My Vision of AI

Everyone who has actually implemented AI solutions knows:

ensuring the efficiency of an AI system requires a deep mastery of the business, data, risks, and side effects.

What AI does not have — and will never have — is human experience, understanding of contexts, weak signals, trade-offs, compromises, internal political stakes.... in short, the experience that is precisely not in the training statistics!

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Entrusting AI to an intern "because they are young" is one of the most frequent mistakes I observe.

In practice:

• More than 80% of what a young profile can produce without experience can already be done faster and better by an AI operated by an experienced professional.

• Neuroscience studies show that intensive use of AI reduces the ability to reason independently among younger generations, especially since they tend not to control the data they expose.

• Our responsibility is to teach them to regain control, develop their critical thinking, and build a sustainable career in a world where AI will be omnipresent — but where discernment will make the difference

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👉 The best AI Delegation (see the concept of 4D in "AI fluency" training): Give strategic thinking and coherence control to seniors, and have it executed with AI.

It's counterintuitive, but true.

Most companies entrust AI to young people trained with the same tools, the same social networks, the same models, with the same biases.

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Result:

➡️ their outputs all look alike
➡️ their AI strategies converge
➡️ their mistakes too

By hiring a young profile, you gain an unexpected advantage:

to anticipate very precisely what competitors will do… and therefore avoid doing the same.

It's a strategic lever that is largely underestimated.

AI is a subject of innovation.

And innovation is managed with methods proven for decades:
•    Think Tank
•    Design Thinking
•    Pain Points
•    Innovation Incubator
•    Digital Factory
•    Management of an innovative project portfolio

My Executive MBA, and in particular my major in Innovation Management in Portland (USA), profoundly influenced me on this point:

AI is never an end in itself.

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👉 Putting AI as the absolute priority without a methodical link to strategy is a guarantee of failure.

Yes, AI can find coherence in the chaos of Data.

But let's be serious:

no sustainable AI project works without clean, structured, governed, and secure internal Data. 

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The urbanization of Data is not a luxury.

It is the first mandatory step.

Working with a paid AI on your PC can give the illusion that everything is simple, fast, and inexpensive.

This is false!

As soon as you scale up:
• recurring costs explode,
• governance becomes essential,
• cybersecurity becomes critical,
• Data must be industrialized,
• models must be supervised.

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Most companies massively underestimate these costs.

The most reliable studies converge:

• RAND (2024)

• BCG (2024–2025)

• McKinsey (2025)

• MIT NANDA (2025) — which establishes a 95% failure rate for generative AI projects

These sources are robust, methodologically sound, and cover a wide range of sectors.

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The major causes are always the same:

• Data not ready (silos, insufficient quality)

• Lack of AI governance and executive oversight

• Projects conducted as technical POCs, without business integration

• Lack of knowledge infrastructure

• Disconnect between technological promise and operational reality

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👉 In a VUCA world, getting support is no longer an option. It is a condition for success.

I propose a free exchange to understand your challenges, constraints, and ambitions, and to help you structure a realistic, strategic, and profitable AI approach