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.

Oracle is expanding its use of Google's AI models across its enterprise software portfolio.

The company is adding Google's Gemini models to its AI Agent Studio for Fusion Applications, a development platform that enables organizations to build, connect, execute, and run AI automation and agentic applications using reusable Oracle, partner, and external agents.

It also plans to use Gemini models for embedded AI use cases in Oracle Fusion Applications and Oracle NetSuite.

“Organizations around the world trust Google Cloud’s full AI stack to power critical enterprise workflows and agents,” said Satish Thomas, vice president of Google Cloud.

“Our expanded partnership with Oracle is designed to make it easier for organizations to use Gemini in the applications and agentic workflows they rely on to automate workflows, accelerate decisions, and drive outcomes.”

The deal builds on customers’ existing access to Gemini models through Oracle Cloud Infrastructure (OCI) Enterprise AI through integration with Gemini Enterprise Agent Platform.

“Our partnership with Oracle brings Google’s most capable AI models directly into the core application workflows global businesses rely on every day," said Kevin Ichhpurani, president of global partner ecosystem at Google Cloud.

“Together, we are making it seamless for enterprises to apply powerful and cost-efficient AI directly where business decisions happen.”

Oracle said customers and partners will get more choice when building Fusion-native agents and agentic applications, along with expanded multi-modal capabilities. They'll be able, for example, to access Gemini 3.1 Flash Lite, a high-efficiency model engineered for optimal price-performance and Gemini 3.5 Flash for more complex reasoning and specialized tasks, including video and presentation creation.

“To achieve the best business outcomes, organizations need the flexibility to choose the AI model best suited to each problem,” said Chris Leone, EVP of applications development at Oracle.

“By bringing Gemini to Oracle AI Agent Studio for Fusion Applications, we are giving customers and partners greater choice as they build and extend agents and agentic applications that reason through complex, real-world business challenges. Oracle Fusion Applications then turn that reasoning into action through governed workflows, approvals, and transactions.”

Oracle also plans to use Gemini models for embedded AI use cases in Netsuite and Oracle Fusion Applications, which include Oracle Fusion Cloud Enterprise Resource Planning (ERP), Oracle Fusion Cloud Human Capital Management (HCM), Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) and Oracle Fusion Cloud Customer Experience (CX).

In each case, it will choose the model that can deliver the best price-performance for specific customer scenarios.

“AI is at the core of how customers use and experience NetSuite and choosing the right model for the right use case is critical to helping them get more value from AI,” said Evan Goldberg, founder and executive vice president, Oracle NetSuite.

“As we evaluate various AI use cases in NetSuite, we are working with leading large language models, like Google’s Gemini, to help customers improve visibility, automate work, and move from insight to action within NetSuite.”

Cognizant has announced the launch of a new EMEA AI Unit, in a move tit says will help organizations scale agentic AI deployments and move projects from pilot stages into production.

The dedicated unit will bring together advisory, engineering, and delivery capabilities to support enterprises across the region as they build, deploy, and manage agentic AI solutions tailored to their business requirements.

The launch forms part of Cognizant’s wider AI Builder strategy, which aims to help customers adopt AI technologies without being tied to a single cloud provider, AI model, or technology platform.

In an announcement, Cognizant’s president of EMEA, Manoj Mehta, said many organizations remain enthusiastic about AI but continue to face challenges translating early projects into measurable business outcomes.

“The EMEA AI Unit reflects Cognizant’s AI Builder strategy by bringing together the people, platforms and engineering expertise needed to move clients from pilots to payoff,” he explained.

“Our approach is neutral by design: we work across clouds, models and ecosystems so clients can build agentic AI solutions that fit their business, integrate into operations and support accountability for outcomes.”

Headquartered in New Jersey, Cognizant provides IT consulting, digital transformation, and technology services to enterprises around the world. In recent years, the company has increased its focus on AI-led transformation, developing services that combine consulting, software engineering, and managed delivery to help customers implement AI across their operations.

Turning AI into business outcomes

At the center of its new EMEA AI Unit is Cognizant’s Frontier Deployed Engineering (FDE) offering, a delivery framework designed to help organizations progress from AI strategy through to enterprise-wide deployment.

The framework consists of three service models: Foundation, Accelerate, and Transform. Foundation focuses on AI strategy, governance, technology selection, and early-stage prototypes, while Accelerate is designed to identify and deploy high-value AI use cases into production.

The third tier, Transform, supports wider business reinvention through multi-agent AI systems capable of automating end-to-end workflows.

Early impact across EMEA

The unit is already supporting several notable enterprise deployments across the EMEA region, according to Cognizant.

The company is currently working with one of Europe’s largest online fashion retailers to move AI use cases into production through an AI factory model that it said can reduce development cycles from months to days.

Elsewhere, the firm revealed it is also supporting a global pharmaceutical company with the use of multi-agent AI systems across research and development – including drug discovery, clinical trial design, and regulatory preparation.

Seagate has said it's all but sold out of its exabyte-capacity hard drives until 2028, thanks to the AI boom sparking a cloud and data centre build-out.

The storage giant made the comments following the release of its results, which showed revenue climbed to $3.6bn from $2.4bn last year, and net income leapt to $1.2bn from $488m. Seagate posted margins of 52.3% for the quarter, versus 37.4% last year, and record cash flow of $3.1bn for the year, saying it expected margins and cash flow to grow throughout this year.

Dave Mosley, Seagate’s chair and chief executive officer, said in a statement that the results were "driven by robust cloud data center demand" and predicted that would continue into 2027.

"Our confidence is supported by the scale, quality, and duration of our data center customer commitments in a strengthening demand environment," Mosley added in a conference call, according to an online transcript. "Data center demand now represents approximately 90% of our exabyte shipments.”

He added: "Based on the long-term supply agreements in place today, the vast majority of our nearline exabytes are now allocated into calendar 2028."

AI versus supply

The AI-driven infrastructure buildout has sparked a shortage in components RAM, driving up prices for companies and consumers — but benefiting suppliers.

Despite growing concerns about the costs associated with AI, Mosley expects investment to continue, saying cloud customers remain the largest driver of growth for Seagate, with no evidence of a slowdown.

"Importantly, we are not seeing customers pull back on planning horizons," he said on the call. "As our strategic relationships deepen, many are actively seeking to extend planning horizons through 2029 and beyond, which we believe reflects growing confidence in their own long-term infrastructure needs."

That is driven by how data-hungry AI has proven, Mosley noted, with new data constantly being created and customers hoarding data for longer — which is good news for Seagate and its hard drives.

"With the transition from AI model training to inference to agentic applications, more data is generated and retained for historical context, compliance, and future reuse," Mosley added. "As these data center environments become larger and more complex, customers must balance performance, energy consumption, and cost across distributed infrastructures."

This means that hard drives still have a place versus SSDs and other storage technologies, according to Mosley. "Cloud providers have long addressed these challenges through tiered storage architectures that combine high-performance memory and SSDs with mass capacity hard drives to optimize performance and economics at scale," he said.

Next up

Mosley also revealed details of Seagate's future roadmap, saying testing with customers was underway for its second-gen Mozaic 4 platform, which can support up to 44 terabytes per drive.

"We expect to achieve our next ramp milestone by exiting calendar 2026 with 50% of our HAMR exabytes on our Mozaic 4 platform," he added. "Looking further ahead, Mozaic 5, our 5-plus terabyte per disk platform, remains on track for qualification shipments in late calendar 2027."

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.

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.

The tokenmaxxing trend has swept the tech industry in 2026 and already left some firms reeling from huge bills – but lessons from the early cloud era could be key to curtailing costs.

That’s according to Patrick Brogan, director of the FinOps advisory team at DevOps firm Harness. Speaking to ITPro, Brogan said the current trend bears similarities to the cloud boom over a decade ago. Enterprises are ramping up adoption, tinkering, experimenting, and testing out what works for them.

In some cases, tokenmaxxing is simply a case of encouraging AI adoption among staff, while for others it’s about justifying lavish investment in the technology and keeping up with AI-savvy competitors.

“There’s a number of factors that go into tokenmaxxing,” he said. “Some of it has to do with organizations deliberately pushing their employees to use the technology.”

“It might come from a sort of subconscious desire, subconscious FOMO. They don’t want to feel like their competitors are leveraging technology in a way that’s going to put their own company at a disadvantage.”

Regardless of the underlying motivations, Brogan said tokenmaxxing shows companies are “throwing everything at the wall to see what sticks” then making decisions on what AI tools should be used – and it’s coming back to bite them.

The trend has reached such an extent that Harness found nearly three-quarters (72%) of organizations have been hit with unexpected cost spikes over the past year.

Uber stands out as a key example here. As ITPro reported in June, the ride hailing firm blew through its entire annual AI budget in just four months after incentivizing staff to engage in the practice.

Going back to FinOps basics

Harness noted that AI now accounts for 23% of the average enterprise cloud bill, while organizations estimate that around 26% of all AI spend is wasted. This needless waste can be tackled, though, and Brogan said time-tested FinOps practices could be key.

“AI cost management has the same problems that cloud had a decade or more ago,” he said.

FinOps is a framework for cloud spend management that aims to bring together engineering, finance, and broader business teams to maximize value and, crucially, establish accountability for spending.

It’s not a practice that’s gone away, but in the AI boom it's somewhat fallen by the wayside. Brogan said these same techniques and processes could be crucial to curtailing excessive AI spending, particularly in terms of accountability.

Harness’ study found that, in many enterprises, nobody is clearly accountable for AI costs. More than half (52%) said they had no clear sense of ownership with responsibility split across engineering, finance, and IT.

The result here is that when spending ramps up in specific areas, such as software engineering, there isn’t a single function designed to answer for it or hold things to check.

“When we were getting started in our FinOps practice there, we felt the same pain points,” he said. “You know, confusion over ownership, gaps in how we govern the cloud bill and the services that our organizations will use, and certainly invoice shock.”

Velocity creates new challenges

Brogan said the challenges posed by AI adoption in terms of cost management aren’t a facsimile of the early cloud era, however.

While that period saw a widespread shift, the scale and pace of AI integration means many organizations aren’t reacting quickly enough to surging costs.

“Those problems are compressed into a fraction of the time because AI technology is developing at such a rapid pace and its usage and adoption have exploded far faster than cloud,” he said.

“We don’t have the same 10-year span to figure out the solutions to the challenges with AI spend but, luckily, we have a lot of lessons we learned with managing cloud spend.”

Brogan noted that Harness’ findings suggest a sharpened focus on ownership and governance will be critical.

“Companies very quickly, if they haven't done so already, need to agree and align on a single owner of the AI bill,” he said.

“Whether you're a company where that's just one person, or you're a large enterprise and that requires an entire team or set of teams,” Brogan added.

“There needs to be a very clear decision on who or what that team is, the responsibilities that that team has, and what they then need to drive [in terms of] responsibility and accountability.”

Boxing clever

A key issue many enterprises fail to acknowledge lies with model choice, Brogan noted: not every task requires a costly frontier model, yet teams frequently fall into this trap.

Speaking to ITPro last month, Nitish Tyagi, senior principal analyst at Gartner, said model selection will be critical to reducing costs moving forward, particularly for smaller tasks.

Tyagi noted that “intelligent model routing” strategies are now a key focus for developer teams, helping them to box clever with selection.

Brogan echoed these comments and urged enterprises to keep closer tabs on the costs associated with individual models.

“There’s probably a lot still to be learned about how we build an application to pick the model that’s best fit for purpose,” he said.

“[So] not using a frontier model for something that can be delivered by an older generation. Maybe it arrives 10% slower at that outcome, but if you can live with that trade-off for the lower cost, then that should be built into the application design from the start.”

Whether enterprises are willing to accept this trade-off is debatable, Brogan noted, and Harness’ study suggests there’s little sign that they’ve learned lessons so far.

More than half (57%) of engineers told the firm they’re still being encouraged to engage in tokenmaxxing practices, for example.

Some organizations have taken drastic measures to cut down on needless spend. As ITPro reported in late June, Accenture told staff to cut down on AI use for basic tasks due to “"soaring token spend"..

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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.

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.

Microsoft CEO Satya Nadella says the company plans to expand use of its MAI model range as the company looks to offer customers lower-cost AI options.

The in-house models, unveiled by the company in June, are designed for specific enterprise tasks and span a range of areas, including image and voice generation, audio transcription, and coding.

In a blog post on 23 July, Nadella outlined the tech giant’s new ‘Frontier Diffusion and Control’ strategy, which aims to help customers reduce reliance on costly frontier models and emphasize the importance of model choice for specific use-cases.

The move by Microsoft comes amidst growing concerns about spiralling AI costs over the last six months. With trends such as ‘tokenmaxxing’ and the shift toward consumption-based pricing, some companies have been left with hefty bills as AI use accelerates.

“In a world where software has real marginal cost for the first time, how do we ensure frontier benefits are diffused across the entire ecosystem?” Nadella wrote.

“The key is to optimize the cost-to-outcome frontier in real-world context,” he added. “In practical terms, that means using the right model for each task, and optimizing the context, skills, tools, and agent harness around it.”

Boxing clever with AI model selection

Nadella’s contention here is that these in-house models can give customers “frontier capabilities” albeit in a cheaper, bespoke capacity.

Simply put, these are domain-specific models based for an enterprise’s individual needs, not a one-size-fits-all frontier model such as those offered by OpenAI or Anthropic.

These still form part of the broader “orchestration system” used alongside MAI, but all amounts to helping users box clever when choosing what model to use.

“These models have been built ground up with clean data lineage and optimized for learning transfer from generalist to specialized skills in enterprise RLEs,” he explained.

This will require enterprises to implement evaluation processes when considering which model to use in which context, he said.

“Product-specific evals and model independence give us the control and a direct hill to climb, and to keep refining until we reach the right quality-cost target,” Nadella wrote.

“We are now seeing MAI models outperform general-purpose frontier models in many use-cases while using a fraction of the tokens.”

Internal testing of MAI for its own products has so far delivered “promising early results”, according to Nadella. The company has piloted the use of these models in GitHub Copilot, Outlook, and other Microsoft 365 services.

The company plans to take the same approach with Copilot Chat, PowerPoint, and other services, he added.

Nadella’s comments align closely with recent research from Gartner on rising AI costs.

While focusing primarily on the use of AI in software engineering, the consultancy told ITPro that enterprises should establish a “use-case-driven decision” framework when using the technology for certain tasks.

This, Gartner noted, will help reduce costs by encouraging users to stop needlessly allocating agents to tasks they’re not designed for.

Microsoft eyes model diversity

Nadella’s blog post marks the latest in a string of comments made by the Microsoft chief hinting at the company’s efforts to diversify model choice - and to stop pushing frontier models on customers.

In mid-July, he warned about the risks of relying too heavily on frontier AI labs, describing what he called the “reverse information paradox”.

Nadella said enterprises are essentially “paying twice” for AI services, handing too much data to providers, and receiving little benefit in return.

These providers, meanwhile, gain valuable insights into customer products that could help them refine - or build - competing options.

"If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself," he said.

"Therefore, it’s imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop.

As ITPro noted at the time, his comments were peculiar given the close ties the tech giant maintains with OpenAI and Anthropic, whose models are deeply entwined across its core product range.

Microsoft's long-standing relationship with OpenAI saw it take the lead as the go-to model for its software services, yet this changed last year after striking a deal with Anthropic to give users greater choice.

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Tokenomics has been a theme of many tech conferences this year and AMD has joined in the fun at this year’s Advancing AI conference.

At the heart of the tokenomics conversation is the move from chatbots and simple inference towards agentic AI. This is a real trend as noted by Digital Ocean and McKinsey and one that OEMs have locked onto hard.

While in previous years enterprise hardware vendors would wax lyrical about the importance of training LLMs on your own data (and how their hardware can help), if you listen for that chatter now you will hear nothing but crickets.

AI agents offer a range of benefits for enterprises – according to Digital Ocean’s research, 53% of the over 1,000 IT decision makers it spoke to saw productivity and time saving gains, while 44% said it had created new business opportunities.

There are also drawbacks, however, and one of the most apparent is the increased use of tokens, which leads to higher bills.

Whereas an individual using prompts to interact with an AI chatbot may use a few hundred tokens, agents can use up thousands.

As McKinsey explains: “Agentic tasks can consume roughly 1,000 times more tokens than code reasoning (single-turn problem-solving without tool interaction) or chat tasks (multi-turn dialogue about a coding problem).”

“The expensive part is not the first answer generated but the checking, repairing, and reverifying that follows,” the report continues. “About 60% of an agentic task’s costs, in fact, are tied to refining answers.”

This fact, and companies baulking at the related costs, hasn’t gone unnoticed among enterprise IT vendors, which are proposing their on-premises services as a way round this.

For AMD, this is a tricky path to tread, given it counts both traditional enterprise vendors like Dell, HPE, Lenovo, and Supermicro and public cloud-based services from OpenAI and Anthropic among its customers.

AMD’s performance focus

Michael Nordquist, CVP of product marketing for AMD’s computing and graphics group, diplomatically said that when it comes to considering the impact of tokenomics “it’s not all one way or all the other, it’s going to be hybrid. For some things local models work great, for some things you’re going to want that frontier model available for agent behaviour.”

From a practical perspective, AMD’s answer to this quandary has been to focus on how its own chips can (theoretically) reduce token-related costs, without taking sides on the public/on-premises debate.

“The theme [at Advancing AI] is really performance is the way that we can deliver the best economic value to our customers,” said Andrew Dieckman, CVP and GM for data center GPU at AMD.

“It’s the strongest dial that we have on the dashboard, so to speak,” Dieckman said, “so we’re very focused on that and then making sure that conveys to favorable economics for our customers because the cost of tokens as the world moves to inference is so important.”

Part of that focus is the company’s MI455X GPUs, announced at Advancing AI 2026 during AMD CEO Lisa Su’s keynote speech.

According to AMD, the MI455X has up to 18x lower token cost than its predecessor, the MI355X, while maintaining up to 34x higher token throughput.

“That delivers, really, the ability to serve more users with faster response times and significantly better economics,” Dieckman said.

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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.

Living off the land (LOTL) attacks are a worst-case scenario for enterprises globally, with hackers lurking in networks, extracting sensitive data, and biding their time before wreaking havoc.

These attacks rely on weaponizing the legitimate software and tools used by enterprises, and they're by no means a new trend.

Research from Bitdefender, for example, found 84% of major cyber attacks leverage these techniques, and security teams globally have developed an array of detection capabilities aimed at combatting the threat.

But the dangers posed by these methods could escalate with the arrival of AI agents, according to Zeki Turedi, CrowdStrike's Field CTO for Europe. Speaking to ITPro, Turedi said the deep access given to agents across enterprise IT estates represents a huge threat if even just one were to be compromised or manipulated.

“Organizations are giving them [agents] full access. They're giving them full capabilities. They will have the full privilege of the human user,” he told ITPro.

Turedi pointed to previous LOTL techniques that involved compromising tools like PowerShell. While this gave users access to valuable data, the risk with agents is drastically higher given how they operate, and, crucially, where they can go within networks.

“They also have further reach across an enterprise,” he said. There’s only so much PowerShell can touch or manipulate or modify, but an agent theoretically can touch every single part of that organization's technology ecosystem and architecture.”

“This is where it makes it so much more dangerous.”

Living off the land attacks are evolving

Findings from CrowdStrike’s 2026 Global Threat Report show these risks aren’t just theoretical, they’re now a reality, and the same logic with traditional LOTL attacks is being applied to agents.

Indeed, the cybersecurity firm has observed threat actors exploiting legitimate AI tools at dozens of organizations globally. In these cases, AI systems such as chatbots and assistant were abused to generate malicious commands and steal sensitive data.

The goal here is often focused on credential theft for initial access brokers, attacks later down the line, and even cryptocurrency theft. All typical hallmarks of financially motivated cyber criminals.

LOTL attacks have also become a go-to method for highly sophisticated, state-sponsored threat actors. As ITPro reported last year, the infamous Salt Typhoon threat group laid low in compromised US National Guard networks for nearly a year, accessing sensitive military and law enforcement data.

It’s here where risks are even higher as government departments and public sector organizations begin exploring the use of agents. A recent study from McKinsey, for example, found agentic AI is emerging as a key focus area for public sector organizations in the US.

Keeping up with hype cycles

Turedi told ITPro the focus on agents isn’t surprising given that threat actors will “follow the technology landscape that their victims are looking to invest in”.

“We've seen this time and time again. So previously, we saw as cloud got adopted, funnily enough, adversaries became very capable in cloud,” he noted.

“When organizations started to be more focused and store sensitive data in SaaS applications, SaaS tools, guess what? We saw adversaries go after the SaaS applications.”

Turedi added that the pace of change within the generative and agentic AI space, combined with rapid adoption efforts, could create additional challenges moving forward.

Enterprises across a range of industries are diving headlong into the hype cycle, and threat actors are keen to capitalize on the confusion and lack of risk awareness with the technology.

“This is just a brand new technology landscape that organisations are investing in,” he commented. “And of course, the adversary is very, very aware that for multiple reasons this is a great opportunity.”

“There’s a lot of confusion,” Turedi added. “There’s a lot of lack of knowledge and expertise in this space.”

An identity conundrum

According to Turedi, a key concern surrounding ‘living of the AI land’ attacks, as CrowdStrike dubs them, is that many organizations lack the visibility or governance capabilities to adequately monitor what’s going on with these bots.

This has become a recurring talking point since the advent of agents. Research from Ping Identity in March this year specifically highlighted IT architecture visibility as a key concern for enterprises rolling out agents.

The sheer volume of agents is jarring for security teams, the study noted, but things are exacerbated by sub-agents, essentially creating chains of untraceable bots – an ideal target for stealthy threat actors.

Identity is another area of concern, Turedi noted, albeit one within a broader range of overlapping considerations for security teams. He noted that efforts to improve processes on this front are falling flat.

Many organizations are working with infrastructure or tools that were struggling to manage human identities in the first place. Adding agents into the mix further compounds the problem.

“The problem we see is that a lot of organizations are only starting to think about things like identity security or IAM (identity and access management) hygiene,” he said. “These are typically infrastructures and architectures that were not designed for modern human identity usage, never mind for the AI identity usage.”

With living off the land attacks, the whole advantage for the attacker is that they’re blending into IT environments. They’re using your tools and software and thrive on the confusion. With agents, Turedi said this adds further complexity in terms of identifying what actions are legitimate and potentially nefarious.

“It's actually quite difficult to figure out if it's AI usage on an identity layer,” he said. “Can you truly say that that's a human accessing the network versus that's an AI agent accessing the network on behalf of a human?”

A recent survey from the Cloud Security Alliance (CSA) raised similar concerns with regard to agent identity. More than two-thirds (68%) of respondents said they couldn’t accurately identify agent activity compared to human activity.

Turedi said it’s crucial to have “data from multiple different domains” when it comes to monitoring agent activity, taking into account applications, endpoints, and broader network traffic and “marrying them together” for a clearer picture.

“You need to be looking at the identity layer, you need to be looking at the endpoint, you need to be looking at the application layer,” he said.

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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.

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.

Companies using AI should be wary about losing control of their data – and the risk of AI developers making use of that knowledge.

That’s according to Microsoft CEO Satya Nadella, who in a recent blog post warned many AI users are "paying twice" thanks to what he calls the "reverse information paradox”.

This is a process through which AI providers, particularly frontier labs, get access to proprietary data about their own customers. Nadella said the trend represents a huge risk for enterprises, and called for protections akin to patents.

The critique is intriguing as Microsoft itself is an AI company, offering plenty of AI-driven products to customers and cramming AI features into its software. Beyond that, Microsoft was an early funder of OpenAI, though that relationship has weakened of late.

Nadella pointed to economist Kenneth Arrow's information paradox, in which the seller of data or knowledge risks giving it away in order to prove it's worth selling. He noted that AI has created the reverse problem, in which the buyer gives away knowledge in order to use the product purchased.

"You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful," he noted. "The better you want the model to perform, the more of that knowledge you have to feed it!"

"Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return."

The ‘AI exhaust’

This concern isn't just about uploading corporate data to systems, or accidentally including proprietary data in a prompt, though those are both concerns. Instead, Nadella said every interaction with AI inherently includes information that could be useful.

"Models learn from 'exhaust,' the prompts people write, the tools agents use, and especially the corrections people make when the model is wrong," he wrote.

"Every correction is distilled into institutional know-how. It’s the kind of knowledge a competitor could never buy, and the kind that leaks almost imperceptibly: trace by trace, correction by correction, eval by eval."

Ironically, Nadella said it was necessary for the "great innovation" of allowing model developers to use public data – some of them face lawsuits for hoovering up copyrighted data to train their systems.

When it comes to business data, providers should share anything learned with customers. Very few actually do this, however.

"If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself," he said. "Therefore, it’s imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop."

Nadella's tips on how to protect your company from AI

Nadella said the existing setup requires a protection system on par with patents, but also advised companies to set a "trust boundary" by considering a few key aspects – some of which may be easier by simply switching to open source AI.

He suggested companies should control their own "evals", or evaluations of the AI system, because that reveals what looks "good" to the organization, and keep ownership of decisions, feedbacks, and other outputs for their own use.

Beyond that, companies should build capability with AI via their own learning environments where models can learn using real workflows without exposing corporate data.

Improving choice can be achieved by decoupling the orchestration layer from any single model, enabling the ability to switch to another model if one is withdrawn. This can also help organizations cut costs by choosing the right model based on their individual needs.

Lastly, he said to "compound" those four ideas to "create your own continuous learning loop".

"In other words, a company should be able to use a model without giving up the knowledge that makes it unique," he added.

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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?

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

Anthropic's Fable 5 and Mythos 5 are back after a three-weeks-long restriction saw the security models effectively banned.

Earlier this month, the US government applied export controls to Claude Mythos and Claude Fable 5, banning any foreign users, inside or outside the US, from either model.

As Anthropic had no way of checking the nationality of its users, both models were de facto banned from use. Last week, the restriction was loosened to allow Mythos to be used by US organizations deemed trustworthy.

Now, Anthropic has said the restrictions have been mostly dropped following the introduction of new safeguards.

"Anthropic has agreed to proactively detect and address security risks associated with the models," US Commerce Secretary Howard Lutnick wrote in a letter to the tech company, according to the BBC,

Lutnick later added on social media: "Over the past two weeks, we have worked closely with Anthropic to analyze and approve Fable 5 to ensure alignment across the US Government and strengthen America’s leadership in AI."

Anthropic said it was still working to expand access to Mythos 5 to a wider set of partners in its Project Glasswing program – which gives access to the security model in a managed way – and hoped that would include domestic and international users.

Fable 5 should now be available again for all Claude users, with access reenabled for AWS, Google Cloud, and Microsoft Foundry as quickly as possible.

Why Mythos and Fable were restricted

Fable 5 and Mythos 5 are both security focused models, sharing the same underlying model, Anthropic notes.

As ITPro reported in early June, Fable 5 was built with "strong safeguards to make it safe for general use".

Mythos, meanwhile, has fewer protections in place, and as such was released to a limited number of trusted partners under Project Glasswing for the express purpose of defensive security.

Just days after the release of Fable 5, the government applied strict export controls. That was driven by Amazon researchers finding a way to dodge Fable 5's safeguards to use it to hunt for flaws in software and produce exploits.

Anthropic argued that most of its models could actually uncover the same vulnerability and exploit demonstration, and that the Amazon research didn't make use of any "unique Mythos-level cyber capabilities".

The workaround discovered by Amazon has now been blocked for most instances, with any users attempting similar techniques downgraded to a lesser model. Anthropic admitted that more "benign requests" may be flagged accidentally now, though it hopes to reduce such false positives as the system is improved.

"One particularly important safety mechanism involves classifiers — smaller automated AI systems that, during an interaction, detect when the model is asked to perform a potentially harmful cybersecurity task (or produces potentially harmful outputs)," the company said.

"When this occurs, the classifiers block the model from responding to requests. The ultimate goal of these classifiers is to prevent the model from engaging in uniquely dangerous behaviors."

Beyond the new safety classifiers and other safeguards, Anthropic is working with industry partners including Amazon, Microsoft, and Google to develop a framework for addressing AI model ‘jailbreaking’ techniques, developing a system for judging the severity of such attacks.

Plus, Anthropic said it would work more closely with the US government, offering expanded early access for models that could impact national security and sharing information about jailbreaks or misuse patterns.

The company called for the same rules to apply to its rivals: "These rules should be codified in strong regulation and applied equally across frontier model developers."

Mixed industry reactions

It's no surprise this was sorted quickly. Keven Knight, CEO, of cybersecurity firm, Talion, noted that the US government would not have wanted to risk slowing down AI development, particularly in security.

"Given that China is steadily advancing with its advanced AI model, with a platform that mirrors Mythos being launched by 360 Security Technology last week, the US could not afford to limit access to Anthropic's platforms for much longer," Knight said.

"Restricting access to the platforms would only leave western organisations on the backfoot, and at a time when the AI arms race is really heating up, this would be dangerous."

Though the export ban is now loosened, Andrew Bloster, senior R&D manager at Black Duck, said that won't end the "chilling effect" the incident has had on the use of such models.

"Security leaders globally are now wary to depend on AI as a service models that can be launched with much fanfare and then pulled from the global market," he said, pointing to the rise of sovereign AI.

Bloster added that "technology leaders are more concerned about resilience, data security, and cost, and Fable being permitted back on the world stage might not be enough to earn back global trust."

Though Anthropic was the first company to jump through these hoops, it actually tried to find a good balance between safety and use via Glasswing, said Mike Britton, CIO at Abnormal AI.

"On Mythos, the controlled rollout to a vetted set of US organizations is actually the right model. Limiting access to trusted institutions, with use cases focused on finding and remediating vulnerabilities, is how you derive real security value from a model at this capability level without creating new risks," said Britton.

"The goal should be more secure software — and that's achievable if the access controls stay disciplined over time."

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Netgear is making strides in helping businesses make the move into AI-powered network operations and management with the launch of Netgear Insight 10.0.

This next-generation cloud network management platform will be game-changing for small and medium-sized businesses (SMBs) and Managed Service Providers (MSPs), plugging the gap between need and resource, according to the firm.

Insight 10.0 is designed to support organizations through delivery of enterprise-grade AIOps, intelligence, and operational simplicity, helping them to make decisions faster and move their business forward.

One key benefit of the new platform is empowering network administrators to move from being purely reactive to being proactive, according to Luca Marinelli, Netgear’s head of Europe.

In terms of launches, Marinelli said this is a pretty significant one for the company and a key focus during his time with the business so far, following his appointment in October last year.

“The opportunity to have a single pane of glass, which will be managing simultaneously – like with multi-tenancy – several networks will free up time for [MSPs] to maybe start providing other additional services to attract new customers, which is always something very healthy to do in business.”

“[Also] if you perform certain types of activities in a reduced amount of time, your margin can be better. This results in a much more profitable business. So, there are multiple impacts [from] using more sophisticated state-of-the-art tools," Marinelli added.

"Sometimes the feeling is that AI is just a nice slogan that you need to just add in everything you say. [But] I think the important thing is to know what to do with this superpower. If you know what you want to do, it is extremely valuable.”

Outside of product innovation, Netgear has been heavily focused on how it partners to do business. Indeed, in November 2025 it unveiled changes to its partner program in a bid to make it easier to work together and drive joint success.

At launch, Netgear’s president and general manager, Pramod Badjate, said partners were at the center of everything the company does.

“Our big play at Netgear is delivering enterprise-grade products with the simplicity that the SME market needs. That all boils down to a product that is ready for those environments, but also, from a TCO point of view, fits that customer experience really well,” said Jordan Hobday, Netgear’s UK country manager.

“The Netgear Drive Partner Program is three tiers. It's there to really incentivize and reward our partners. There's a commercial benefit to that, but equally it's about how we [can] provide a good experience to our partners, like certifications, self-serve material, all the good things that you would expect.

“A big part for us is simplicity, and how easy it is to work with or do business with Netgear.”

Netgear’s EMEA business is in good health, according to Luca, but he said the company’s turnaround journey was not finished yet. Indeed, one priority for him is not just growing partner numbers for numbers' sake, but really focusing on deeper engagement.

“I think we have, in my opinion, enough partners that we are working with. The game is to keep them interested; I don't necessarily think we need to increase the number of partners broadly. It's going to be a very selective move in both the AV and IT markets to capture the real players that we need to work with, in a given territory, in a given market,” he said.

“We work with a lot of partners, but the number of partners we have this business intimacy with is not at all at the level where it should be. It’s about ways of working with partners; it's about tools that we keep evolving; it's about the value we bring to those partners and the way we spend time. We do spend time [with partners] already, of course, but [it’s about] the way we spend [that] time. It has to evolve.

“It's [about] more quality, more depth, more intimacy, more shifting the way we work with partners, which takes a little while. It doesn't happen overnight.“

Anthropic has launched Claude Sonnet 5 in a move the company said offers users more powerful agentic capabilities.

The new option is the “most agentic Sonnet model yet”, according to Anthropic, capable of building workflow plans for users, accessing browsers and terminals, and operating autonomously for longer periods.

Cost and performance are key talking points here for Anthropic, with the company noting that Sonnet 5 “narrows the gap” with its flagship Opus range in terms of efficiency.

“Claude Sonnet 3.5, 3.6, and 3.7 were the first models that showed impressive skills in coding and tool use. More recently, though, the clearest gains in agentic capabilities have been in our Opus-class models,” the company said in a blog post.

“Its performance is close to that of Opus 4.8, but at lower prices,” Anthropic added. “It’s a substantial improvement over its predecessor, Sonnet 4.6, on important aspects of agentic performance like reasoning, tool use, coding, and knowledge work.”

SWE-bench Pro results show that show that Sonnet 5 recorded a 63.2% score on agentic coding capabilities. That marks a solid increase compared to Sonnet 4.6, which came in at 58.1%.

The model still lags behind Opus 4.8 (69.2%), but clear gains are being made on this front.

Anthropic eyes cost improvements with Claude Sonnet 5

With concerns mounting over token consumption, Anthropic is keen to emphasize that Sonnet is a far more cost-efficient model.

Performance of the model at “different effort levels” – or the intensity and timeframe of tasks – on BrowseComp shows a “strict improvement” over Sonnet 4.6.

Compared to Opus 4.8, meanwhile, Sonnet covers a “much wider range of cost-performance options”.

“It provides substantially improved cost efficiency at medium effort,” the company explained. “Its higher-effort performance can match Opus 4.8 on some tasks.

Users will also be able to adjust effort levels to help them “find the right balance of cost and performance” with both Sonnet 5 and Opus 4.8.

Sonnet 5 safety improvements

On the safety front, Anthropic noted there has been an “overall improvement” with Sonnet 5, which is far more likely to refuse “malicious requests” than 4.8 and is capable of resisting hijack attempts, such as prompt injection.

“The model shows lower rates of hallucination and sycophancy than Sonnet 4.6,” the company noted. “On our automated behavioral audit, which tests a wide range of misaligned behaviors such as cooperation with misuse and deception, Sonnet 5 scored lower (that is, safer) overall.”

It’s worth noting that Anthropic admits Sonnet 5 displays higher rates of “misaligned behavior” compared to Opus 4.8 and Claude Mythos, urging caution by users.

Safety tests for Sonnet 5 come at a critical time for Anthropic, with the company having launched Claude Mythos earlier this year as part of a gated preview with select industry partners.

How to access Sonnet 5

Claude Sonnet 5 is available across all plans from today, according to Anthropic, including Max, Team, and Enterprise users. It will also act as the default model for Free and Pro plans.

For Claude Code and Claude Platform users, Sonnet will come with an “introductory” pricing of $2 per million input tokens, and $10 per million output tokens until 31 August.

Thereafter, Anthropic said prices will rise to $3 and $15 across input and output tokens respectively.

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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.”

Accenture has told some staff to roll back the use of AI for basic tasks due to skyrocketing prices.

According to reports from 404 Media, leaked audio from an internal meeting at the consulting giant highlighted growing concerns about "soaring token spend".

To battle that, leaders at Accenture are pushing back against token use by "non-engineers" who are driving excessive use of the technology on unnecessary tasks, such as converting PDFs to slides, according to internal company data.

The move comes after a report in February revealed the consultancy has introduced new measures to monitor staff AI use, prompting a rise of “tokenmaxxing” by employees to showcase their uptake of the technology.

“What we’re seeing right now is just rapid escalation in AI token spend,” Justice Kwak, Accenture’s agentic AI strategy lead reportedly said in the meeting, according to the report.

He added that Accenture has hit an "inflection point" with unpredictable spend, saying C-level executives are "still asking the question of whether they’re getting value from what we’re spending on in the context of AI."

Kwak reportedly noted that Accenture's problem isn't "niche" but one that will be faced by every AI-using enterprise – and that offers an opportunity to the consultancy to offer services unpicking token use to its own clients.

ITPro approached Accenture for comment, but did not receive a response by time of publication.

Accenture isn’t alone on rising AI costs

Accenture is by no means the first company to introduce measures aimed at limiting excessive AI use in recent months. Indeed, a host of major firms have issued warnings to staff due to rising token costs.

As ITPro reported last week, rising AI costs are the result of a combination of factors, most notably the shift to consumption-based pricing on the part of providers, and the tokenmaxxing trend.

Anthropic, for example, has shifted to usage-based billing and raised costs for some users, though also increased caps for others.

GitHub in April rejigged its pricing model, leading to credits now being consumed based on token usage, a model known as token-metered or token-based pricing. The more AI tokens used, the more a company is billed.

Combined with the rapid acceleration of AI use, some firms have been hit with huge bills. One unnamed company reportedly failed to set usage limits and accidentally spent $500 million on Claude.

This has prompted some businesses to take drastic action to rein in AI spending, including the introduction of token caps and spending limits.

Uber capped employees to $1,500 a month in tokens after blowing through its entire AI budget in just four months, for example. Elsewhere, Walmart limited staff usage of its internal AI agent while Microsoft removed access to Anthropic's Claude Code over costs.

Amazon and Meta have also reportedly removed their AI "leaderboards" designed to encourage AI usage in an effort to end "tokenmaxxing".

Costly even when useful

The leaked meeting notes from Accenture suggest the consultancy is keen to limit AI use for unnecessary tasks, but continue to give engineers plenty of tokens to play with.

Reducing tokens in one part of the business and allocating more to engineers might not be a concrete solution, however. Indeed, Gartner analysts told ITPro last week that AI coding costs could soon surpass developer salaries.

With this in mind, the consultancy said a concerted focus on cost optimization practices will be needed to limit surging costs on this front – yet many are woefully underprepared to introduce such measures.

“Most organizations still lack the maturity and frameworks to effectively measure cost versus business impact," Nitish Tyagi, Senior Principal Analyst at Gartner, told ITPro.

"Software engineering leaders are increasingly concerned as token-driven AI spend becomes harder to justify, with budgets often being depleted earlier than expected."

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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

Three years ago, a Californian man claimed that he'd been rejected hundreds of times by Workday's AI-driven HR system – now a judge has ruled that a class action based on the complaint will be heard in court.

US District Judge Rita Lin has ruled that Workday must face the claims brought against it, which includes claims that the company's AI-based candidate screening system discriminated on the grounds of age, race, and other protected characteristics.

With the case set to proceed, this makes it the first to consider the impact of letting AI-powered software make decisions on job candidates – a significant case given the rise of recruitment.

Indeed, a report from HireVue suggests seven-in-ten companies are using AI in HR and recruitment processes. The shift to AI hiring isn't popular, either, with two-thirds of Americans telling Pew that they didn't want to apply for a job via AI.

A spokesperson for Workday told ITPro the “claims in the suit are false” and dismissed claims that AI recruiting tools make hiring decisions.

“Workday’s AI recruiting tools don’t make hiring decisions in California or anywhere else,” the spokesperson said. “Our customers maintain full control of their hiring process and our tools are designed with human oversight at their core.”

Workday added that its technology “looks only at job qualifications, not protected traits like race, age, or disability”.

“We rigorously test our products as part of our Responsible AI program to confirm our tools do not harm protected groups.”

Workday case considerations

Lin ruled that anti-discrimination laws do indeed apply when Workday's software is used outside of California, where the company is based and where the claim was filed.

Workday's lawyers had argued that "it makes no sense for a Texas employer with a Texas applicant who will perform work in Texas," but Lin disagreed.

Lin also refused to drop a claim alleging that the software filters out candidates using "proxy indicators", including gaps in employment history, which could impact those with disabilities or illnesses.

One claim was dismissed, however. This sought to include discrimination against Asian Americans in the suit, on the grounds that proper procedure wasn't followed.

History of the case

In 2023, Derek Mobley first sued Workday claiming that he had been rejected by the company's software, which is used by other companies to sift through candidates, between 80 and 100 times.

Mobley believed that the Workday pre-selection system was discriminating against him because he is a black American, over 40 and suffers disabilities. At the time, Workday said the lawsuit was "without merit."

Workday attempted to have the case thrown out in 2024, but Judge Lin dismissed those challenges, ruling that the company wasn't an employment agency but could for the case be considered an employer.

She dismissed claims that the discrimination was intentional. At the time, Workday said it was confident that the remaining allegations would be "easily refuted."

Earlier this year, Mobley was joined by four other would-be candidates, with the judge ruling they could be considered together as a class action.

One of the new plaintiffs, Jill Hughes, said she submitted hundreds of applications that were rejected, with some responses saying she didn't meet the job's minimum requirement, when she actually did.

The AI recruitment conundrum

With the case set to go ahead, it could reveal much sought after details about how AI is being used in recruitment and how it influences who is being hired – and not.

The case comes as AI has overrun recruitment, with companies using it to power everything from sifting through CVs to conducting interviews, while would-be candidates use AI to find open roles, apply automatically, and boost their responses in interviews.

This has created a situation where AI is essentially talking to AI in order to hire a human for a job. Anthropic raised eyebrows last year when it told candidates not to use AI when applying, for example.

Workday isn't the first company to be caught out by the risks of using AI for recruitment. Back in 2018, Amazon was forced to pull an AI tool it was testing for recruitment after it became clear the system was methodically filtering out CVs submitted by women.

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Microsoft has announced that Copilot Cowork is now generally available for users globally, following a beta period via the tech giant’s Frontier program.

First announced in March this year, Copilot Cowork marks a significant milestone for Microsoft 365 Copilot users, integrating Anthropic’s highly popular tool within its flagship product.

According to Microsoft, Initial testing of the platform by a host of major companies, including Accenture, has proved highly successful.

In a blog post detailing the launch, Charles Lamanna, Microsoft’s EVP for Copilot, Agents, and Platform, said Cowork is the “fastest growing feature” in the history of the firm’s Frontier program.

“Cowork has among the highest user satisfaction of any Copilot or agent experience we have shipped,” he wrote.

“We learned from what we saw, engaged with you along the way, and used everything we heard to improve quality and add new features, including model choice, extensibility through plugins, and new cost management controls. Read more below and watch the full demo.”

Here’s what users can expect with Copilot Cowork.

What is Copilot Cowork?

Copilot Cowork is an integrated version of Anthropic’s Claude Cowork, which launched in January this year. The platform provides users with industry-specific AI capabilities to automate tasks across a range of professions.

Users can direct agents to carry out tasks on their behalf - such as file management or sending emails - by giving bots access to their files. The company described the new platform as essentially providing every worker with a “specialist” for their individual role.

Notably, the launch of the Cowork sparked a sell-off in the software market amid fears the AI tool could render dedicated software services obsolete.

Copilot Cowork features and capabilities

Users at Microsoft shops can expect all the typical capabilities offered by Cowork, albeit with a few new features, according to Lamanna.

Notably, users will be able to choose which model they use with the service. Copilot Cowork currently runs on Anthropic models such as Claude Opus 4.8 and Claude Sonnet 4.6.

Customers will also be able to use GPT-5.5 further down the line with the launch of Cowork 1, the company revealed. According to Microsoft, this will deliver “optimal” cost and quality, and is intended for “enterprise-grade use”.

The tech giant noted that tweaking has helped remove model bias.

“It’s designed to handle everyday Copilot tasks at a substantially lower cost, making it a strong option for cost-sensitive workloads,” Lamanna wrote.

New partner plugins have also been added to Copilot Cowork, according to Microsoft, with a host of partners such as Miro and Monday.com already available.

Other partner plugins include:

• Enosix
• Harvey
• LSEG
• Moodys
• Morningstar
• S&P Global Energy
• TeamsMaestro

Several more are coming soon, including plugins for Atlassian, Canva, Box, Databricks, and Adobe.

Microsoft also touted a number of security-focused features for Copilot Cowork, aimed at shoring up governance and compliance. Cowork, prompts, responses, and artifacts, for example, now flow through existing Microsoft 365 controls.

Copilot Cowork pricing

To use Copilot Cowork, users will need to go through the Microsoft 365 Copilot User Subscription License (USL). This means that users are billed on a usage-basis, and charges are based on what particular tasks they use these agents for.

In terms of costs, customers have two payment options: PayGo and P3 (Credit Pre-Purchase Plan). The former is aimed at customers aiming for flexibility in terms of how they use the service, with credits priced at $0.01.

P3, meanwhile, is for those who want to commit to a usage volume “in advance in exchange for a discount”.

The cost of Copilot Cowork comes at a tricky time for businesses globally, particularly since the rise of the ‘tokenmaxxing’ trend which has resulted in some firms racking up sizable AI bills.

Cost control features are available for customers, however, with Microsoft noting that customers can “decide when Cowork turns on, who gets access, and how much can be spent”.

Spending limits will be available at the tenant, group, and user levels, meaning admins can control costs and usage rates.

“Admins create scoped billing policies and define budgets, including user-level caps set inside group policies,” the company noted. “Admins set the thresholds that matter for their organization and groups, and choose who gets notified when spend crosses them.”

In the event that users exceed usage limits, credit requests can be made to complete tasks. These can be requested from inside Cowork.

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If it looks like AI professionals are in demand right now, just wait for 2028, when the UK's set to have more than half its AI roles unfilled.

New research from Native Teams and Robert Walters has found that demand will reach nearly 300,000 roles, against an estimated domestic supply of just 137,000.

"The scale of projected demand for AI talent is expected to significantly outpace domestic supply growth in many advanced economies, including the UK," said Phill Brown, global head of market intelligence at Robert Walters.

"Historically, major advances in technology only translated into meaningful productivity growth once organisations had the workforce capability to implement them at scale. The same dynamic is now emerging with AI, where access to experienced talent will play a defining role in how quickly businesses can convert investment into measurable economic output."

The answer for many organizations is to hire staff internationally, rather than simply throw money at the problem. And doing this, the researchers found, could lift UK productivity growth by 0.5 to 1.5 percentage points annually, by helping organizations scale AI capability more quickly and reduce deployment delays linked to talent shortages.

The supporting infrastructure behind a globally distributed AI team – global payroll, work payments, and compliance systems – has widely matured over the past few years, the researchers found, making global hiring far more practical at scale.

With the biggest AI deficit, the UK is also the most active global AI talent hirer. In technology specifically, the UK generates 20% of all cross-border tech transactions, ahead of the US at 15%. And it's also the fastest-growing market, adding more than 50% year-on-year.

"Organizations are now able to access critical AI capability more quickly and respond faster to changing technology demands, while also creating greater access to worldwide opportunities for professionals," said Jack Thorogood, founder and CEO of Native Teams.

The researchers found that the top 10 origin markets generate two-thirds of all cross-border hiring, with each one offering cost savings of between 40% and 68%. Europe-to-Europe hiring generated 41% of all hiring activity.

Four-in-ten roles are either senior or leadership – signalling, said the researchers, a shift from cost arbitrage to capability building.

The two main sources of talent, Spain and the Philippines, are rather different. Spain is seen as a hub for international hiring, supported by strong digital infrastructure and favourable remote work policies. It's also popular for its time zone and EU access.

The Philippines, meanwhile, has a strong position as a global talent hub, particularly in IT services, customer support, and remote operations. Language and cost are also factors.

"Global hiring is no longer merely a contingency for local talent shortages; it is rapidly becoming the primary workforce strategy for businesses focused on growth and scale," the report concludes.

"Organisations are turning to cross-border hiring not just to support expansion, but to accelerate AI adoption and enhance overall productivity. This shift is driven by the reality that domestic talent supply, particularly across technology and AI-related roles, has reached a ceiling that current local pipelines cannot sufficiently meet."

Kaseya has outlined an ambitious plan to unify its sprawling product portfolio around a common data layer, open APIs, and AI-driven automation, as the vendor seeks to move beyond its acquisition-led past and position itself for what it calls the era of autonomous IT.

The announcement, made at Kaseya Connect Europe in Prague this week, marks a strategic shift for a company that’s spent much of the past decade expanding through acquisitions. Rather than adding more products, execs are now focused on integrating the technologies already inside the portfolio.

“We’re doing the hard work to connect the data and open up the APIs,” Kaseya CEO Rania Succar told ChannelPro.

Focus shifts to platform architecture

The strategy is being led by CTO Pratik Wadher, who joined the company last year and has been tasked with building a common platform architecture across Kaseya’s product set. The long-term goal is to create a unified data layer capable of supporting AI agents that can automate ticket triage, remediation, reporting, onboarding, and other routine operational tasks.

Succar argued that many vendors have focused on adding AI features without first addressing the underlying data challenges.

“We’re actually doing the hard work to connect the data and open up the APIs,” she said, adding that AI becomes easier once those foundations are in place.

Elsewhere, Kaseya is expanding API access to allow MSPs to build their own integrations, customer experiences, and business intelligence capabilities on top of the platform.

Larger MSPs in the spotlight

The move reflects a growing focus on larger and more sophisticated MSPs. Historically, Kaseya has been strongest among small and mid-sized service providers, but execs now believe larger MSPs increasingly want access to the underlying data and APIs to build their own solutions.

“We are going to really focus on helping the largest MSPs be successful,” said Succar.

Partners at the event suggested that many of the changes introduced under Succar’s leadership are already being noticed, particularly around openness, product integration, and a greater focus on engineering execution.

“That change of management has really seen that progressive change,” said Simon Gurner, managing director of UK-based MSP Sunrise Technologies. “They were very, very siloed before, and they’re really pulling together now.”

Execution remains key

Alongside the positive reception, MSPs acknowledge that delivering a truly integrated platform will take time.

Jason Fry, managing director at MSP Global Four, said his organization had already invested heavily in building its own automation and reporting layer across multiple Kaseya products. And while welcoming the direction of travel, Fry cautioned that the work is only just beginning.

“It’s going to take them another year or so to fully develop and get all of that in place.”

The first capabilities built on the new platform are expected to become available later this year, with Kaseya planning to expand the functionality over time as more products are brought onto the common architecture.

With financial support from several other tech giants, Google has committed $915 million in new funding for carbon removal through the purchase of carbon removal credits.

Chipping in are Stripe, Google, Shopify, Salesforce, H&M Group, and Anthropic, which will contribute via Google's Frontier advance market commitment (AMC), made in 2022, to buy $1 billion of carbon removal by 2030.

The goal, Google said, is to spur innovation by sending a clear demand signal to researchers, entrepreneurs, and investors that there is a growing market for these technologies.

"Our renewed support helps scale novel climate solutions that benefit both ecosystems and communities – ranging from improving soil health through enhanced rock weathering to strengthening local economies through biomass carbon removal," said Randy Spock, Google's head of carbon credits and removals.

"These long-duration removals fold into our broader climate solutions portfolio, which includes restoring natural ecosystems and eliminating superpollutants. Together, these approaches are more than the sum of their parts; they can be combined to neutralize the warming impact of emissions over every timescale."

The company is focusing its efforts on two main areas. On the supply side, it plans to concentrate purchases on a narrower portfolio of companies where, it said, it's thoroughly convinced that the technology has gigaton-scale potential.

On the demand side, meanwhile, it will require every deal to have a clear line of sight to robust, long-term demand, be it compliance markets, industrial regulation, or direct government procurement.

The program includes methods such as enhanced rock weathering (ERW), inland water alkalinity enhancement (IWAE), ocean alkalinity enhancement (OAE), biomass injection, and waste-to-energy with carbon capture.

"Surficial mineralization and ocean alkalinity enhancement have the potential to be huge and low cost, but outstanding technology risks mean large error bars on both metrics," said Google.

"Biomass-based approaches and enhanced rock weathering are capped in their scale potential, and the cost of direct air capture is likely to remain relatively high, but these technologies are better understood and enjoy more existing policy support."

Currently, it's Microsoft that's the world's largest buyer of carbon removals by far: in fiscal year 2025, it signed agreements to remove a record 45 million metric tonnes of carbon dioxide with 21 companies across the globe.

But over the last four years, said Google, Frontier firms have also made a great deal of progress. Last year, seven portfolio companies delivered around 23,000 tons – roughly twice as much as the year before, and broke ground on 1.4 million tons of new annual removal capacity. This year, Frontier portfolio companies are forecasting more than 50,000 tons removed.

"Hundreds of companies are being built, and tens of thousands of tons have been delivered. We have real-world data across most major pathways and growing confidence in which technologies could be a meaningful part of a gigaton-scale future. The major question now is whether demand will materialize at the magnitude required," said the firm.

"To get to gigaton-scale, companies and governments will need to work in concert. Policy takes years to develop, and governments are best served by a range of proven, derisked technologies. Corporate buyers bridge that gap, providing the reliable revenue companies need today to get their technologies to commercial scale."

Cloudflare has announced the launch of a new Cloudflare One Design Partner designation, alongside an AI-powered toolkit aimed at simplifying security platform migrations.

The initiative is designed to help organizations modernize legacy security environments and accelerate adoption of secure access service edge (SASE) architectures.

According to Cloudflare, migration away from fragmented security and networking environments can often throw up operational challenges such as configuration risks, security gaps, and lengthy deployment timelines.

Housed within the vendor's PowerUP Partner Program, the new high-priority designation will provide partners with additional technical enablement and resources to counter these issues through its Cloudflare One platform.

The pool of initial partners selected for the program includes Arctiq, Consortium, CMT, Presidio, and The Missing Link. In an announcement, Cloudflare chief partner officer Tom Evans said the company is increasing its investment in partners as demand grows for modern security platforms.

"Cloudflare One has evolved into a partner-led engine, and our new Design Partner Designation is built to propel long-term growth," he commented. "This new framework represents our deepest channel co-investment yet."

Cloudflare One Stack

Alongside the new designation, the vendor has also introduced the Cloudflare One Stack, a collection of AI-powered deployment and management tools designed to help partners and customers evaluate, deploy, and manage Cloudflare One environments.

Built directly on top of the platform, the toolkit includes structured knowledge libraries, decision trees, automated workflows, and blueprint configurations that can be used with AI agents to assist with projects and reduce manual provisioning.

The offering draws on deployment knowledge developed through customer migration projects and is designed to automate and assist with a range of tasks typically associated with SASE and zero trust rollouts.

Capabilities include network assessment, VPN replacement planning, security policy translation, troubleshooting, and migration support for organizations moving from platforms such as Zscaler and Netskope.

Additionally, the toolkit can integrate with the Cloudflare API, allowing AI agents to inspect configurations, recommend changes, and automate deployment workflows.

Partner focus

Cloudflare said its new partner designation and resources mark the latest phase in its wider efforts to strengthen its channel ecosystem around the Cloudflare One platform.

Partners participating in the program will gain access to the Cloudflare One Stack, alongside technical expertise and deployment support aimed at improving large-scale security transformation projects and customer migrations.

"We are equipping our elite partners with the financial runway and technical mastery they want to scale the Cloudflare One platform," Evans added. "By blending our unified SASE architecture with partner expertise, we are turning complex network migrations into high-margin, high-value consulting opportunities for the AI era."

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.”

Databricks has released an AI coworker for business teams, including marketing, finance, and sales.

Genie One, available on the web, iOS, and Android, is designed to automate work, answer questions, and take action using structured or unstructured, analytical or operational company data.

Visual interfaces include interactive charts and graphs, and teams can set up alerts for always-on monitoring, schedule tasks, create repeatable skills, and take action with MCP tools.

The product is based on the newly-launched Genie Ontology, which, said the firm, brings together an organization's entire data. It's a self-improving context layer that automatically extracts and continuously updates business knowledge from Databricks, as well as AI tools and connected workplace apps.

It can access curated, authoritative data through SQL, rather than having to reason from fragments spread across documents, said the firm.

"Most enterprise AI today is just guessing with false confidence. That is not good enough for business. If you're a CFO and AI can't tell you why margins changed, or you're a sales leader, and it can't find your next upsell, that's not an AI problem, that's a context problem," said Ali Ghodsi, co-founder and CEO of Databricks.

"Genie Ontology continuously learns context from data everywhere, so our answers are much faster and our agents are more accurate. That's the difference between an AI chatbot and an agentic coworker who knows your business inside out – every metric, every data source, every answer."

Genie connects to all major AI tools, said Databricks, as well as more than 50 popular apps and data systems for business users across databases, files, tickets, chats, and meetings. Integrations include Google Drive, Jira, Slack, Confluence, SharePoint, and more.

It can, said the firm, retrieve real answers from governed data and take action, with higher accuracy, reduced latency, and lower costs. And with Genie Agents and Genie App Builder, teams across the business can create reusable agents and applications – all connected to their data with access controls, permissions, and cost governance built in.

Genie Agents allow teams to save any Genie conversation as a reusable agent that inherits the conversation's memory, including its sources, instructions, and behavior, so coworkers can repeat trusted workflows across teams. Staff can also create and share agent skills with teammates for consistent, repeatable answer formats and workflows.

Genie App Builder, meanwhile, is a fully managed vibe coding environment built for the enterprise. Teams can upload business context, with Genie App Builder generating a live build plan and working app preview connected to real, governed enterprise data. These applications – built for internal teams or customers – have Unity Catalog permissions and access controls built in from the start.

Genie Code is an AI agent that helps data teams plan, build, and run data engineering, machine learning, and analytics workflows, and now includes a dedicated workspace to track progress, review steps, and easily switch between threads to work across projects.

And Genie ZeroOps is a new background agent built into Databricks that autonomously monitors, investigates, and proposes fixes for data and AI assets such as pipelines, jobs, tables, ML models and more.

"At Foot Locker, Genie Agents are transforming how we lead. They provide our executives and business teams with a centralized space to harness AI-driven insights across every North American banner we operate," said Krish Lakshminarayanan, VP, AI, data & analytics, enterprise architecture at Databricks customer Foot Locker.

"As we scale Genie to the enterprise, it's reshaping the way our business interacts with data and makes the decisions that matter most."

The UK government has launched a new national network to coordinate standards for quantum technologies.

With £10 million in government funding, the National Quantum Standards Network (QSN) will be managed by the National Physical Laboratory (NPL).

The aim is to align standardization priorities across sectors and strengthen the UK's presence in key global standards forums, making sure that UK priorities are reflected in the still-emerging global regulatory landscape.

Government, industry, and academia will work with UK companies to make sure their products are developed to internationally recognized standards, with input from the British Standards Institution and UKRI's National Quantum Computing Centre.

This will include the creation of training resources and guidance to build UK expertise in quantum standardization, along with specific help to support SMBs and startups engaging with standards.

The QSN will, said the government, oversee everything from the linewidths of the ultra-narrow lasers needed to control qubits inside a quantum computer to the size, weight, and energy-efficiency requirements that will ensure one quantum sensor's reading can be trusted against another's.

"Standards are the backbone of responsible, scalable innovation," said Dr Peter Thompson, at the National Physical Laboratory (NPL). "By coordinating expertise across the UK quantum ecosystem, the network will accelerate technology adoption, boost UK competitiveness , and support the safe and ethical development of quantum technologies." 

The launch, set for the third quarter of this year, follows a pilot scheme that ran from 2023 to 2025, initiated by NPL, DSIT, BSI, and UKQuantum. This brought together leaders from across the UK quantum landscape to test new collaborative models and identify priority areas for future standards-focused collaboration.

"A collaborative approach to standardization is an essential element for the successful realisation and adoption of quantum technologies," said Tim Prior, UK QSN programme director at NPL.

"The QSN is a major component in maintaining the UK as a world leader in this area and turning the UK's ambition into coordinated action. We are building the foundations needed for quantum technologies to scale securely with real-world impact."

Earlier this year, the government announced a £2 billion investment in the technology, including £1.2 billion towards the procurement of large-scale quantum computers.

Quantum, it said, has the potential to add £212 billion to the UK economy by 2045 and create 100,000 jobs, boosting workforce productivity by 7% over the next 20 years.

"Quantum could bring benefits to our society as significant as what we are seeing with AI, with the potential to deliver new medicines, better public services, and protect our finances," said science minister Lord Vallance.

"The UK's quantum sector is already a global leader. With the National Quantum Standards Network we will accelerate its growth, meaning more British jobs and investment into our economy from all over the world."

Gartner has warned that organizations are facing mounting competition for AI talent in supply chain roles, as demand ramps up for workers with both domain expertise and AI skills.

According to the analyst firm's latest research, demand for supply chain positions requiring AI capabilities increased by 387% between the first quarter of 2023 and the first quarter of 2026 – significantly outpacing growth across the wider labor market.

The findings arise from Gartner's analysis of more than 35 million job postings, including almost 600,000 vacancies across the supply chain. The company said this sharp increase in demand is creating a wide talent gap, with organizations facing longer recruitment timelines and rising hiring costs as they compete for a limited pool of qualified candidates.

"The demand for AI skills in supply chain is accelerating at a rate that far exceeds the broader labor market, creating a widening talent gap that organizations cannot close through hiring alone," explained Tess Frenzel, director analyst in Gartner's supply chain practice.

Demand was found to be particularly concentrated among experienced professionals, with 58% of AI-related supply chain roles sitting at the mid-senior level. Director-level positions are also overrepresented, the firm added, highlighting a need for experienced professionals capable of combining domain expertise with AI skills.

Addressing the imbalance

The findings build on previous Gartner research that highlighted the need for chief supply chain officers (CSCOs) to shift to an autonomous-ready workforce with versatile roles that guide and improve AI systems.

With its latest data, the analyst firm said organizations must now also look beyond traditional recruitment strategies as they seek to build and evolve their AI capabilities. Instead, CSCOs are advised to prioritize internal upskilling of staff while making greater use of entry-level talent to establish a sustainable pipeline of AI expertise.

Despite entry-level roles representing a significant proportion of overall hiring activity, Gartner noted that these positions represent a much smaller share of roles requiring AI skills – highlighting an opportunity for organizations to develop talent internally.

"CSCOs should take a balanced approach by accelerating internal upskilling and more effectively leveraging entry-level talent to build a sustainable pipeline of AI capabilities," added Frenzel.

"Organizations that invest in both experienced professionals and emerging talent will be better positioned to scale AI adoption and prepare their workforce for the future of supply chain."

Small and midsize businesses (SMBs) in the UK have doubled their use of AI tools over the last year, according to Google Cloud.

That includes Google's Gemini models but also Gemini Enterprise and AI Studio, said Maureen Costello, vice president for UK and Ireland at Google Cloud, in a blog post.

Costello said that the UK's five million-plus SMBs were the backbone of its economy. "Now, we're seeing these critical businesses begin to put AI to work, in order to operate more efficiently, move faster, and ultimately deliver better outcomes for their customers," she wrote.

She added: "At Google Cloud, we have a front row seat to this shift: SMBs have long utilized platforms like Google Workspace, and today, they're transforming with Google AI."

Partner bookings grew more than 100% last year, and the company told ITPRO that its partners contributed nearly 80% of Google Cloud's incremental revenue growth last year.

SMB struggles

Companies of all sizes have struggled with the AI rollout, in particular as return on investment (ROI) has proven elusive – a problem as costs of AI have started to rise.

Earlier this year, OpenAI said that SMBs were finding success with AI tools, though take-up remained sluggish in some parts of the UK. While more than nine in ten London-based SMBs were using AI, that was true of just one quarter in Yorkshire and Humber.

Microsoft noted that fear of falling behind was driving record adoption rates among UK businesses, with 84% now using AI for competitive advantages, versus 40% in 2025.

How SMEs use AI

According to Costello, SMBs have turned to AI for customer support, to automate repetitive actions in payroll and accounting, and to leverage data at work – without training to become data scientists.

She added that AI is helping smaller companies "rapidly create and implement new designs" for marketing. Smaller companies are also starting to let employees build their own AI agents for their day-to-day work.

She points to fintech Neural Alpha, which is using Gemini to read unstructured reports to automate digging out key facts, while security firm Sep 2 has developed autonomous AI agents for always-on threat modelling using Gemini Enterprise.

Design agency Sunhouse turned to Gemini Enterprise to hunt through its archives that are stored on Google Drive, events company Terrapinn automated manual tasks, and telecoms provider used Google Cloud AI tools to build autonomous workflows and make better use of data.

Google Cloud for British Business

Google pointed to a new set of resources designed to help smaller businesses navigate the new AI world.

That includes a new SMB Learning Path and Gemini Enterprise Agent Ready programme, which offers specialised agentic AI training. There's also Google Skills for Organisations, a free, on-demand learning platform that pulls together 3,000 AI courses, as well as Google's own certification platform.

"Whether you want to train your existing technical teams or democratize data tools across your entire workforce, these programs will help you build an AI-ready organization," said Costello.

HPE and Nvidia have announced new features for HPE AI Factory, aimed at improving customers' experience with AI agents.

Announced at HPE Discover 2026, HPE AI Factory, which is part of Nvidia AI Computing by HPE, will now offer the Nvidia Agent Toolkit software, which includes Nvidia Nemotron open models, Nvidia NemoClaw (the chip maker's answer to OpenClaw), and Nvidia OpenShell secure runtime.

The new capabilities will, the companies say, help customers deploy enterprise-ready AI with greater observability, efficiency, and control.

There are also new hardware additions to HPE Private Cloud AI, which now has HPE ProLiant Compute DL394 Gen12 servers with Nvidia Vera CPU for, in the words of the company, "a compute-optimized foundation for agentic AI and high-performance data processing, including security and management features".

On the security side, the update also includes new Zerto Software capabilities – HPE acquired Zerto in 2021 – which the company says will help customers identify when a "rogue agent" starts carrying out actions that it shouldn't. If this happens, the Zerto capabilities can "use continuous data protection to rewind to a clean state".

In addition, HPE Private Cloud AI also now has support for secure local agent registration, which the company says will allow customers to approve AI models, skills, and tools while following centralized governance and security policies.

Finally, HPE Private Cloud AI also has HPE Alletra Storage MP X1000, which enables customers to apply metadata and governance policies that prepare data for AI applications. According to HPE, this allows businesses to boost token throughput by up to 20% and improve prompt processing efficiency.

Of the announcement, HPE president and CEO, Antonio Neri, said: "Across networking, servers, storage, and software, HPE is delivering full-stack AI solutions with NVIDIA that build the foundation for agentic enterprises, helping customers move from experimentation to production with control and confidence."

ITPro's managing editor Jane McCallion is at HPE Discover 2026, and you can follow all the latest announcements and analysis from the event here.

HPE has announced a number of new networking products and capabilities that it believes will help enterprises achieve success in agentic AI projects.

HPE has positioned networking technology – and its own networking capabilities in particular – as foundational to developing agentic AI systems that can increase business automation and, thus, efficiency.

The company increased its networking capabilities in 2025 when it acquired Juniper Networks. There has since been a process of 'cross-pollination' taking place with the company's existing Aruba networking technology, which has been part of its portfolio for just over a decade.

New Juniper switches for AI workloads

The first two new products announced were the HPE Juniper Networking QFX5140 Switch and HPE Juniper Networking QFX5252 Switch tray for AMD Helios, both of which are available as part of HPE AI factory.

The QFX5140 Switch "was truly purpose built for the next wave of AI infrastructure with a special focus on inferencing clusters at the edge", said HPE networking president and GM, Rami Rahim, in a press conference. "And of course we're seeing explosive growth and demand for AI inferencing today," he added.

The company added that the QFX5140 is "instrumental in driving the HPE AI Factory validated design strategy to the edge".

The QFX5252 is a module specifically created for the AMD Helios AI rack-scale platform. HPE was one of the first companies to offer AMD Helios infrastructure and the new Juniper switch "[delivers] the low-latency, high-bandwidth switching required to maximize AI infrastructure performance at scale", the company said.

New self-driving networks and 'cross-pollination'

HPE also announced expanded data center operations within the HPE Mist platform, which came through the Juniper deal, including proactive maintenance with predictive analytics and an advanced reasoning agent for high-confidence remediation.

"By continuously analyzing telemetry across all of the infrastructure, and this includes things like power and temperature and optics and system health, we can identify potential issues before they become outages that impact experience," Rahim explained. "This leverages more than a decade's worth of AI expertise that we've built, and it helps customers improve uptime, reduce risk, and operate just far more efficiently."

In terms of "cross-pollination", HPE's term for how it's bringing together Aruba and Juniper technologies, the HPE Networking CX switching portfolio (Aruba) is being integrated with HPE Mist AIOps (Juniper). Additionally, HPE Marvis (Juniper)-powered self driving capabilities are coming to HPE Aruba Central.

"Mist customers consistently tell us that these capabilities – Marvin Marvis actions capabilities – are absolutely indispensable because they don't just identify problems, they proactively resolve them," Rahim told the press. "So now we are extending those same proven capabilities to central, giving customers AI-driven insights and recommended actions across wired, wireless, and SD WAN environments that are all operated through HP Aruba Central."

HPE Discover 2026 is taking place between 16 and 17 June, and our managing editor, Jane McCallion, is on site for the duration. Follow all of our analysis and coverage, including live blogs, here.

AI is creating a 'two-track' labor market, with jobs requiring more human-intensive skills seeing better pay and greater growth than those where AI makes the role easier for non-experts.

PwC's 2026 Global AI Jobs Barometer has found that 'professionalized' roles – such as radiologists or recruiters – are seeing twice the growth in available jobs and 42% faster salary growth than those categorized as 'democratized', such as IT service managers or medical secretaries.

This applies even to entry-level jobs, where AI appears to be increasing demand for more 'senior' skills from junior workers.

Based on 2.4 million entry-level jobs analyzed in the US, the roles most exposed to AI are now seven times more likely to require traditionally senior-level 'human-intensive' skills like leadership, creativity, or face-to-face interactions – and job openings for these 'seniorized' entry-level roles have grown 35% since 2019, while other entry-level roles shrank by 10%.

"Across the global economy, we're beginning to see a new divide emerge between different models for talent and value creation," said Joe Atkinson, PwC's global chief AI officer.

"The companies seeing the greatest returns on AI are using it to amplify human expertise, accelerate innovation, and create entirely new sources of value. As a result, they are pulling further ahead on productivity and growth than companies that focus primarily on automation."

The report also found that the gap between companies most and least exposed to AI is widening. Companies in the most AI-exposed sectors recorded 34% productivity growth between 2018 and 2025, compared with just 24% for the companies least able to use AI.

Even within this group, a pronounced 'super-star' effect is emerging, with the top 20% of the most AI-exposed companies achieving average labor productivity growth of 163% – nearly five times higher than the most AI-exposed companies overall.

And perhaps most surprisingly, said PwC, headcount at the most AI-exposed companies is growing more than at the least AI-exposed companies – 52%, compared with 36%.

The average wage premium for workers with AI skills continued to grow, hitting 62%, up from 57% last year. But it varies by industry, reaching as much as 118% in some sectors, such as consumer markets, but only 16% in government and public sector work.

Jobs requiring specific AI skills – such as prompt engineering or machine learning – have also soared, growing roughly eight times as fast as the overall jobs market. The number of AI jobs is almost twice as high as in 2024, with growth in AI jobs outpacing all jobs since 2015.

Sectors including technology, media and telecommunications, and professional services saw the highest share in AI job growth, with health at the lowest end.

"The traditional relationship between experience and expertise is changing," said Pete Brown, PwC's global workforce leader.

"AI is removing some of the routine work that once acted as an apprenticeship, while increasing demand for judgement, leadership, and adaptability much earlier in careers. Organizations need to rethink how they develop talent if they want people to thrive in this new environment."

The findings may represent a backlash as organizations find that using AI to eliminate entry-level jobs isn't always a great idea.

With AI taking over repetitive and knowledge-based tasks, it can be hard to find candidates with the necessary skills for higher steps on the ladder.

In a survey late last year by Deel, just under seven-in-ten organizations said there are now fewer on-the-job development opportunities for junior employees, while 71% said it was getting harder to recruit and train future leaders because of the loss of entry-level learning pathways.

Anthropic has suspended most of its advanced AI models just days after the company made them public, after sanctions were imposed by the Trump administration.

On Friday, Anthropic models Fable and Mythos were hit with export controls by the Department of Commerce, barring foreign nationals from using either model. In response, Anthropic has suspended both systems for all users.

In a statement posted on its website, Anthropic said that it was complying with the government's legal directive and removing access to Fable 5 and Mythos 5 for all users. But it disagreed with the government's reasoning as it only offered verbal evidence of a "potential narrow, non-universal jailbreak", according to Anthropic.

For AI systems, a jailbreak is a technique used to manipulate an AI model into bypassing its built-in safeguards, guardrails, and content filters. An AI chatbot, for example, which is designed to assist, has vulnerabilities that can be exploited by malicious actors and forced to do things it was specifically programmed not to.

Anthropic believes the jailbreak method claimed to have been found in Fable and or Mythos is widely available in other models, such as OpenAI's GPT-5.5, and is used daily by the security teams that keep it safe.

"We disagree that the finding of a narrow potential jailbreak should be a cause for recalling a commercial model deployed to hundreds of millions of people," the statement reads. "If this standard was applied across the industry, we believe it would essentially halt all new model deployments for all frontier model providers."

"Perfect" jailbreak resistance, according to Anthropic, is not currently possible for any model provider. And that every safeguard used in the industry is vulnerable to non-universal jailbreaks. The company added that it was likely that universal jailbreaks would eventually be found in the future, a point it stressed when it first released Fable 5.

Pentagon, AWS, and Anthropic

The sanctions on Anthropic come not long after the Pentagon named the company as a supply-chain risk to national security. A number of senior government figures publicly clashed with Anthropic's leadership over issues including AI regulation and the use of Anthropic's technology in domestic surveillance and autonomous weapons.

The specific directive, however, followed a report by Amazon researchers who identified the potential jailbreak in Fable. Reportedly, Amazon first shared its findings with Anthropic. CEO Andy Jassy is also said to have discussed the issue with US officials, raising broader concerns about frontier-model capabilities rather than issues focused specifically on Anthropic.

The UK and Japan have signed a broad £18 billion investment deal that includes plans to work together on high priority frontier technology challenges.

The idea is to combine the UK's software and research capabilities with Japan's hardware and manufacturing power, and collaborate on crucial technologies.

Support will be provided through government co-operation and coordinated, targeted R&D support, along with efforts to mobilize private capital towards strategic technologies​, fostering commercial collaboration and supporting high-potential companies to emerge and scale.

The two countries will also cooperate on international standards and regulations.

"As G7 economies and close security partners, we are working together with Japan on some of the most innovative technology in the world, harnessing the best of British and Japanese research and industry to deliver growth and security to every corner of the United Kingdom," said Prime Minister Keir Starmer.

Building on last year's Quantum Memorandum of Cooperation, the Frontier Technology Partnership (FTP) will include the development of globally competitive, commercially scalable, and deployable quantum technologies, including computing, sensing, and communications.

UK and Japanese businesses will export, invest, and conduct R&D in one another's markets, with joint, long-term collaboration on quantum high-performance computing (HPC) integration. In parallel with this, the government said, the two countries will advance practical applications of quantum technologies across the computing, sensing, communication, and networking sectors, strengthening cooperation on testbeds, evaluation frameworks, and system integration.

There are also plans to work together to make the most of the two countries' AI semiconductor strengths, working with partners and suppliers to enhance the resilience of AI supply chains, as well as deepening cooperation between their respective AI Security Institutes (AISIs).

The agreement brings an export deal for British firm ORCA Computing – one of the first times a major corporation anywhere in the world has bought a quantum computer.

Meanwhile, a formal partnership between the UK Semiconductor Centre and Rapidus, Japan's state-of-the-art manufacturing facility, should help the UK semiconductor sector to manufacture cutting-edge chips used in mobile phones, vehicles, and other devices.

"Jointly maintaining and developing our respective strengths and indispensability in critical technologies will help drive resilience, productivity, and economic growth in both our countries and sustain essential manufacturing and technological capability, the declaration reads.

"We will increase collaboration among our innovation, VC, and startup ecosystems and relevant agencies, including by encouraging greater two-way investment and supporting joint commercialization in emerging technology, including dual-use, and jointly exploring approaches to understand and mitigate potential risks to innovation and commercialization."

The announcement builds on previous cooperative efforts, including this year's Strategic Cyber Partnership, last year's Economic Security and Industrial Strategy Partnerships, the 2023 Semiconductor Partnership and 2022 Digital Partnership.

And it forms part of a deal that, said the government, will include investment creating tens of thousands of new jobs and more than £18 billion in economic gains.

"These landmark agreements will bring multibillion pound investment into the UK, creating tens of thousands of new jobs and driving new developments," said Starmer.

"As G7 economies and close security partners, we are working together with Japan on some of the most innovative technology in the world, harnessing the best of British and Japanese research and industry to deliver growth and security to every corner of the United Kingdom."

IT leaders are struggling with unexpected AI-related costs, along with legal, reputational, and compliance issues.

In a poll conducted by Asana, more than eight-in-ten UK-based IT leaders said they’d encountered unplanning AI cost increases over the last 12 months.

Just over six-in-ten said they are held highly or fully accountable for AI-driven business outcomes . However, with AI adoption increasingly happening across departments and outside traditional governance processes, those responsible for AI ROI are unable to fully track and measure its usage.

The survey by Asana comes amid rising concerns over the skyrocketing costs associated with AI, particularly agentic AI.

As ITPro reported earlier this month, Uber was hit with an eye-watering bill following a push to drive AI adoption and use internally at the ride-hailing giant. Indeed, the company is believed to have blown through its entire annual AI budget in just four months.

This incident, as well as the rise of ‘tokenmaxxing’ across the incident, has prompted some enterprises (including Uber) to introduce caps on AI token use and rethink how productivity with the technology is measured.

AI costs aren't the only worry

Elsewhere in the Asana survey, governance was identified as a particular headache among IT leaders.

More than half (53%) said that an AI tool or agent has taken an action in the past 12 months that resulted in financial, legal, reputational, or compliance harm.

Nearly half of UK workers say they're not aware of how their company uses their work – including emails, documents and tasks – to train or improve AI systems. One-in-four say that they often use AI tools that are not formally approved by their organization, while 38% regularly use personal AI accounts for work tasks.

UK IT leaders estimate, indeed, that three-in-ten new AI tools and agents that have been introduced over the past year have been adopted without formal IT review or approval.

"Most organizations are past asking whether their people will use AI – they already are. The challenge now is turning that into measurable business value, without losing the governance and visibility needed to manage risk," said Christina Francis, head of UKI and Northern Europe at Asana.

"People want faster, smarter ways to work. The question is whether organizations can give them trusted, approved ways to do that, or whether they end up working around the tools entirely."

Stalled AI projects still plague IT teams

The research also revealed problems with execution, with 46% of UK IT leaders saying that AI initiatives often or always fail or stall because AI lacks complete organizational context.

Meanwhile, 37% of UK knowledge workers spend at least 30 minutes a day fixing or reworking AI outputs due to missing context – a trend also highlighted in a recent study by Glean.

Workers are also struggling to access the information AI needs to perform effectively: four-in-ten say they need to check three or more tools to gather the context needed to complete their work, while two-thirds say they have to re-explain context to AI tools at least some of the time.

"AI is most powerful when it has context: the goals, decisions and workflows that sit around the work. But that only lands if there's transparency built in. Employees need to understand how their work feeds AI systems and what that makes possible," said Francis.

"The organizations that get ahead will be the ones that combine visibility, governance and context not as competing priorities, but as the same thing."

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The vast majority of UK digital workers (90%) now use AI in their daily activities, but productivity gains are being hampered as many fight to make the technology “usable”.

That’s according to a new study from Glean’s Work AI Institute, which found that AI appears to be having a positive impact on productivity. More than three-quarters (77%) said AI is making them more productive, helping to automate tasks and saving roughly 12 hours per week on average.

Yet these AI-related productivity gains are often offset by a new trend called “botsitting,” the study found.

Many workers are left “feeding it missing context, checking its outputs, debugging its mistakes, rerunning prompts, and cleaning up the confident-but-wrong answers AI leaves behind”.

All told, workers waste around 6.4 hours per week on average making AI usable, according to Glean, equivalent to nearly a full working day.

Notably, this is a problem that’s hitting UK workers harder than their international counterparts. The study, which polled digital workers in the US, UK, and Australia, showed that Brits spend a larger share of their time botsitting on average.

Cutting corners with AI

The long-term effect of this trend could have a significant impact on broader enterprise productivity, Glean warned. Frustrated workers cut corners with the technology to get projects over the line or simply file work that’s essentially slop.

“They stop checking outputs and deliver work they can’t fully explain or defend,” the report notes.

“That’s when botsitting turns into something more dangerous: botshitting – shipping AI-generated work that workers haven’t reviewed, don’t fully understand, or couldn’t defend if asked.”

Glean noted that more than two-thirds (69%) of workers polled admitted to ‘botshitting’ at work.

The AI productivity paradox

The research from Glean marks the latest in a string of studies warning about mismatched productivity gains with AI.

As ITPro reported in April, research from Accenture found workers are reporting higher levels of output and speeding up project delivery timelines with AI.

Yet these productivity boosts aren’t reflected on a business-wide level – and a key factor lies in the fact that underlying IT infrastructure or working practices haven’t evolved to compensate for the technology.

This same phenomenon was highlighted in the Glean study, with just 18% of respondents saying AI has “significantly improved” their organization’s overall performance.

Context matters

A sharpened focus on organizational change is critical for broader productivity gains, according to Glean – and shoehorning AI into the business is a sure fire way of scuppering plans.

Indeed, the study noted that those succeeding on this front typically “start with the work, selecting tools and platforms that fit the job instead of letting vendor contracts dictate their AI strategy”.

“Leading AI organizations resist AI addition sickness: the reflex to solve every problem by buying more AI, adding more tools, or pushing people to use AI whether or not it helps,” the study noted.

This tracks with a recent survey from Gartner, which found a leading factor in failed AI projects in integrating the technology in areas that aren’t applicable.

Similarly, these leading firms “understand that giving AI access to data is not the same as giving it context”. This is a major factor in why workers are spending so much time making the technology usable, the study warned.

More than half (53%) of workers said critical information they need to do their jobs isn’t accessible through their AI systems. In “context-poor” AI environments, workers are more than three-times more likely to feel worn out by AI.

By contrast, those in “context-rich” organisations are 52% less likely to ship AI slop work, spend less time botsitting, and are 31% less likely to “botshit”.

Rebecca Hinds, Head of the Work AI Institute at Glean, suggested that overcoming these challenges will require a rethink on how AI use is tracked in the enterprise.

“Too many companies are treating AI adoption like a vanity metric: more seats, more prompts, more usage,” she said. “But the UK data shows why that is not enough.

"British organizations have moved quickly to put structure around AI, and that is a real advantage,” Hinds added. “The next step is making sure AI is grounded in the right enterprise context, governed in the flow of work, and measured by whether it actually improves outcomes.”

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Are businesses heading for an AI pricing time bomb? With companies like Uber suddenly raising concerns about the inability to draw a clear line between token usage and visible improvements, there is a growing question about the real cost of AI and agents.

On this episode of the ITPro Podcast, Bobby Hellard and Ross Kelly are joined by the CEO of Mendix, Raymond Kok, to discuss the different scenarios facing businesses as the true cost of AI hits home.

Highlights

"I think more and more the conversation is shifting in that direction. So, starting with what are the business-relevant use cases, and I'm saying business-relevant on purpose, because there are plenty of interesting use cases around generative AI, but it's about the business-relevant use cases. Then it's about what is the job that needs to get done, and then to your question, how do you actually budget for agents like you budget for your human workforce in any company? And I think slowly but surely that narrative is changing, right, and I think what we're going to go see is that after the IPO of Anthropic and OpenAI, the prices will further soar, and I think this conversation will become more and more important."

"I'm glad that actually you're talking about it in your podcast, because I think it's now really becoming an issue, meaning that CEOs and CIOs were obviously promoting their employees to become AI literate and started to make AI tooling available to their workforce. 24 months ago, they were able to sign attractive deals with the big technology providers and get their people going, but now the bill is starting to show up. It used to be maybe 10, 20, maybe 50k a month for a larger company, but it's now moving into the hundreds of ks, and guess what, because this is also usage-based, it's very hard to control, right, so most of the monetization models, as you know, of these large language providers are based on usage."

Footnotes

• Could rising token costs boost interest in on-premises hardware?
• 'One-size-fits-all' agent governance sets enterprises up to fail
• IT leaders are bullish on agents, but keeping falling at the final hurdle – here's why

Most companies are ramping up agentic AI adoption, but it largely remains in the pilot stage with no return on investment (ROI) on the horizon.

That's according to a new report from Forrester, which found that three quarters of enterprise leaders said they were adopting the technology, but only a small minority are using it operationally beyond "agentish" chatbots.

"Agentic AI is no longer theoretical – it’s technically real in 2026 – but most enterprises are still unprepared to operationalize it," said Brian Hopkins, VP and analyst at Forrester.

The research marks the latest in a string of studies highlighting an inability to move agentic AI projects past the pilot stage. A study from Dynatrace earlier this year found roughly half were stuck at this point within their adoption journey and unable to progress.

Notably, the Dynatrace report found this wasn’t putting IT leaders off investing in the technology, with a significant portion planning to increase investment despite a lack of concrete returns.

Agents aren't chatbots

In a blog post detailing the Forrester research analysts noted that AI agents had arrived but enterprises simply weren't ready – which shouldn't surprise anyone, as "we've seen this story before".

Part of the problem, Forrester said, was treating agents like chatbots when they should instead be viewed as a distributed system that demands infrastructure and support.

Companies can't simply stitch together isolated agents without shared data, registries or routing, the consultancy noted.

"The gap we see isn’t about models or ambition; it’s about orchestration, control, and trust," Hopkins added. "Companies that treat agentic AI as a feature experiment will stay stuck in pilots, while those that invest in agent‑native design, executable governance, and nonhuman identity will be the ones that actually capture value at scale."

Expensive chase

The lack of ROI is one reason enterprises are slow to escape pilot mode and fail to scale, according to Forrester. Simply put, the technology isn’t quite living up to some expectations, and that’s spooking IT leaders.

"ROI uncertainty traps enterprise ambition in pilot mode because most companies can’t justify production beyond narrow efficiency gains," the blog post added. "And platform confusion freezes commitment while teams argue over whether to bet on a SaaS agent, [a system integrator]-built system, or a custom build."

Forrester argues that a major cost is the "trust tax", as auditing adds costs to agentic rollouts – of course, many companies, in particular in finance or healthcare, are heavily regulated and with good reason.

"Every autonomous action has to be logged and defensible to an auditor, and right now that cost is too high," Forrester claimed.

Another challenge is risk management, with Forrester admitting that AI security issues are a real threat that require new governance and identity policy that's "enforced as code rather than written down and hoped for."

What can IT leaders do?

Forrester said enterprises can stop chasing agentic AI and catch up with the technology by laying the right groundwork. Agentic AI needs a solid data foundation before IT leaders should even think about diving head-first.

"The companies pulling ahead aren’t the ones with the most agents," the company said in that blog post. "They’re the ones laying the track the train will run on."

Practically, that means investing in orchestration before adding agents, with Forrester noting that shared registries are necessary for agents to work inside conventional systems.

Plus, the analysts called for companies to redesign the work being done, not just the tooling.

"Agents bolted onto human-paced legacy workflows produce task savings, not step-change value," the post added. "Pick a few high-friction workflows and rebuild the roles and approvals around autonomy."

Notably, companies need to treat every agent as its own governed identity, with unique credentials, least privilege, full logging and a named owner to watch over it.

With those three key aspects in place, companies can begin to step away from pilots towards scaling, but even that should be done in stages with autonomy added only when appropriate controls are in place, Forrester added.

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People have been using storytelling to educate, influence, and inspire their audiences for centuries. Tech firms are now suddenly rushing to recruit communication specialists to shape the stories being told about artificial intelligence (AI).

The number of US job postings including the term ‘storyteller’ doubled in the year to 26 November 2025, according to LinkedIn. And there’s been no slowing down in the months since.

In March, Anthropic was hiring a head of product communications responsible for “translating complex AI capabilities into compelling stories”. Similarly, OpenAI has been on the hunt for an infrastructure communications lead who can turn “complex engineering work into clear, credible storytelling”.

While some companies have begun to rely on storytellers to promote their large language models (LLM), others are turning to storytelling to help cut through AI slop and strengthen their brand. Compliance tech startup Vanta recently recruited a head of storytelling who has a knack for making topics such as security and online “feel modern, human, and worth paying attention to”.

Storytelling is becoming more and more an effective marketing tool because it can help companies to connect with their audiences on a human and emotional level – something that an LLM can’t (currently) do.

"Storytelling is a fundamental principle of communication. At its core, it’s about taking a message and shaping it in a way the human brain is wired to understand, with a clear beginning, middle, and end. As humans, we naturally look for structure and meaning, and when that’s missing, it creates confusion or even resistance," Bekki Bushnell, director of growth at Element Communications tells ITPro.

Stories can create AI buy-in

Companies may be mastering how to tell stories to sell to customers, investors, and stakeholders, but they’re generally not so good at using storytelling to educate employees on the benefits of AI in the workplace.

‘’Too often, companies roll out AI tools without a clear narrative, leaving employees unsure of what it means for them. It’s not enough to explain what the technology does. You need to bring people on the journey,” stresses Bushnell.

Charlotte Stoel, managing director at B2B tech PR agency Firefly Communications, echoes this. When AI tools are introduced into a workplace, employees will be questioning what it means for their skills, their role, and their future career path. Facts alone rarely ease their concerns, she tells ITPro.

“Without that structure [of storytelling], AI initiatives can feel imposed, which is where resistance often begins.”

McKinsey research backs this up. Seven out of every 10 transformations fail, and when leaders reflect on what went wrong, six of the top 10 reasons relate to poor communication between themselves and employees.

According to the consulting firm, narrative-led communication is more effective than just presenting employees with data and spreadsheets, as it gives context as to why something is important. This can inspire employees and create the much-needed buy-in in order for AI implementation in the workplace to be a success.

The arc of a good story

So, what does a good story look like? Both Bushnell and Stoel agree that there has to be a clear arc. A good story should start by outlining the current situation, such as inefficiencies in the tech stack.

Then, it should explain why change is needed, which in this case would be why AI tools are being implemented. Finally, it should set out what success will look like if the AI tools are adopted successfully, using real-world examples to create that buy-in from employees.

Bushnell describes these three stages as awareness, relevance, and credibility. She cites the example of integrating an AI agent into the tech stack. A good story should explain to employees how the agent will accelerate productivity and, potentially, unlock new business opportunities. Then, it should show employees how their roles will evolve, backing this up with evidence using case studies and early wins, as well as visible support from senior leaders.

The evidence part is crucial as it can bring clarity and put employees’ minds at ease. Stoel explains: “We often see that employees don’t resist technology itself, they resist the uncertainty surrounding it. Storytelling reduces that uncertainty by creating context and continuity. It allows companies to ‘own the narrative’ internally, rather than letting fear or speculation fill the gaps.”

The skill set to be a storyteller is very similar to that of a journalist – the role requires an ability to turn complex ideas into a compelling narrative with relatable outcomes.

The strongest storytellers should help senior leaders “move away from abstract promises about efficiency” and show tangible examples of how AI can benefit the workplace, says Stoel. They should also display empathy to those employees resisting AI and have the confidence to deliver messages with authority, Bushnell adds.

If done correctly, storytelling can build trust among employees. And, in the context of AI adoption, trust is the currency that can turn uncertainty into adoption.

OpenAI is reportedly planning a major redesign of ChatGPT, with the company set to sharpen its focus on agents and coding capabilities.

ChatGPT's launch in 2022 kickstarted the AI boom, and it remains one of the most popular generative AI applications. But reports suggest OpenAI is looking to post stronger revenue, in particular ahead of a potential initial public offering (IPO).

The plans, as reported by the Financial Times, include the creation of a "super app" that includes coding tools and an AI agent, replacing the traditional chatbot-focused format of the highly popular tool.

The move is designed to help drive revenue, sources told the publication, a front on which OpenAI has been pushing heavily over the last 18 months.

"Chat is dead"

OpenAI's plans could see the company shift its focus away from simple chat, as the FT reports executives at the company view it as merely a gateway to signing up users to paid-for products.

ChatGPT has a billion monthly users globally, according to Sensor Tower data, though most of those are free rather than paid users, with roughly two million business customers globally.

The newspaper quoted one employee as saying "chat is dead" – hinting at the future direction of the platform.

Instead of chat, the aim is to convince users to step up to using AI agents to complete tasks, rather than merely using the chatbot for information. The so-called "superapp" will also include OpenAI's Codex coding product, which allows users to create their own custom agents.

Changes to ChatGPT are expected to begin rolling out in the coming weeks, according to the report. However, rather than a total overhaul, these will surface as small changes to the app and website that will push users towards other tools for coding, image generation, and even third-party apps.

The aim, sources told the FT, is to build a personal agent that can help users across work and their personal life, and be accessible everywhere – even in the car.

OpenAI’s big revenue push

At the end of last year, OpenAI was rumoured to be considering everything from advertising to personal assistants as it sought to find ways to pay for more than $1 trillion in spending it had lined up at the time.

The company has also targeted corporate customers via a new consultancy designed to make it easier to roll out its tools in enterprises. At the same time, OpenAI shuttered the video generation app Sora, as part of efforts to focus on the new app.

Earlier this year, OpenAI said the company's revenue topped $20 billion in 2025, up from $6bn the year before. CFO Sarah Friar said at the time that was "never-before-seen growth at such scale."

But that comes with high costs as well as a string of multi-billion dollar infrastructure deals with industry counterparts such as Oracle.

Competition is also heating up for OpenAI, particularly from Anthropic. The key rival recently confidentially filed to make a public debut, and has been the subject of intense corporate attention targeting key services and software in recent months.

Jenny Xiao, partner at Leonis Capital and former researcher at OpenAI, told the FT that OpenAI started by trying to gain as many users as possible, while Anthropic targeted revenue.

"Now the two are converging, because both of them are trying to aim for an IPO and investors care more about money than dreams," she said.

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Anthropic has unveiled a new Mythos-class AI model designed specifically for public use.

The new model, dubbed Fable 5, boasts significant performance improvements on previous models, with the company noting that it “exceeds every model” previously made available to the public.

Notably, Fable 5 has been tweaked to include new safeguards that “block responses in specific high-risk areas” to prevent misuse – a key concern with the launch of Claude Mythos.

Claude Mythos was rolled-out as part of a gated release in April this year, with industry partners such as Microsoft, Amazon, and Cisco selected to test the model’s capabilities behind closed doors.

With the launch of Fable 5, Anthropic revealed that the model will “fall back” to Claude Opus 4.8 in the event that potential high-risk uses are detected.

Alongside the launch of Fable 5, Anthropic also revealed it will release Claude Mythos 5, again in a restricted capacity.

“Beginning today, all users who currently have access to Claude Mythos Preview (for example, our cybersecurity partners in Project Glasswing) will be able to upgrade to Claude Mythos 5,” the company said in an announcement.

The company added that it plans to expand access in due course through a “more systematic trusted-access program”.

Performance boosts with Claude Fable 5

According to Anthropic, Fable 5 shows “exceptional performance” in software engineering-related tasks, as well as knowledge work and scientific research.

Testing of the model by analytics company Hex revealed the model was the first to record a score of over 90% on its core analytics benchmark, which consists of running the model through “complex, long-running analytical tasks”.

Indeed, Hex noted that Fable recorded a 10-point improvement over Opus.

Similar testing by agentic workspace platform, Genspark, also showed significant performance boosts compared to other models, particularly in areas such as coding.

“Fable came out #1 on our evals, winning head-to-head against every model we tested,” the company said. “It was significantly stronger on the hardest tasks in the set — UI design and game coding."

Robust guardrails for Fable 5

With Fable 5’s new safeguards, the model will be restricted from performing tasks in a range of areas, including cybersecurity and biology, and will revert back to Opus 4.8 to provide answers to queries on these topics.

“Releasing a model this capable comes with risks,” Anthropic said in a statement. “Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage.”

Anthropic said it “extensively red-teamed” classifiers to test the model’s defenses against potential jailbreaks as part of a bug bounty.

“Internally, we ran an external bug bounty that produced no universal jailbreaks in over 1,000 hours of testing,” the company said. “We then worked with external red-teaming orgs which also failed to find universal jailbreaks.”

Fable 5 pricing and access

According to Anthropic, pricing for both models will stand at $10 per million input tokens, with $50 per million output tokens.

Token consumption costs have become a hot topic in the tech industry over the last two months with the rise of ‘tokenmaxxing’.

Uber, for example, revealed it blew through its entire annual AI budget in just four months amidst skyrocketing use by staff.

Developers will be able to use Fable 5 through the Claude API while Mythos 5 will only be accessible to Project Glasswing participants.

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Most CIOs and CTOs are accountable for systems they don't fully control, as AI adoption outpaces current governance capabilities.

A new global study of 2,000 C-level technology executives by IBM has found that two-thirds are forced to take responsibility for AI systems that they can't realistically supervise, with only 11% saying they're completely prepared for the scale of AI agent deployment.

Respondents said that by 2027, they're expecting a 38% increase in the number of AI agents deployed; and while eight-in-ten report being given transformation mandates driven by the CEO, 77% said their AI adoption is already outpacing current governance capabilities.

"For CIOs and CTOs, the challenge now is scaling AI systems that operate continuously and autonomously, often within governance models and architectures designed for a far slower, more predictable environment," said Matt Lyteson, CIO at IBM.

"It is no longer just about deploying AI faster. It's redesigning how organizations control, govern, and invest in it and embedding control and visibility from the start, so they can scale with confidence."

The firm's analysis revealed that in organizations relying on manual governance, incident risk increases as AI adoption scales, whereas those that embed control directly into their AI systems experience 25% fewer incidents.

Nearly six-in-ten of the CIOs and CTOs surveyed cited security and compliance concerns as top barriers to scaling AI agents.

Organizations experienced an average of 54 AI agent incidents last year in which an unintended and/or harmful occurrence required human correction. And of those, 17% were high severity, requiring more than four hours to contain: 37% resulted in data exposure or security breaches, 33% caused cascading system failures, and 17% triggered compliance issues.

"The pressure is only intensifying. CEOs want AI scaled faster. Business leaders want autonomy. And expectations for accountability continue to rise," said Lyteson.

"Yet many organizations are still operating with architectures, controls, and funding models designed for human-speed decision making, in systems that now operate at machine speed."

While AI spend is projected to grow from just under 15% of IT budgets in 2025 to nearly 25% by 2027, 84% of tech CIOs and CTOs haven't fully operationalized AI financial management, and 85% still lack full visibility into real-time AI spend.

However, organizations that build control into their AI systems deploy 16 times as many AI agents as those relying on manual governance, deliver 18% higher operating margins, and spend a quarter of their AI budget.

Those with strong financial discipline deploy 2.4 times as many AI agents, without driving up their AI/IT budget, and are three times as likely to say they are fully prepared for AI scale.

Meanwhile, those that have designed for adaptability early – keeping workloads portable and models replaceable rather than locked into hard dependencies – reported a 10% higher return on AI investment in 2025.

"The key constraint is adaptability. Most organizations were built for control, standardization, and efficiency," said Lyteson.

"But in an AI-driven landscape, it is now architecture, governance, and investment decisions that establish how fast they can move, what risks they can absorb, and which opportunities remain viable."