Artificial intelligence has become a significant area of investment for UK businesses. More than £6 billion of new AI-related investment was announced during London Tech Week in June, while the UK remains home to the largest AI sector in Europe and the third largest globally.
From customer service and knowledge management to software development and internal operations, organizations are looking for places where AI tools can improve productivity, decision-making and business performance.
Principal Industry Advisor at ShareGate and Microsoft MVP.
But as investment accelerates, another gap is becoming harder to ignore: organizations are often scaling AI faster than their ability to measure, govern and explain its value. That matters when CIOs and CFOs are increasingly being asked not simply whether AI is being adopted, but what the organization is getting in return.
As businesses move from individual copilots towards agents embedded across workflows, the economics become more complicated. A user making a single prompt is relatively easy to understand. An agent may make multiple model calls, retrieve information, invoke tools and take actions to complete one task.
Depending on the platform and pricing model, that can introduce additional consumption, infrastructure and oversight costs. CIOs therefore need to understand not only where AI has been deployed, but what it is doing, what it costs and whether the outcome justifies that cost.
The not-so-hidden cost of AI
AI investment is increasingly being scrutinized in the same way as any other major technology investment. The difficulty is that measuring its return can be unusually complex.
Usage may be distributed across departments, applications, models and workflows, while the benefits can range from time saved to improved quality, reduced risk or increased revenue. Without agreeing what success means first, organizations can end up measuring activity rather than value.
That becomes difficult when organizations expand AI without first defining where it sits in the workflow, who owns the outcome, what success looks like and how costs will be measured. Spending can become fragmented across licenses, models, infrastructure, platforms and consumption-based services, with no single view of whether those investments are delivering value.
The scale of the challenge is becoming visible. Research commissioned by Emergn estimates that large UK businesses lose £67 billion annually across transformation and AI initiatives that fail to deliver. Separately, a Censuswide survey of 500 senior UK decision-makers found that just 31% of businesses already using AI reported a positive return on their investment.
Governance is part of that measurement challenge. A 2026 ShareGate survey of 851 IT leaders across seven countries found that cost visibility was the most commonly cited barrier to measuring AI ROI, identified by 51% of respondents, followed closely by governance complexity at 47%. The challenge isn't simply knowing what AI costs. It's connecting that cost to the use case it supports, the information AI interacts with and the outcome it creates.
Much of the AI debate to date has focused on model selection, skills and productivity. But as adoption spreads, another gap is becoming visible: confidence in governance does not always match what happens in practice.
The same study found that 93% of IT leaders believed their Microsoft 365 governance was ready to support AI responsibly, yet 29% reported that AI tools had surfaced sensitive internal data that should not have been accessible. Another 8% weren't sure whether it had happened at all.
Those gaps carry costs as well as risk. Duplicated tools, additional validation, rework, security investigations and time spent establishing whether an output can be trusted all create a hidden tax on AI adoption. For CIOs trying to demonstrate value, reducing that friction starts with making AI usage more visible, accountable and measurable.
How organizations can regain control
Good governance doesn’t begin and end at procurement. Knowing how many licenses have been purchased and where they have been assigned is useful, but regaining control requires a broader view: visibility into how AI is being used and what it costs, clear ownership of the outcomes, and a well-governed information environment for AI to work from.
Clear ownership matters just as much. As AI becomes embedded in business processes, responsibility can easily become fragmented across IT, security, business teams and individual employees. That makes some basic questions surprisingly difficult to answer: Who owns the outcome? Who monitors the cost? Who decides whether a use case should scale, change or stop?
Cost governance is only part of the picture. Organizations also need to improve the information environment in which AI operates. That means reducing redundant and outdated content, managing access appropriately and helping employees and AI systems find authoritative sources.
Cleaner, better-governed information does not guarantee a correct AI response, but it can reduce ambiguity and make reliable grounding easier. That can mean less time spent searching, validating and reworking outputs.
Employees have a role here as well. Clearly distinguishing drafts from approved material, keeping trackers and priorities current, recording decisions and maintaining authoritative sources all make organizational context easier for people and AI to interpret. Where organizations use AI meeting assistants or similar tools to capture context, those tools should be subject to the same privacy, retention and access controls as the information they create.
The question for leaders, then, is no longer simply whether AI is worth the investment. It is whether they have enough visibility and control to understand where AI is creating value, where it is creating cost and what they should do differently as a result. AI investment is likely to continue, but under different expectations.
Deployment alone is not evidence of value, and governance is becoming more than a risk-management exercise; it is increasingly part of the business case. Leaders need to understand what AI costs, who is accountable for its outcomes, whether the information supporting it can be trusted and what measurable benefit it creates.
The organizations best positioned to scale will not necessarily be those deploying the most AI, but those that can explain what it is doing, understand what it costs and make informed decisions about where it belongs.
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