Why so many companies abandoned their AI project last year — and what the ones that succeeded do differently

The latest round-ups of US business trends describe a sobering up after the wave when companies tried to deploy AI practically anywhere it was theoretically possible — including at companies that are at the same time investing aggressively in robotics and other automation. It is not that AI stopped working. It is that it is becoming clear which projects never had a clear definition of what should happen and how to tell that it works.

We see a similar pattern among the small and medium companies in the Czech Republic we work with. The share of companies that abandoned an in-progress AI project over the past year rose sharply year on year.

What the projects that survive have in common

From experience with clients, and from what shows up in the data on AI adoption, a few recurring traits can be traced.

It is not a limit in the model's capabilities. Companies that abandon a project usually do not complain that AI "couldn't" do what it was meant to. The problem tends to be elsewhere — the team could not set up the process around the AI, there was no clear responsibility for the outcome, or after the first week of enthusiasm no one tended to the pilot regularly.

They have one specific process, not a vision of "AI everywhere." Projects that do not prove out in the pilot often started ambitiously — deploy AI across the whole operation at once. The companies that succeeded usually started with one process with a clearly measurable impact and expanded only after validation.

They defined what success looks like from the start. Without a baseline number — how many hours the process takes today, what the error rate is, how long the response takes — you cannot tell after three months whether the pilot really worked or just looked interesting in a demo.

The difference between a demo and operations

Many companies today are moving from trying an AI tool to actually replacing a specific working procedure — and this is exactly where it shows whether a project survives. A demo looks good almost every time. Operations is a different discipline: it needs integration with real data, a clearly defined moment when a person decides, and a way to measure that something genuinely improved.

Companies that manage this phase typically have one thing in common: they do not map the process once and then let the AI coast. They have a rhythm in place for evaluating and adjusting the results regularly.

What this means for smaller companies

A smaller company does not have the capacity to try five AI projects in parallel and quietly bury three of them within a year. That makes it all the more important to pick one process with a clear return at the start, instead of trying to catch up with competitors through a blanket rollout all at once.

That is exactly why our AI Opportunity Sprint does not start with a solution proposal, but with an audit — to find the process where AI has the clearest and fastest measurable impact, and to prove the value on it before the company commits to anything larger.