and embrace emerging technologies, such as agentic AI.

The solution to this challenge came via technology specialist Snowflake’s data platform.

“We wanted to create a golden source of data for each of our foundational domains,” he says.

“We wanted to overlay the data quality checks, the business rules, all those good things, and then everything would be built off that data lake. That's where we found ourselves working with Snowflake’s AI Data Cloud.”

Taking a long-term view

Wood, who spoke with ITPro at the recent Snowflake Summit 2026 conference in San Francisco, says this process began two years ago, when RAM, which is part of the Rathbones investment group, was looking for a way to manage its information assets more effectively.

The shift to Snowflake is part of a broader digital transformation to support business growth. The organization’s assets under management grew quickly before the move to Snowflake. However, RAM was still using a portfolio management system that was customized for the wealth management element of the wider Rathbones group.

Fortuitously, a solution came from within the broader Rathbones group. The firm was in the first phase of its Snowflake deployment, and Wood suggested to senior executives that the technology could help his organization manage its data headache:

“We said, ‘Look, we can take advantage of this technology,” he adds. “We want to lead the way in terms of building out our core foundational data domains in a Snowflake environment.’”

Transformational stages

Stage one of the transformation project involved a point-to-point data share between the existing Charles River investment management solution and the Snowflake platform. Wood says this data-sharing process covered a trading view of the organization’s funds, such as securities bought and sold.

Stage two, meanwhile, involved building out RAM’s data store and the static reference information for its funds. Wood says this process allowed his team to build the Open Funds template, which external vendors use as a standard for consuming the organization’s data.

The success of this initial work meant senior executives could see further opportunities, with Wood adding: “Then we started to get into conversations where we were saying, ‘Okay, now we need historical fund performance, our quant risk calculations, what we call subscriptions and redemptions, and the cash flow in and out of our funds from clients.’”

Today, RAM has nine domain-related data sets established in its Snowflake data platform. These data sets cover a range of areas, including performance, risk, sustainability, and unstructured information, such as the decisions behind the firm’s investment activities.

Foundation for future success

Wood says the integrated data sources provide a foundation for change, including potentially embracing emerging technology. Work undertaken so far means his team can start exploring AI, including Snowflake’s agentic services. At the San Francisco event, Snowflake announced new features for CoWork, its personal agent for knowledge workers, and CoCo, its coding agent for developers and data engineers. Wood can see the agentic technology’s potential.

“When you combine all the information, you can start to do some powerful things,” he says.

“We're now in a scenario where we can say, ‘Looking over all this data, we can start to analyze the performance of our investment decisions and give product specialists new insights.’”

RAM’s agentic explorations are already underway. The organization has a test environment for CoCo and CoWork that’s running with millions of rows of data. “At this stage, the outputs look very decent, so it's a near-production environment,” Wood says of the explorations, before outlining other potential applications.

He adds: “The future direction is that you start to get into sales and distribution. The most valuable data in our CRM system today is the call notes written up after every meeting that say clients are interested in some funds and pulling away from others. No one could analyze that data until now, because, after you've had two or three calls with the client, the information disappears down the chain in Salesforce.

“Now we're looking at the idea of using agentic technologies to consider the long-term buying trends of clients. The salespeople know their clients, and they know what they're buying. But if we see the general trends, then that insight can go into a product cycle that makes us think, ‘Do we need to be looking at new funds, do we need to broaden our horizon? Are we at risk in certain areas?’”

Fuelling innovation

The work Wood’s team has undertaken means they can investigate native applications and emerging technologies with confidence, rather than focusing on point-to-point data shares with Snowflake. That investigatory work is crucial because, as a smaller business, RAM will never have a team of engineers to build out a big AI platform.

“That approach doesn't make any sense,” he says. “What does make sense is that we've got Snowflake technology that is central to our data strategy. Now, our work is about how we can find the tools that interplay with the platform, so we reduce the Frankenstein's monster of connectivity and integrations that can slow projects down.”

Wood reflects on the digital transformation program he’s overseen and says that, as with any financial services firm, the key challenge is ensuring exploratory projects are prioritized in an industry that often contends with a regulatory burden and technical debt. Sometimes, due to these intractable challenges, innovation gets stifled because of people and budget constraints. Wood says his organization has circumvented these issues.

“I think Snowflake, and how Rathbones operates, has allowed us to take control of our environment,” he says, referring to the split between the group implementation of the platform and his organization’s deployment. “There's a broad Snowflake environment, and then we've got a ring-fenced environment, with all our data sets, which means the implementation has been built in a way that allows more self-service capability.”

Wood suggests the key to long-term success will be staying on top of Snowflake’s product advances. For example, his team received buy-in for its CoWork and CoCo explorations by explaining how a trial of the agentic technologies could benefit the group.

“There are good ways to try and ensure that you get that prioritization if you need to,” he says.

“When you've got a real-world business case and value proposition that is going to materially benefit the business, versus just being a nice technology idea, the investment ultimately comes through.”

AI continues to dominate conversations across the technology industry. Every week seems to bring a new model, platform, or product announcement, alongside predictions about how quickly AI will transform the way organizations operate.

For channel partners, the challenge is deciding where to focus. Customers are looking for guidance, but keeping up with every new development isn't a realistic strategy for most businesses.

What many organizations need right now is more practical. For many customers, the challenge is no longer understanding AI's potential but addressing the data, integration, and operational hurdles that stand in the way of adoption. Information is often spread across disconnected systems, while growing attention to AI costs is increasing demand for more disciplined approaches to deployment and governance.

They are trying to work out how AI fits into their existing technology environment, how it can support day-to-day operations, and how to deliver measurable results. That's where many customers are looking for support - and where partners can have the greatest impact.

Why promising AI projects get stuck

Over the last few years, organizations have invested heavily in AI pilots and proof-of-concept projects. Many have demonstrated what is possible, but moving from experimentation into wider business use has proven more difficult.

The obstacles are rarely limited to the AI technology itself. Firms often face challenges integrating AI into existing environments, governance requirements continue to evolve, and teams can struggle to connect AI initiatives to clear business priorities. Even when a pilot is successful, scaling it across different teams, processes, and systems introduces a new set of challenges.

Customers need support identifying where AI can make a meaningful difference and how it fits into existing workflows. Just as importantly, they need help addressing the operational issues that can slow adoption long after the pilot phase has ended.

All roads lead to data

For many organizations, progress with AI is constrained by their ability to make data accessible, trusted and usable across the business. Many have accumulated years of information across different applications, cloud environments and departments.

Making that data accessible, reliable and usable remains one of the biggest barriers to successful AI adoption. Half (49%) of UK organizations lack the internal skills needed to integrate AI with existing analytics and business intelligence systems, according to our research. While 37% say gaps in real-time data integration are a major obstacle.

Without a strong data foundation, organizations will struggle to trust AI outputs or apply them consistently across the business.

For partners, this creates opportunities that extend beyond a single deployment. Helping customers improve data quality, connect systems and establish effective governance addresses challenges that continue long after an AI project goes live.

As organizations roll out AI across more areas of the business, these foundations become even more important. This creates ongoing demand for partners that can help customers manage complex data estates, link disparate systems and put the right policies and processes in place.

Complexity isn’t going away

Alongside data challenges, organizations are also managing increasingly complicated technology environments. Few businesses operate within a single vendor ecosystem. Most are working across multiple cloud providers, applications, analytics platforms and AI tools, many of which have been introduced at different stages.

Customers want the flexibility to adopt new technologies without disrupting existing investments. They also want confidence that systems can work together effectively and that today’s decisions will not create unnecessary constraints in the future.

As AI projects move beyond the pilot stage and into wider deployment, organizations will increasingly need support connecting technologies, improving interoperability and reducing operational complexity. Partners that can provide that expertise are likely to become trusted advisors.

Building services customers will continue to need

As AI adoption progresses, some of the strongest opportunities for partners sit in areas where customers will need ongoing support.

Workflow automation is a good example. Many organizations continue to rely on manual processes that slow decision-making, create inefficiencies or make information harder to access. AI can help address these issues, but success depends on understanding how technology, processes and people work together.

The same applies to services focused on AI readiness, data quality, governance and integration, for which there is already demand. UK businesses and IT leaders believe better data integration and analytics would help demonstrate AI's value to stakeholders, while more than half want greater visibility into how AI models reach decisions, our research found.

Organizations are also becoming more disciplined in how they evaluate AI investments. Beyond model performance, there is growing focus on managing operational costs, reducing unnecessary processing, and ensuring that AI systems are working from relevant, trusted data. Strong data practices not only improve outcomes but can also help organizations use AI more efficiently as deployments scale.

These engagements help customers address immediate challenges while creating a stronger foundation for future AI initiatives. They also lend themselves to repeatable service offerings that can be applied across different customers facing similar problems.

The opportunity is already here

For many channel partners, the biggest AI opportunity lies in helping organizations move beyond experimentation and into everyday use.

Many organizations already have the technology required to move forward. What they often need is a clearer path to adoption across the business. Partners that can provide that guidance will be well positioned to strengthen customer relationships and support the next phase of AI deployment.

Sovereign AI has quickly moved from policy language into boardroom planning. For CIOs, the appeal is easy to understand: more control over where data goes, which systems can access it, and how far an organization depends on overseas providers for increasingly critical AI workloads.

In 2026, the pressure is only growing. Governments want national AI capacity, regulators want clearer accountability, and enterprises want to know whether their data, models, and infrastructure are exposed to legal or operational risks that they cannot easily see.

In that context, regional hosting, sovereign cloud services, and local AI infrastructure can all look like a sensible response.

Gartner has predicted that 35% of countries will be locked into region-specific AI platforms by 2027, up from 5% in 2026, underlining how quickly sovereignty is becoming a question of procurement, platform choice, and long-term flexibility.

But sovereignty is not a single switch that makes an AI deployment safer, simpler, or more compliant, and can actually narrow model choice, add procurement friction, raise costs, complicate integration, and create fresh dependencies.

The real question for IT leaders is what level of control each workload actually needs, what the organization is prepared to give up in return, and whether a sovereign approach reduces risk or simply moves it somewhere less obvious, not whether sovereign AI is “good or bad”.

To help understand these trade-offs, ITPro spoke to Forrester senior analyst Dario Maisto about what enterprises really mean when they talk about “sovereign AI”, where local control can reduce risk, and where it can create fresh complexity.

What enterprises really mean by “sovereign AI”

For many businesses, sovereign AI still starts with a fairly narrow concern: keeping data in the right place, which might mean running inference in a specific region, storing prompts and outputs locally, or making sure sensitive data is processed under a particular legal regime.

While these are important controls, especially for regulated sectors, they are only one part of the sovereignty picture.

Maisto says most enterprise projects have not yet reached the point where organizations are wrestling with sovereign AI at full production scale. “Clients' journeys are not that advanced in AI deployments that they can worry about the depth of sovereign AI at scale for production environments,” he says. “They have mostly data residency concerns.”

Maisto’s insight explains why the term can become slippery. A vendor might describe an AI service as “sovereign” because the data is hosted in-country; a public sector buyer might use the same term to mean local legal accountability or domestic infrastructure; and so on.

Distinctions like this are important because AI does not sit neatly in one place. A single deployment can involve cloud infrastructure, foundation models, APIs, orchestration tools, identity systems, logging, monitoring, and a lot more besides.

Once AI agents – perhaps 2026’s buzziest trend – enter the picture, the stack becomes even harder to contain, as systems start pulling from enterprise data sources and triggering actions across multiple applications.

Why local hosting is not the same as control

The first trap is assuming that sovereignty begins and ends with location. Keeping data in a specific country or region can be important, but local hosting does not automatically decide who controls the service, which laws apply, or who can access the underlying systems.

Maisto is blunt on this point: “Local hosting does not protect workloads and data with regard to sovereignty concerns. This is where organizations overestimate the potential of local hosting and in-region infrastructure to improve their digital sovereignty posture.”

“If the infrastructure and the tools are owned by a third party operating under a different jurisdiction, the sovereignty risk is not mitigated,” he says.

None of these makes regional hosting pointless. It can still help with latency, regulatory alignment, audit requirements, and internal assurance, and can also reduce the number of cross-border transfers and make it easier to show that sensitive workloads are being handled within a defined legal environment.

The problem comes when residency is treated as a substitute for sovereignty. For CIOs, the due diligence needs to go beyond the region on the invoice and into the architecture behind the service.

Sovereignty can limit choice

The next cost to businesses is flexibility. AI buyers are used to a market that moves quickly (perhaps too quickly), with new models, tools, and managed services appearing every few months or even weeks.

A sovereign AI strategy can slow that down, especially if an organization has to use approved regions, certified providers, local infrastructure, or a smaller set of compliant services, alongside additional procurement, legal, or regulatory scrutiny.

While that trade-off may be perfectly reasonable for the right workload, it still needs to be visible. A government department handling citizen data should not treat model access like a marketing team testing copy variations, and a bank, healthcare provider, or critical infrastructure operator may decide that tighter controls matter more than immediate access to the newest frontier model.

But a sovereign deployment may mean fewer models, less access to specialist AI services, slower feature rollouts, or more integration work for tools that would otherwise be available through a major cloud platform.

That risk is already visible in agentic AI, where ITPro has reported that many UK companies are moving ahead with deployments despite gaps in governance and visibility over where data is stored, processed, and accessed.

Maisto argues this is where buyers can underestimate the knock-on effects. “Organizations mostly tackle this theme from a data residency and inference location perspective, although problems like agents' sovereignty could be more important, and the solutions look very immature as of now in the market,” he says.

“Integration is another underestimated aspect of agentic AI as agents are pervasive and have the potential to increase vendor lock-in with non-sovereign vendors.”

As AI becomes more embedded in enterprise systems, sovereignty becomes harder to separate from day-to-day architecture. The key question is how deeply it connects to the business, and whether those connections can still be governed, audited, or replaced.

Skills and procurement can slow everything down

Even when the case for sovereign AI is clear, delivery can be harder than the strategy suggests. An organization still needs people who can assess suppliers, design the architecture, check operational controls, and so on, and these skills are already scarce in mainstream AI projects, before sovereignty requirements narrow the pool further.

Talent may become one of the biggest constraints, according to Maisto. “According to our forecast, [the] tech workforce is the only sovereignty index that is going to decrease in the next five years,” he adds.

“The more specialized the skills, the less the chance to have the right talent with the needed sovereignty requirements at scale. With an outlook to the future, skills from a sovereign pool of talent would be the most relevant component.”

Procurement adds another layer of friction.

Sovereign AI buyers may need to test where data is stored, how support access works, and many other features of the wider operational chain. Most of the time, the process is likely to be slower than buying a standard AI service from an existing cloud marketplace.

For CIOs, this creates a now-familiar tension: The business wants access to AI quickly, while security, legal, and compliance teams need confidence that the deployment will not create exposure later.

Sovereign AI can reduce some risks, but only if the organization has the skills and procurement discipline to define what “sovereign” means before it buys.

Should companies prioritize sovereign AI?

The safest approach is to treat sovereign AI as a design choice with a defined purpose.

Maisto says Forrester uses a “Minimum Viable Sovereignty Model Decision Framework” to help technology leaders weigh “compliance requirements, risk appetite, budget, tech and business requirements, and available sovereign products and services.”

He adds that they should assess “the tradeoff between sovereignty and functionality, looking at the viability, feasibility and desirability of a sovereign solution.”

Any assessment should include the practical value of sovereignty, the compromises it introduces, and the maturity of the products available. “Not all vendors are scoring equally on AI capabilities and even less so when it comes to sovereign AI capabilities,” says Maisto.

For CIOs, the aim should be proportional control. Some workloads will justify strict sovereignty requirements because the data, operational risk, or public accountability demands it; others may be better served by strong governance, clear deployment controls, and encryption.

The danger comes when enterprises buy sovereign AI too vaguely, without defining which risks they are trying to reduce, which freedoms they are willing to give up, and what they will need back if the strategy changes.

The memory market continues to reel from shortages created by the diversion of resources to the manufacture of higher-margin AI infrastructure memory. DRAM (Dynamic Random-Access Memory) and NAND flash memory used in mainstream devices are now in such short supply that prices have been pushed up by 130% leading to PCs increasing by an average 21%.

While device shortages were also apparent during the COVID pandemic, prices then remained stable. Instead, today’s situation is more akin to the shortages in new car production which saw a spike in demand for second-hand cars with prices rising accordingly. OEMs are experiencing similar issues with respect to stock availability which means they are struggling to fulfil contracts, with shipments expected be down by between 6.6% to 23%, resulting in lead time slipping.

To make matters worse, geopolitical events are further compounding matters. The war in Iran is now disrupting the production of helium, which is essential for chip manufacturing, for example, with around a third of the world’s supply of helium comes from Qatar. The war is also seeing the oil needed by chipmaking nations such as South Korea and Taiwan delayed or rerouted. Consequently, there’s no real way of knowing when production might normalize.

Lack of supply chain resilience

What the shortages have revealed is a considerable lack of resiliency in the supply chain. Rigid procurement and device refresh strategies now seem woefully out of step and stockpiling inventory is not a viable option because it risks purchasing at the peak and saddling the organisation with too much stock. Instead, we need to look at how we can plan, buy and manage technology to accommodate these kinds of events.

Some organizations are playing for time by delaying device refreshes. They’re moving from a proactive three-year cycle to a “replace on failure” approach, with average life cycles then stretching to 4.3 years. But this can have a knock-on effect on efficiency and productivity, not to mention higher break-fix support costs. Others are considering second-hand devices or switching to vendors with more available stock, but they don’t necessarily have the market knowledge or the capability to put those devices through their paces.

Then there’s the software issue. The memory shortages are also forcing organizations to look at how and where they run their software and whether they can make changes to compensate for hardware shortages. For example, some are looking to load-balance servers to use capacity more effectively.

It’s these kinds of issues that Channel partners are ideally placed to help with. Their knowledge of the market can prove indispensable, as they can draw on their relationships with manufacturers to lock down shipment schedules and pricing. They can also provide flexible financing to ease budget constraints and source or even road-test equipment from alternative suppliers on behalf of their customers. That makes it more likely that organisations will end up with a ‘mixed estate’ of devices that those partners can then help to manage.

How the channel needs to adapt

But to fulfil those roles, the channel must adapt. Rather than continuing to focus on the same sourcing strategies, partners need to begin looking at where they can create efficiencies by offering advice, lifecycle management and ‘intelligent refresh’ strategies. The latter is particularly interesting because it steps away from the cyclical nature of the traditional refresh approach. Instead, it seeks to stagger and prioritise deployments by assessing device health, performance and user requirements.

Intelligent refresh uses a data-driven approach to lifecycle management. Tools such as benchmarking and Digital End User (DEX) solutions are used to assess device utilization and map personas and workloads, for example. Potential devices from different OEMs can then be tested to see if they will align with the performance requirements of the organization, and financial options explored such as CAPEX, leasing or DaaS. Once selected, the devices can be configured according to those personas, secured and asset tagged. Then DEX tools can be used to track health, usage and UX, ensuring high uptime and devices remain matched to the needs of the employees.

Taking a data-driven approach enables partners to utilize real-time performance data, which can then be used to help with planning for refresh. Because this forecasting is based on utilization, it sees blanket refresh cycles replaced with smaller strategic waves, making it easier to fulfil those orders and maintain a consistent inventory.

In addition to assisting with the sourcing of hardware, partners can also offer device lifecycle management. They can provide a range of support, from security covering patching, policy enforcement and endpoint analytics, through to rapid break-fix support such as field-based repair to keep employees productive, and device management by tracking warranties, managing spare or loaned devices and extending asset value. They can even help claw back revenue by assisting with the resale of second-hand components.

In this way, partners can step up to help their customers adapt to this new normal. The memory shortages, initially expected to last into 2027, could now last years, according to Nvidia CEO, Jensen Huang, which means organizations will have to become more strategic in how they manage, maintain, and replace their end-user devices. That will drive an uptick in demand for partners who can advise on the optimization of hardware and software to improve utilization and longevity, who can manage a mixed estate, and draw upon data to devise intelligent refresh programmes that then forecast exactly where and when new devices are needed.

At the end of June, Apple released the iOS 26.5.2 update, containing a long list of security fixes. The timing of the update differed from Apple’s typical approach, which sees the iPhone maker issue mid-cycle patches for emergency security fixes or urgent bugs only.

Yet when iOS 26.5.2 was released, Apple told Reuters the move was part of an overhaul of its strategy as AI tools find vulnerabilities at scale. Indeed, some of the flaws patched in its update were found using AI, painting a clear picture of what security teams can expect in the future.

Apple is not the only vendor to supercharge its patch release cycle fuelled by AI-enabled vulnerability discovery. This June was Microsoft’s biggest Patch Tuesday ever, fixing over 200 bugs, while a recent Google Chrome patch fixed 400 issues in the browser.

As ITPro has already reported, patching velocity must increase as Anthropic’s frontier AI model Claude Mythos and others like it start to find flaws en masse.

But Apple’s accelerated release schedule highlights the impact on mobile devices – especially when part of a bring your own device (BYOD) policy. What can security leaders do to regain control?

Shrinking attack windows

Experts say shrinking attack windows are making patch management an urgent problem. The time between a flaw being discovered and weaponized is “falling dramatically” as AI tools “accelerate both research and exploitation”, says Tristan Shortland, CTO at Infinity Group.

“Traditional patch cycles that measured response times in weeks are increasingly at odds with a threat environment that moves in hours.”

With this in mind, most industry commentators see the move towards accelerated patch releases as positive. “From a risk perspective, I'd much rather see organizations patch quickly and in a controlled way than wait for monthly cycles and leave vulnerabilities sitting there, waiting to be exploited,” says Nathan Davies-Webb, principal consultant at Acumen Cyber.

Yet rapid patch release cycles add pressure in scenarios where employees use personal devices to access corporate networks. “Apple, Google and Microsoft can remind users to update, but they don’t always enforce it,” points out Jake Moore, global cybersecurity advisor at ESET.

For those that run BYOD policies, the impact of late patching could be vast, agrees Sriram Kakalara, chief product officer at Scalefusion. “Personal iPhones and iPads often access corporate apps and data from outside the IT team's direct control,” he warns.

More updates, less warning

Another challenge is the sheer number of mobile fleets to be updated. The trend towards increasing numbers of mobile device patches could be a problem for larger distributed organizations such as retailers, according to Kakalara.

“These might have thousands of devices running on iOS, and every accelerated release means pushing updates more often and with less warning,” he says.

He points out that many organizations “fail to scale their patching and access strategies as their endpoint estate expands, leaving known vulnerabilities unresolved”.

The onus is now on organizations to operate “faster and more efficiently”, he says.

Beyond simply deploying updates more rapidly, the challenge will be ensuring every endpoint has received them, adds Andy Ward, SVP International at Absolute Security.

“The rise of hybrid working over the last decade has made this increasingly difficult. Remote work and BYOD leave organizations with gaps in visibility that attackers can easily exploit. Every day a critical patch is delayed extends the window of opportunity for cybercriminals.”

While there’s no doubt AI can be beneficial in vulnerability discovery, the pressure on patching as a result of the constant flurry of security fixes is only going to get worse.

Shortland expects more vendors will follow Apple's lead, issuing patches more rapidly in line with vulnerability discovery. “AI is exposing vulnerabilities at a scale we've not seen before, which means the industry will have to move towards more continuous patching and risk-based remediation models, rather than relying on predictable release cadences.”

Security teams should treat Apple's move as “the new normal”, agrees Kakalara. “The precedent means patches can now arrive unexpectedly and without notice, so waiting for a scheduled maintenance window is no longer a viable strategy. Teams need to be on permanent alert, ready to test and deploy at speed.”

No more patch day

It might seem complex, but there are some simple steps security leaders can take to ensure fixes are applied as soon as they’re released, including to BYOD devices.

Rather than banning BYOD, Moore thinks the solution is implementing conditional access controls to ensure outdated devices can’t access company systems. “This means it becomes nearly impossible to have devices running outdated software.”

But at the same time, companies need to avoid treating every new flaw as an emergency. Instead, the focus should be on “what is actually being exploited – rather than what is possible”, Moore advises.

Ian Thornton-Trump, CISO at Inversion6, believes security leaders must make themselves aware of the size of their company’s attack surface, and be able to identify legacy devices “either properly supported by vendors, or unsupported”.

In tandem, Thornton-Trump outlines the importance of firms testing their own defences. “Prioritize red teaming and penetration testing to try and find weaknesses before bad guys do, and before security researchers encourage firms to respond.”

Moore recommends automated patching as an ideal scenario. “Long gone are the days where Tuesday was the patch day,” he says. “Manual processes were built for a far slower time, but as we shift into the AI era, we are going to have to adapt and evolve as quickly as the technology does.”

Davies-Webb concurs. He thinks the point has been reached where not using automatic patching “is becoming harder to justify”.

“There was a time when concerns around stability and functionality made organizations cautious about patching too quickly, but for most, the risk now sits firmly on the other side of the equation,” says Davies-Webb.

“AI is accelerating how quickly vulnerabilities are discovered and weaponized, which means organizations need to be reducing exposure as quickly as they can."

Digital twins have been a key tool for organisations to create virtual replicas of operating environments. From transportation to manufacturing and oil & gas, they help simulate performance and prepare for 'what-if scenarios'. But they're evolving.

On this week's episode, Bobby talks to ITPro's news and analysis editor, Ross Kelly, to ask what exactly a digital twin is and how they are evolving since the arrival of generative AI.

The term 'digital twin' is even taking on different meanings; in some cases it's not just a virtual replica of a factory floor, for example. We are now seeing digital twins of people, with Forrester describing them as 'Digital Doubles'. Gartner also predicts that some organisations will begin creating 'Digital Twins of Customers' as a way to simulate how customers might respond to specific scenarios.

Highlights

"We covered this around two years ago on the website: 'AI and digital twins are a match made in heaven'. AI within the context of a digital twin can help improve visualization of real-world dynamics that you're trying to replicate, make sense of the data that you're gathering here, and reduce workloads. For the people that would have traditionally, taken all this on board and been operating and leading the charge on this front."

"I think a great example of AI being used in digital twins at the moment is in Dubai. They launched a digital twin of the entire city, 190-5000 buildings, hundreds of 1000s of infrastructure assets, hundreds of 1000s of public facilities as well. Again, this is part of Dubai's broader digital transformation strategy, but fundamentally this comes down to urban planning and just making it easier, allowing them to create a comprehensive map of everything there."

"Over the last year or two, we've heard chatter about digital doubles or digital twins of workers as well. I think we're going down the rabbit hole here, so to speak. But Forrester's spoken about this quite frequently. The idea of creating a digital double of a worker that's based on all of the information available on that worker internally, their skill sets, what particular unit of the business they're in, and what role they have. There was a fascinating article on the BBC in April this year around Digital Richard, which is essentially an AI, a digital twin of an individual worker. Here, it's not so much a chat bot."

Links

• Are AI digital twins a match made in heaven?
• Does your business need a digital twin?

Ten years ago, a server outage might have affected a single office or application. IT was simpler, stakes were lower and customers were willing, and able to accept a reactive relationship. Managed Service Providers (MSPs) built their value on ticket closure rates, uptime stats, and how quickly they could put out fires. It worked. But that era is over.

Today, a single point of failure can ripple across hybrid cloud environments, disrupt customer experiences globally and trigger regulatory risk. Businesses aren’t looking for faster helpdesks anymore; they’re demanding strategic IT partners that can provide true visibility into their environments and clear guidance on how to use technology to drive business outcomes. And they want all of this while budgets tighten and risk intensifies.

IT leaders are also operating under financial pressures. They’re no longer judged solely on system stability, but are expected to help drive revenue, support innovation and manage risk.

At the same time, many IT leaders are being asked to deliver AI projects, strengthen cybersecurity and drive digital transformation projects, without proportional increases in IT budgets. This is resulting in a more detailed examination of infrastructure spending, particularly around refresh cycles and long-term support strategies.

In this landscape, MSPs that still rely on a break-fix model aren’t just behind the curve; they’re becoming irrelevant. Support capabilities are still important, but the real difference now is made by those who go beyond fixing issues to become more of a strategic partner to their clients.

From visibility to insight

Today’s enterprise estates are fragmented by design, spanning on-premises infrastructure, colocation, multiple public clouds and an expanding edge. Layer in legacy systems, cloud-native applications and a patchwork of vendors and operating models, and the result is an environment that isn’t just complex; it’s opaque.

It is that opacity that’s the real challenge.

It’s no longer sufficient to detect when something breaks. By the time an alert triggers, the damage is often already underway. What IT leaders need now isn’t more monitoring noise; it’s clarity. They need partners who can cut through the sprawl to reveal how infrastructure is actually performing, where risk is accumulating and where inefficiency is quietly draining value.

This is where infrastructure intelligence comes in. Infrastructure intelligence goes beyond traditional monitoring tools to bring together data from across hybrid environments and layers in analytics, automation and contextual awareness. Rather than generating more noise, infrastructure intelligence is about filtering, correlating and interpreting signals in real time.

This enables MSPs to understand not just that something is happening, but why it’s happening and what the downstream impact will be. For example, instead of flagging a spike in CPU usage as an isolated event, it can tie that signal to broader workload behaviour, capacity trends or emerging resource constraints across environments.

This shift allows MSPs to move away from reactive incident management and towards predictive and preventative operations. Patterns can be identified before they escalate into outages, capacity can be optimised ahead of demand, and risks can be surfaced long before they impact users. In this model, insight replaces hindsight, giving both the MSP and the customer far greater control over performance, cost and resilience.

Redefining infrastructure lifecycle strategy

Traditional OEM-led refresh cycles are also coming under scrutiny. It no longer makes financial sense to replace hardware on a fixed timeline when it’s still running effectively. As a result, organizations are more likely to extend infrastructure lifecycles, provided they have the expertise to manage risk and performance. For MSPs, this creates an opportunity.

Partners that can help customers balance performance, reliability and cost optimization are playing a far more strategic role in IT decision-making. Third-party maintenance (TPM), once seen as purely a cost-cutting tactic, is now playing a key part in broader infrastructure optimisation. In hybrid environments, it gives organizations the flexibility to keep reliable systems in place longer, while putting their investment into the areas that really need it.

This allows MSPs to go beyond service delivery and play a more strategic advisory role, helping customers understand what to replace, what to keep and how to align support with what the customer actually needs. By combining lifecycle data, performance insights and support histories, MSPs can make far more informed recommendations about when to extend, when to replace and where to optimize.

This evolution doesn’t diminish the role of OEMs or traditional managed services. It reflects a shift towards the idea that customers now expect flexibility. In multi-vendor, hybrid environments, MSPs are increasingly judged not by vendor alignment, but by their ability to deliver outcome-driven, objective guidance.

The future of the MSP

The definition of value in managed services is being fundamentally rewritten. Closed ticket metrics still matter, but they no longer define the leaders in the market. Value is now measured by what doesn’t happen - outages avoided, inefficiencies eliminated, and unnecessary spend reduced. It’s reflected in better utilization, longer asset lifecycles and infrastructure that performs predictably under pressure.

To meet these expectations, MSPs need to evolve both what they offer and how they deliver it. They also need a mindset shift away from simply managing infrastructure and towards making sense of the data it generates and turning it into meaningful business insight.

This shift is changing the growth trajectory of the channel. MSPs that stick to a break-fix model risk being reduced to a commodity, competing primarily on price as margins continue to shrink.

The next phase of growth belongs to MSPs that can move upstream, translating data into insight, insight into action, and action into measurable outcomes. That requires building infrastructure intelligence capabilities that go beyond visibility, integrating optimisation and lifecycle management into a continuous, insight-driven model of service delivery.

As IT estates continue to expand and fragment, success will depend on the ability to provide direction as much as delivery. MSPs that can simplify complexity and help organisations make better decisions will move beyond day-to-day operational support to become true strategic partners, trusted not just to run infrastructure, but to help shape how it develops over time.

Open source can't catch a break. As the world of software development marches forward with new tools and capabilities expanding productivity – an evolution driven predominantly by AI – the community endures significant headwinds.

"In the age of AI-driven security threats, protecting customer data has to come first," wrote Bailey Pumfleet, co-founder and CEO of scheduling software maker Cal.com, in April.

After five years as "open source champions", the company announced it would go "closed source". Cal.com is far from the only example, with several organizations and projects including Tailwind, curl, Jazzband, Godot – and even the U.K. National Health Service (NHS) – also struggling in the wake of AI.

Tension has always plagued open source, with concerns around funding, infrastructure, and even expectations management, negating any meaningful green shoots. The threat has even been described as "existential", but the community has nevertheless survived and thrived, celebrating incredible adoption rates in recent years. But things feel different. AI poses an existential threat in many areas — but will it continue to hack away at the beleaguered open source community, or is there a viable path forward for the movement?

Open source is battling AI-centric headwinds

"In the past, exploiting an application required a highly skilled hacker with years of experience and a significant investment of time to find and exploit vulnerabilities," wrote Cal.com's Pumfleet in the blog post. The reality is that humans don’t have the time, attention, or patience to find everything. Today, AI can be pointed at an open source codebase and systematically scan it for vulnerabilities."

Security is just one aspect in an increasingly fraught dynamic. Tailwind, the organization behind a popular CSS framework that web developers incorporate into their projects, announced on 7 January it had slashed its four-person dev team to just one. The reason, CEO Adam Wathan cited, was "the brutal impact AI has had"; web traffic to documentation – which is the only way people can find out about its commercial products – was down 40% from early 2023.

There are also examples of projects struggling to cope with an onslaught of low-quality contributions. Take Godot, the open source game engine that's drowning in AI slop code pull requests (PRs), according to one of the primary maintainers, Rémi Verschelde, who described this as "increasingly draining and demoralizing".

The Jazzband collective, a Python project ecosystem, was sunset this year after ten years of activity due to the "slopocalypse" with floods of PRs and AI-generated spam rendering "Jazzband’s model of open membership and shared push access untenable".

There are plenty of other issues to contend with. They may include the fact that using advanced AI tools may be gated on the need to spend money, and that it's unclear whether you're speaking with a human or an AI agent at any given time.

How open source projects are coping with AI

Amanda Brock, CEO of OpenUK, tells ITPro that the industry is still grappling with AI as a novel force and different projects are handling it in different ways – with some handling it much better than others. "For some projects, they have not been able to manage the scale of it. And for some small companies, they have said they've had to close," she says, but added this isn't a universal experience, with others managing to get a grip on the headwinds.

"I'm skeptical about their business plan," Brock added on Tailwind, "and I could be wrong, and I'm not an authority on this in terms of each company's business and whether they were doing well enough. But I'm a bit skeptical when many are surviving about the few that are closing, and I don't know if that's the most vulnerable or the ones without the right business structures or teams."

However, Brock states the challenges are very real and unlike much else the community has experienced. For example: "When AI comes to Wikipedia, it not only comes at a huge scale by volume of eyes on the site or the pages, it looks at every page, and it does it in an instant. And the overwhelm is huge. And that's the equivalent of what open source projects feel when their repos are scraped; when they suddenly have this inbound volume from AI."

An example of a project that's fared better in wrestling with AI is Homebrew, the package manager. Its project leader Mike McQuaid tells ITPro Homebrew's response was to fight fire with fire. "We have had an increase in low-effort activity, but we respond with low effort," he says. "Our bots automatically close issues, we’ll close without review, we don’t dignify them with a response, et cetera."

McQuaid adds that, despite some notable examples, it's overstated that many projects are going closed source or winding down, and it's not representative of the wider experience. He says projects weather the storm "the same way we always have," with one measure including prioritizing maintainers' time, effort, and enjoyment over contributors, and prioritizing contributors over users. Responding to rudeness is also met with low effort, no effort, or blocking. He also suggested leaning very hard onto guardrails.

Resolving the fate of open source

McQuaid says the notion that AI could lead to the end of open source is "far too dramatic" and that there are plenty of positives to enjoy in a grand trade-off, despite there being an element of entering the unknown. These positive improvements include the way that AI allows some people to go much faster, that there are plenty of free AI tools available, and that many of these can help with code review, fixing bugs and triaging.

"It isn’t making huge negative changes without any positive remediations," he explains. "It’s just resetting people’s expectations of what 'working on open source' might be like for them. Some people won’t enjoy it any more. Many people equally are contributing who didn’t or wouldn’t before."

Despite many positive steps taken in the open source world over the last decade, Brock dwells in a sense of pessimism – but not entirely because of the immediate effects of AI. "I have been very pessimistic in some ways for a long time," she says, "because after lockdown everybody came out seeming to think that 'we'd won'." And what we'd won was, we've got a scale of adoption — but that scale of adoption wasn't matched by a scale of understanding, or funding.

"My worry was that you were going to see it, go full circle and end up back in proprietary because of exactly this kind of thing. I did not imagine AI, but I could see that things might happen, that meant that we were just overwhelmed because we got into a position where people expected an SLA-type delivery and support for free because the code was free."

Building on open source's legacy

"If it hadn't been AI, it would have been something else," Brock reiterates, saying the challenges have been piling up "at a time when people are reeling" one after another in the last few years.

"We've just had a decade since we really saw all this adoption start. We're a small group of experts [and scaling from] the small to the many hasn't worked."

Brock wants to see more gatekeeping, because "good projects have always gatekept". She adds: "There is this sense in the wider world that open source has always been a wild west, and that anybody can contribute – and that's just not the case."

Beyond that, she sees hope in models like the German Sovereign Tech Agency, which brings together dozens of maintainers into standards development. There are calls in the U.K. for a similar system, with a foundation in the model of the Linux Foundation, or China's OpenAtom, that brings together expertise at a national level to work on intellectual property (IP), managing GitHub repos, and other key elements, for public sector open source.

By professionalizing the industry, however, you run the risk of losing projects when things go wrong. CHAOSS, a Linux Foundation project that measures open source health, is an example of a project in dire straits, having just lost its funding. Former director of data science Dawn Foster left her role in March, and the project has returned to the community.

But, Brock says, "it's hard to reignite the volunteers" when they see funded people doing the work. "So it all has to be done in a very careful, measured, and joined-up way across borders," she explains.

When it comes to the future, Brock remains adamant that urgent action is needed or open source will be consigned to history. "We're already seeing eight companies controlling the AI landscape," she says.

"If we allow open source to fail, the only organizations in the world that will be able to manage that are some of those big tech ones that will already have it in place. So I don't think it's in the human and public interest to allow open source to fail."

Email phishing has always been a simple yet effective form of attack. While the method isn’t going away, research is showing attackers evolving beyond email to multiple channels such as Slack and cloud-based platforms.

That’s according to security firm KnowBe4’s Phishing Threat Trends report, which found hackers are leveraging new “touchpoints” when targeting victims, with calendar invites and messaging tools a frequent tactic. The company recorded a 41% increase in Microsoft Teams-based attacks between October 2025 and March 2026 as adversaries strived to create more avenues for success.

With AI offering cybercriminals the ability to supercharge phishing attacks further, what should businesses be doing to manage the risk?

Phishing evolution

In the past, phishing attacks relied on adversaries targeting a wide range of users in the hope that some of them would engage with malicious content, or give away valuable information. But since then, businesses have improved their security, with tools such as multi-factor authentication (MFA) used as standard. This has forced attackers to utilize more sophisticated techniques.

“When attackers cannot compromise technical controls, such as MFA, they are instead seeking to bypass the technology and move the attack onto end users through social engineering,” says Luiz Simpson, head of offensive security at Bridewell.

He cites the example of illicit consent grant attacks in Microsoft 365, which trick users into granting access to data. These attacks often “go completely under the radar”, says Simpson. “Users will accept an untrustworthy app while logged into their cloud workspace and under the legitimate Microsoft or Google workspace platforms. These attacks don’t require any bypass of MFA and almost always aren’t flagged by detection and response.”

Another reason attackers are targeting multiple channels is the fact that the workplace itself has changed. “Employees now spend far more time in collaboration platforms, cloud applications, messaging tools, and video conferencing environments,” says Ray Canzanese, director of Netskope Threat Labs. “Attackers are following that behavior.”

Canzanese describes how phishing lures are increasingly delivered through platforms such as Microsoft Teams, Zoom and fake meeting invitations. “These are designed to exploit the trust users place in familiar collaboration workflows.”

From a criminal perspective, multi-channel phishing is becoming “progressively structured”, according to Benson Varghese, a criminal lawyer and founder and managing partner at law firm Varghese Summerset. Rather than flooding potential victims with random messages, phishers will prepare their sequence, test the response rate, and react to engagement in real-time, he says. “This makes attacks more focused and effective.”

Security shift

Experts believe the evolution of phishing requires businesses to change the way they think about securing communication channels.

The shift means no longer treating phishing as purely an email security problem. “Security teams need visibility across a much broader digital environment that includes collaboration tools, cloud platforms, browsers, unmanaged devices and AI applications,” according to Canzanese.

Almost all social media platforms include user messaging, which supports the distribution of links and images. Yet historically, the focus on preventing social engineering attacks has been around traditional email-based messages, encouraging users to avoid clicking links. “None of these defences will help prevent attackers from contacting end users and sending malicious content across other channels,” says Simpson.

“As these interactions do not arrive via traditional email, there is no URL rewriting, sandboxing or inspections of content,” he points out. “This leaves you with just endpoint and identity-based controls to protect users.”

However, endpoint detection and response (EDR) solutions traditionally fall short in having visibility of what goes on within a browser, Simpon explains. “If a user is to bring your own device (BYOD) with access to enterprise resources, you are very much on the back foot to prevent identity-based attacks.”

Managing multi-channel phishing

The move to multiple channels is just one way phishing is changing. In tandem, AI is accelerating the quality and scale of attacks, while lowering the barrier to entry for new cybercriminals, according to Danny Jenkins, CEO of ThreatLocker.

“Attackers no longer need to spend hours researching targets or carefully crafting convincing messages,” he tells ITPro.

“AI can generate highly personalised, context-aware phishing content in seconds – whether that’s a Teams message, a fake document-sharing notification, or a voice deepfake impersonating a colleague or executive.”

There is also a rise in adaptive social engineering, where AI-driven phishing attempts evolve in real time based on how a user responds, he warns. “Instead of relying on static messages, these interactions can mimic natural conversations, making them significantly more convincing,” explains Jenkins.

The threat from multi-channel phishing is certainly growing, but firms can help boost defenses by making several key changes. Some of these are cultural.

From a security perspective, organizations need to accept that they will not stop every phishing attempt across every channel, says Jenkins.

“Humans are imperfect, and mistakes will happen, and no amount of training can prevent every phishing attempt.”

Instead, the priority should be reducing the impact of an attack and tightly controlling access, rather than trying to eliminate exposure, he advises.

Passkey use can help, says Simpson, pointing out that the UK National Cyber Security Centre (NCSC) has started to actively recommend the adoption of passkeys over passwords and MFA.

“Passkeys help address many of the shortfalls of traditional passwords and MFA. Notably, they’re resistant to phishing because they’re tied to the legitimate website only; they are fast and convenient, and cannot be stolen if a website is breached.”

As with traditional phishing, organizations should adopt a strategy of user awareness and technical controls to defend against multi-channel attacks, according to Simpson.

This means ensuring users are aware that a spectrum of social engineering attacks are possible, beyond solely email-based phishing. Users, especially those in high-risk roles, such as executives and finance teams, should be targeted with training to empower them to identify and report attacks, Simpson advises.

“This should be an ongoing message and constantly refreshed based on active attacks seen in the wild, rather than just an annual policy that’s read and signed off.”

Most Managed Service Provider (MSP) service desks already have several layers of automation. From chatbots and self-service portals to AI routing and virtual agents, the tooling is widely used. Some of it resolves tech issues well, ensuring that skilled engineers are not wasting time on password resets and basic access requests.

However, problems usually start when a ticket is passed between multiple teams or arrives without the context needed for troubleshooting.

Even though MSPs are already tracking the usual metrics: ticket volume, average handling time, first-contact resolution, and technician workload, L1 teams can spend huge chunks of the day just trying to gather context. Some support teams jokingly call it the “20 Questions” phase. Who is the user? What device are they using? Has anyone already touched the machine? Did the VPN fail before the update or after it? Is this even the right queue?

A lot of that happens because support teams still lack clear visibility into what users are experiencing on the endpoint. Most MSPs now operate across ticketing, endpoint management, monitoring, remote access, and documentation tools that do not always share information particularly well. So technicians end up jumping between systems trying to piece together context that should already be sitting in front of them.

According to our analysis presented at the Gartner Digital Workplace Summit London recently, saving just one minute of context-switching time across 10,000 monthly tickets equates to 166 hours of recovered support capacity.

That is basically a full-time technician disappearing into tab-switching and context rebuilding, which is not exactly a great use of skilled people.

Escalation is where things really start getting expensive

In reality, many escalations just restart the troubleshooting process from scratch. Support teams have all kinds of names for this: verification tax, re-diagnosis, or ticket ping-pong. In some environments, AI-assisted triage has actually made this harder to spot because tickets arrive looking neatly categorized while still missing critical context.

Nobody fully trusts the notes attached to the ticket, so the next technician repeats the same checks anyway. And honestly, sometimes they have a point. By the third reassignment, half the ticket notes barely make sense anymore.

That gets expensive fast once senior engineers start getting dragged into tickets that never should have reached them. L2 teams can spend 15–30 minutes rechecking information already confirmed earlier in the support chain. L3 engineers may still insist on verifying the root cause themselves before touching anything important. Leaving some tickets basically on a doomed escalation path from the moment the initial diagnosis goes wrong.

If the underlying ticket data and context are weak, automation can just accelerate bad routing decisions. Tickets land in the wrong queues, bounce between teams, or get escalated before anybody has properly understood the issue in the first place. Some queues basically become dumping grounds for badly categorized tickets.

Why AI support struggles with unpredictable problems

Every MSP wants users to handle the simple stuff themselves rather than flooding the queue with password resets and printer tickets.

The trouble is that self-service tends to fall apart pretty quickly once the issue is no longer straightforward. Somebody reports a “slow laptop.” The system suggests a few generic fixes. Nothing changes. The ticket lands in the wrong queue anyway. Then an L1 tech has to start from scratch, figuring out whether the problem is Wi-Fi, memory usage, a bad update, or some background process chewing through the CPU.

Failed self-service gets expensive fast. Failed self-service attempts can push incident-handling costs from roughly $6 to $53 once escalation and repeated troubleshooting are involved.

Part of the problem stems from AI-driven support tools that are trained on historical ticket data and static workflows, rather than real-time device information. After all, real support environments drift all the time. Devices fall out of policy. VPN issues hit one office but not another.

That is where the bad assumptions creep in. Tickets look neatly categorized even when the underlying diagnosis is wrong. By the time the issue escalates, half of the support recommendations are due to AI hallucinations. Skilled technicians recognize that almost immediately. However, the other automations usually do not.

Reducing wasted effort in the service desk

Just throwing more AI at the service desk doesn’t fix the underlying diagnosis problem. In reality, technicians still spend way too much time piecing together context, rerunning the same diagnostics, and trying to figure out how a ticket even ended up in their queue after failing First Correct Assignment.

Smart MSPs are starting to tackle the problem at the source. They’re rebuilding their self-service portals so users can just explain what’s wrong in plain English, rather than forcing them to pick from clunky categories. After all, no one ever reports a “DHCP lease failure”; they say the internet’s down. L1 techs are also gaining real-time visibility into endpoints, rather than relying on patchy ticket notes and whatever the user happens to mention.

When you can instantly see failed updates, VPN drops, crashed services, or a machine grinding to a halt, you waste a lot less time playing detective. But even with those improvements, most MSPs still hit the same wall: all the important information lives in separate systems.

Ticketing, RMM, monitoring tools, and endpoint agents don’t communicate effectively. So every time a ticket escalates, the next person basically has to start from scratch. That’s why some of the better-run MSPs are now creating a single shared record for endpoint health and diagnostic history. When L1, L2, and L3 are all looking at the same up-to-date facts, you cut out the endless re-checking and stop tickets from getting stuck on a doomed escalation path.

At the end of the day, automation and AI are only as good as the foundation they’re built on. If self-service keeps generating dirty tickets, or tickets keep getting escalated too early, or rebuilt every time they move queues, AI is just helping you make the same mistakes faster. The teams getting real results are the ones giving every support tier access to the same live endpoint context; that’s when AI actually starts pulling its weight.

The rise and fall of tokenmaxxing has been dramatic, not least because of how quickly it happened. From a darling of CEOs wanting to show how AI-forward they are to a balance sheet nightmare, the journey this particular strategy for measuring AI lasted mere months before its star started to fade.

In this episode of the ITPro Podcast, Jane and Bobby are joined by Frank Böhmer, CEO of Ninox, to talk about what prompted the trend in tokenmaxxing, why it fell apart, and what comes next.

Highlights

"[Gamification] leads to a pretty high stress factor for the employees, because they feel really pressed, even if it's not a direct performance metric, if it's just ... something that creates visibility, and so they feel really pressured to utilize LLMs to the highest possible amount."

"If you put this kind of as a success metric and monitor it, then it becomes useless, because people kind of optimize for that ... so even just looking at the output, so the number of tokens does not say a lot, and it's very easy to use models in a way that is not efficient. It can easily, for example, pollute the context window by just cramping in all the information you have."

"When you have to work with within the constraints of a budget or a token budget, then there are clearly ways to optimize the outcome I can create with models, for example, by providing the right information within the context of the model, by making sure that all the data that the company processes is readily available for models to work with by giving the right guidelines for teams to utilize that by giving them access to the right models in the right context."

Footnotes and sources

• Could rising token costs boost interest in on-premises hardware?
• Uber’s eye-watering AI bill shows enterprises are ‘still measuring AI success through consumption rather than outcomes’ – and it's warping our perception of ROI and productivity
• IT leaders are being stung by "unexpected" AI costs
• The AI pricing time bomb
• ‘What we’re seeing right now is just rapid escalation in AI token spend’: Accenture tells staff to stop using AI for unnecessary tasks amid surging costs
• Surging AI costs could exceed developer salaries by 2028 – analysts say context engineering could be the key to optimizing token consumption
• Companies are regretting investing in generative AI so quickly – here’s how to avoid buyer’s remorse
• Goodhart's law - Wikipedia

Louise Newbury-Smith, head of UK&I at technology specialist Zoom, describes her role as busy and exciting. With a remit to help the firm’s customers make the most of its technology and a general management responsibility for the business across the UK and Ireland, Smith says the busyness in her role comes with a side order of variety.

“What's rewarding is when we're doing amazing things for customers, very real things for customers, and actually, that's where you know the power of the platform comes in,” she says.

“Technology for technology's sake is fine, but it only gets really interesting when it's delivering something for people.”

Newbury-Smith joined Zoom in May 2025, having previously held senior-level positions at BT Engage IT, Daisy Group, and RingCentral. She reports to Steve Rafferty, Zoom’s head of APAC and EMEA. On a day-to-day basis, she manages 40 people directly but also oversees the work of 400 employees across the UK and Ireland.

“I’m supporting the team to deliver what they need to do for their customers, representing the brand, and making sure that Zoom is front of mind for people and businesses,” she says.

“We're doing quite a lot with the UK government, as an example. We're on one or two task forces looking at how AI can support small businesses.”

Innovation at speed

Newbury-Smith says there’s a rapid pace of innovation at Zoom. While many people associate the technology company with its video-conferencing tools, the breadth of the platform, including its AI-powered capabilities, continues to extend. A pull factor for joining the firm was the chance to help shape this strategy across the UK and Ireland.

“What really attracted me was the opportunity to take our strong brand awareness and increase the perception,” she says.

“The platform takes all the data we're producing through our interactions with other human beings and turns that into actions to support the employee in an AI-centered workplace. It was exciting for me to be part of that process.”

While the broader business strategy is set at Zoom’s San Jose headquarters in California, Newbury-Smith says there’s a high degree of autonomy for executives in the UK and Ireland to make important decisions. To support this process, the company has built a dedicated UK data center that’s designed to align with the country’s data residency requirements.

“We've now got all the services live on the UK data center,” she says.

“Zoom has empowered its regions to deliver what they need for their own markets. Clearly, having control over where your data is stored is hugely important, not just for public sector organizations in the UK, but beyond that sector in areas like healthcare and financial services because of all sorts of global data sensitivities.”

The combination of rapid technological transformation and geopolitical challenges means data residency is a key consideration for digital leaders in the age of AI and cloud. Newbury-Smith says her team’s approach and the provision of the UK data centre mean CIO requirements can be considered effectively.

“Our customers have got that choice with us about how they want to operate and what level of AI they want to tap into, and whether that's delivered locally or part of the global infrastructure, so data sovereignty is very much top of mind,” she says.

“There's well-documented information that is available to customers, so that they can make the right choices in how we set up their accounts.”

What’s clear from the discussion with Newbury-Smith is the extent to which adaptability is a core element of the Zoom business strategy. Six years ago, during the height of the coronavirus pandemic, the technology specialist went from being an enterprise staple to a brand that everyone around the world not only recognized but relied upon.

Newbury-Smith says her colleagues at HQ delivered great results in challenging circumstances. Now, the company faces a different challenge. Zoom must continue to hone its brand perception and develop innovative services in the AI era. She recognizes that the company faces a fresh burning platform for change and is eager to deliver for its customers.

“In its simplest form, our approach is about taking information and creating automated workflows,” she says. “There's an expression that I’ve been quoted on before about automating the predictable and humanizing the unpredictable. I think that's a powerful way of looking at the challenge. It’s about how we can enable those workflows and AI.”

Making a difference

Since joining Zoom last May, Newbury-Smith has worked to develop a culture where people on her team feel valued, and their professional and personal goals are recognized. She describes her leadership style as servant leadership, which aims to remove barriers so people can be the best version of themselves.

“I'm passionate about seeing the difference that we can make,” she says.

“We need to hit our KPIs and all those things, but will we be remembered for that achievement? Probably not. We’ll be remembered for how we make people feel, which is the folks around us, our colleagues, my team, and our customers.”

To that end, Newbury-Smith also recognizes her standing as a high-profile woman in the UK and Ireland technology sector. She’s eager for more women to step forward into senior IT roles and supports broadening diversity across the industry.

“That’s an easy thing to say, but it's what you do that makes a difference,” she says.

“I quite often ask the question in an interview: ‘What have you done to support diversity in your role?’ And it's interesting because some people think diversity is the responsibility of people and HR teams. And it's not, because we're all advocates for our industry, and we can all be evangelizing to encourage more people to join.”

Zoom runs events to encourage diversity, according to Newbury-Smith. One recent session focused on AI prompting. The event was run close to International Women's Day to encourage people to feel confident using AI. Her key message for other women looking to develop an IT career is to be themselves.

“There's a huge amount of pressure to be something else. I think women – as have all people – must be their genuine selves at work, because otherwise it's too exhausting. People need to know they don't need to fit into another persona. Being yourself is good enough,” she says.

“Success for any IT professional is about having that level of confidence in themselves, which is very easy to say, but not always easy to tap into. And I think if people feel they can be authentic, then that helps.”

Managed Service Providers (MSPs) were once viewed primarily as IT support partners. Today, they operate something far more critical: digital trust infrastructure.

MSPs manage identity systems, remote monitoring platforms, cloud environments, endpoint protection frameworks, and data backup architecture across dozens — sometimes hundreds — of client environments. In doing so, they have become central operational nodes in the digital economy.

That centrality also makes them strategically attractive to cybercriminals.

From service providers to trust custodians

Modern organizations depend on MSPs not just for operational efficiency, but for secure access management, infrastructure resilience, and regulatory alignment. MSP platforms sit at the intersection of customer networks, cloud services, SaaS applications, and identity environments.

This position makes MSPs stewards of inherited trust across entire business ecosystems.

When that trust is compromised, the consequences extend far beyond a single organization.

MSPs now operate critical digital infrastructure. When that trust is compromised, the impact multiplies across entire client ecosystems.

Why attackers are targeting MSP ecosystems

Threat actors increasingly mirror the scale economics of managed services themselves. Rather than targeting organizations individually, attackers focus on centralized service environments that provide multiplier effects across downstream clients.

The logic is simple: compromising one MSO can provide access to dozens, sometimes hundreds, of connected organizations.

Remote monitoring and management platforms, multi-tenant administration consoles, and aggregated identity systems have become high-leverage entry points. Once inside, attackers can move laterally, harvest credentials, conduct reconnaissance, and deploy payloads across multiple customer environments simultaneously.

Campaigns attributed to groups such as DragonForce demonstrate how exploitation of MSP tooling can enable credential theft, data exfiltration, and ransomware deployment at scale.

Attackers have adopted the same logic as manager services: centralize access, standardize operations, and scale efficiently.

The industrialization of cybercrime meets the managed services model

This convergence reflects a broader shift in cybercrime operations. Criminal groups now prioritize scalability, automation, and repeatable processes — principles that mirror the operational models used by MSPs.

Cybercrime has become industrialized, adopting structured affiliate programs, service platforms, and monetization strategies designed to maximize efficiency.

Managed service environments naturally align with this approach because they aggregate infrastructure, identities, and administrative access into centralized systems.

For attackers, this represents an opportunity. For MSPs, it raises the stakes of operational resilience.

Resilience maturity as a competitive differentiator

As threat exposure grows, resilience maturity is becoming a defining factor that separates strategic MSP partners from commodity service providers.

Customers are increasingly evaluating MSPs based on governance transparency, identity security controls, incident readiness, and third-party risk management practices. Regulatory frameworks such as NIS2 are further reinforcing expectations around operational accountability and supply-chain oversight.

Security is no longer just a technical feature; it is a business trust signal.

Resilience maturity is becoming the dividing line between strategic MSPs and commodity providers

Forward-looking MSPs are strengthening privileged access controls, monitoring behavioral anomalies across multi-tenant environments, segmenting client infrastructure, and conducting continuous supply chain risk assessments.

These measures do more than reduce exposure; they demonstrate strategic commitment to protecting customer ecosystems.

Intelligence-led defense in interconnected ecosystems

As digital environments grow more interdependent, reactive security models are proving insufficient. MSPs increasingly benefit from adversary-centric threat intelligence that tracks how specific attacker groups operate, which tools they exploit, and how campaigns typically unfold.

This approach enables earlier detection of suspicious behavior and faster disruption of attack chains before compromise spreads downstream.

Predictive threat intelligence also allows MSPs to anticipate emerging risks across their customer base, rather than responding only after incidents occur.

The future role of MSPs in digital trust

The role of MSPs will continue expanding as organizations seek partners capable of managing both operational complexity and cybersecurity risk.

That reliance reinforces the MSP’s position as a custodian of digital trust — a role that extends beyond service delivery into governance, resilience, and ecosystem-wide risk management.

The providers that succeed in this environment will be those that recognize this responsibility and invest accordingly.

MSPs are no longer just managing infrastructure. They are safeguarding the trust architecture that modern business depends upon.

Britain might be in the middle of a heatwave, but cast your mind back a few weeks, and you’ll remember the rain.

In fact, Met Office estimates suggest 2026 has seen well-above-average rainfall, with some areas recording their wettest conditions in over 100 years during January and February, followed by a notably unsettled and wet spell across May and early June.

Such are the fluctuations in weather conditions that sudden rainfall can result in flooding, creating huge challenges for residents and public sector organizations. It’s into this fast-changing environment that an innovative project between Ordnance Survey (OS), Britain’s national mapping service, and technology specialist Snowflake aims to bring data-led clarity.

The two businesses have worked together to create an Intelligent Flood Readiness Model (IFRM), which has identified 1.2 million undefended buildings at risk of flooding in England, many in the most deprived parts of the country.

Harnessing the power of data-led insight

The initiative is opening eyes to data-powered opportunities, according to Tim Chilton, managing geospatial consultant at OS.

“We’re trying to create a broader conversation around the types of things technology can do across government,” he says.

“Several parties have come to us and said, ‘This is interesting, can we talk?’ What I'm trying to do on behalf of OS is to create those conversations and ask, ‘What could you achieve with our data and this kind of technology?’”

Chilton helps OS explore new commercial opportunities from its treasure troves of data. The organization started working with Snowflake in 2024. The organization uses the Snowflake Marketplace to share open data with public-sector organizations and explore commercial avenues. The priority now is ensuring OS embraces other data-led advances.

“We have a platform strategy, which is about putting our data where the customer works,” he says.

“The person who uses our data might work in geospatial, they might be a data scientist, and increasingly, as you'll find with the IFRM, they could be a businessperson who wants to ask a natural language query and be confident the results are correct.”

That approach resonates with Snowflake’s longer-term business strategy. At its recent Summit 2026 event in San Francisco, senior executives outlined the company’s plans to use agentic AI to open data access to IT and non-IT professionals. Chilton says the IFRM is an exemplar of the joined-up initiatives that will help organizations make the most of data in the age of AI.

“The model is about Snowflake and us talking about how AI can supercharge the timelines around having a problem and trying to solve it. Geospatial data really brings that data-led journey to life. A lot of government strategies are based around multi-year plans, but the climate is changing more frequently and regularly in more extreme ways than before,” he says.

“The question we asked was, ‘How could we use technology and bring in the latest data to check that those plans are still right for specific areas and even individual buildings?’ We hoped the model could help us keep on top of the issues and monitor and measure the impact of that plan on the ground.”

Finding the path forward

OS and Snowflake started the initiative in early 2026. After spending a couple of months working on the model, including ensuring the right data sets were being tapped, the organizations recognized that other sources could provide further depth, particularly the National Geographic Database. Chilton says the model will continue to be honed iteratively.

“If someone says, ‘Ah, but you didn't include this data that we've just released,’ we can say, ‘Good point, give us a week, and we'll give you some early results with your data included,’” he says.

“So, the model is a collaborative way of working, where we’re reacting to those changes in the model and the data that it uses.”

The model combines six data streams into a single layer. This layer produces key insights. For example, cross-referencing OS building datasets with the Indices of Deprivation in England identifies where physical vulnerability intersects with social risk. This insight is then layered against other information, such as Environment Agency flood and risk data.

Chilton says three key steps were crucial in the development of the model. First, project staff used the Snowflake CoCo agent to turn text-heavy documents, such as Flood Risk Management Plans, into data that the model could read in natural language.

Second, Snowflake’s semantic layer technology helped the model to understand, with guidance from OS experts, the meaning of attributes in the organization’s data sets. Finally, project staff used the Snowflake CoWork agent to create a natural language interface that allows anyone to ask questions about flood readiness.

“That capability was amazingly well received,” he says. “People can use the interface to query six well-organized and well-formatted data sets. The extraction of insights from documents, the semantic layer, and the CoWork frontend combined to create this powerful capability.”

The right data, in the right hands

"Data is at the heart of making informed decisions. As this project shows, it's rare that one body holds all the relevant data or that this data is in the same format,” says Fawad Qureshi, global field CTO at Snowflake.

“But we're now in an era where technology can bring together the right people and the right data to collaborate on making better-informed decisions."

By combining OS building data with information on flooding, social deprivation, and 3,000 pages of Flood Risk Management Plans, the model allows decision-makers to understand the potential impact of flooding in more detail than ever before. Chilton says these insights will help policymakers better understand the likelihood of flooding.

“There are already flood risk models; that idea is not new,” he says. “What is new is the ability to bring in much more granular data tied to flood risks. Not everyone in government was aware we had that level of detail. This initiative has provided an effective, practical application of some of our knowledge.”

Further work is planned in the wake of the model’s success. OS aims to make it easier for users to ask questions of data. The organization is exploring new technologies, including AI, to make data more accessible, enabling a wider range of users to unlock its value.

“We're actually ahead of the curve in comparison to many of our customers and our partners,” says Chilton.

“We're educating through doing and sharing demonstrators and great case studies like this model, and gradually you can see that cultural shift with people saying, ‘I get it, I know why there's a lot of fuss about AI and data, and we're going to give it a go.”

Other potential data sources include information from the Office for National Statistics, the British Geological Survey, and the National Underground Asset Register. In fact, OS aims to deliver an integrated approach to data within the next 12 months. Chilton returns to his key message for other digital leaders in the public sector considering AI projects.

“Give it a go,” he says. “I've seen the governance, security, data protection, and robustness of these tools. We've gone through the due diligence process, and Snowflake is a system that we trust to hold our data.”

People in the technology sector often joke that anything new in winter will be obsolete by spring. This is especially the case in cybersecurity, where hacker groups and cybersecurity teams are locked in an ever-escalating war of attrition.

The speed of obsolescence far outpaces the legislative process: it can take up to two years for government bills to be enacted into law. This is due to the parliamentary process, where various readings, committee hearings and reports are required to ensure that bills are adequately scrutinized.

An outdated system

“The legislative process in this country is essentially a Victorian process, in the sense it takes a long time,” says James Morris, chairman of CSBR, who spoke to ITPro earlier this month at InfoSecurity Europe in London.

“Meanwhile, the world is changing every day. How you deal with that is, I think, a question of not wanting to try and do everything at once.”

Legislation tends to lag behind technology as the speed of technical innovation far outpaces the law-making process. Therefore, cybersecurity legislation risks becoming redundant before it is enacted.

Compounding this challenge is the intensification of cyber threats, in particular the problem of state-sponsored hacker teams and organized crime groups repeatedly targeting national infrastructure for financial or political gain.

“The Cyber Security and Resilience Bill represents a step change to our national security that will protect the services people rely on every day - reducing the risk of disruption to public services and businesses, and ensuring a faster national response when threats emerge,” said a spokesperson for the government.

“The Bill gives government powers to update cyber regulations as risks evolve, so our defences can keep pace with evolving threats. But there is also a lot we can do, and are doing, outside legislation. For instance, we're urging organizations across the economy to boost their resilience by signing up to the Cyber Resilience Pledge.”

As few politicians have an in-depth understanding of cybersecurity, they hence rely on committee hearings in order to become informed on the subject. However, the expert speakers have purely an advisory capacity, so governments are not bound to abide by their guidance.

As a consequence, there is often friction between government regulators and private enterprise. The government regulators could perceive emerging technologies as a potential risk to the public, whilst technology companies might consider that regulatory guard rails hamper development and innovation.

Regulation vs innovation

“There's a tension between regulation and innovation, and wanting the UK to be a technological cyber power in its own right,” says Morris.

“That’s a constant tension in policy, and that applies to the Cybersecurity and Resilience Bill as well, because as that regulatory system starts to get embedded, it's going to have an impact on small and medium-sized companies. We don't want to over-regulate markets which present the UK with growth opportunities.”

Given the rate of progress in technology, acts of law set broad expectations, with associated government regulation intended to give specific minimum expected requirements. Regulations are far easier to revise than government acts, as they often incorporate the so-called Henry VIII powers.

Henry VIII powers are clauses within government regulation that bestow upon government ministers the authority to change secondary legislation (such as regulations) without oversight from Parliament. However, the lack of transparency in Henry VIII powers means they often have restrictions on the extent of their authority, in order to limit potential abuse.

In order to ensure accountability for Henry VIII powers, the authorising partner can be cross-examined before a parliamentary committee on the reasoning behind regulatory updates they actioned.

Organizations can help by engaging with the legislative process, through offering their expertise to parliamentary committees, which regularly seek expert views.

“Any responsible government needs to be engaging. One of my concerns around the Cybersecurity and Resiliency Bill has been it's a bit top down. It gives government a lot of power and doesn't really talk about consulting with business,” says Morris.

“I'd like to see more of that because you're not going to make progress in improving standards across the board if you just impose stuff top down.”

The role of AI

AI is one of the key areas of current focus and technological development. It is one of the most rapidly developing fields of technology, and there are growing calls from many sectors for regulation.

“The challenge is about timescale and the rapidity of innovation, because when the Cybersecurity and Resilience Bill was introduced 16 months ago, AI was being talked about,” says Morris.

“But in that period, if you think of all the different innovations and things that have happened around AI, it's become much more of a central focus in discussions about resilience and critical national infrastructure.”

In addition to this, data sovereignty is coming to the fore as one of the key issues surrounding the use of AI. “I think we should be developing more sovereign technological capability, because we are very dependent on US data processing, and so on,” says Morris.

Many state-run organizations are colossal institutions with massive amounts of data, which could potentially be used to train AI tools to help government departments become more efficient. For example, Microsoft’s Dragon Copilot is being trialled to transcribe patient consultations with a clinician and automate the associated note-taking and form-filling.

Also, with current geopolitics, there is a question regarding whether the government and national infrastructure should move towards using systems developed within the UK. The UK government has previously found itself reliant on external technology providers, such as Huawei, which could potentially cause security concerns and data sovereignty issues.

“We need to look at how the legislative process could be improved, as it not just affects cybersecurity, but lots of other aspects, particularly around infrastructure development,” Morris adds.

“In the age we're living in, we need to look at how we can speed up legislation, because parliament is still operating like it was on a very traditional model.”

In the age of AI, perimeter defense alone is no longer enough. Traditional methods for securing IT estates are horribly vulnerable to increasingly sophisticated AI tools, leading to a rapid degradation of many organizations’ defenses.

On a global scale, in April, the news broke of Claude Mythos’ limited release and the danger it could pose to cybersecurity worldwide. The hyper-powerful AI model rapidly uncovered flaws and vulnerabilities in defense systems that had lain dormant for years, threatening the integrity of banking systems, energy networks, and more.

In the UK, more than three-quarters of UK businesses have suffered a cyber incident in the past year. What’s more, 43% of UK IT decision-makers identified AI-powered attacks as the single biggest risk they face over the next 12 months, ahead of traditional threats such as ransomware, phishing, and data breaches.

Turning lemons into lemonade

Clearly, there’s plenty of cause for concern here – the challenge is significant, and the potential damage could reach far beyond companies’ bottom lines. But that doesn’t mean the IT industry should throw up its hands and accept the inevitable. Rather, the rapid growth in AI-driven breaches is a sign that a new approach is needed.

For managed service providers (MSPs) in particular, the evolving threat of AI presents a business opportunity rather than just another security burden. As customers seek to handle cloud patching complexity, regional compliance differences, and increasingly automated attacks, MSPs that can unify security, operations, and automation in a single offering will be best placed to improve service quality and usability – and so unlock increased profitability.

This isn’t wishful thinking: organizations are making plans to invest in technology that can help them tackle the AI challenge. AI and advanced threat preparedness is the top spending commitment for UK organizations over the next 12 to 24 months, cited by 41% of 1,500 IT decisionmakers ManageEngine recently surveyed.

Simpler, faster

There is also a growing gap between how quickly organizations detect incidents and how long it takes them to recover, which is where MSPs can provide real value. The majority (94%) of UK organizations detect incidents within 24 hours, and nearly half recover within 10 days. However, 26% said recovery can extend beyond 10 days, with a smaller proportion taking more than 20 days.

In response to that inefficiency, MSPs can help by reducing tool sprawl, standardising workflows, responding quickly to incident reports, and packaging up more resilient service tiers. Clients will pay for this kind of rationalisation and streamlining, providing, as it does, a crucial way to reduce the time between a security incident and a successful resolution.

Putting operational tech at the center

MSPs can also provide value in the battle against AI-driven cyberattacks by including operational technology (OT) in the development of security systems as a priority rather than an afterthought. OT is becoming part of the managed risk surface and requires the same disciplined approach MSPs already apply to IT – not least because OT software may traditionally have been seen as ‘lower-risk’, and therefore less diligently patched.

Again, the core benefit MSPs can provide clients with here is acting as the single point of contact that draws together oversight of all potential vulnerabilities. As organizations’ digital estates become ever more complex, applying security policies and automations to OT as well as back-office apps and systems can be a major headache.

MSPs with expertise across the board can not only build a unified policy to defend the entire attack surface – they can also radically simplify day-to-day management for client IT teams.

AI is turning the threat of cyber attack into a many-armed monster, hitting harder and in more places than ever before. In the face of this ramped-up threat, MSPs are uniquely placed to offer a unified, simplified service – and in that sense, the rise of AI breaches could be a real business opportunity.

While every CTO’s job varies, some stand out more than others, and being CTO of one of the USA’s national laboratories is one such case.

In eastern Tennessee, south of the city of Oak Ridge and close to the Clinch River, sits the aptly named Oak Ridge National Laboratory – or ORNL for brevity – and its CTO, Scott Atchley.

ORNL is one of two facilities – the other being Argonne National Laboratory in Illinois – that make up the Leadership Computing Facilities. These two locations, both of which are funded by the US Department of Energy (DoE), are home to some of the fastest and most powerful supercomputers not just in the US but in the world: Aurora at Argonne and, at ORNL, Frontier.

Atchley, a 16-year veteran of ORNL, tells ITPro that there have only been two weeks in his tenure as CTO that he’s regretted taking the job.

“That’s when we had deployed Frontier,” he says.

Full power failure?

Yes, even in one of the most advanced computing facilities in the world, it’s still entirely possible to have a relatively small fault cause chaos.

“[Frontier is] our current supercomputer, it's an HPE Cray EX system and one of the benchmarks – not the goal, but one of the benchmarks – for the system is that it would reach exascale, or 10 to the 18 flops,” says Atchley.

Supercomputers’ power is only announced twice a year – at ISC in Germany, which takes place in June, and SC Conference in the USA, which takes place in November. It’s from the announcements at these conferences that the Top500 list of most powerful supercomputers is drawn.

With the deadline for ISC 2022 fast approaching, Frontier was failing to perform.

“We were only about halfway [to 10¹⁸ flows], and we could not figure out why,” says Atchley. “We were having daily calls with HPE and AMD, and the deadline was coming.

“We were in a full panic, and this was the first time I thought, ‘my gosh, we might have bought the wrong machine’.”

Fortunately for Atchley and everyone else involved, he was wrong, and once the problem was identified, it was a “simple fix”.

“It was a one-line software change, and all of a sudden the performance shot up to right at just under an exaflop,” he says. “When they're new, [supercomputers] are very fragile. So, we rebuilt the machine during the day, and the next night we did the runs, and we finished it at about six in the morning.

“There were probably 50 people in line watching … and when the job looked like it was going to finish, all the chatter went quiet, nobody wanted to jinx it – we had been trying all night long, and it was really exciting when we saw the result that we actually exceeded an exaflop.”

Putting power to use

Top500 does have its limitations – only companies that run the LINPACK benchmark and submit the outcomes are measured, for example. There are also claims that it’s not particularly scientific, and Jack Dongarra, one of the creators of LINPACK, has himself admitted that its usefulness is limited.

“This benchmark reports the performance of this one application with its floating point operations and message passing. So there is one application and one number that represents the performance for the computer system. The weakness is that this is just one application and one number,” he told HPCWire in 2002.

Limitations and weaknesses don’t mean useless, however, and Atchley pushes back at the idea that Top500 is entirely meaningless.

“A lot of people will knock the Top500 … but we've actually had jobs on the system doing real science exceed an exaflop in performance, which is really cool, and so we're glad,” Atchley tells ITPro. “Our users love the machine, it's doing some incredible science.”

From aerospace to outer space – and back

Speaking of users, who is making use of Frontier? One case study Atchley gives is from the engineering firm GE. In partnership with French firm Safran, they have been developing a new type of jet engine.

“If you look at engines when commercial airliners came out in the 60s and early 70s, the engines were really narrow, really long,” says Atchley. “If you flew[today] and looked out the window, the engines now are really short, but really wide.

“Making them wider makes them more energy fuel efficient, but they're to the point now that if they make them any bigger, the gains they get in the efficiency they lose because of the drag on the nacelle.”

One option is to get rid of the nacelle – the housing around the engine – altogether. This isn’t a new idea, Atchley explains: “NASA has known about this for 50 years.”

The only issue is that using them on a commercial aircraft would deafen the passengers and crew, because they are – or would be – so loud.

It was only once Frontier came online with its exascale capabilities that GE and Safran were able to measure in high resolution the swirls of turbulence coming from the engine.

“Now they're tweaking the design to minimize those, and so that is the huge breakthrough, is understanding turbulence at a really fine scale. They can't do that in a wind tunnel. This engine is so big, there's no wind tunnel on earth that would fit it, so the only way they can do it is in a computer,” says Atchley.

As powerful as Frontier is, however, it can only model a 14-blade engine. In 2028, though, Frontier is expected to be superseded by the Discovery supercomputer, which may open up new opportunities for the likes of GE.

Once it’s online, ORNL will be home to three supercomputers: Frontier, Lux (which is expected to come online in 2026), and Discovery. At least for a time. Frontier will, ultimately, be decommissioned just as its predecessor, Summit, was.

“The challenge you get on these machines is even if the hardware doesn't run out or doesn't fail, the software, the vendors don't want to support something that's now 6,7,8 years old,” says Atchley.

“You think you want to have the latest software, you want to have the latest security patches, and so eventually you have to turn the machine off, but we had universities [and] industrial partners asking us, ‘can you run Summit longer?’ – we'd love to, but we can't.”

Sometimes there is a second life for these apparently beloved behemoths that isn’t just the recycling route.

“IBM was able to sell a portion of it to an oil and gas company, and so it is still doing work for them somewhere else.”

When the CrowdStrike update and associated outage adversely affected Windows devices in July 2024, many organizations were impacted. Just outside Houston, IT operations at the Spring Bank Independent School District (ISD) were among those affected.

Spring Branch ISD has over 31,000 children under its care across 46 schools, spanning 57 buildings and 6,000 staff members.

The organization faced significant challenges when the CrowdStrike outage hit, and it came at the worst possible time, given that summer school classes don’t run on Fridays.

“During the summer, school districts are primarily off on Friday, we work 4-10, so we’re closed on Fridays," Troy Neal, Spring Branch ISD’s executive director of cybersecurity and technology, told ITPro at Pure Accelerate 2026.

“[We] woke up Friday morning, turned on the news, and there’s this banner running across that says ‘CrowdStrike outage’ and I’m like ‘we’re a CrowdStrike customer, is this really affecting us?’

“Outages can be lots of things, so I looked at my phone, had some messages, and [I said] let’s start checking the systems, and [I] notice[d] the phone system was down.”

This posed a problem for the organization, given that these systems are hosted on-prem, Neal explains. Further investigation showed staff members couldn’t access some critical services.

At this point, the scramble to recover, or at the very least establish what was happening, was on.

Disaster recovery with Everpure

Spring Branch ISD works with a range of hardware vendors for storage, recovery, and backup purposes, Neal explains. Among these are Hitachi, Dell, Veeam, and Everpure, formerly Pure Storage.

High-capacity, high-performance storage is critical for the organization, which manages more than 60,000 endpoints spanning Chromebooks, iPads, and various Windows devices at schools across the district.

When Neal started at Spring Branch ISD some seven years ago, the organization was in the process of refreshing its storage capabilities due to the sheer volume of CCTV footage they stored.

“We have 4,400 cameras in our school district,” he explains. “We store about four to six petabytes of data on just CCTV, so we started doing our due diligence.”

This exploratory process saw Neal and colleagues scour the market for options, eventually narrowing the field down to Dell, Everpure, and Hitachi. Budgetary constraints were a concern, he notes, particularly given that Spring Branch ISD is publicly funded.

An initial offer saw Neal faced with a $7.1 million price tag for six petabytes across two locations over 10 years. He felt this was actually good value, but it was outside of his budget, which was just over half that amount.

Hitachi ultimately met Spring Branch’s requests, and the organization secured vital storage infrastructure to meet its increasingly demanding data needs. But it was here that the relationship with Everpure blossomed.

“We were really impressed with Pure at the time,” he says. “In the cyber space, 3-2-1 is the standard for backup strategies. I like 4-2-1, [as] you can never have enough backups.”

“I’m a layered defense kind of guy for everything we do, and so we had an opportunity with some extra bond funds to bring in another solution to be our secondary backup.”

That deal saw Spring Branch ISD adopt FlashBlade//S and FlashBlade//E solutions, which provide users with high-speed, scale-out backup storage. Notably, Neal says that when the CrowdStrike outage hit, these proved vital.

The road to recovery

After the initial furore of the CrowdStrike outage and the impact on Spring Branch ISD dissipated, Neal says he was in some way glad that it was a “CrowdStrike outage, not a Spring Branch ISD outage, and caused by an adversary.”

Focusing on the task at hand, Spring Branch found itself in a similar situation to many organizations elsewhere in the world: dealing with a botched patch.

“We’re in the office by 6.30 am that morning,” he says. “We pulled out our incident response plan, and [said] this is a live exercise.”

“Our first thing was their patch,” Neal adds. “That didn’t work too well.”

The initial workaround saw Neal’s team revert to a Veeam backup, which he says was “just a regular-sized virtual machine (VM) that we had”. This was expected to take around 45 minutes for a single VM.

Spring Branch ISD has over 200 VMs across its IT estate, meaning the recovery time could take days. With summer school students and staff expected back in on Monday morning, this could prove disastrous.

“We [said] this isn't going to work, let’s try Pure too.”

The resulting backup “took minutes,” Neal notes, adding “this is the performance we paid for and why we chose Pure.”

Leaning on Everpure backups, Neal says critical services were back up and running within around four hours. Thereafter, ancillary services were up by around noon.

“About six hours later, we were up and running, and then we did some cleanup work over the weekend. We also had to wait on some of our owners of some of these systems to test them, validate them, and then by the end of the weekend, we rank a whole other snapshot of every single VM,” he says.

“We had some good clean states going forward, and Monday came around, and summer school went on. Nobody knew the difference.”

Looking ahead, Neal says Spring Branch ISD plans to continue - and expand - on its relationship with Everpure. The organization is going back to a 3-2-1-style cluster, and recently brought in another FlashArray//C solution for additional backup.

Notably, the school district is also moving to EverGreen//One, the company’s storage as a service (STaaS) platform. This aims to unify on-prem and cloud storage resources, providing users with a single data storage platform based on a subscription. Neal notes that the flexibility and ease of use associated with this setup were particularly appealing.

“I’m going with a subscription-based model. So I don’t care what the hardware is, because that’s Pure’s problem to figure out and manage,” he jests.

It's a very uncomfortable end to June with temperature records once again being broken across Europe. The technology world is moving forward unabated, though, still talking about AI and, in particular, the workforce and infrastructure that underpins it.

Do we have the talent to meet the demand for AI? Do we have the power to keep our data centers operational? And can the European Union get us off our dependence on Google Workspace and Microsoft 365?

ITPro's news editor Ross Kelly joins Jane and Bobby to look back on some of the most interesting stories of the month.

Highlights

"It's a paradox, isn't it. AI is going to remove and destroy half of the entry-level roles within the next decade. White-collar roles are going to be impacted, but at the same time, we're dealing with a massive deficit, specifically for AI roles. The messaging here is so all over the place, so consistently, that I think a lot of people are just maybe a bit jarred by it all."

"The higher occupancy, the greater the energy draw, and if you've got a problem with energy supply, then greater tenancy or greater occupancy rather becomes a problem. I understand the problem of electricity demands, sitting here in a heat wave. People are already talking about how if everybody deploys air conditioning into their houses, that it's going to cause a problem for the national grid. I understand that, but the bit about occupancy being so high, I'm baffled that this is allegedly a negative part of this story."

"Not to offend any guests of said podcast, but it does sometimes feel like a bit of a 'no true Scotsman' argument when it comes to open source. That this isn't quite actually open source because this tiny bit of it is not actually, or this isn't open source because it's actually based on this thing that is a commercial thing belonging to somebody else. I think it's possible that the Document Foundation has a point that what the European Union has declared is like this open source saying truly sovereign whatever isn't, it's kind of halfway there, but I do also wonder if there's sort of almost a back compatibility stepping stone type thing going on, because already lots of organizations within the European Union will have been using Microsoft, so it might just be easier to have something that's based on Microsoft standards initially before then transitioning to something else."

Links

• UK faces huge AI talent shortage
• The UK is running on fumes as data center build-outs can't keep pace with demand
• Euro-Office 'sovereignty' claims questioned in scathing open letter by LibreOffice maintainers

UK Managed Service Providers (MSPs) have embraced flat-rate support because it’s made delivery scalable: standardize the stack, package the service, and give customers a predictable monthly cost.

However, support has expanded faster than fixed fees can keep up with. Users expect instant fixes, boards expect resilience, and compliance teams, insurers, and auditors increasingly expect proof.

At the same time, the modern customer estate - SaaS, cloud, remote endpoints, third parties, and integrations - creates more noise, more incidents, and more out-of-hours work.

The somewhat predictable result is margin erosion: more tickets, more tools, more engineer time, and the same recurring revenue.

That’s why, for MSPs, observability needs to become a clearly defined, billable capability focused on outcomes customers recognize - fewer avoidable incidents, faster resolution, better user experience, and evidence that stands up in audits and insurance questionnaires.

Flat-rate contracts stay profitable when demand is reduced, not just responded to, and managed observability is one of the most practical levers MSPs have to do that.

Support is scalable when demand is predictable

Flat-rate support only works when MSPs can predict and reduce demand. But customer estates keep getting more complex while contract values stay flat, so every extra ticket and escalation eats margin.The usual culprits are ticket volatility, tool sprawl that slows triage and drives up costs, and escalation creep that pushes routine issues onto senior engineers. When resolution slips, SLA penalties and reputational damage follow.

That’s exactly what observability should fix, preventing avoidable incidents and cutting time-to-resolution, but too often it’s treated as “free”, and the service desk absorbs the cost.

Observability becomes billable when it’s auditable

In the channel, monitoring can sound like a checkbox. Customers assume it’s already included, like antivirus used to be, until you translate it into outcomes they care about:

• Less downtime (and fewer productivity losses)
• Faster incident resolution (and fewer escalations)
• Better user experience (especially for SaaS and remote work)
• Evidence for audits, regulators, and cyber insurers (not just “we think it’s fine)
• Capacity and cost control (no surprise renewals, no shadow IT growth)

For many organizations, that evidence now affects audit outcomes and cyber insurance terms, including whether cover is available and how premiums are priced. That shift in language matters because it changes observability from a feature to a service line: something with a scope, deliverables, and a price.

Tier it: response vs assurance

MSPs don’t need to rip up contracts to monetise observability; they can add outcome-based tiers that make proactive work visible and separate reactive support from assurance.

A simple model:

• Baseline covers availability checks, basic alerting, and standard response
• Managed Observability is a paid add-on: alert tuning, SLOs, dependency mapping, service reviews, proactive fixes, plus monthly reporting that supports audits and insurer questionnaires
• Service Assurance is the premium layer for customers who need stronger governance: continuous optimisation, capacity planning, change-impact analysis, and board-ready resilience reporting

The key differentiator isn’t more alerts, it’s less noise and more action. Fewer false positives, faster diagnosis, and a credible narrative of what you prevented, not just what you fixed after the damage was done.

Packaging that doesn’t feel like platform marketing

The fastest way to make observability feel promotional is to centre it on a platform. The fastest way to make it feel valuable is to centre it on operations.

Three practical packaging moves MSPs are using:

1. Define the observable estate: Be explicit about what’s covered (networks, cloud, critical SaaS, key user journeys, core systems) so the scope is clear.
2. Make proactive work visible: A monthly assurance summary showing issues removed, top drivers, automation time saved, and early risks - reusable for audits and insurance.
3. Tie it to business-critical moments: Position it as risk control during migrations, site moA simple model: ves, mergers, major upgrades, and security hardening, not just BAU monitoring.

SLOs, not uptime: measure what users feel

Packaging won’t protect margins if the NOC is already drowning. Observability only becomes a revenue engine when it’s operationally credible, and that starts with alert quality. Duplicate, low-value alerts create noise, slow triage, and trigger unnecessary escalations, inflating delivery costs. Good alert hygiene means de-duplicating, setting thresholds that reflect real user experience, and tuning continuously based on what actually drives incidents.

Consistency matters, too: standard runbooks keep first-line triage predictable and reduce senior-engineer drag. Measure what customers feel with SLOs (latency, transaction time, failed logins, API errors), not uptime alone, and “single pane of glass” should mean one accountable operational picture, not one tool.

The unglamorous truth is that MSPs protect margin not by collecting more telemetry, but by turning telemetry into fewer tickets.

Compliance pressure is rewriting the value conversation

Budgets are tight, suppliers are being rationalised, and procurement is increasingly asking why they are paying if things still break.

Cyber insurance and regulation are sharpening that pressure. Insurers want evidence of monitoring and operational oversight, and frameworks like FCA operational resilience, DORA, and NIS2 are raising expectations for mapping and proof. In many cases, observability is moving from nice to have to a commercial or contractual requirement.

That’s why monetising observability works now: it shifts the conversation from reactive support to measurable assurance, differentiates you from low-cost providers, and protects margin without adding headcount by preventing failures from becoming tickets in the first place.

IT, in its current form, relies entirely on networks. From office LANs to transatlantic fibre-optic cables, to WiFi and cellular connections, no business can really carry out its day-to-day tasks without using at least one network.

Yet, with all the attention focused on AI and its vociferous appetite for GPUs, CPUs, and even storage, the network risks being overlooked.

Without both investment and new ways of engineering the network, connectivity could derail progress in digital transformation, the user and customer experience, data science, and, of course, AI.

Recent research from vendor Cisco found that three in four enterprises expect to hit the limits of their campus and branch networking capacity within two years, and they are seeing network traffic double year on year. Already, some enterprises say they are holding back on scaling up AI investments because of network constraints.

Unlocking the problem

“You have to look at where bottlenecks are,” Paul Stringfellow, CTO at IT consultants Gardner Systems, told ITPro.

“This is less an enterprise network issue, but more a backend issue. So, looking at how you ensure high capacity [connections] between CPUs, GPUs, and back-end storage.”

As Stringfellow points out, there have been advances in high-speed connectivity, including faster Ethernet speeds and RoCE (RDMA over Converged Ethernet), which allows remote direct memory access over Ethernet technology.

These, though, along with Infiniband and 400Gbps and even 800Gbps Ethernet, are mostly targeting data centres and cloud providers.

For client devices, developments in WiFi should improve both connectivity for users and for IoT devices. Technologies such as WiFi 6 and WiFi 7 – and in the future, WiFi 8 - look to increase bandwidth and provide reliable connections, even as more users or devices connect to the WLAN.

“WiFi also needs to be included in this conversation, so many users now use WiFi as the primary office connection. The idea of cabled connections is foreign in a lot of places,” says Stringfellow.

The move to cloud and SaaS-based applications, as well as widespread home working, means that much of an enterprise’s data traffic has shifted to WiFi, with users never reaching for an Ethernet cable. Effective WiFi has become essential, with poor connections directly affecting productivity.

“Enterprise Wi-Fi has evolved significantly and now carries far more than simple user connectivity,” Phil Huang, business development and field application manager for the UK and Ireland at vendor D-Link, told ITPro.

“Modern Wi-Fi networks routinely support collaboration platforms, voice and video applications, IoT devices, and a wide range of cloud-based services,” he says. “In that sense, Wi-Fi has become a strategic access layer rather than just a convenience technology.”

But, he says, there are still significant differences between wired and WiFi networks. And improvements in WiFi connection speeds are pushing bottlenecks further into enterprises’ core networks. This, in turn, is driving investments in the wired LAN too.

“Newer Wi-Fi 6E and Wi-Fi 7 access points are capable of exceeding 1Gbps throughput, meaning a standard Gigabit uplink can quickly become the bottleneck,” says Huang.

As a result, businesses are moving to both 10Gbps connections and 2.5GbE. This is moving closer to “standard” 1Gbps connections in cost. “The premium over traditional Gigabit Ethernet has reduced dramatically, making 2.5GbE an easy investment for organizations refreshing their infrastructure,” he says.

Installing 2.5GbE, and even 10GbE, is simpler than fibre-optic or even higher-speed copper LANs. But it still works well for most office deployments, at distances under 100 metres.

Campus to cloud

The bigger challenges lie both in connectivity from businesses to data centers and the cloud, and within the data center itself.

Here, growing data volumes and the need to reduce latency are prompting CIOs to look at how their networks are designed, as well as the hardware they run on.

“As SaaS and AI adoption grows, hybrid work reshapes access patterns and digital security follows users beyond traditional perimeters,” Nauman Raja, a director analyst at Gartner, told ITPro.

“Enterprise networks are no longer built around the data centre. Instead, they now gravitate around the internet.”

AI too is playing its part: expensive GPUs are a waste of money if the network cannot keep up. Network architects need to meet the high-bandwidth demands of uploading data for model training, usually to the cloud, and the low-latency needs of inference.

Organizations also need to consider how they connect their existing enterprise applications to AI models. Conventional networks are rarely built with these demands in mind.

“I don't think we're having a bandwidth problem. We're mostly having a design or infrastructure problem,” Wim Remes, a technology consultant who has also run operations for a managed security service provider (MSSP), told ITPro.

“Compute and data are not always in the same place. Either we bring data to the compute or we bring the compute to the data. The paths we create on the network need to be the shortest.”

“The top-level issue, to me, is that we’ve ignored ‘the network’ for about a decade and a half,” he adds.

“Does a full-stack developer know the basics of networking? The clear answer is no. The stack in ‘full stack’ stops at the web server at best. At the same time, many a CIO also doesn't really care about the network anymore.”

The challenge is to bring together new and emerging hardware technologies, such as 400 or 800Gb Ethernet and RoCE, with a network architecture giving the performance data-heavy, latency-intolerant applications need.

“Bad network design, such as routing internet-bound traffic through centralised data centres for software as a service (SaaS) application, or for end-user collaboration, such as video conferencing, will result in poor user experience and inconsistent access for both remote and on-site users,” Gartner’s Raja warns.

“As demand fluctuates with remote working patterns, network architectures must allow dynamic bandwidth allocations,” Raja adds.

“In many cases, smart traffic management and automated policies can reserve sufficient capacity for business-critical applications even during peak usage.”

That, though, means thinking less about the network as hardware, but as an operational issue across the business. After all, no one wants colleagues, or worse still, customers, locked out of applications because someone “tokenmaxxing” is blocking the LAN.

AI is already transforming the workplace, but does today’s workforce have the necessary skills to use it productively, appropriately, and safely?

The short answer, experts agree, is no. According to Forrester, most employees lack the baseline understanding, skills, and ethics to use AI tools like Microsoft Copilot or Google Gemini in this way. Among information workers in five countries, the analyst firm found that High AIQ only grew from 12% to 16% between 2024 and 2025, and front-line workers are likely further behind still.

“While we don’t have a survey of the AIQ of frontline workers at this point, we assume that they lag further due to gaps in education and immersion into tool usage,” notes J.P. Gownder, vice president and principal analyst on Forrester’s Future of Work team.

While natural language input and multimodal models can lower the operational barrier of AI tools, ease of input isn’t the same as the competence of use. Vendors consistently underplay the human side of AI, says Gownder, which includes the skills, motivation, ethics, and behaviors employers need to cultivate for employees to use AI successfully at work.

“[It’s not] a straight line to success: AI hallucinates, generates coherent nonsense that sounds right but isn’t, can violate privacy standards, and reinforce poor human decisions through its sycophancy.”

Natural language removes one barrier but quietly raises another: that of judgment, says Professor Rose Luckin, founder and CEO of EDUCATE Ventures Research. “The tool will answer almost any question fluently, including those it shouldn’t and when it’s wrong. So the claim that these tools are accessible to anyone confuses producing an output with producing a reliable one.”

What’s the worst that can happen?

Lack of ‘AIQ’ can lead to misuse and mistakes, and so a business opens itself up to a lot of risk if users aren’t properly trained, notes Holly Chate, coo of FutureDotNow – a coalition of UK-based leaders working to close the digital skills gap.

Untrained users might unwittingly share customer or company data: “If you put confidential information into an open source GPT, suddenly that data’s now publicly available,” she points out. They may also pass along incorrect information to customers or prospects, or generate confidently wrong answers about their next best step on the job.

There’s a further risk beyond individual error, Luckin adds, noting that people who don’t understand the tool are the most likely to outsource their judgment to it, accepting what it produces because questioning it feels beyond them. “Over time, that erodes the very capabilities they need to use it safely. The gap isn’t only about access, it’s about who retains the judgment to stay in control of the tool and who gradually hands that control over.”

The four key AI skills employees need to have

In reality, the use of AI tools can widen the digital confidence gap between employees, with the confident getting more capable, while the hesitant fall further behind. The solution to this, and to reducing the other related risks, is to ensure your staff gets the necessary training. But what exactly does this entail?

FutureDotNow developed evidence-based AI skills recommendations to embed into the UK Department for Science, Innovation and Technology’s (DSIT) Essential Digital Skills Framework. Its research found four key AI skills that every worker needs to have. These are AI literacy and understanding, effective communication with AI, critical evaluation of AI outputs, and responsible use of AI.

In the case of effective communication, prompting matters, as this is something that doesn’t come naturally to most people, notes Luckin. “The better the prompt, the larger the pattern you give the model to match, so structured prompts with clear context produce better output.

“My caution is about sequence and proportion,” she continues. “I once sat through a training session that spent 45 minutes on prompt engineering and never once explained what a large language model (LLM) does or why it hallucinates. That’s the wrong way round. Teach it after understanding, not instead of it.”

The ability to critically evaluate AI output is the skill most worth investing in, Luckin says, which has three parts. She recommends starting with the why, as people evaluate output far better once they understand that the model generates plausible text rather than checking facts. Next is to build habits.

“I describe this as the ‘wobble test’: ask for sources, challenge the detail, press on the claims and see whether the answer holds together or collapses,” Luckin says.

“Practical checks help too, such as confirming that cited references actually resolve, since invalid digital object identifiers (DOIs), broken links, and fabricated citations are common signatures of generated text.”

The third part is domain grounding. Critical evaluation depends on knowing enough about the subject to sense when something’s off, so training is most effective when it’s contextual, practiced on real tasks the learner cares about, and uses genuine examples of failure, rather than delivered as abstract drills, Luckin explains.

Getting training right

A 2025 survey from McKinsey found that while 88% of organizations use AI, only 6% are generating meaningful returns. Work by BCG explains why, says Luckin: “Algorithms account for 10% of AI success – people and processes make up roughly 70%.”

This highlights the importance of staff training, but it’s just as critical to get that training right. Gownder notes that too few organizations currently invest in sufficient training: “It’s typically one-and-done.” AI requires iterative training, he says, as it changes all the time, and employees need practice and reinforcement. He adds, however, that social learning and practice are even more important.

Chate recommends designating ‘digital champions’ within teams, as “humans learn from humans. If you identify a handful of people to give extra training to, and incentivize them to support their colleagues, that’s been proven hugely successful.”Another idea is to build AI training into the day-to-day, she notes, such as adding 10 minutes to the end of a weekly meeting where people share their top tips or ask questions. “Having that little bit of support and knowing where to go is really helpful,” she adds.

Windows may be a timeless OS, and practically synonymous with modern PCs, but that doesn't mean it's well-loved by its users. In fact, the opposite might be true in some instances, according to anecdotal evidence as well as reports by publications like Windows Central.

While the development world is far from a homogeneous blob, anecdotal consensus suggests that Windows 11 is the least enjoyed mainstream platform for developers, with systems like macOS and Linux overwhelmingly preferred. But why does Windows 11 have such a bad reputation among developers, anecdotally at least?

Whether it's on internet forums, in blog posts, or in our conversations with experts, Windows 11 is often snubbed in favour of macOS or Linux, depending on the context. In fact, one of the best advantages of Windows, developers say, is the capacity to run Linux on mainstream hardware. There are, of course, some advantages to using Windows 11 for development – but what could Microsoft do to get more developers on board?

Which OS do coders and programmers really prefer?

ITPro consulted with more than a dozen programmers, engineers, and developers to understand why they use the platforms they do, and whether Microsoft has the potential to transform Windows into an attractive destination for coding.

Little research exists into which OS developers truly prefer, although Stack Overflow findings suggest that Windows is the dominant platform in which they work; nearly half (49.5%) use it over alternatives, including macOS (32.9%) and Ubuntu (27.7%), among others. But just because Windows is the "primary [OS] in which you work" — that doesn't mean it's the OS of choice, with organizations and administrators usually calling the shots more often than not.

The developers we spoke with were divided evenly between the three major systems – Windows, macOS, and Linux — with some even using all three depending on the task. However, when we asked which OS they prefer to code on, none answered Windows.

The vast majority say they preferred using macOS when coding, including Mike McQuaid, project lead of Homebrew, the open source package manager. "Apple and the wider ecosystem seem to care more about design and building high-quality applications for free or a reasonable one-off price."

What's the trouble with Windows 11?

McQuaid tells ITPro that "Microsoft’s developer experience on their platforms has never been great", with critique among those we spoke with ranging from concerns with the tools available to the notion that Microsoft is simply less easy on the eye. Where developers use Windows, it's largely because "that's the corporate choice", as Egor Danilkov, global development director at Innowise, neatly summarized it.

The OS is, however, "cumbersome", according to Nikita Kuznetsov, founder and developer of GameAP, an open-source game server management panel, and "ships with tools I don't need"; "the ones I do need have to be installed and configured manually". Some tooling is built for POSIX systems (Linux and macOS) first and is then ported to Windows, so these versions don't always behave as expected.

Many complaints center on inconsistency, with the notion that different developers on identical Windows hardware may end up with environments that behave differently. Windows is also a massive target and attracts far more malicious attention.

There's also the question of how "managed" company machines are. That's something that Jessica Kerr, engineering manager of developer relations at Honeycomb, an observability platform, can attest to.

"Companies can put more restrictions on Windows machines, and these restrictions hamper developers. As a software engineer, I install software. I run unverified, unapproved apps (for instance, when I’m writing them)," she says. "I access the file system from the command line and write scripts and run ad-hoc programs. Windows computers can be locked down, and Macs… if they can be locked down like that, I’ve never seen a company do it."

Much of the feedback around Apple's flagship OS is around the Unix-based terminal, which feels more natural to use and can be very powerful, as well as the fact that shell scripting, SSH, and command-line tooling all behave as you'd expect. Some of the positive feedback also centers around the user interface, which is universally considered intuitive, clean, and well-designed – not that Windows 11 doesn't have its fans. An additional benefit for Danilkov is the way that Apple devices work so well together.

Windows has its problems – but is the criticism unfair?

Danilkov describes some of the criticism as "outdated", suggesting Windows 11 is "leaps and bounds better" than its predecessors and that "many reactions to Windows are based on a version of the platform used years ago". This is an idea that Jitesh Keswani, managing director at e intelligence, a digital marketing agency, expands on.

"In the past, developers found it difficult to get their development environments up and running," he explains. "This typically involved a plethora of command-line tools. In addition, many of the development environments set up on a developer’s Windows desktops would not work in the same way on the Linux-based production servers that they were deployed to. This is a legacy problem that Windows still has to overcome."

Windows does have its advantages – and that primarily comes in the form of Windows Subsystem for Linux (WSL), which lets developers run full Linux environments directly on Windows 11 platforms without the need for a dual-boot setup or virtual machine (VM). Dwellwell Analytics' data science engineer, Olga Kuzmich, humorously quips: "The best feature for developers on Windows is the one that lets you run Linux."

This system repeatedly came up in our conversations. Many of the compatibility issues simply aren't relevant anymore thanks to this platform. It also means developers can run Windows applications, Linux tools, Docker containers, and more, all on the same machine. The next iteration, WSL3, is also getting a major upgrade, with new features such as the ability to take full advantage of your machine's GPU and NPU without a "performance tax".

The other advantages come in the form of Visual Studio, which Danilkov and Taimur Ijlal, information security leader at Proxy Coupons, UK, both describe as one of the strongest Integrated development environments (IDEs) available. Ijlal also describes Windows Terminal as "top shelf". The advantages of coding on Windows also extend to certain development platforms. As Jiří Fabián, co-CEO & founder of TopMonks, put it: "I would choose Windows only if I were a .NET developer working on Windows desktop applications or a game developer."

Giving Windows a new lease of life for coding and programming

With so much hesitation and opposition to using Windows as a system for coding, barring the ability to run WSL, is there anything that Microsoft can do to tempt the developers that ITPro spoke with?

There isn't a clean fix, says Omari Wright, web development manager at digital marketing agency seoplus+. "A lot of what makes macOS work well for development teams is structural, and changing it would mean Microsoft moving away from the openness that Windows users value," he says, adding that the tech giant could push harder on WSL and "make it feel less like a workaround and more like a first-class development path".

Nothing, however, would pull Kuzmich back. "Linux is just too natural now, and I think a lot of developers feel the same. That's exactly why, in my opinion, Microsoft had to ship WSL in the first place, and this is very telling."

Doubling down on WSL and improving this as a feature is where the majority of developers we spoke with centered their feedback around. Microsoft could make the remaining gaps between Windows and Linux file systems less noticeable, for example, and improve performance when working with larger repositories, containers, and some tools.

Keswani also suggests that Microsoft should push Windows as close as possible to a native Linux environment without sacrificing the user-friendly elements. This focus on user-friendliness is great "for end-users who don't need to know much about what's going on under the hood," Fabián adds, but to win more developers over, "Microsoft would need to base the core of Windows on Unix, which will never happen".

He continues: "With the rise of AI and heavier terminal usage, I need all core Unix utilities bundled with the system, without having to install and maintain them myself — tools for file manipulation, SSH, search, and so on."

McQuaid, meanwhile, centers his criticism on the direction that Microsoft has been taking Windows in the last few years, lamenting: "Stop trying to overrule my preferences on how to use my computer". He tells ITPro: "I use Edge and Bing there now just because they keep overruling or pressing me to change my defaults.

"There are ads (or what feels like ads) and upselling all over the OS. It’s gross and feels bargain-basement compared to Apple. I'd love to see Microsoft make Windows feel like “spammy” and more of a cohesive, overall pleasant developer experience."

There are various reasons why developers prefer other systems over Windows 11, macOS especially, with the main benefit of Microsoft's flagship OS, according to the programmers that we interviewed, being the fact that it lets you run Linux and bypass Windows entirely.

Nobody, Microsoft included, would describe Windows 11 as perfect, hence why there's such an active Insider program with updates shipped frequently and continuously. There are some unrealistic demands on developers' wish list, like making Windows Unix-based, but more realistic seems to be giving WSL more love – making it streamlined, more functional, and more powerful so that developers feel more supported by their devices at work.

Cybersecurity spending is increasing, yet many organizations still experience breaches, operational disruption, and ongoing security challenges.

As businesses expand their digital environments and adopt new technologies such as AI, many are also finding that their larger security budgets are not translating into stronger protection.

For managed security service providers (MSSPs) and channel partners, this is an important opportunity. Many customers are looking for support that goes beyond simply deploying additional security products. They want a partner who can help them manage cyber risk more effectively, while also helping them operate in a complex security environment.

This shift in customer priorities is even more visible as organizations are managing increasingly fragmented IT environments. Hybrid working, cloud adoption, remote endpoints, and connected applications have all expanded attack surfaces considerably over the past few years. In addition, many businesses have accumulated multiple security tools across different parts of the organization, often from different vendors and with varying levels of integration.

Too many tools

Although each tool may exist to address a specific security requirement, the overall result is difficult to manage. Security and IT teams may need to monitor numerous dashboards, respond to large volumes of alerts, and manually correlate information between systems. In some environments, this will create visibility gaps and operational inefficiencies, making it harder to respond quickly to threats.

For MSSPs, this is likely to be a key focus for customers. Many organizations are reassessing whether adding more standalone tools is the most effective way to improve resilience. And there is growing interest in approaches that simplify management, improve visibility, and support measurable risk reduction.

This is particularly relevant for mid-market organizations that may not have large in-house security teams. Many smaller IT departments are being asked to manage increasingly sophisticated environments, while also responding to complex threats, regulatory requirements, and user demands. A channel partner that can reduce this operational pressure while also improving security outcomes will be able to demonstrate and deliver greater long-term value to customers.

Unsurprisingly, AI is also influencing this. Businesses are adopting AI-enabled technologies across their operations, while cybersecurity vendors are increasingly integrating AI into security tools and platforms. Cybercriminals are also using automation and AI-supported techniques to increase the scale and sophistication of phishing campaigns, social engineering, and vulnerability exploitation.

Keeping safe and secure, but without spending more

Organizations are faced with growing pressure to improve detection and response capabilities without significantly increasing operational overhead.

For MSSPs, this creates the opportunity to help customers manage security more practically and sustainably. Rather than focusing solely on deploying additional technologies, many are positioning themselves around outcomes such as improved visibility, faster response times, and simplified security operations.

Integrated security platforms are becoming increasingly relevant in this context. Platforms that bring together endpoint protection, identity security, network monitoring, and threat detection capabilities help organizations reduce fragmentation and improve operational efficiency. By consolidating visibility and automating some routine tasks, businesses may be able to reduce alert fatigue and simplify day-to-day security management.

Automation is also playing a growing role in helping organizations manage their rising security demands. Many MSSPs are using automation to support threat detection, incident response, patch management, and policy enforcement. For customers with limited internal resources, this helps to improve consistency and reduce the burden on internal IT teams.

Technology alone is not the solution

But technology on its own is unlikely to solve every security challenge. Many organizations still require support with prioritisation, governance, and practical risk management. This is where MSSPs can differentiate themselves from those providers who are focused primarily on product deployment.

Customers are increasingly evaluating cybersecurity investments based on operational effectiveness rather than simply the number of tools deployed. In some cases, this may involve consolidating overlapping products, improving configuration management, or streamlining response processes rather than introducing additional technologies.

This approach also aligns with the growing industry focus on cyber resilience. Many organizations recognize that security strategies should include detection, response, and recovery capabilities, alongside prevention. MSSPs that can help customers improve resilience and reduce operational disruption will strengthen their role as long-term strategic partners.

The channel also has an important role to play in helping organizations adopt AI-enabled security capabilities responsibly. While AI may help improve efficiency and threat analysis, it can also introduce additional management challenges if implemented without proper oversight or integration. This means customers will value partners that can help them adopt new technologies in a manageable and commercially practical way.

Organizations need to balance growing cyber risks with operational and financial pressures. As a result of this, there is likely to be increasing demand for MSSPs and channel partners that can simplify cybersecurity management while supporting stronger resilience outcomes.

This is an opportunity for the channel to move conversations away from tool proliferation and towards measurable risk reduction. The conversation with customers needs to be about helping them manage complexity, improve operational visibility, and align security more closely with business priorities.

What is HPE? For just over a decade, since its split with what's now HP Inc, the answer has been clear: HPE is an enterprise hardware company specializing in servers and storage.

The additions of Cray and SGI to its portfolio bolstered this position: While they primarily opened the door to HPC and supercomputing for HPE, fundamentally there's a lot of crossover between typical data centers and these behemoths.

Even the name – Hewlett Packard Enterprise – invokes a sense of a company with a long history of making specialist kit for organizations' data centers or server rooms.

At HPE Discover 2026, though, you would be forgiven for thinking HPE's primary competitor wasn't Dell or IBM but Cisco.

Both of the first day keynotes were chock full of networking. I'm not just talking about announcements, of which there were some, I'm talking about the overall vibe as well. Both CEO Antonio Neri and head of networks, Rami Rahim, spoke about the importance of networking as a foundation for modern enterprise architecture, particularly for AI workloads.

Rahim offered a cautionary tale about the Millennium Tower in San Francisco, a residential high-rise that began sinking into the ground and listing to one side shortly after its completion due to problems with its foundations. His argument: if businesses don't lay solid foundations based on networking technology, then they're setting themselves up for failure. Their agentic AI ambitions, in particular, will become weak and start to wobble.

Such was the emphasis on networking all week, you would be forgiven for thinking that the company had moved away from its traditional business areas. In fact my first question to Antonio Neri when I spoke to him was: "Is HPE a networking company now, then?"

"Yes, it is," came the answer. He expounded on how this plays into HPE's expectation that it will grow into an $11 billion company, adding that "in my mind, together with our competitor, Cisco, [HPE has] the most complete networking portfolio".

"Our competitor Cisco" – what a phrase, and one that would have seemed slightly crazy just a few years ago.

It's not that HPE has had no networking business before this – Aruba Networks has been a core part of what HPE has to offer for years – but I don't think anyone would have openly declared Cisco a competitor in 2015.

Undeniably the Juniper acquisition does strengthen HPE's position in the data center – it can now offer the whole package, from scale up to scale out. Neri was keen to point out the AMD Helios, which should be coming this autumn, uses HPE Juniper Networks for scale up. Possibly more to come on that in July when we get to hear from AMD's CEO Lisa Su at AMD Advancing AI summit, though we shall see.

The question that I've been asking myself throughout the conference and I've voiced already on the 19 June edition of the ITPro Podcast and reaction video, is this: is the focus on networking a perpetual state of affairs, or is HPE just burnishing its latest acquisition?

My expectation is that for the next 6 months minimum Juniper's star will very much be in the ascendence at HPE. After that? Well, maybe the foundations will have settled and networking will have equal importance to the rest of what the company does. HPE Discover Barcelona, the company's European conference that takes place 2-3 December this year, may offer some more insight into the direction of travel.

Until then, you can catch up on all my coverage from Las Vegas here and if you want to know more about the tower and its ongoing issues – including attempts to remediate that made it worse – I recommend this blog, and also the Wikipedia page. It makes for quite a read.

This week's episode comes to you live from Las Vegas, where we link up with Jane McCallion at HPE Discover and also ITPro's news editor, Ross Kelly, at Pure Accelerate.

Jane talks us through all the major announcements at HPE, with a heavy focus on how its Juniper Networks acquisition is impacting its new products and services. She also gives her thoughts on CEO Antonio Neri and the company's approach to agents.

Across the Las Vegas Strip at Resorts World, Ross gives us his insight into Everpure, the impact of rising hardware costs on its business, and how organizations can get their data AI-ready.

Highlights

"Despite the fact that Juniper has been part of HPE for nearly a year now, this has been the first chance, really, to celebrate it ... [so] the focus has been solidly on networking, and it's slightly hard to say whether this is because this is the first time they've really got to talk about the acquisition that they've really fully integrated everything."

"This idea of cross-pollination has come to fruition. There are parts of Mist, which was a Juniper offering that are now available on Aruba Central and ditto, parts of the Aruba offering that are now available through Mist. So the idea is to give everybody the opportunity to use the full product portfolio without having to move from the platform that they're already using. That's got to change at some point, but there has to be some kind of integration of the two platforms at some point, but I think you know there's a slow and steady let's not upset anybody type approach, which is fair enough."

"[Everpure has] been on a big pivot over the last 18 months. This time last year, Pure announced the launch of their Enterprise Data Cloud, which was part of this sort of pivot towards more of a data management approach. And then they've definitely continued on that vein this year. A couple of big product announcements all focused on this area, it's something that they're really, really moving towards, pushing towards, hard."

"I think on a broader sense, with all of these announcements, Everpure is going into not uncharted territory, but they are to some extent on a collision course, I think, with a lot of other organizations in the industry ... you know, your Snowflakes, Data Bricks, even, like I said, Salesforce."

Links

• Everpure CEO Charlie Giancarlo says firm will share the burden with customers amid rising hardware costs
• Forget tokenomics – agents are a personnel cost
• HPE unveils a raft of new networking products for AI workloads at Discover 2026

The Taliban has had enough of smartphones in the workplace so much so that it is smashing up devices belonging to its own members.

And this isn't just about the typical concerns that the internet is immoral. It's well known that Taliban leadership has repeatedly tried to restrict access to the World Wide Web. But the focus here is specifically on the smartphones themselves.

An unnamed government employee is quoted in The Guardian as saying, "About two months ago, they said not to bring your mobile phones to the office. Me and a few colleagues didn't take it seriously. They confiscated them, and after we made a fuss about it, they smashed our phones".

Why? Well, according to the report, written by Zahra Nader, the editor-in-chief of the Zan Times, the Taliban is worried that its people are online too much, consumed by their phones, and, as a result, not working. Allegedly, the Taliban believes that smartphones don't belong at work. And they are not the only government that is coming to this conclusion.

The reputation of the smartphone has taken a bit of a hammering of late. There's been a large focus on social media, infinite scrolling, and protecting children. But there is a growing community of adults online (ironically) looking at ways to reduce the use of smartphones, either to protect their mental health or, like the Taliban, to be more productive.

Now, many of us have simply deleted apps that cause distractions. I myself have written about the dangers of using Slack at night. And there are also lots of people going for somewhat retro options – dumb phones, actual cameras, iPods – in a bid to claim back their attention spans.

There are also more modern methods on the rise, including Brick, a tiny box that you can tap your phone on to lock you out of those troublesome apps. There are also smartphones with limits, such as the Balance Phone (review coming soon), which is simply a phone with the bare essentials (camera, banking, email). The company has drawn a line on what apps help and which ones don't, using the infinite scroll as the deciding factor (although, technically, business email is an infinite scroll).

How did it get so bad? Do we need to rethink BYOD initiatives for the sake of business productivity? Should we have distraction-free smartphones or Bricks around the office? Or is that asking too much of our already busy IT teams and their stretched budgets? My hand twitches thinking about these – it wants to pull up Google and ask.

The situation is so bad that the mere presence of your phone is distracting. Comedian Josh Widdicombe joked that once he used Brick to block social media, he still used his phone absentmindedly, scrolling through Uber to find out what cities it operated in.

Back in 2017, a consumer research study called Brain Drain: The mere presence of one's own smartphone reduces available cognitive capacity suggested that cognitive function was impaired by the phone being in reach. It conducted two experiments that showed even when users avoided the temptation to check their phones, the mere presence of the device negatively affected cognitive performance, specifically available working memory capacity.

What can be done about this? Unfortunately, very little. The phone is a tool for business, and apps keep us connected to colleagues, tasks, clients, and so on. It's also an important part of the verification process – I can't get into my laptop without it. But there is a growing movement, both anti-social media and anti-phone. And it might not be long before we see IT departments and businesses taking a different approach to the phones employees use.

If an organization is surprised to see IT costs explode when it starts running agents at scale, the problem might not be in the IT budget – it might be a category error. At least in the opinion of HPE CEO Antonio Neri.

“I don’t think of [AI agents] as an IT cost,” Neri tells ITPro at HPE Discover 2026. “I think about the cost of the workforce because, to me, an agent is no different than any other employee I have to hire.”

“So if I hire [a person], there is a cost to onboard him and make sure that he's productive by giving him the tools and everything else,” he says. “When I train an agent, it's going to cost me a number of tokens to train it to drive the best productivity. If I'm going to spend a million dollars to train an agent, it has to be way more productive than [the person]. Otherwise, why am I doing that?”

This turns on its head much of the conversation around agents, tokenmaxxing, and costs as agentic AI starts to eclipse generative in organizational use.

AI sticker shock

ITPro wrote in May about “tokenomics” and so-called sticker-shock when the AI bill finally came due for many businesses. Famously, Uber used up its entire annual AI budget in four months and has now capped the amount each employee can spend on agents per month.

One answer that’s been given to the problem is a shift to on-premises IT rather than ‘cloud-based’ AI like Anthropic’s Claude Code or OpenAI’s Codex, and moving agent costs from OpEx back to CapEx. For an enterprise hardware company like HPE, surely this should be an opportunity to increase hardware sales?

“Sure,” says Neri. “But we think about it as a total cost of [the] workforce, not as an IT expense.”

And is this something that he’s educating his customers about, or do they see it another way?

“Yes, I am,” he says. “I’m spending more than 50% of my time with our customers, and when I bring that concept to life for them, they say ‘ok, ok!’”

“I’d rather give a little bit more budget to IT to reduce my entire cost of the workforce, which is – depending on what you do – probably one of the biggest cost levers,” he continues. “I have only two large cost drivers: people and materials. The materials, in networking, we have a lot of that under control because we have the IP, but a lot of servers, obviously, CPUs, and GPUs I don’t control.

“[The people] I have 65,000 employees. It’s not about reducing employees, which in some cases will happen, but it’s about making those 65,000 employees way more productive with the aid of AI by freeing up capacity for the humans to do more value-added [work] while the agentic models do more work at much bigger scale. I think about that, the total cost of [the] workforce”.

Human / AI interaction

This is an idea that’s been seeded here and there throughout the conference. In Rami Rahim’s keynote, which took place just a couple of hours after Neri’s, one of the demonstrations floated the idea of taking humans out of the loop once a process has been approved.

Katrina Pickett, senior product manager, Juniper Networks, showed how HPE Marvis might help detect and remediate a problem with a sluggish network. The conversation between person and machine was in natural language, but the fixing was agentic. At the point that the fix was applied, Marvis asked if Pickett wanted to have an agent apply the same fix automatically in the future, without human intervention, which would present a time saving.

Human-in-the-loop (HTL), however, has been a key mantra of not just AI but automation more generally. Is it time to take another look at this attitude?

“Well, once you’re confident about [what’s happening], then you can remove the human from the loop and just put on the loop,” says Neri. “I think there needs to be some sort of oversight, but not doing the actual work.”

“In the case of networking, a lot of things were done manually through [the] command line interface, by logging in the console, and putting hands on a keyboard,” Neri continues.

“The scale is now so big that humans cannot possibly manage that scale. Therefore, there is no other choice than to automate things and use artificial intelligence to manage massive amounts of data.”

Naturally, there are caveats. The story of the company that had its entire codebase and all backups erased by a rogue agent is quickly passing from fact into legend; a cautionary tale for any business thinking of letting agents roam free in their network.

“I always say, you don’t get what you expect, only what you inspect [so] I think there needs to be some sort of oversight,” Neri says. “Human beings make mistakes, agents may make the same mistake, right? But as you’re doing it at a bigger scale, it may have a huge impact.”

Mistakes happening are “inevitable”, says Neri. Ultimately, though, there has to be an element of trust that the technology will work as intended. Guardrails can help with this, and, from Neri’s perspective, what HPE is doing with GreenLake Intelligence and Marvis Actions means customers can deploy agents with greater confidence to whatever extent they wish.

HPE still makes everything it always did – it has storage appliances, and it has servers a-plenty, and they’re popular too. According to the company’s most recent earnings call, traditional server orders are booming, growing triple digits year-on-year, while Alletra Storage MP orders also increased triple digits.

The conversation, however, will always revert to networking when it comes to visions of and for the future.

“There was a question in a previous session [that was posed] to Rami (Rahim), which said how far are we to really being fully autonomous, and we believe it’s within the next 12 months,” says Neri.

“It’ s not going to be everywhere across the network, but there are many aspects of the network just running itself.”

“Our opportunities are in the three domains: networking, cloud, and AI. Networking is a huge opportunity; we see it is. And it’s driven both at the edge to the campus and branch, the on ramp into the AI. You need that connectivity.”

AI may have become ubiquitous in the workplace, but new research shows that some employees are increasingly pushing back on AI tools by sabotaging their rollout.

Generative AI company Writer and research firm Workplace Intelligence found that 29% of 1,200 employees across the UK, US, and Europe had engaged in sabotage. Examples include opting out of AI training, ignoring company guidelines on AI use, and refusing to use tools altogether. There is a greater resistance to AI tools among younger workers, with 44% of Gen Zers admitting to sabotage.

In extreme cases, workers have even resorted to uploading sensitive company data into unapproved tools and tampering with performance metrics to make it seem as though tools are underperforming.

Charles Radclyffe, founder of SaaS enterprise provider EA Global AI, says that workers pushing back in this manner shouldn’t come as a shock. “In many cases, what is being labeled as ‘sabotage’ is simply a rational human response to how AI is being introduced.”

He adds: “We’ve spent decades asking people to behave like robots – follow processes, fill forms in, and move data from one system to another. Now that the robots can finally do that work, we’re surprised that people push back.”

This view is echoed by the report, which shows that the main reason for the sabotage is that employees don’t want to be replaced by AI (30%). Tools diminishing employees’ value and creativity (26%) and adding to workloads (20%) were other cited reasons.

So, what can leaders do to prevent acts of sabotage from happening?

Be clear on what AI tools will and won’t do

For Radclyffe, the solution is fairly simple: “If your teams are resisting using the tools you adopt, or are using something else, it’s telling you something, so I’d suggest listening. Either the technology isn’t fit for purpose, or the incentives are broken. If you fix those, adoption tends to resolve itself.”

Leaders also need to communicate to their employees that AI is more than a “tooling upgrade”, he adds. If there are implications for roles, then they ought to know.

Adam Gaca, managing director for UK&I at tech strategy adviser and tech partner Future Processing, echoes the importance of communication. “People are being encouraged to use AI more often, but they don’t always have a clear view of where it actually improves outcomes or how their work will be evaluated. In this situation, switching tools, ignoring recommended solutions, or working around the official setup is a fairly natural response,” he says.

“Leaders need to define where AI will be applied, how results are evaluated, and how teams are expected to work with it on a day-to-day basis.”

This will give them reassurance and confidence and means they’d be less likely to push back against rollouts. “Once that’s in place, adoption becomes more consistent and behavior settles naturally,” he adds.

Empower through AI governance

The biggest risk of employees using unauthorized AI tools in the workplace is undoubtedly shadow AI.

Data from the Writer and Workplace Intelligence survey shows 76% of 1,2000 C-suite executives are aware of the dangers posed by AI sabotage and how it could threaten their company’s future. When employees choose to bypass internal AI guardrails and reject approved tools in favor of unauthorized ones, they increase the risk of shadow IT. Nefarious actors can use the unauthorized tools as a backdoor to exploit unpatched systems, leak data, and expand the attack surface.

“Shadow AI often occurs because the approved tools aren’t very good. People will always gravitate towards whatever helps them get the job done faster and better,” says Radcylffe.

The onus is on leaders to establish governance frameworks outlining what constitutes improper use of AI tools and how sensitive data should be handled. Oliver Simonnet, lead cybersecurity researcher at cybersecurity and AI usage control platform CultureAI, says that employees will often avoid approved tools and seek alternatives because “governance has become a blocker rather than an enabler”. In other words, employees are wary of the unknown and need to be empowered to use the approved AI tools.

Leaders should start by positioning AI training for governance compliance – this has been a legal requirement in the EU since 2025, but isn’t yet in the UK – as a career development and learning opportunity, as opposed to a box-ticking exercise. They should also recognize employees for responsible use of approved tools and incentivize them to flag possible bias, hallucinations or data leaks. For example, cash bonuses and rewards could be tied to compliance behaviors.

Ultimately, leaders need to show to employees that the AI tools are being implemented for their benefit and not to their disadvantage. “If employees see AI governance as something that helps them do their jobs better, adoption accelerates. If they see it as something that slows them down, they'll find a way around it,” Simonnet adds.

Place trust in employees

While putting guardrails in place is crucial to prevent the use of unauthorized tools in the workplace, companies shouldn’t necessarily put a blanket ban on tools that sit outside of company policy.

If the above has been followed, then employees shouldn’t fear AI tools being adopted. As a result, it’s less likely they’ll sabotage rollouts, and they should be more inclined to use the tools responsibly and take care to protect sensitive data. They should be trusted to use tools outside of company policy outside of the workplace if they help them to produce better work.

As Ash Gawthorp, CTO at tech consultancy Ten10, puts it: “Trust plays a huge role in whether people engage. People are more likely to engage when they can see how changes support better ways of working and understand the role they continue to play.”

At some point, every software company reaches the same crossroads. Integrating a capability that the product doesn’t yet have can be time-consuming and costly.

In many cases, it can take between 12 and 18 months to build (depending on the product), while somewhere else in the market, a competitor is already offering it.

This is the point at which product development leads need to decide whether they build it themselves or find a partner who has already developed it.

At first glance, it seems like a straightforward trade-off between control and speed. In practice, however, it’s more complicated than that.

Building in-house is expensive

For product teams, the instinct is to build. If you own the capability, you have full control over how it works, how it’s scaled, and how it integrates into your central solution.

That logic is sound for core capabilities that sit at the heart of what makes the product valuable. However, customers often require capabilities that depend on new features outside of the team’s specialization. That’s where the instinct to build starts to cost more than it delivers.

Take cybersecurity as an example. If a company in an adjacent market wants to offer dark web monitoring, stealer log detection, or credentials exposure alerts on its platform, the underlying data infrastructure is enormously complex to build. It takes years to develop reliable sources, significant investment to maintain them at scale, and dedicated teams to keep pace with criminal ecosystems that constantly evolve.

That’s all before a single line of product code is even written.

The same challenge shows up in other sectors. Fintech teams address it through fraud detection and identity verification; compliance platforms address it through data sourcing. The specifics differ, but the shape of the problem is similar.

The full cost of building is rarely visible initially. Hiring specialists with the right expertise, building and maintaining the infrastructure, discovering (often after launch) that there are sources not being monitored or threats not being caught, and managing the ongoing operational work that follows every proprietary capability indefinitely.

When those costs are added up, it becomes clear that building these capabilities in-house often makes far less financial sense than it first appeared.

What an integrated partnership looks like

With an integrated product partnership, instead of building a capability completely from scratch, the partner’s technology is connected to the product via an API. From the customer’s perspective, it looks and feels like a native feature. There’s no separate login, no new vendor to onboard, and no visible complexity. The plugin simply enables them to gain a new capability inside of a product they already use.

This brings with it the opportunity for premium pricing tiers and natural upselling, giving sales teams something differentiated to talk about. And because customers are getting more value from the product over time, they’re less likely to move to a competitor.

But it’s the speed to market that makes the biggest difference. Delivering new capability in weeks rather than months or years means revenue starts flowing in faster, and along with it, customer feedback that can start informing the roadmap. A customer who starts using a feature this quarter rather than in a year’s time compounds positively across the whole customer base by accelerating revenue, customer feedback, retention, and competitive advantage simultaneously.

Five areas to examine before committing to a partner

Collaborations only deliver on these areas if the right partner is chosen. These five areas are well worth examining carefully:

1. Is the data proprietary? If the partner’s value comes from data (e.g., threat intelligence, dark web content, identity exposure signals, etc.) the key question is whether that data is unique to them, or whether it’s data that is available from multiple vendors. Shared sources create parity with competitors, not an advantage over them. If there’s no clear difference between this partner and a cheaper alternative, customers will eventually reach the same conclusion.
2. Is the integration genuinely well built? A working API isn’t quite the same as a well-designed one. Good partners invest in consistent data structures and engineering teams who are genuinely available when something doesn’t work. Poor integration experiences only serve to slow down launches and create ongoing maintenance burdens.
3. Do the economies scale? Pricing models that make sense at current volumes can become a problem as the customer base grows. Volume-based pricing (where costs scale proportionally with growth) indicates a partner who is aligned with the business’s success. Fixed-fee or rigid per-seat models can start to squeeze margins precisely when the product is performing well. Before signing, forecast what the partnership would cost if adoption grew by 10x.
4. Does the partnership preserve roadmap freedom? A partner who solves a major setback should not create a dependency that limits what the product can do moving forward. It’s worth understanding what would happen if the partner’s technology changed, degraded, or a key integration point was deprecated. Contracts should include service level protections, and the product architecture should not rely entirely on one partner’s continued performance.
5. Will customers notice? Within a few months of launching a new integrated capability, there should be clear evidence that it’s delivering value. Therefore, it’s critical to track security outcomes (e.g., how many exposures were identified, how many credentials were addressed, etc.) alongside business outcomes like adoption rates, revenue generated, and customer retention. If the numbers aren’t adding up, the capability hasn’t earned its place. Set the success criteria before launch, not at the point of renewal.

Regular evaluation

Partnerships require regular and honest evaluation. If data quality has declined, integration support has become more difficult to access, or customer adoption has stalled despite genuine effort, those are all signals worth acting on. The cybersecurity landscape moves quickly, and a partnership that was delivering strong results in its first year may need significant re-evaluation by year three, particularly if the threat environment has evolved and neither side has kept pace.

The best approach is to treat an integrated partnership the same way any feature is treated on the product roadmap, by acting on what the data shows, and being willing to make changes when it stops delivering.

Integrated product partnerships, done well, are one of the most effective ways to accelerate a product roadmap without overextending an engineering team.

The decision to partner rather than build is often the sharper strategic choice. The real key is to be rigorous about selecting who you partner with, being honest about whether it’s working, and being willing to act if the situation changes.

Hello again from HPE Discover 2026, this is managing editor Jane McCallion. It's the second (and final) day of HPE Discover, with CTO Fidelma Russo presenting the final keynote of the event at 9am PT.

We can expect more on the technical side of the announcements that came from CEO Antonio Neri and president and GM of networking Rami Rahim. Follow along here for everything that happens, as it happens.

I've made it to the auditorium. Despite it being the second day, and the third keynote of three, it still looks pretty well attended.

Everything from the staging to the lighting is very on brand, which is to say very green!

Russo is on stage talking about how fast things are changing in the business worls. "The question now is not whether AI will change an enterprise" she says, it's about how it can be done safely and securely.

She's now talking about how AI systems – and agents in particular – need a structure to work properly. This means having the right data, the right infrastructure, and the right operating model, all underpinned by the right platform.

Announcement: The release of HPE Data Fabric 8.2. This offers agentic workload integration, global data catalog, and turnkey deployment.

Oh! I think we're going to do some tokenomics:

Russo points out that if you're working in the cloud, token costs can rack up quickly as agents make repeated requests back and forward, not just in one direction – this, is the classic tokenomics conversation.

HPE has its own experience of this, she says. It's not quite the $3,400 cost in 24 hours that we heard from Dell Technologies in May, but Russo says: "We saw this firsthand ... our storage systems and our support systems process billions of operational signals coming in from our customers, and as those environments became more autonomous, and as we looked at those systems, our token consumption scaled with the amount of signals"

She continues: "So our engineers, really smart people, built an AI source support platform, we built it on-prem with Greenlake Intelligence and Private Cloud AI, and running AI in our own infrastructure gave us control over the economics and allowed us to govern that really important customer data, and it gave us better performance, and it also helped us significantly minimize the token spend associated with operating AI at scale.

"We stopped being consumers of AI, and we became producers of intelligence. So, let's look at the results. We have more than 30x lower cost. We have nearly $100,000 saved per month, and that gave us the capacity to scale even further, faster. So, that is an example of how we took all of our infrastructure, all of our intellectual property, and put it to work within HPE for the goodness of our teams and also to react to customers faster."

We're moving on from data to infrastructure. There's a myth that AI reduces infrastructure, Russo says, but it's the opposite. "AI doesn't shrink your infrastructure, it expands it," she says.

Naturally, HPE has an answer to this: a single control plane consisting of HPE SimpLivity PC1000, HPE Private Cloud PC3000, HPE Private Cloud PC7000, and HPE Private Cloud AI. These last three are all now available air-gapped.

Announcement: The release of HPE Morpheus 9. "[The] most advanced platform for operating modern and traditional infrastructure, as well as AI," says Russo.

"Morpheus 9 introduces several major innovations and advances our vision of a more complete enterprise platform," Russo says. "Morpheus Central provides a single operational view from the GreenLake platform across sites and regions, integrated software-defined networking based on ... Juniper technology brings policy security and micro segmentation directly into the cloud.

"Stretched clustering extends resilience across sites, helping organizations maintain availability of critical workloads together," she adds.

We're talking HPE VM essentials. apparently there are over 2,000 customers using it with HVM, Kubernetes, VMware and more.

Announcement: This is an interesting one – the company's expanding its relationship with Citrix.

Citrix Desktop as a service and Citrix Virtual Apps are coming to HPE VM Essentials, "enabling customers to deliver modern digital workspaces with cloud-like operations in the environment of their choice", she says.

Right, next up: OpsRamp. This is one of HPE's newer acquisition (though not as new as Juniper).

"There is literally a lot more stuff running in your environment," says Russo. This makes management more difficult. "You need to understand exactly what is happening, which agents are running, which models are consuming resources, where are you spending the token," she says.

From an HPE perspective, that is HPE OpsRamp observability for AI.

We're now talking about HPE Zerto. "OpsRamp helps you understand the impact of AI across your environment. What happens when something goes really wrong?"

Russo alludes to the start up that got wrecked by a rogue agent earlier in the year.

"We're already seeing stories about agents who modify code they weren't supposed to have access to, deleting information and creating unintended consequences, and that's why we are extending HPE Circle to support NemoClaw and OpenClaw agentic AI environments," she says.

"It helps organizations recover from unintended agent consequences and return to a trusted state. Critically, critically important as you move forward into the era of AI," she says.

I thought Russo was about to sum up, but we have (I think) one more thing to talk about: GreenLake Intelligence

Announcement: HPE Copilots and Agents on GreenLake Intelligence.

Announcemnet: HPE is partnering with ServiceNow.

"GreenLake Intelligence helps you understand what's happening across your AI factories, your infrastructure, your agents, and your workloads and ServiceNow helps you operationalize that understanding through autonomous service delivery," says Russo.

"Together they connect infrastructure intelligence with operational execution," she says.

WORLD CUP MENTIONED! What you see on the score board, says Russo, is just the tip of the iceberg.

"Every great result is powered by much larger efforts behind the scenes," she says. "It takes extraordinary technical teams, go to market teams, and partners and customers all working together to bring these innovations to life."

And that's a wrap for this live blog! Keep an eye out for our podcast episode from the event, which will be published on Friday, and bookmark this link for even more from the event.

The National Cyber Security Centre (NCSC) recently declared passwords to be fundamentally flawed, so what are the concerns and what can be done about them?

Access and identity management are an essential part of our online lives. They protect our data and ensure sensitive information is only shared with the appropriate people. However, following recent password leaks, billions of access credentials have been exposed, thereby fundamentally weakening the systems relying on them.

Passwords have been used to confirm identity for thousands of years, for example, by guards and sentries to identify friends and official visitors. However, even then, passwords were prone to interception and being used by enemies to falsify identity.

The problem with passwords

The key problem with passwords is that they do not confirm identity, only that someone knows the correct response. The recent leaks have been compounded by the poor password hygiene followed by many users, who use the same password credentials for multiple platforms and services.

Furthermore, recent advances in high-performance computing (HPC) and quantum computing have meant that computers are becoming increasingly powerful and able to crack passwords in a far shorter time than was previously possible.

In 2024, China announced it was able to decrypt 50-bit RSA encryption using quantum computing. Although modern encryption is a minimum of 2048 bits, the research is a fascinating proof of concept, showing where the technology is heading and the implications for the future of cybersecurity.

As the processing capabilities of modern computers continue to grow rapidly, the recommended minimum length and complexity of passwords are becoming longer. It is now recommended that passwords be at least ten characters long, with a mix of letters, numbers, and symbols, and not be a name or word from a dictionary.

Compounding the issue is that not everyone follows appropriate password hygiene, such as not using the same password credentials across multiple accounts or avoiding words/phrases that have a personal connection. Furthermore, the most common passwords are still “123456”, “admin” and “12345678”. At this point, we may as well just give the bad actors our keys.

“The VIPs are the worst security users in the company – they don't want to type even eight characters. I saw in the past some CEOs who are asking their IT people to have only three characters as a password,” says Jean-François Aliotti, co-founder of Almond.

“Now, because of all the leaks that we have seen, there are passwords leaked everywhere. Some of my passwords have been leaked. I use a unique password for each access I have. It's a rule that I follow strictly, and I use password managers for that. But most people don't do that – they have an Excel file with all their passwords, or they have the same password everywhere.”

Regular changes of passwords are commonly enforced, especially for business account login details. The recommended duration varies depending on the sensitivity of the data, but a password change every three to six months is the most common requirement. However, the NCSC has argued against changing passwords due to the potential vulnerabilities it causes, as users can be tempted to rely on passwords that are easier to remember.

It is no longer recommended that access security and identity management be solely reliant upon a password. Instead, there needs to be a layered approach to security, with multiple levels of authentication before granting access.

Alternative solutions

The most common form of additional confirmation is Multi-Factor Authentication (MFA), whereby short-term single-use codes are sent to personal devices held by the user. Codes are typically sent via text, email, or authenticator app, but could also be generated by a 2FA token. However, if someone has already gained access to the secondary device or token, they will be able to confirm the additional verification.

Emails are potentially the most vulnerable form of MFA, as they are equally reliant on passwords, and many personal email accounts are not as strongly protected as they could be.

Biometrics (fingerprints, facial recognition, and voice recognition) are unique to each person, but are not as strong as many believe. Fingerprints can be forged, and voice recognition can be fooled using high-definition recording. Facial recognition can easily be bypassed if a user is caught off guard, as anyone with teenagers will know when friends ‘borrow’ their phones.

“Biometrics are a good thing, but not alone. If someone stole your fingerprint, it's over. You can change your password, but you cannot change your fingerprints,” says Aliotti.

“Biometrics alone are quite dangerous, because if they are stolen, then it's over.”

Passkeys instead of passwords

An alternative authentication system is passkeys. Although the technology is comparatively new, the NCSC has recommended that people use passkeys instead of passwords.

“Adopting passkeys wherever you can is a strong step towards a safer, simpler login experience, and I am pleased that we can now support uptake,” according to Jonathon Ellison, Director for National Resilience, NCSC.

“The headaches that remembering passwords have caused us for decades no longer need to be a part of logging in where users migrate to passkeys – they are a user-friendly alternative which provide stronger overall resilience.

“As we aim to accelerate the UK’s cyber defences at scale, moving to passkeys is something all of us can do to improve the security of everyday digital services and be prepared for modern and future cyber threats.”

When a user seeks to confirm their identity, a push notification is sent to their smartphone. Once their device, such as a smartphone, has been unlocked, a unique passkey is created and sent to the platform/website/service they wish to access, confirming their identity.

Unlike MFA, which relies on traditional methods of user verification, this method does not rely on login information or biometric data being transmitted, thus mitigating interception and key-logging attacks.

“MFAs will continue to be deployed, but what we are seeing right now is that passkeys are the best way, but it will take a lot of time to deploy them at a large scale right now,” says Aliotti.

“Pass keys will be more and more used, and we hope that it will be the dominant way for credentials, as we don't have any other system right now that we are seeing as a brand-new thing.”

The decreasing effectiveness of passwords means they are no longer viable as a sole form of access management. Instead, a layered authentication process, where users need to prove their identity through two or more methods, is strongly recommended.

Furthermore, given the inherent weakness of passwords overall, the robust nature of passkey technology means that passkeys are the NCSC’s recommended access management protocol.