A first AI project that fails rarely costs a lot because the technology doesn't work. It fails for more ordinary reasons that come up again and again from one project to the next.
Choosing the tool before defining the problem
This is the most common mistake: starting from a tool seen elsewhere or heard about, rather than from a specific task to solve. A tool that looks impressive in a demo but is poorly aligned with the company's actual business almost always ends up serving no one after a few weeks.
Confusing a standard tool with a suitable tool
81% of SMEs that invest in AI ultimately choose a custom-built solution or specific development rather than a standard tool (Bpifrance, Baromètre IA 2026). Many discover this need only after already paying for a generic tool that didn't match their business.
Underestimating maintenance
A delivered tool is not a finished tool. Business needs evolve, data changes, requirements become clearer. A project designed as a one-off, without a maintenance budget or plan, deteriorates within a few months and ends up abandoned.
Not clarifying regulatory questions upfront
52% of SMEs don't know exactly what they're allowed to do with AI (Bpifrance, Baromètre IA 2026). Discovering a compliance issue after building the tool costs far more than checking it beforehand.
Trying to do everything at once
Projects that last over time handle one specific, measurable task before adding a second. Projects that fail often try to solve everything at the same time, without ever delivering something usable quickly.
An AI tool only matters if it helps the company grow, not if it impresses in a meeting.
That's the role of the audit: to precisely identify the problem, quantify the gain, and verify feasibility before any commitment to building.
