What 24 years in digital taught me about AI

I delivered my first web project in 2002. Back then, we told clients that having a website was optional — a nice-to-have. Most Quebec SMBs didn't have one. The few who ordered one would often ask us to explain why it was worth having.

Twenty-four years later, I'm having the exact same conversation, with the exact same objections and the exact same fears — except now we're talking about AI. And it's precisely that repetition that should interest us, because it reveals something no McKinsey report can capture.

Three cycles, one same pattern

I've lived through three major technology cycles from the inside. The web (2002-2008). Mobile and SaaS (2010-2018). And now AI (2022-today). Each time, the script is identical:

1. A new technology shows up. A few entrepreneurs see the opportunity right away; most shrug.
2. The first spectacular use cases emerge. The press goes wild. The gurus appear.
3. A wave of empty promises floods the market. Disappointment sets in. Many conclude that it was overhyped.
4. The real operators keep building, quietly. Three years later, the companies that took the transformation seriously have built a gap that's impossible to close.

That's exactly what happened with the web. Exactly what happened with SaaS. And it's exactly what's happening now with AI.

Technology doesn't reward those who talk about it the loudest. It rewards those who deploy it with discipline, on the right problem, at the right time.

The mistake that repeats every cycle

The same mistake comes back every wave: confusing the tool with the transformation. In 2003, SMBs ordered a website and believed they had become "digital." In 2014, companies bought a CRM and thought they had a sales department. In 2025, I see entrepreneurs subscribe to ChatGPT Enterprise and believe they have an AI strategy.

A website doesn't grow a business. A CRM doesn't either. ChatGPT doesn't either. What grows a business is what happens around the tool: the processes you accept to question, the people you train, the decisions you make differently because you now have access to new data.

The tool is an entry point, not a destination. If you stop at the tool, you've just added a cost. To go further, you have to accept that AI isn't a layer you put on top of an existing organization — it's a reason to rethink how the organization works.

What's really different this time

Now, let me push back on my own analogy. Yes, the pattern repeats — but AI has something the previous cycles didn't: the speed at which it rewrites the rules of the game.

The web took ten years to become unavoidable. SaaS, seven or eight. Generative AI became critical for entire sectors in less than two years. That compression of time has a brutal consequence: the gap between those who adopt and those who watch is no longer measured in years, but in months.

Concretely, in the professional services firms I work with — lawyers, accountants, consultants — here's what I see in 2026:

• Firms that have automated client intake and document research are freeing up 30 to 50% of billable time to reinvest.
• Those who haven't done anything are starting to lose mandates to competitors who respond in hours instead of days.
• Leaders who understood that AI frees their team rather than replaces it see their retention rates go up, not down.

That last observation deserves attention. I repeat it in every conversation: technology should free humans, never replace them. This isn't a moral slogan. It's a strategy. Companies using AI to lay off get short-term productivity gains and lose their culture over 24 months. Those using it to give their best talent more time build a durable competitive advantage.

Three principles I share in every mandate

1. Start with the problem, never the tool

The worst question an entrepreneur can ask in 2026: "How can I use AI in my business?". The right question: "What's the bottleneck costing me the most in time, margin, or lost clients — and can AI help me solve it?". That inversion changes everything.

2. Measure before deploying

If you don't know how many hours your team spends on a task today, you won't know how much AI is saving you tomorrow. Before any serious deployment, I spend a week measuring real current operational costs. It's not glamorous. It's what prevents wasting six months automating tasks that weren't worth automating.

3. Build systems, not hacks

A brilliant ChatGPT prompt isn't a system. It's a trick. Tricks don't survive their author leaving. Systems are documented, tested, integrated into a workflow, and keep working even when the employee who used them moves on. The difference between an SMB that has actually transformed its operations and one that has just added a few tools is exactly that.

To wrap up

If I had to summarize these 24 years in one sentence for someone hesitating today in front of AI, it would be this: the entrepreneurs who succeed at technological transitions are not those who adopt first, nor those who adopt last. They are those who adopt with discipline.

They take the time to understand where they can win. They refuse the empty promises. They measure. They deploy systems rather than hacks. And above all, they never forget that technology serves humans — not the other way around.

That's what I learned in 2002 with the web. That's what I confirmed in 2014 with SaaS. And that's what I see playing out again today with AI. The technology changes. The principles don't.