AI in IT service management (ITSM) departments may be delivering a reasonable return on investment, but it's not yet making life any easier for IT professionals.

A new survey by SolarWinds found that 84% of respondents believe AI has met or exceeded ROI expectations, and many have unlocked meaningful time savings across core tasks.

Yet despite these apparent benefits, more than half (52%) revealed their overall workload has increased since adopting the technology. Notably, just 7% said the cost of AI adoption has matched what they planned for.

Even after an average of about 16 months using AI in ITSM environments, most teams are still managing AI’s overhead rather than realizing its full potential.

Real gains, but new workloads

SolarWinds said the survey highlights the productivity benefits of using the technology in ITSM tasks.

Respondents reported that AI is saving them an average of 3.2 hours per week detecting and flagging issues, 3.0 hours on end-user requests, and 2.9 hours on ticket triage.

Most of the time saved is being swallowed up by new work, however. Nearly half (48%) said they now spend time managing and maintaining AI tools and integrations.

A similar number (47%) spend more time reviewing and validating AI-generated outputs, while 37% end up training and fine-tuning AI models.

As for unexpected costs, 48% cited staff training, 47% data quality and clean-up, and 45% tuning and maintenance - all of which are ongoing, rather than one-time costs.

More than four-in-five respondents (83%) said they now spend three or more hours per week just keeping their AI systems running reliably.

ITSM teams still stuck in reactive mode

Most ITSM teams seem to be taking a reactive rather than proactive approach. According to SolarWinds.

When asked where AI has had the greatest impact across the incident lifecycle, three-in-ten cited identifying issues before they impact users and 23% pointed to prioritizing and routing issues.

Only 19% cited preventing issues before they occur as the area of greatest impact.

The answer, according to SolarWinds, is to concentrate efforts where they're most likely to pay off – high-frequency, well-defined tasks where gains are measurable and feedback loops are tight, such as ticket triage, issue detection, and incident.

Teams should consolidate AI closer to existing service workflows rather than spreading it across disconnected tools and strengthen the data foundation.

Notably, data quality is the top reason AI fails to deliver expected value, and treating it as part of the AI strategy rather than a separate clean-up project directly determines output quality.

“We’re at an inflection point in IT service management. AI adoption is no longer the hard part — the hard part is building the organizational discipline to make AI actually deliver,” said Brad McGinity, GM of ITSM, SolarWinds.

“The teams that get this right aren’t just running a faster service desk; they’re running a fundamentally different operation.”

Lingering ROI woes

ROI with AI has become a common recurring talking point for IT leaders over the last three years, with the financial benefits still up for debate at some enterprises.

Recent IDC research found one-in-five firms admitting they're investing aggressively in AI with little evaluation of the likely ROI.

However, ROI isn't necessarily all about the money. According to recent research from KPMG, other key metrics include the performance and quality of work and the speed and accuracy of decision making.

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AI Insurance, also known as affirmative AI insurance, has been developed to specifically address the risks associated with the use and performance of AI technologies and systems, and covers liabilities that are excluded from existing business policies such as general liability, directors and officers, errors and omissions, and cyber insurance.

“Many of the existing policies companies have in place have AI exclusions,” highlights Lauren Kornutick, senior director analyst, Analytics and AI at Gartner.

“As AI is used more, and in more strategic and high-stakes use cases, new risks are being introduced. This is a gap in the existing risk management approach that could result in large-scale financial penalties, which could be detrimental.”

What does AI insurance cover?

AI insurance can cover a wide range of risks. On the errors and misinformation side, this can include financial losses stemming from AI hallucinations, such as a chatbot dispensing bad advice, or flawed decision-making.

It can also extend to algorithmic bias and discrimination, picking up legal defense costs and settlements when an AI model inadvertently disadvantages a group in contexts like hiring or lending.

IP and copyright exposure is another area of coverage, protecting businesses against claims that an AI model was trained on, or generated, copyrighted material without permission.

Then there’s performance guarantees, which are leading to warranty-style offerings that will refund license fees or cover associated costs if a model fails to hit specific benchmarks like accuracy or fairness. At the more severe end of the risk spectrum, AI insurance can cover large-scale physical damage or property loss resulting from poor AI-generated advice, device hacking, system failures, or harmful actions taken by AI agents.

Why should IT leaders take notice?

AI insurance is still a nascent market, highlights Tomas Novosad, technology analyst and founder of Full Fibre Checker, and one that’s currently a collection of discrete coverage options – standalone, endorsement and exclusionary – instead of a single type of insurance category.

But while IT leaders may consider AI insurance a matter for the legal or finance departments to attend to, the reality is that AI risk is increasingly becoming an operational responsibility for IT teams.

“[This is because] once AI is embedded into core systems, accountability sits within the technology stack and the processes that support it,” explains Indranil Roy, managing partner and global head, Industry Solutions Group at IT solutions and consultancy firm Mphasis.

“In practice, IT teams are already seeing issues such as inconsistent AI-driven decisions across different channels, or difficulty tracing how an output was produced once it passes through multiple legacy and cloud-based systems.”

For IT leaders, he continues, the key issue isn’t the policy itself, but whether the organization can evidence how AI-driven decisions are made, logged and explained in live production environments, not just in design documentation. If something goes wrong, it’s the system design, data flows, and governance controls that determine whether the issue can be traced, identified, and resolved.

“This is why AI insurance is becoming directly relevant to enterprise IT governance, because it increasingly reflects how well organizations can operationalize control, not just define it,” Roy states.

The growing importance of AI governance

To underwrite such policies, insurers must have stringent procedures to evaluate the AI capabilities and risks of those companies seeking to buy insurance, and Gartner predicts that by 2030, they will mandate strong AI risk controls as a condition of coverage.

We’re already seeing movement in this direction, reflected in more detailed underwriter questions, AI exclusions, and dedicated limits for AI risks. “In financial services, regulators are already requiring explainability and bias audits for AI-driven decisions,” notes Pragati Awasthi, assistant teaching professor at Drexel University’s School of Computer and Information Sciences.

“Insurers will follow the same logic: if they can’t assess the risk, they won’t cover it.”

Insurers will look for ways that deployers of AI are addressing the operational complexity of AI governance and not just rely on dated and static governance, risk, and compliance (GRC) controls, notes Kornutick, adding that Gartner recommends IT leaders deploy a hybrid approach consisting of centralized and decentralized policy, team, and system as a complete piece.

“This includes layered (enterprise and department level) policies and rules, clearly designated responsible teams who deeply understand the application’s operation, and adopting hybrid technical platforms, such as an enterprise-level AI governance platform (AIGP) paired with decentralized, application-specific guardrails,” she says.

One key factor to consider is whether organizations are deploying AIGPs as part of their governance architecture, she adds. According to Gartner’s 2025 State of AI-Ready Data Survey, organizations that do are three times more likely to achieve ‘high effectiveness’ in their AI governance practices than those that don’t, with 42% of organizations effective in AI governance having already deployed AIGPs.

“The organizations building governance infrastructure now will have a meaningful competitive advantage in insurability by 2030. The ones waiting will face either exclusions or prohibitive premiums,” Awasthi warns.

Practical steps IT leaders can take today

For those organizations just getting started, Awasthi recommends three priorities. Firstly, build a model register. Know every AI system you're running, what decisions they influence, and who owns them. Secondly, implement logging for automated decisions so you have an audit trail if something goes wrong. Finally, run a bias and drift assessment on any model touching customers or employees.

"Underwriters are increasingly looking for documented model inventories, human-in-the-loop checkpoints for high-stakes decisions, monitoring for model drift and bias post-deployment, and a clear incident response plan specific to AI failures. Governance that exists on paper but isn't operationalized won't satisfy a serious underwriter."

As with every technology before it, AI will continue to bring new risks, and those who get ahead of them now will be far better placed than those who wait for something to go wrong.

With 2030 fast approaching and underwriter requirements only tightening, the window for IT leaders to get ahead of this is narrowing. With this in mind, there’s no better time to build the necessary foundations.

Snowflake has announced new dynamic model routing capabilities for flagship products along with expanded access to open-weight models for customers.

The new capabilities build on Cortex AI Gateway, according to the firm. It was unveiled in July this year and provides customers with a “unified foundation” for agent governance and routing requests.

The aim here, the company noted, is primarily focused on optimizing AI token consumption, which has become a recurring pain point for enterprises over the last 12 months.

Snowflake said the new capabilities mean Cortex AI Gateway will “automatically select" a specific model based on the quality and cost for particular tasks.

Put simply, this will see Snowflake assign more efficient models for lower-complexity or repetitive tasks. Meanwhile, tasks that require “deeper reasoning” are routed to frontier models.

This means customers can reduce unnecessary inference spend and remove the need for laborious manual model selection for each individual task, according to the firm.

Snowflake CEO Sridhar Ramaswamy said the update comes in direct response to growing concerns over AI-related costs. Token costs have surged so far across 2026, while a host of major providers have switched to new consumption-based models, resulting in increased costs for enterprises.

“Enterprises are becoming much more rigorous about the economics of AI. The question is no longer how much AI they are using, but whether that AI is translating into meaningful business value,” said Ramaswamy.

“Achieving intelligence efficiency requires the flexibility to use the best model for each task as the landscape evolves. Snowflake’s role is to absorb that complexity so customers can focus on outcomes while we optimize model choice underneath.”

Dynamic routing will also be integrated across Snowflake CoCo and Snowflake CoWork, and will be made available for third-party AI agents.

Snowflake expands model library with open-weight options

In addition to model routing capabilities, Snowflake revealed it will expand access to several leading open models, such as DeepSeek-V4-Flash 0731 and GLM-5.3.

The addition of these models adds to what is already an extensive library available for customers, Snowflake said. Enterprises can already choose from a variety of models from Anthropic, Google, Meta, Mistral, and OpenAI.

“This portfolio gives customers more freedom to choose the right combination of performance and cost for each workload,” the company said in a blog post.

Once again, the focus here is firmly on helping to manage costs by providing a broader range of options.

Internal testing conducted by the firm found that enterprises using a combination of open and proprietary models can “deliver comparable quality while materially improving efficiency”.

In one evaluation detailed by the company, agents using dynamic model routing through Cortex AI Gateway were able to build a data build tool (dbt) pipeline with up to three times greater token efficiency compared to a frontier model-only approach – all while maintaining the same quality.

“In a separate test, engineering teams completed the same number of pull requests with 25 percent greater token efficiency,” the company said in a blog post.

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Spirit Airlines collapsed in May this year, but its data is set for a new lease of life after Google acquired it in an auction.

The tech giant recently outbid AI company Mercor to acquire the de-identified data, according to court documents. Google bid $10 million, versus Mercor's $7.5 million, though reports note the bidding started around the $5 million mark.

A third bidder also took part but was not named.

A spokesperson for Google told ITPro the aim behind acquiring the defunct airline’s data lies in its potential use for AI training and product refinement.

"We acquired part of an enterprise dataset from Spirit Airlines, which can be helpful in improving our products and AI models,” the spokesperson said.

That same sentiment was highlighted by a spokesperson for Mercor, per reports from Business Insider.

"Companies are sitting on decades of records that show how real work gets done, and that data is now some of the most valuable material for training and evaluating AI," the spokesperson said.

"We partner with leading companies to license their operational data to the labs building the next generation of models. Spirit was that same process applied to a bankruptcy estate."

What Google bought

The data bought by Google includes all productivity and collaboration data, which the filing notes includes everything from emails to chats, spreadsheets to calendars, as well as "core business systems and business applications data".

This includes data on employee behavior and productivity, aircraft operations, and inventory, among other topics. Beyond that, Google has purchased workflow and process data from departments such as HR and marketing.

In total, Google is picking up 100 million emails, 500 million Microsoft Teams chats, 30 million lines of code – but not records of loyalty program members or other customer data.

Indeed, the data set does not include any consumer data or personal data, the filing noted, or any other information that could fall under data protection laws. The buyer was required to agree to not attempt to re-identify the data and link it to any person.

The buyer is allowed to transfer the data set to third parties, however.

"We will not receive any personal information from this dataset," the Google spokesperson added. "Any data we receive will be rigorously scrubbed of any personally identifiable information by a third party before receipt."

Alongside the data, Google's $10 million also buys it all of Spirit's internally developed software and applications, including all the source code, plugins, data files, libraries, APIs and documentation.

The battle for data

The battle for the business data reveals how desperate AI developers are for fresh material to feed AI, in particular for training models, though Google didn't specifically say that was the purpose of the data set.

Anthropic has raised eyebrows via a campaign of buying up pre-2022 books to scan for training data, while Google was reportedly trying to buy code from Google Play Store app developers.

SimpleClosure helps failed startups sell their old code, chats, and emails to AI companies, and Meta had to pause plans to track its own employees for AI training purposes amid internal backlash.

All of this is to avoid issues like data exhaustion or hitting a data wall, in which they lack enough good quality data to build bigger models, and to avoid model collapse, when training data isn't of good enough quality — or is full of AI generated content.

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IBM and OpenAI have partnered up to help customers deploy AI at scale for core business operations.

Under the deal, IBM will create a dedicated OpenAI Practice, training thousands of consultants and engineers for expert-level certifications through the OpenAI Partner Network.

OpenAI models and products will be combined with IBM Consulting technology and expertise, the duo said, with the first areas to be targeted including financial services, government, telecommunications, and financial services.

“The organizations pulling ahead with AI are the ones turning it into a trusted part of how their business operates,” said Denise Dresser, former chief revenue officer at OpenAI.

“IBM Consulting and OpenAI are helping organizations make that shift, combining deep transformation expertise to deploy AI that is secure, operational, and aligned with real business priorities.”

Driving adoption rates

According to the two firms, the partnership will focus on applying AI to specific business industry processes, while also modernizing applications, redesigning back-office workflows, and strengthening cybersecurity.

"We’re starting with the business use case,” said Michael Healy, managing partner of offerings, assets and generative AI at IBM Consulting. “Then we bring IBM’s process transformation and industry expertise together with OpenAI’s models to change how that work actually gets done.”

OpenAI frontier models like GPT-5.6 and products such as Codex and ChatGPT Work will be embedded into IBM’s AI platform for delivering consulting services, IBM Consulting Advantage.

This will allow operating procedures and workflows to be analyzed to identify inefficiencies and help teams automate and streamline work using AI across finance, procurement, customer operations, and HR.

Tech modernization

As part of the deal, IBM said the duo also plan to help clients modernize legacy applications and accelerate software development by bringing together OpenAI Codex and ChatGPT Work with IBM’s industry, technology and domain expertise, including integration.

Here, the aim is to help organizations simplify engineering processes and speed up the delivery of new digital products and services.

“Codex as a coding assistant will accelerate the application development modernization cycle in combination with IBM Consulting Advantage’s coding harness and OpenAI’s models,” said Healy.

Following IBM’s participation in the OpenAI Daybreak Cyber Partner Program, the duo will expand their collaboration on cybersecurity.

This will involve combining OpenAI frontier AI capabilities with IBM Autonomous Security. This is an agentic AI service designed to deliver coordinated decision-making, response, and intelligence for cyber professionals.

“While enterprises are rapidly investing in AI, they are looking for practical ways to apply it across their core operations to deliver measurable business outcomes and create new commercial models,” said Andy Baldwin, global senior vice president, IBM Consulting.

“The challenge is not access to AI technologies — it’s integrating AI securely and at scale into complex enterprise environments and workflows. By embedding OpenAI’s technology with IBM Consulting’s AI assets, industry solutions, and cybersecurity capabilities, we can help clients accelerate secure, AI deployments at scale.”

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Want people to actually use AI? Then make it more useful according to Airbnb CEO Brian Chesky.

Speaking during a recent Power Players podcast appearance, Chesky said people are still using AI as an "interactive search product" but predicted further progress when it comes to interfaces, rather than just typing.

"I think part of it's a narrative issue that we're not talking about AI correctly," he said. "But part of it is we need to actually be developing more products that just regular people can use and say, 'I love AI because AI allows me to have a doctor on demand and I can't have that. I can't afford that.' And so I think we need more regular things."

Given Airbnb’s market, Chesky noted that the focus on largely enterprise-related AI innovation could be holding back broader adoption. He noted that 159 out of 175 startups at Y Combinator, where Chesky is a board member, focused on enterprise AI rather than consumer products.

"Maybe a simple way of saying it is we're getting really, really deep on enterprise, but there's been very little progress on consumer AI," he added.

AI backlash

Chesky said that it's in Silicon Valley's interest to make consumer apps that improve people's lives in order to shift increasingly negative opinions about the tech, warning that "regular people in the United States do not like AI."

This year has seen the start of a mainstream backlash against the technology, perhaps inevitable given the hype, but concerns have also been boosted by rising prices and environmental concerns around data centres.

Survey after survey also suggests many people simply don't like AI. Research via YouGov showed two-thirds of people weren't confident in the way businesses use generative AI, in particular with customer service.

A poll by Pew suggested just 16% of Americans think AI will prove to be a positive tool for society.

Chesky's argument is that building actually useful products powered by AI could help to undercut some of that negative sentiment – and he believes that will happen.

The Airbnb chief predicted that within in the next two or three years there will be a "renaissance around consumer AI that is going to begin to change daily life."

AI booster

Chesky has long been positive about AI. In 2023, he compared the technology's impact to electricity and said it will "change everyone's life."

In the podcast, he noted there haven’t yet been any major changes to our daily lives on par with the internet or iPhone.

Chesky also called for moving beyond the chatbot model, saying later that year that it should "augment humanity in a positive way".

Airbnb bought GamePlannerAI in November 2023, and earlier this year said AI was the "best thing that ever happened to Airbnb," in part thanks to a third of customer service tickets now handled by the technology.

After its last round of results, that number had jumped to 45% – despite those previously mentioned concerns about AI driven customer service.

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Part 2: Managing performance in a human-AI workforce

Managers have traditionally organized work around people: assigning responsibilities and holding individuals accountable for results. AI changes that model. When employees delegate tasks to AI assistants and teams include autonomous agents, performance emerges from a system of people, technology, data, processes, and managerial choices.

Part 1 of this series explored the rise of the intelligent employee experience and how AI assistants and connected workplace platforms are changing everyday work. However, as intelligent systems take on a more active role, organizations must reconsider not only how work is performed, but how it is managed and measured.

Part 2 examines what happens when AI becomes a permanent member of the team. It considers how leaders must manage blended human-AI workflows and how employee development can preserve judgment and accountability as routine tasks move to intelligent systems.

Microsoft reports that 36% of managers expect to supervise AI agents within five years. Leaders also anticipate that teams will build multi-agent systems and redesign processes. Nearly one-third expect to hire AI agent specialists within 12 to 18 months, while 28% are considering specialist AI workforce managers.

Yet adding digital workers does not automatically create business value. Only 14% of workers were using generative AI daily in 2025, according to PwC, while 56% of CEOs said their AI investments had delivered neither revenue growth nor cost savings. The gap suggests that managing a human-AI workforce is not primarily a software challenge. It requires organizations to rethink how people develop expertise, and where accountability rests when machines influence decisions.

Performance moves beyond output

Traditional performance systems often reward visible activity: cases closed, documents produced, calls handled, or hours billed. AI can increase all these numbers without necessarily improving the work. A poorly designed system can generate reports, code, marketing copy, or customer responses at extraordinary speed, but greater volume has little value if people must correct inaccuracies or repair damaged trust.

Dr. Janet Bastiman, chief data scientist at Napier AI, says quality should be the target rather than volume. “It would be trivial to automate slop, and that never results in a good company outcome,” she argues. Performance measures should instead connect work to organizational purpose and assess effectiveness and accuracy, regardless of whether AI was involved.

Good performance increasingly includes framing the right problem and accepting responsibility for the result. The most productive employee may be the one who prevents a plausible AI error from reaching a client or helps develop a more reliable workflow.

Margarita Lindahl, head of AI at Panasonic Connect Europe, explained: “The strongest performers will not necessarily be those who produce the most with AI. It will be those who use it to create better decisions, stronger customer outcomes, and build knowledge, ultimately benefiting the wider organization.”

Existing performance practices may not be ready for this change. Only 48% of UK employees report even a basic level of formal performance management, defined as having specific objectives for their roles, according to the CIPD. Meanwhile, nearly half of employees say their work feels chaotic and fragmented. Measuring tasks and activity in that environment risks confusing busyness with contribution.

Performance frameworks must combine output with quality, judgment, collaboration, customer value, and responsible AI use. Results also depend on suitable tools, reliable data, training, and time for human review—not talent alone.

These conditions connect directly to the intelligent employee experience covered in Part 1 of this series. Performance cannot be separated from the environment in which employees work. Fragmented platforms and technology that add friction will affect results, regardless of how sophisticated an organization’s performance measures become.

Managers become architects of human-AI teams

AI expands rather than removes managerial responsibility. Systems may allocate tasks, recommend priorities, monitor work, or provide performance insights, but leaders still decide how they operate and when employees can override them. Management becomes less about controlling activity and more about designing complementary human and machine capabilities.

“Business leaders need to increasingly become architects of human-AI systems,” Lindahl tells ITPro. That requires enough technological literacy to distinguish genuine capability from hype, where human judgment remains essential, and to establish clear accountability. As AI influences more decisions, she says, leaders must create responsible guardrails rather than surrender authority to the technology.

Deloitte found that managers spend almost 40% of their time resolving immediate problems and completing administration, but only 13% developing people. More than one-third feel unprepared for the people-management aspects of their roles. AI could return time to coaching, or create another stream of alerts and scores.

Andrew Avanessian, CEO of Haiilo, emphasized that leaders will need stronger systems thinking because AI often exposes inefficiencies elsewhere rather than solving them. They must also communicate what is changing, why it is changing, and where employees continue to create value.

Managers must also preserve permission to challenge the machine. If an agent allocates a task or recommends an action, employees should know whether they are expected to follow or overrule it. Otherwise, accountability becomes blurred, and people may defer to a system simply because its recommendation appears authoritative.

Development must protect human expertise

As AI absorbs routine and analytical tasks. The larger challenge is ensuring that people still acquire the domain knowledge and practical experience needed to evaluate machine output.

Employers expect 39% of workers’ core skills to change by 2030, according to the World Economic Forum. If the global workforce were represented by 100 people, 59 would require training, yet 11 may not receive it. Although 85% of employers plan to prioritize upskilling, only 33% of UK businesses using or considering AI say they are training or retraining existing employees in AI-related skills.

This gap particularly threatens early careers. Routine work has served as an apprenticeship: junior employees gather information, produce drafts, check details, and observe how experienced colleagues turn evidence into decisions. AI may remove those activities, but not the need for underlying knowledge.

Jenny Briant, director of talent Strategy at Scale Factory, explained that employers must create deliberate opportunities for employees to practice, make decisions, and receive feedback. “Employers cannot assume that people will develop judgement simply because they have access to better tools,” says Briant. “They need to create deliberate opportunities for employees to practise, make decisions and receive feedback. Practitioner-led training, mentoring and supervised work on real problems will become more important because they connect technical knowledge to the situations employees actually face.”

The goal is not to make people compete with machines on speed. It is to strengthen critical thinking, problem framing, creativity, relationship-building, and contextual judgment. Bastiman emphasized, “Ensuring meaningful human oversight of AI systems through various types of checks will ensure that the employees keep their core skills sharp for the task, but allowing the shift of work into new tasks will allow the building of new skills that were not possible prior to the overload of routine tasks.”

Employee development must also include the ability to explain how AI contributed to an outcome. Asking someone why they selected a tool, how they checked its response, and which elements required personal judgment reveals more about professional competence than banning the technology during an assessment.

Developing this combination of technological fluency and human judgment will become increasingly important as organizations prepare for 2030. Part 3 of this series examines how skills-based workforce planning and internal mobility can help businesses build the capabilities required as AI, automation and other emerging technologies reshape work.

Trust determines whether measurement improves work

AI promises more personalized and evidence-based performance management. It could identify patterns that a busy manager might miss or reveal where workflows routinely break down. Yet the same technology can intensify surveillance and give questionable judgments a veneer of objectivity.

Algorithmic management is already widespread. OECD research found that such tools were used to instruct, monitor, or evaluate workers in 90% of surveyed US firms and an average of 79% across France, Germany, Italy, and Spain. Managers identified unclear accountability, limited explainability, and insufficient protection of employee health among their concerns.

Bastiman warns that automated evaluation can remove empathy from performance management and embed assumptions from the culture in which the system was developed. Briant similarly notes that an algorithm may count output or response times but miss an employee who supported a struggling colleague or challenged a poor decision. What is easiest to measure is not always what is most valuable.

Transparency and consultation are therefore operational requirements, not optional ethics statements. Employees should understand what data is collected and how they can challenge it. OECD evidence indicates that training and consultation are associated with better outcomes when workplace AI is introduced. Trust matters because only 58% of workers currently trust their direct manager and feel able to speak openly with them, according to PwC.

Organizations that manage human-AI performance successfully will redesign work, protect time for learning, measure the value of outcomes, and keep accountability visibly human. AI can expand capacity, but leadership determines whether it produces better decisions or simply more activity. Performance will depend on how well people and machines work together, and whether employees emerge more capable and responsible.

Managing today’s human-AI workforce also requires organizations to prepare for the more extensive transformation ahead. The final part of this three-part series looks toward the workplace of 2030, identifying the emerging technologies and workforce strategies that will determine whether businesses can turn continual disruption into lasting competitive advantage.

As the old saying goes, sometimes “you have to fight fire with fire”. That’s certainly true in 2026, with cyber threats increasingly coming from ever more sophisticated use of AI tools.

There’s a problem with that, though: most businesses are not yet fully set up to deal with AI-based attacks and don’t trust the tools to do the job. A 2026 Proofpoint AI and Human Risk Landscape Report revealed that half of organizations using AI-based security controls still experienced suspicious or confirmed AI-related incidents.

There is a fundamental security gap in many organizations: AI adoption has outpaced the right security measures. In the report, nearly 9 in 10 (87%) organizations had moved AI assistants beyond the pilot stage, and 76% were actively piloting or rolling out autonomous agents. But that activity has outpaced security maturity: 63% had AI security controls in place, but 52% weren’t completely confident those controls would detect a compromised AI.

AI tools are embedded directly into communications to increase productivity and speed, but in the rush to be efficient and compete, AI permissions and access to sensitive data are being left unchecked.

These aren’t complex security engineering problems: they are organizational and process gaps that can be addressed without waiting for the tooling market to mature. Currently, only a few organizations have developed their incident response playbooks, logging coverage, or forensic tools needed to investigate a compromised AI agent.

For many businesses lacking the in-house skills needed to take on these items, which can be significant, contracting a trusted managed service provider (MSP) can help bridge the gap. Many MSPs have been assisting businesses with AI adoption for some time now, and many have the deep understanding and technical skills needed to help businesses of all sizes see tangible benefits to AI while keeping critical data safe.

The origins of AI attacks

Most attacks start with unrestrained access and end with autonomous systems exposing sensitive data from these environments. When threat actors target agentic systems that lack proper controls, they don’t need to trick employees to access internal intelligence; they only need to manipulate the AI.

Prompt injection attacks are a common way to do this. A bad actor might send a target user seemingly helpful AI instructions while posing as a trusted authority or co-worker. A well-intentioned employee may then ask an AI to answer what seems like a simple inquiry.

Depending on the attacker’s instructions, the AI agent may instead be tricked into reading manipulated webpages (i.e., white text on a white background) to unwittingly extract internal data and send it to the attacker’s server.

For prompt injection attacks, it’s important to limit AI agents’ access to only the tools and data they need to complete the designed task. This is a good solution to prevent AI from giving out more information than required. This can limit the scope of an external threat actor’s reach.

That said, the employee’s role isn’t lost in all AI-based attacks. An ongoing cybersecurity skills gap severely impacts defenses, and threat actors know this.

Over the last decade, multi-factor authentication (MFA) has been an important step toward stronger security authentication. But today, attackers can pair AI-generated phishing with ‘MFA bypass kits,’ such as open-source Evilginx (known as a penetration testing utility for these styles of attacks) and the W3LL panel (a private phishing kit) to deceive employees into handing over that ‘extra step’ of security.

Tools like Evilginx and the W3LL phishing kit are used to create realistic sign-in pages that mimic those of Google, Microsoft, and others. Without proper security training, employees may unwittingly be signing into these while attackers capture their session tokens – even those with MFA.

A good defense against MFA bypass kits is adopting phishing-resistant MFA technologies such as FIDO2 hardware keys, Windows Hello for Business, Certificate-based Authentication (CBA), and Passkeys. These methods are tied to legitimate sign-in pages and don’t work on fake pages.

However, stopping the threat from ever materializing starts with having proper cybersecurity awareness training. Nearly half of all organizations lack this training or simply adopt a checkbox approach, which is why implementing these programs is an important step to closing the gap.

These programs teach users to spot a myriad of cyber threats, including AI-based threats. Tools of this type are also a good example of using AI in defensive security, as some can leverage AI to customize the training an end user receives based on their performance in past training.

This is another area where MSPs are highly qualified to assist. MSPs typically run training programs across a vast number of users and industry types. They understand what training works and what doesn’t, and can help position the best security awareness training for a given organization.

How does AI enhance threat detection?

To understand the power and importance of AI-powered cybersecurity, it helps to understand how it works. Once trained, a detection model becomes exceptionally good at spotting the characteristics of malicious activity. It can take into account thousands of different characteristics to spot anomalies, outliers, and similarities that humans are unable to correlate.

It’s important to have a reliable cybersecurity service provider with REAL AI skills, because machine learning systems aren’t perfect and (while rare) can produce false positives. A strong partner can minimize these false positives while offering real-time threat detection and analysis, as well as faster, well-informed response times.

A trusted MSP will have a wide range of capabilities and will have a deep understanding of the protection methods that work well within their target industries. By leveraging that deep knowledge from their partner MSPs, businesses will benefit from great protection, even with today’s AI-powered attacks.

Preparedness in 2026 and beyond

To operate in this new era, businesses must treat every AI agent as a high-risk workload identity.

In practice, this requires working with reputable MSPs to implement strict least-privilege access to avoid data leaks, constant monitoring to protect the integrity of the data, and comprehensive employee awareness training.

Microsoft has imposed limits on staff AI use in a bid to cut ‘tokenmaxxing’ practices among software engineers.

First reported by 404 Media, an internal memo warned staff “need to be aware of how [they] consume tokens” on platforms such as GitHub.

“Tokenmaxxing is now what we are optimizing for. I want all of us focused on maximizing outcomes that move the needle for customers and our business,” wrote EVP Jay Parikh.

“As such, we are updating our internal guidance and managing token spend with the same discipline we apply to every other critical resource."

Under the changes, cheaper models such as OpenAI’s GPT-5.6 will be the default option for engineers moving forward.

GitHub introduced a new usage-based billing scheme for Copilot in April this year due to rising compute and inference costs.

At the time, executives said the move aimed to accommodate the increased use of agents, which are far pricier than reasoning-based models.

Microsoft is still an ‘AI-first’ company

Parikh noted in the memo that the changes don’t represent a change in Microsoft’s approach to internal AI use, adding that the firm is still “AI-first”.

Employees do have certain guidelines to follow, however. An internal Copilot usage guide cited in the memo requires departments to operate with budget targets. Staff also have tools at their disposal to track token usage.

According to 404 Media, the guidelines state: “While there is no target spend value being shared at this time, the data shows that many engineers spend in the range of hundreds of dollars a month to a few thousand dollars in tokens,.”

When asked by ITPro whether 404 Media’s report is true, Microsoft declined to comment.

Tackling rising AI costs

If the reports are correct, Microsoft is the latest in a string of companies to impose AI usage limits amid rising costs. The ‘tokenmaxxing’ trend has taken the industry by storm over the last 12 months, with organizations pushing hard to ramp up AI use.

Companies such as Meta introduced internal leader boards highlighting power users while others have incentivized staff to increase their use of the technology.

The trend has caused serious issues, however, with businesses facing spiraling costs.

Uber, for example, blew through its entire annual AI budget in a matter of months due to staff accelerating their use of AI. Reports from Bloomberg in June revealed the company introduced a $1,500 monthly cap per employee.

In June, Accenture told staff to cut back their use of AI for basic tasks due to what the company described as “soaring token spend”.

“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 a leaked meeting transcript.

Microsoft’s internal limits come as the company actively pushes new internal AI models designed to reduce costs.

As reported by ITPro, CEO Satya Nadella suggested in July that customers should leverage its more economical MAI model range rather than costly frontier models.

FOLLOW US ON SOCIAL MEDIA

Hallucinations are an eternal problem in generative AI in particular, and while it’s true that large language models (LLMs) require vast amounts of data, the quality of that information will affect the quality of the output.

What can businesses do to ensure they’re using AI both effectively and responsibly?

In this episode of the ITPro Podcast, Jane and Ross are joined by Amanda Stent, head of AI strategy and research in the office of the CTO at Bloomberg, to examine what responsible AI is, how organizations can use it, and what has been achieved at Bloomberg.

Highlights

Links

• Why doesn't more data produce better results?
• This new technique could improve AI accuracy by 80%
• AI hallucinations, accuracy still top concerns for UK tech leaders as adoption continues

Tel Aviv-based startup Irregular has found itself at the center of the 'rogue AI' debacle, after it came to light all the incidents so far revealed involved its test environment

Irregular was named by Meta, OpenAI, and Anthropic as the environment from which their so-called rogue AI agents escaped. On its website it also lists Google as a customer.

In a recent statement detailing incidents involving its own models, OpenAI claimed a “testing environment misconfiguration” by Irregular allowed agents to access the public internet.

Anthropic, meanwhile, also said that Claude models accessed the internet while “interacting with the evaluation environment of Irregular”. Both cases resulted in AI agents waging attacks on organizations and individuals.

ITPro contacted Irregular in response to these findings, but hadn’t received a response at the time of publication. However, a spokesperson told BBC News the Meta incident was the "exact same evaluation-environment issue that was already disclosed by Anthropic last week”.

The spokesperson added the firm is working to improve security when conducting agent evaluations.

Irregular, formerly known as Pattern Labs, describes itself as a “frontier security lab with the mission of protecting the world in the time of increasingly capable and sophisticated AI systems”.

In September last year, the Israeli startup raised $80 million in funding across seed and Series A rounds, valuing it at $450 million. The investment round was led by Sequoia Capital.

Speaking to Forbes in the wake of the funding round last year, CEO and co-founder Dan Lahav raised concerns about increasingly powerful AI models and their potential security risks.

Lahav told the publication at the time that Irregular aims to “build in the mitigations and defenses that are going to be relevant later on” as more powerful models hit the market.

Anthropic and OpenAI have issued repeated warnings about the new capabilities of cyber-focused AI models across 2026 so far.

When Anthropic launched Claude Mythos earlier this year, for example, the firm did so as part of a gated release with industry partners to avoid potential misuse.

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In this year’s Verizon Data Breach Investigations Report, exploiting vulnerabilities is the most common route that leads to data breaches, at 31 percent of the 22,000 confirmed attacks.

Looking at the most serious software issues that are Known Exploited Vulnerabilities tracked by the Cybersecurity Infrastructure and Security Agency, patching those critical vulnerabilities is down from last year as well. Only 26 percent of CISA KEV issues were fully remediated during 2025, compared to 38 percent in 2024.

Alongside this, Anthropic’s Mythos brings the prospect of even more vulnerabilities being discovered and exploits being created. While Mythos itself is still off-limits for the vast majority of companies, the advent of frontier AI will mean that security teams have more issues to deal with and less time to get their tasks done.

Customers will need help to understand the practical steps that they can take around security in the future. Cutting through the hype around AI and keeping things focused on real-world processes will go a long way.

The current picture for security

Traditionally, customers need security help around product selection, deployment practices and managing those activities over time. Managed Detection and Response (MDR) for endpoint security has been the bedrock of many partners’ approaches, followed by more specialist products and incident response where there are opportunities.

However, what the advent of AI shows us is that getting the basics around security and IT operations is still a challenge. Areas like IT asset management and maintaining an accurate inventory are still not solved, particularly as companies grow in size. While security programmes advocate for complete insight into what is on the network, the reality is that getting to even 90 percent accuracy is a significant challenge. This means that there is already a blind spot in security.

As AI gets adopted, many teams will want to improve their existing patching processes and speed up what they currently do. Automation around security and asset management can help in this regard, but the problem is that speeding up a process that does not cover everything already is not going to be enough. Repeating those same processes faster is not the full answer.

So how can channel companies help their customers in practical ways, and without making them feel despair at the situation? The answer here is to focus on practical responses that companies can take to fix these problems.

Patching is hard work

The first element here is that patching has always been hard work. Enterprises normally have different teams managing each element of the IT estate, so getting a consistent and fast process in place around patching or fixing misconfigurations was difficult before AI came in. Today, security teams have to do more than point out the problems; instead, they have to provide remediation guidance and ideally solve those issues in full.

With AI pointing out more problems, teams have to concentrate on potential business impact. In practice, this means understanding what issues exist in systems and what the risks involved are. What makes this different from previous attempts to manage cyber risk quantification is the level of detail involved. This ‘hyper-personalisation’ around specific software assets, systems deployment and exploitability should guide what vulnerabilities exist and what to fix.

The second element to this is how to automate patching. Traditionally, IT teams have been scared to implement automated patching due to experience with bad patches. In response, channel partners can take customers through how to structure automatic deployments using some lessons learned from software deployment at scale. For example, your customers can tier their assets into groups for deployments - the initial tier will be endpoints that get those patches first and are then checked for potential problems or configuration issues. If that tier of machines is deployed successfully, then the next tier of machines can be updated, and so on. This tiered approach can flag issues early.

Alongside deployment tiering, testing the patches themselves using AI can flag potential issues or where more human oversight for deployment is needed. Lastly, not all patches are created equal - your customers might want expert insight and expertise on hand when they have to patch their mission-critical applications that are responsible for revenue. However, a patch to Google Chrome can be automatically deployed at scale because it is less likely to be problematic.

For customers, these changes around patching can be big. They can involve working across departments and team boundaries, which can be problematic. For partners, just being an external provider can help internal teams talk to each other and solve problems. However, the bigger opportunity is around how to make changes at scale.

The sheer volume of patch updates and remediation work that is expected due to AI vulnerability discovery means that it won’t be possible to stick with the same manual processes. Helping your customers understand where they will have to make changes, and where they can take advantage of AI to improve their processes, is a market opportunity.

Getting ahead of issues before they get added to exploit lists like CISA KEV requires understanding the risks involved within customers, and every customer will have their own priorities and risk tolerance. The challenge is how to help them get the right process in place and ride the wave of AI-discovered vulnerabilities, rather than drowning in them.

Given a prompt, an AI model will do anything to achieve its aims – even hack into a company without specifically being instructed to do so. In July, firms pushing AI as a solve-all technology learnt that lesson the hard way, when OpenAI admitted two of its frontier models had breached Hugging Face after escaping a misconfigured sandbox during a benchmark test.

But the story didn’t end there. Days later, OpenAI’s competitor Anthropic claimed its AI agents had also hacked companies when they were mistakenly given internet access by engineers.

On the surface, it’s a tale of AI that’s so powerful at solving security issues, it can’t be contained. Yet underneath this marketing-driven exterior, it’s about a lack of control and failure to put guardrails in place when dealing with the fast-developing technology.

Taking this into account, how do IT leaders need to change the way they think about AI within the business?

From theory into reality

Until recently, the idea of an AI agent breaking containment and hacking other systems was largely theoretical. In May 2026, Palisade Research released a paper claiming that AI agents could autonomously hack and then self-replicate onto the breached systems.

However, the test was only performed at the “junior capture the flag level” and, crucially, “happened in a contained environment”, says Jeff Watkins, chief AI officer at consultancy NorthStar Intelligence. “The most important takeaway for me was that the trajectory of language models’ ability to hack showed we probably didn’t have long left before this became a serious issue.”

But while the OpenAI-Hugging Face breach is the first documented and well-publicized instance of this happening, experts do not see it as unprecedented.

Daniel Card, cybersecurity consultant at Xservus Limited, describes how often things can go wrong in testing. In fact, some reports suggest OpenAI had already been experiencing the issues – such as agents breaking out of sandboxes – that led to the hack of Hugging Face and others.

“Anyone who has experience doing offensive security – let alone offensive with AI – will know the risks involved here, be that from a script or via a large language model (LLM),” Card tells ITPro.

“It’s commonly known in pen testing circles that things sometimes do not go as expected,” he points out. “Anyone doing research with an LLM has probably had something go a bit funny when they didn’t mean it to. This is not new; it's not something we couldn’t have predicted.”

OpenAI calls the Hugging Face breach “an unprecedented incident”, and “an important moment for AI safety”.

OpenAI describes how the firm is conducting a “thorough review along with external advisors” and with oversight from its Safety and Security Committee. “Once the review is complete, we will publish a technical report of our learnings for everyone,” an OpenAI spokesperson says.

Anthropic points out that the agents in each of its evaluations ran without the standard safeguards it deploys when it makes the model generally available.

Claude did not exploit a novel vulnerability to escape isolation, because the models accessed the internet via an open path, rather than breaking out of a sandbox, according to Anthropic. At the same time, the most recent model, on realizing that it was working in a real environment, stopped its pursuit of the evaluation goal, according to the firm.

Issues with technology deployment

But the OpenAI incident and Anthropic’s following admission do raise questions about how firms use AI and the controls they have in place to govern it. The issues the incident highlights are “less about AI” and “more just around how people and organizations approach technology deployment”, according to Card.

“Security comes from the details; it comes from care,” says Card. He thinks the incident shows “more care needs to go into systems” – even more so when they are autonomous. “We can't just set them off and hope they are ok. They need monitoring.”

At the same time, with AI being able to move quickly through networks at scale, it’s important to be aware that “failures can become serious very quickly”, says Watkins.

He thinks things are moving “far too quickly for comfort in the capabilities and autonomy of AI agents, and our defenses likely aren’t keeping up”.

“Ironically, the advancements that make AI more commercially valuable also increase the potential for things to go wrong, or for misuse to be highly damaging,” points out Watkins.

Dana Simberkoff, chief risk, privacy and information security officer at AvePoint, thinks the OpenAI and Anthropic incidents “really expose the limits of treating containment as a one-time design decision”.

“Sandboxes matter, but autonomous systems can reason through small openings, use tools, and keep acting toward an objective,” says Simberkoff. “In my experience, the question cannot be, ‘was it sandboxed?’ It has to be, ‘can we prove what it accessed, whether it crossed a boundary, and how quickly we can stop it?’”

AI security now goes beyond simply protecting data, points out Tristan Shortland, CTO at Infinity. “Organizations also need to think about behaviour, permissions and autonomy, given the reported attack appears to have involved a model finding weaknesses in its environment and exploiting them to achieve an objective.”

Risks internally and externally

With all this in mind, IT leaders need to change their own mindset, as well as educate senior management about the issues posed by AI. CISOs must take the conversation to the business around how the organization is planning to “not just deploy, but manage, monitor and secure their AI capabilities”, says Card. “They need to talk about the risks they face internally and externally – or in this case, both.”

Card advises a mentality of “assume breach, assume adversarial intent, and deploy defence in depth”.

Do not assume your systems will work as expected, he advises. “Go and test those assumptions and red team them.”

Watkins believes AI agents should be modelled as “privileged digital workers, capable of becoming insider threats if poorly contained”.

“We need to start thinking of our AI agents in the context of what permissions they have to see or do things: Can they reach APIs, data, authorise transactions, contact third parties or modify systems?”

Equally important is understanding what those agents are doing in their deployed environments – something that many implementers have little visibility over, says Watkins.

“These are insider threats that need to be managed through good access controls, but also rate limiting, monitoring and alerting, better air-gapping of sandboxes, clear allow-lists for tools and access that [is] time-bound, rather than perpetual.”

While on the face of it, the OpenAI and Anthropic incidents might seem scary, simple changes will make a difference, experts say.

The most urgent initial step is to discover where you are using LLMs and put in place policies for AI usage and monitoring, says Card. “It's a really wide subject, but if you treat these systems as if they may do harm, it's a good starting point.”

TL;DR

• Data quality has a direct impact on the success of AI deployments
• Intuitive data management and automation capabilities help bridge gaps, clean up data, and deliver tangible results
• High-performance infrastructure underpins any successful AI project

AI is by no means a plug-and-play solution and requires significant investment and expertise across a range of areas.

From networking and storage infrastructure to data management and analytics capabilities, AI projects can be daunting for enterprises – and many are encountering acute challenges in moving from pilot to production.

MIT’s 2025 report found that 95% of AI pilot projects fail, while separate McKinsey research shows that just 7% of companies have successfully scaled AI across their organization.

A key factor lies in "AI readiness", according to McKinsey. Put simply, many organizations have not done the necessary groundwork to ensure that their data is cleaned up and prepared for AI use.

Other considerations, such as security and governance, are also often overlooked during the early stages of AI adoption. This creates a confluence of issues that impedes progress and, in many cases, ultimately results in failure.

Given that AI systems rely heavily on unstructured data, which Forrester describes as the “messy stuff” including video, text, images, and even behavioral signals, it’s no surprise that IT leaders encounter challenges when embarking on AI adoption projects.

This is where solutions such as the Dell AI Data Platform can help enterprises make sense of their data estate in preparation for AI adoption.

The Dell AI Data Platform allows IT leaders to consolidate their IT environments within a unified platform, with tools designed to prepare, manage, secure, and maximize the value of their data

How do I clean up my data?

When embarking on an AI adoption project, one of the first questions many IT leaders ask is how to clean up data in preparation for its use.

Data quality is paramount in this process: if you put garbage in, you will get garbage out. Nearly two-thirds of CEOs cite low-quality or disconnected data caused by siloed infrastructures and fragmented technology stacks as a major barrier to scaling AI, according to Dell’s summary of Futurum Group and Kearney research.

“High-quality data determines how well an AI model can perceive, predict, and act—all critical performance criteria,” Dell notes. “Without sound data, your AI foundation will collapse.”

According to a recent eBook published by Dell and Nvidia, there are several steps IT leaders can take to begin cleaning their data, including identifying recurring issues.

"Accelerating AI outcomes starts with clean, well-labeled, and accessible data," the eBook states. Put simply, IT leaders should assess and audit the quality of their data from the outset.

Common problems with data quality include:

• Duplicate datasets
• Missing values
• Syntax errors
• Irrelevant data
• Inconsistencies

These are all questions IT leaders must consider when auditing their data, but assessing data management capabilities is equally important. This allows enterprises to establish a clear baseline in terms of their expertise and readiness.

To underscore the point, Dell’s own guide to AI and data, ‘The IT Leader’s Guide to Feeding AI High-Quality Data’, says: “Establishing organizational standards for data structure, consistency, and completeness helps ensure the models your business relies on are learning from the right signals.”

How can the Dell AI Data Platform help?

The Dell AI Data Platform includes tools designed specifically for this process, such as the Dell Data Processing Engine. Powered by Apache Spark, this engine helps enrich and transform new data sources.

According to Dell: “Data is efficiently organized into Parquet columnar format, optimizing storage and query performance.

"This engine supports a wide range of use cases, including ETL, advanced analytics, and machine learning, enabling teams to derive insights and build models faster."

The Dell Data Search Engine also plays a key role in discovery, retrieval, and making unstructured data usable. This solution allows users to parse and index unstructured data, making it more easily searchable by adding metadata tags.

What does clean and structured data look like?

In this context, clean data is accurate, consistent, free from errors and duplication, and ready to be used in AI systems.

A fundamental distinction is that clean, structured data is easier to identify and retrieve because it is organized using labels, definitions, and consistent structures.

What kind of infrastructure and tools do I need to prepare my data?

The data pipelines that feed generative and agentic AI applications are a key component in the success of any adoption project, however. Clean, usable data will have little impact if an organization is still contending with fragmented, disparate siloes across its IT architecture.

That is where the Dell AI Data Platform is a key differentiator for enterprises. As a unified platform, the solution allows organizations to consolidate data sources and create a more comprehensive view of their greatest asset in the age of AI.

The Dell Data Orchestration Engine is a critical tool in this regard. This platform helps consolidate data ingestion, preparation, retrieval, and inference within a single end-to-end pipeline.

This not only has a direct impact on data flows and broader AI innovation, but also delivers benefits in governance and security. Visibility is crucial to ensure that mission-critical data remains safe and secure.

“Simplifying how data moves, how it’s processed, and how it’s governed ensures systems can scale without adding complexity,” Dell notes. “A unified, flexible approach enables faster experimentation, better performance, and long-term adaptability.”

From a broader infrastructure perspective, best-in-class AI storage solutions are also a vital component of AI innovation. Dell PowerScale, the core storage foundation of the Dell AI Data Platform, is designed specifically to support enterprises throughout their adoption journey.

The storage platform offers the flexibility and scalability required to meet evolving needs as AI projects mature and expand in scope. PowerScale is intricately woven within the Dell AI Data Platform, once again providing users with a unified ecosystem designed for end-to-end project delivery.

If you think the Dell AI Data Platform is the right solution for your business, find out more on the Dell website.

OpenAI and Anthropic both revealed this month that their "highly advanced" agents broke free of containment during a cybersecurity training exercise. Both businesses said this shows how advanced their AI agents are but is that the whole story?

This week, Jane and Scott are joined by news editor Ross Kelly to discuss the biggest story of July, plus Jane's takeaways from AMD Advancing AI.

Highlights

"It is to me, at least in my opinion, quite coincidental that this [story] has broken as the company's heading for an IPO. They've somewhat taken a backseat to Anthropic with Mythos. These cyber-focused models, this is definitely the sort of flavor of the month for big tech. Microsoft released its own in-house cyber specialist model yesterday, so the 28th of July. Cisco, as we reported, released their own small language model, the Antares series, last week."

"So I have to say that my post conference wrap up of AMD advancing AI was as many puns as I could get away with about Helios and it being the center of everything and the star and on the rise and all that kind of thing.

"But you know, kind of jokes aside, it really was the very literal star of the show. This wasn't the first announcement of Helios. It was introduced in 2025 at the same conference, but we now know a little bit more about the MI455X chip that's inside it, and it's finally rolling off the production line, which means we also get some more information about launch partners."

"So, from an OEM perspective, you've got HPE, who we also already knew about from last year, but also. Lenovo and Supermicro, you'll note that there's no Dell in that line-up, despite them having a relationship with AMD as well."

Links

• How OpenAI models breached Hugging Face
• Hugging Face CEO calls for 'radical transparency' in wake of OpenAI incident
• AMD hops on the agentic bandwagon at Advancing AI 2026
• Helios was the star of the show at AMD Advancing AI
• Claude Cowork escapes sandbox on mac devices

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."