One thing is clear: we’ve moved from fantasy to obligation. The question is no longer whether to integrate AI into our organizations, but how to implement it quickly, effectively, and autonomously.
This obligation is coming from all directions:
- From European institutions, which regulate, provide guidance, and try to keep pace with innovation that’s moving faster than legislation.
- From governments, aiming to make AI a driver of industrial competitiveness and public sector performance.
- From investors, injecting billions into infrastructure, talent, and startups.
- And most importantly, from users—employees and citizens—who are already interacting with AI tools, often unknowingly… and sometimes without any training.
AI won’t become second nature without a clear framework
Even though AI is no longer optional, it’s not yet second nature. Why the gap? Because AI adoption can’t be mandated. It must be built—use case by use case, sector by sector, within a framework that provides both security and motivation. And today, that framework remains unclear, unstable, and fragmented.
The European AI Act is a major step forward, but it’s already facing criticism. Digital sovereignty is a declared priority, but its boundaries are still hard to define. Generative AI fascinates, but scalable industrial use cases remain rare.
This vagueness feeds two pitfalls:
- On one side, inaction: “It’s too risky, too soon.”
- On the other, blind imitation: “Let’s just copy others, even if it’s neither ethical nor sustainable.”
Neither path is viable. What we need is a culture of AI, grounded in our economic, social, and regulatory realities.
Building a useful, collective AI culture
This culture is not just about training data scientists or publishing reports. It requires a collective effort in education, awareness, and experimentation. It also means bridging the gap between worlds that are too often disconnected: labs and factories, ministries and startups, IT departments and HR teams.
AI will not be transformative if it remains in the hands of a few experts. It will only become a tool for productivity, reindustrialization, or public sector efficiency if it is understood, accepted, and controlled.
Embracing AI in service of chosen progress
That’s why we must think of AI adoption not as an IT project, but as a societal project. This involves making strong political choices: about data governance, trust standards, interoperability, and the role of digital commons.
It also means investing in the points of contact between AI innovations and the real world: local governments, industrial SMEs, public services, and healthcare institutions. That’s where AI will prove its worth—or not.
The key issue today is not so much to regulate AI as to give it an operational, shared, and ambitious framework. A framework that allows us to say no when necessary—to opaque practices, to technological dependencies—but above all to say yes to what works, to what brings transformation, to what serves.
In a world where everyone is talking about AI, it’s urgent to move beyond fascination and turn it into a tool for collective action. It’s time to dare to use AI—not as an end in itself, but as a tool for deliberate progress.
And for that, talk is no longer enough. It’s time to act.
