Nearly Half of Enterprises Blew Their AI Budget This Year. The Number Everyone Missed Is 31 Percent.

Enterprise AI budgets are missing their targets at a rate that has stopped being newsworthy on its own. The Futurum Group's survey of 1,636 global technology decision makers, published September 10, found 46.9 percent report AI spend running over budget in the second half of 2026, split between 35.6 percent moderately over and 11.3 percent substantially over. Only 31.8 percent landed on plan, 10 percent never had a formal AI budget to miss, and just 5.6 percent came in under. Mitch Ashley, the Futurum analyst who led the study, put the shift plainly: "the AI budget question has moved from whether to adopt to how to fund it."

Here is the part of the story most coverage of that survey skips. A separate Flexera report on enterprise AI spending, published in July, found that only 31 percent of organizations have accurate visibility into their own AI software spending, and 59 percent report more wasted AI spend than a year earlier. Put those two numbers side by side and the overrun figure stops looking like a forecasting miss and starts looking like the predictable result of budgeting for a cost nobody is actually tracking as it happens.

The steelman here matters, because plenty of that overrun money is buying something real. Reporting on Gartner's own enterprise research, published by PYMNTS in late August, found at least eight in ten enterprises plan to increase AI spend again next year, with none of the surveyed firms planning to slow down. That is not blind momentum. TD Bank booked roughly 141 million Canadian dollars in measured AI value through the first three quarters of fiscal 2026, and Lowe's reported its AI shopping assistant drove 15.7 percent year-over-year growth in online sales, with triple the conversion rate among shoppers who used it. Boards funding a budget miss for results like that are making a defensible bet, not panicking.

The problem is that the same Flexera data shows most companies cannot tell which category they are in. Flexera cites KPMG's own AI research finding that only 24 percent of organizations have clear executive-level accountability for AI spend, yet the ones that do report three times the return on investment of everyone else. That gap is not about whether AI pays off. It is about whether anyone owns the number closely enough to know. A company with no accountable owner and no usage visibility cannot tell the difference between TD Bank's kind of overrun and a router misconfiguration burning tokens all weekend, and Futurum's data shows what happens next either way: among the 767 over-budget organizations in its survey, 47.6 percent went and asked for supplemental funding and 43.3 percent just absorbed the miss into the next planning cycle. Only around one in six actually paused or scaled back the initiative causing it. Overrun gets funded by default, not investigated by default.

That default has a cost beyond dollars. Among the subset of Futurum's respondents who reallocated budget to cover an AI overrun, 60.9 percent did it by cutting external contractors, and separately, of the organizations that made AI-driven headcount reductions, 60.2 percent hit IT support and helpdesk staff while 58.4 percent hit software and application development roles. Those are specific people losing specific jobs to pay for a line item nobody sized correctly going in, which is a worse outcome than the spending itself.

None of this argues for a spending cap or a compliance mandate on AI budgets, and I would make the free-market case against one directly: a rule forcing companies to under-provision AI compute would lock in whatever capability gap they already have against competitors willing to eat the variance. The fix is not less AI spend. It is treating AI cost like the metered utility it actually is instead of the fixed software license the annual budgeting cycle still assumes. Ashley's own forecast for next year points the same direction: expect finance departments to demand outcome-based vendor pricing and push budget ownership down to the business units actually running the workloads, rather than funding whatever the platform team's invoice says in December.

The practical move, whether you run a five-person team or a division: instrument your actual AI usage by team and task before you set next year's number, name one person who owns that number, and expect it to move quarter to quarter instead of holding still like a subscription fee. That governance layer, not a smaller budget, is what separates TD Bank's kind of overrun from the kind that just quietly funds itself. It is the first thing we build with a client before we let them commit real budget to an AI initiative at Mojo AI Services. If your AI bill is a surprise every quarter, let's fix the metering before you set next year's number.