Why 95% of AI projects don't deliver
“Many talk about it, few get there. What makes the difference?”
The observation
The figure is well known to anyone who has looked: according to MIT, 95% of generative AI projects never reach production. Gartner goes further — more than 40% of agentic AI projects will be outright canceled by the end of 2027. And of the thousands of vendors claiming to be agentic AI, Gartner estimates that only about 130 truly are. The rest is what it calls "agent washing": renaming a chatbot to sell it for more.
How we handle this topic
Three causes of failure, cited by Gartner
Costs that spiral, unclear business value, insufficient risk controls. We attack all three from the assessment onward: costed scope, explicit expected outcome, supervision planned in.
We measure before we promise
On your real volumes, not on assumptions. If the math doesn't hold, we say so — before the signature, not after.
We put things into production, not into pilot
A pilot that never converts is the most common failure pattern. We deliver in working order, with monitoring from go-live.
Supervision, from the start
Deployments with human supervision reach 83.6% go-live, against 73.9% without (barometer of 200 French deployments). It's not an add-on: it's a condition of success.
What it changes for you
- You know what a failing project looks like, and why
- The three pitfalls cited by Gartner are handled before you start
- You have criteria to judge any vendor, including us
The limit, stated plainly: We can't guarantee that a project will succeed: nobody can, and whoever claims otherwise is precisely the one to rule out. We commit to a method, a limited scope and a measurement — not to an outcome we don't control alone.
Is your situation specific?
This topic is handled case by case. The assessment frames it around your numbers, not in the abstract.