Only 17% of CIOs know when to walk away from an AI project

On October 19, Gartner published a survey of 2,002 CIOs and technology executives, run from May to July 2026. According to Gartner, 13% say their enterprise has realized significant or transformational value from AI so far.

The more useful finding is about stopping. 31% of CIOs have rules for deciding when an AI pilot should not go into production. Only 17% have rules for ending AI that is already in production and not paying for itself.

Those rules go with better results. Gartner calls its best-performing CIOs "vanguards": they report better business results from AI, and their CEOs see high value in it. CIOs with rules for stopping pilots are 2.3 times as likely to be vanguards. CIOs with rules for ending AI in production are 2.6 times as likely.

Starting has a process. Stopping rarely does.

Most companies have a way to approve an AI project: a business case, a sponsor, a budget line. Few have a way to end one.

A pilot that works well enough keeps running, because nobody said what "not enough" would look like. A system in production keeps its license, its monitoring and the people who check its output, whether or not anyone still uses what it produces.

That cost does not stay still. An AI system in production is not a one-time purchase. The vendor bills it every month it runs, and the team pays for it every week in the time spent reviewing its work. A portfolio that only grows becomes more expensive each quarter, even if no single project looks like a problem.

Stopping a project is not a verdict on the team that built it. It shows the organization can tell a system that pays from one that does not.

Write the stop rule before the start

A stop rule is cheapest to write on the day a pilot is approved, when nobody yet has a stake in keeping it alive. Three things are enough.

First, the result that would justify going to production, in numbers the team already tracks today.

Second, the date of that decision and the name of the person who makes it.

Third, for systems already live, a regular review in which each one has to earn its place again. The review looks at what it costs to run, including the time of the people who check its work and keep it running, and what it returned in the same period.

Three questions for the leadership team

1. Which of our AI pilots has a written condition for not going to production, and who decides?
2. When did we last switch off an AI system that was live, and what did we learn?
3. For each AI system in production, do we know what it costs to run each month, including the time of the people who check it?

Return on intelligence is one of the services Wyrework offers clients: we measure what AI actually returns, across the whole life of each system. Before a project starts, we agree with the client's team what result it must reach and by when. While it runs, we count what it costs, including people's time. And when it stops paying, the decision to end it has already been made, so nobody has to argue for it.