In 2026, most large companies have already invested in artificial intelligence.
Copilot licenses, conversational assistants, generative AI platforms, internal chatbots, process automation... Budgets are committed and expectations are high.
Yet, one question systematically recurs in executive committees:
"What ROI are we actually getting?"
The answer is often embarrassing.
Because deploying a technology is not the same as creating value.
The Myth of Automatic ROI
Many organizations have followed the same pattern:
✅ Purchasing licenses
✅ Technical deployment
✅ Internal communication
✅ A few enthusiastic demonstrations
Then...
❌ Employees who continue to work as before
❌ Irregular use of tools
❌ Usage limited to a few "power users"
❌ Difficulty in measuring real gains
The problem is not the AI.
The problem is thinking that adoption will naturally follow deployment.
No digital transformation has ever happened that way.
Why would it be any different with AI?
The Most Costly Gap: Skills
Generative AI does not just require new tools.
It demands new skills:
- Knowing how to formulate effective prompts;
- Understanding model limitations;
- Verifying produced results;
- Identifying the right use cases;
- Integrating AI into existing processes;
- Respecting security and compliance requirements.
When an employee masters these skills, value appears quickly.
When they do not, the tool remains underutilized.
And an unused license generates zero ROI.
Training is No Longer a "Nice to Have"
With the arrival of the European AI Act, the issue takes on a new dimension.
Article 4 of the regulation requires organizations to ensure that individuals using AI systems possess a sufficient level of AI literacy adapted to their role.
In other words:
Upskilling is no longer just a performance lever; it is also becoming a compliance requirement.
The companies that succeed will not necessarily be those that buy the most AI.
They will be those that help their employees use it intelligently.
Train Yes, But Train What?
There is a great temptation to offer identical training to everyone.
Yet, this is rarely effective.
A CFO, a salesperson, a legal counsel, or a developer have neither the same needs nor the same use cases.
Before launching massive training programs, a fundamental question deserves to be asked:
What is the actual AI maturity level of my teams today?
Without an answer to this question, the risk is simple:
Training everyone in the same way, at the same cost, for very different results.
What Distinguishes the Most Advanced Companies
The most mature organizations generally apply three principles:
- They measure skills before investing.
- They identify priority populations.
- They track adoption and impact over time.
They view AI training as a strategic investment rather than an awareness expense.
Because in the end, the real question is not:
"How much have we invested in AI?"
But rather:
"How much value are our employees capable of creating thanks to AI?"
And that answer always starts with skills.
💬 In your opinion, what is the main barrier to AI ROI in companies today: technology, usage, or skills?