Why Does AI Fail in So Many Companies?
It’s not a technology problem
Studies keep multiplying: between 60 and 80% of corporate AI projects fail to meet their objectives. Yet the technology has never been more accessible. So where is the problem?
Mistake #1: believing AI does everything on its own
AI is a tool, not an autonomous employee. It amplifies what you give it — if the data is poor, the processes unclear, or the questions poorly phrased, the results will be disappointing. Many companies invest in a tool without preparing the ground.
Mistake #2: choosing an overly generalist solution
A large generalist model knows a great many things — but nothing about your firm, your clients, your internal procedures. Without grounding in your professional data, the answers remain superficial and of little practical use.
Mistake #3: neglecting team adoption
The best technical solution fails if users don’t adopt it. A complex interface, lack of training, or legitimate distrust of the tool is enough to derail an otherwise well-funded project.
Mistake #4: underestimating the data question
Data is the fuel of AI. Poorly structured, incomplete, or low-quality documents will produce mediocre answers — regardless of the model’s power.
What works
Successful projects share common traits: a limited and well-defined scope, quality professional data, a solution adapted to real use, and human support from the outset.
AI does not transform a company. It amplifies what already exists — for better or worse.
