Enterprise AI spend has outgrown the governance, attribution, and ownership infrastructure needed to manage it, new research from AI software company Harness found.

Their report, The 2026 State of AI in FinOps, based on a survey of 700 engineering leaders and practitioners across five countries, finds that AI costs are climbing across every category and provider at once,  while visibility into that spend remains one of the biggest blind spots in enterprise finance.

The findings extend a pattern Harness has tracked across its 2026 research. Just as The State of Engineering Excellence 2026 found that engineering organisations are measuring AI’s productivity gains with instruments that miss what actually matters, this report finds the same gap on the financial side: AI spend is moving faster than any organisation’s ability to see, attribute, or explain it. That gap is no longer a rounding error.

AI Spend Is Rising Fast, and Running Blind

AI spend is no longer isolated to a single team, tool, or budget line. It is climbing across infrastructure, software, and models at once, faster than most organisations can track it.

The report finds that when the bill spikes, most have no way to explain why.

Nobody is clearly accountable for AI cost, as 52% say there is no clear owner, with responsibility split across engineering, FinOps, finance, and IT – so when spend moves, there’s no single function positioned to answer for it.

This ownership gap shows up as recurring surprises, with 72% experiencing an unexpected AI cost spike or bill in the past year, and a third of organisations (33%) have been caught off guard more than once.

Even after the surprise hits, most cannot trace it back to a cause. If spend were to double overnight, only a fifth (20%) could identify the reason within hours.

The result is money wasted with no owner and no explanation. Organisations estimate 26% of all AI send is wasted. For organisations spending $1M per month, the reality for one in five respondents, that’s almost $260,000 a month with no measurable return.

“AI spend has moved from a line item that occasionally surprises people to a budget category that regularly does,” said Patrick Brogan, Director, FinOps Advisory at Harness.

“The patterns we’re seeing, invoice shock, ownership confusion, governance gaps, are the same ones the industry saw with cloud a decade ago, just compressed into a fraction of the time.

“Ownership is really the crux of it. When no one is clearly on the hook for a number, no one is positioned to explain it after the fact either.

“The hardest part of this problem has nothing to do with technology. It’s organisational: getting teams to build with cost in mind from the start, not as a constraint, but as a design principle.”

An Organisational Problem

The report points to a bottom-up dynamic compounding the governance gap.

It starts at the point of build, as most engineers have no idea what the features they create actually cost. Less than half (45%) say they understand the cost of the AI features they build.

More than half (56%) say anticipating AI spend is based on guesswork, not data.

The incentives in place reward more usage, not less. 57% of engineers say their organisation actively encourages “tokenmaxxing,” maximising AI usage regardless of tangible value.

Organisations have tried to rein this in with policy, but the policy has nothing to enforce against. 73% have AI cost policies in place, yet only 13% have basic visibility into their AI spend — you can’t enforce a policy against data you can’t see.

The end result: most organisations can’t tell if any of this spend is even paying off. Only 26% have a robust method for measuring the business value of their AI spend.

“A year ago, our customer conversations were about cloud cost attribution, commitment coverage, and rightsizing,” said Harish Doddala, VP Product, Cloud & AI Cost Management at Harness.

“Today those conversations are increasingly interrupted by a more urgent one: why did my AI bill do that, and how do I make sure it doesn’t happen again? We’re also seeing organisations write policy well ahead of building the visibility to actually enforce it, which just pushes the same problem downstream.

“These gaps show up the same way whether we’re talking to a 200-person startup or a Fortune 500 company. Cost visibility has to get built into the infrastructure from day one, or teams end up bolting on dashboards after the damage is already done.”

Recommended reading

• Worldwide AI Spend to Total $2.5 Trillion in 2026, Gartner Says
• Most UK Businesses Plan to Increase AI Spend Despite Lack of ROI
• Comment | What To Expect if the AI Bubble Bursts

Closing the Gap

The report finds that organisations that have reached full AI cost maturity follow a consistent sequence: naming a clear owner before buying a tool, building a single, unified cost view before optimising spend, embedding cost data directly into engineering workflows before writing policy, and tying spend to measurable business outcomes before chasing ROI.

To close the visibility and ownership gap, Harness recommends that organisations:

• Name a single, accountable owner for AI cost before adding new tools or providers.
• Build one unified view of AI spend across infrastructure, software, and models before attempting to optimise it.
• Push cost data into engineering workflows — at the point of model selection, prompt design, and deployment — rather than treating it as a finance-only concern.
• Tie AI spend to business outcomes, establishing unit economics before measuring ROI.