The demonstration worked, the enthusiasm was there, and then... nothing. The prototype sits gathering dust. This is the most common fate of AI projects: it is estimated that nearly 80% of proofs of concept never make it into production.
Why so many projects stop at the prototype
- No prioritised use case: a technical feat was demonstrated, but no real business problem was solved.
- Unprepared data: the prototype ran on a clean sample; reality is messier.
- No integration with existing systems: an isolated tool that nobody connects to existing processes.
- Adoption neglected: without training and support, teams go back to their old habits.
- Compliance forgotten: the constraints of law n°1.565 are discovered too late and the project is frozen.
The method: 4 milestones
1. Scoping
Business interviews, process mapping, selection of the highest-impact use case and quantification of the expected return. Nothing gets coded before there is a measurable objective.
2. Measured prototype
A prototype built on real data, with success indicators defined upfront. Value is validated before investing in industrialisation.
3. Progressive production rollout
Integration with existing systems, sovereign hosting, testing in real conditions, phased deployment starting with the safest uses.
4. Handover & steering
Team training, documentation, gain indicators and continuous improvement. The tool lives on, measures its results and keeps getting better.
A successful AI project is judged in production, not in a demonstration.
Before you launch: the checklist
- Is the business problem clear and quantified?
- Is the necessary data available and usable?
- Is the architecture sovereign and compliant?
- Who will use the tool, and how will they be supported?
Answering these four questions before the first line of code already puts you among the 20% who succeed.