Britain’s biggest listed companies are investing heavily in artificial intelligence but are failing to show shareholders whether the technology is delivering measurable financial returns, according to new research that raises questions about accountability for the corporate AI spending boom.
Just 3 per cent of FTSE 100 companies report a specific financial return or outcome from AI, despite 36 per cent disclosing substantive deployment or use of the technology and 32 per cent linking it to a particular business outcome, an analysis of public company reporting by technology intelligence group Flexera has found.
The figures expose a significant gap between corporate enthusiasm for AI and the financial evidence being provided to investors. Companies are increasingly presenting the technology as a driver of productivity and competitive advantage, yet few are quantifying what those improvements are worth or how much they cost to achieve.
Disclosure of spending is even more limited. Only 2 per cent of FTSE 100 companies identify a specific figure for AI expenditure, while none disclose AI investment as a separate budget or cost. At the same time, more than half — 54 per cent — identify AI as a material or company-specific risk in their annual reports.
The findings point to a growing challenge for investors attempting to distinguish genuine productivity gains from expensive experimentation, particularly as companies expand their use of generative AI tools and autonomous agents.
Marlon Oliver, senior vice-president for EMEA at Flexera, said businesses needed to move beyond measuring technology consumption and demonstrate its contribution to commercial performance.
“Companies are burning through annual AI budgets in months, and always-on agents will put those budgets under even more pressure,” he said.
“Token consumption, compute, and license numbers tell you what AI costs and how much it is being used. They do not tell you whether it is creating value.”
Oliver argued that investment should be tied to measurable business indicators, including reduced downtime, resolved customer-service tickets, processed insurance claims or additional revenue.
“When shareholders are told AI is improving productivity, they should be able to see what that improvement is worth, what it cost and whether the economics are improving,” he said.
The problem is compounded by the increasingly fragmented nature of enterprise AI expenditure. Spending can accumulate across software licences, applications, models, agents, data and computing infrastructure, making it difficult for finance departments and investors to establish the total cost of deployment.
Steve Daheb, head of AI go-to-market strategy at Flexera, said one company he had recently spoken to discovered that just four users accounted for 35 per cent of its AI costs.
The emergence of autonomous agents could make that spending harder to control. Unlike conventional software, agents can initiate repeated workflows independently, consuming additional computing resources as they attempt to complete tasks.
“A single instruction can set off repeated attempts to complete the work, each drawing on more computing power and adding to the bill. It’s an AI cost domino effect,” Daheb said.
He warned that companies risk allowing AI expenditure to become an “enterprise cost black box” unless they connect consumption and spending with measurable results.
The disclosure gap comes as investors increasingly scrutinise the enormous sums businesses are committing to AI infrastructure and software. While companies have been quick to highlight adoption plans and productivity ambitions, the research suggests that transparent financial reporting has not kept pace.
For shareholders, the distinction is increasingly important: widespread adoption does not necessarily mean profitable adoption. As AI systems become more autonomous and consumption-based costs grow, boards will face mounting pressure to demonstrate not simply that they are using the technology, but that it is generating returns commensurate with the investment.
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