The companies doing the most to control their AI spending are the ones getting burned worst by it. A survey of 500 finance leaders by Sapio Research, published by DoiT, found that organizations with the most mature financial operations practices report an 89% rate of AI-related cost overruns — with a mean overspend of 30.9%.
That is not a typo. The firms that invested most heavily in cost governance still overran their budgets by nearly a third. The reason, according to the researchers, is structural: mature programs are larger and far better at detecting problems that less experienced organizations never even notice.
Across all respondents, 79% experienced AI cost overruns in the past twelve months. Only 15% said they could calculate AI return on investment without significant bottlenecks. And accountability for AI spending is split almost evenly between technology leadership at 55% and finance at 53%, with no clear single owner at the operational level.
The patience window is closing fast. Eighty-three percent of the finance leaders surveyed expect clear, quantifiable AI returns within twelve months. Among C-suite respondents, 65% are already adjusting their AI spending or plan to within six months. But the barriers remain stubbornly familiar: the pace of technological change (40%), finance and engineering defining success differently (37%), and a lack of clear financial attribution (36%).
Mid-size organizations — those with 1,000 to 4,999 employees — are getting hit hardest, experiencing overruns more frequently than large enterprises at 81% versus 76%, despite running smaller absolute AI budgets. Their mean overspend is also higher. The survey, which was conducted in February 2026 and covered organizations with 1,000 or more employees across the United States and United Kingdom, paints a picture of an industry that has formalized AI investment faster than it has built the financial infrastructure to manage it.
What does this mean for the business owner who does not have a FinOps team, a governance framework, or a dedicated finance function tracking AI attribution models?
It means the enterprise cost problem is not your problem — if you choose the right tool. Viktor was designed so that you always know exactly what you are spending. There is no enterprise license. No annual contract. No hidden inference costs buried in a cloud bill you need a specialist to decode.
Viktor runs on Claude, GPT-4, and Gemini — all three models accessible through one credit balance, with Viktor selecting the right model automatically for each task. You spend credits when work gets done. You see what was spent and why. That is the full picture.
While enterprises build FinOps teams to track their AI burn rate, Viktor handles work inside your existing tools — email, documents, spreadsheets, scheduling — and charges transparently by the task. No cost overruns because there is no opaque budget to overrun. No twelve-month ROI calculation because the value is visible from the first task completed. No accountability gaps between departments that cannot agree on what success looks like.
You get $100 of free credits to begin — no credit card, no time limit, no commitment. Explore Viktor properly. Do real work. When you are ready to go further, $50 comes straight off your first bill.
Disclosure: Some links in this article are affiliate links. If you choose to get started with Viktor using the links provided, I may receive a commission — at no additional cost to you. I only recommend tools I use and believe in.