New Harness Report Reveals Enterprise AI Spend Has Outgrown the Systems Built to Track It
As AI spend climbs across the enterprise, a new survey of 700 engineering leaders and practitioners finds that organizations are spending faster than they can track, attribute, or govern
SAN FRANCISCO, July 29, 2026 – Harness, the AI Software Delivery Platform™ company, today released the 2026 State of AI in FinOps, a new report revealing that enterprise AI spend has outgrown the ownership, visibility, and governance needed to manage it. We surveyed 700 engineering leaders and practitioners across five countries to ask about their organization's FinOps practices. The report finds that AI costs are climbing across every LLM provider and spend category, including infrastructure, software, and models. But basic questions go unanswered: who owns the bill, why it spiked, and whether the spend is paying off.
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 organizations can keep up with. The report finds that when the bill spikes, most have no way to explain why:
- Nobody is clearly accountable for AI cost. 52% say there's no clear owner, with responsibility split across engineering, FinOps, finance, and IT; so when spend increases, there's no single person well-positioned to answer for it.
- That ownership gap shows up as recurring surprises. 72% have experienced an unexpected AI cost spike or bill in the past year, and a third of organizations (33%) have been caught off guard more than once.
- Even after the surprise hits, most can't trace it back to a cause. If spend were to double overnight, only 20% could identify the reason within hours.
- The result is money wasted with no owner or explanation. Organizations estimate 26% of all AI spend is wasted. For organizations spending $1M/month, the reality for 1 in 5 respondents, that's $260,000 a month with no measurable return.
This mirrors what Harness found earlier in 2026. The State of Engineering Excellence 2026 report showed engineering teams measure AI's productivity gains with instruments that miss what matters. The same blind spot now shows up in finance: spend is moving faster than any organization's ability to see it, attribute it, or explain it.
"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. This is fundamentally an organizational challenge. Getting teams to build with cost in mind from the start, as a design principle rather than an afterthought, is the harder and more important shift.”
The Problem Starts When Engineers Build the Features
Here's how it plays out:
- 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.
- Planning ahead becomes total guesswork. More than half (56%) say anticipating AI spend is based on guesswork, not data.
- The incentives in place reward more usage. 57% of engineers say their organization actively encourages "tokenmaxxing," maximizing AI usage regardless of tangible value.
- Organizations can't enforce spend policies without data. 73% have AI cost policies in place, yet only 13% have basic visibility into their AI spend.
- As a result, most organizations can't tell if their 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 organizations write policy well ahead of building the visibility to actually enforce it, which just pushes the same problem downstream. We're seeing this pattern the same way whether we're talking to a 200-person startup or a Fortune 500 company. Cost visibility has to be part of the infrastructure from day one. Waiting until after the damage is done means teams are always reacting instead of preventing."
What Mature Organizations Do Differently
The report finds that organizations that have reached full AI cost maturity follow a consistent sequence:
- 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 optimize 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.
To learn more, download the full 2026 State of AI in FinOps report here: www.harness.io/state-of-ai-in-finops-2026
About the Research
This report is based on an online survey of 700 engineering leaders and practitioners, asking about their organization's FinOps practices, conducted in May and June 2026 by Sapio Research. All respondents work at organizations that actively use AI/LLM services and are employed in software engineering/development, DevOps, IT operations, or executive leadership roles. The sample included 300 respondents in the United States and 100 each in the United Kingdom, France, Germany, and India.
