How to Stop Killing AI Projects Before They Start

S&P Global surveyed more than 1,000 firms across North America and Europe in 2025 and found that 42% abandoned most of their AI initiatives before production. In 2024, that number was 17%. The abandonment rate nearly tripled in one year, while companies were simultaneously spending more on AI services. That combination is not a technology story. It is a measurement story.

The failure isn't in the model

MIT's NANDA initiative estimates that roughly 95% of generative AI pilots fail to produce measurable revenue impact. Practitioners who track this pattern consistently point to the same cause: unclear goals and absent measurement criteria, not model performance. The models did not get worse between 2024 and 2025. The organizational conditions around them did.

The average company in S&P Global's survey scrapped 46% of its AI proofs of concept before reaching production. When pilots fail at that rate, the instinct is to blame the technology or the vendor. The data points elsewhere. HBR's coverage of AI program failure names a specific failure mode called "pilot purgatory," where experiments cycle without ever producing a go/no-go decision. That is a test-design problem, not a capability problem.

What open-ended exploration actually produces

JPMorgan Chase Institute data shows small businesses shifting toward consistent AI payments while most still self-describe as "testing and exploring." A reasonable reading of that posture is: these businesses are building capability incrementally, and the exploratory phase is normal. That reading is worth taking seriously.

It fails on the time dimension. If open-ended exploration were a healthy adoption phase, the abandonment rate would stabilize or fall as organizations accumulated experience. S&P Global's data shows the opposite. The rate accelerated sharply. Organizations running longer, looser pilots are not producing better outcomes. They are abandoning more projects faster, without documented reasons, and without redirecting the resources they spent.

The counterargument's legitimate point survives: a three-week test on the wrong workflow produces a clean negative result on the wrong question. A founder who picks a low-leverage process, runs a tight experiment, and hits a hard stop will conclude AI does not work in their business, when the actual finding is that AI does not work in that specific workflow as currently configured. That is a real risk. A structured test does not solve the selection problem.

The structure that forces a decision

A three-week test built around one pre-defined metric, a documented baseline, and a hard stop condition is more likely to produce a production decision than an open-ended pilot. Not because three weeks is enough time to prove everything, but because the structure forces a binary outcome. A clean negative on the wrong workflow is still a production decision: stop this project, redirect resources, pick a different workflow. An open-ended pilot on the same wrong workflow produces no decision at all.

Neoteric's validated learning approach frames this as the core discipline: define what you are measuring before you start, document where you are now, and set a condition under which you will stop. One metric. One baseline. One exit condition. The exit condition is not a failure state. It is the mechanism that prevents sunk cost from accumulating around a project with no exit.

The baseline matters more than most founders expect. Without a documented starting point, a three-week test produces a data point with no reference. You need to know how long the workflow takes today, how often it fails, what it costs. Write that down before you run anything.

What the escape hatch is actually for

The hard stop condition is not there to kill projects. It is there to prevent the specific failure mode S&P Global's data captures at scale: projects that drift, accumulate organizational weight, and get abandoned without a recorded reason. When you define the escape hatch before the test starts, you are not being pessimistic. You are building the mechanism that makes a real decision possible.

Set the condition in plain terms. If response time does not drop by a specific percentage relative to the baseline by day 21, the project stops. Not pauses. Stops. Resources redirect. The team documents what it learned. That documentation is the output, regardless of whether the metric moved.