The boring stuff pays.

THE EXPERIENCE

I'm an aerospace engineer by training, and I spent the last five-plus years running digital programs inside large companies. At CAE, I was product manager of a BI dashboard for senior leadership, then manager of a $13M CAD international project portfolio. At MTY Food Group, as product manager, I led the group's Data and AI platform with the internal teams, built on Databricks. The platform brought together data from more than 90 restaurant banners and supported a technology rationalization representing more than $1M USD in annualized savings and avoided costs. Two decisions illustrate the approach: replacing an underused enterprise procurement platform with an internal application tailored to the business need, then reproducing a recurring report directly in the data platform rather than adding another BI tool. Three discovery workshops fed the product roadmap for the following 12 months.

Those big transformations don't succeed because of the technology. They succeed because someone maps the real work before recommending anything. That was my job: understand the processes, break them down, find the smallest change that produces real value, then prioritize the rest by return. That's product thinking, and it applies to any business, not just software.

Let's be clear: I love AI. I use it every day, for real work. But you can't throw AI at everything.

AI is no different from other technological revolutions. The only way to get a return out of it is to tie it to a real problem. You map the processes, you map the problem, then you attach a skill or a specialized AI agent to one very specific task. The gains look small. They compound.

Then my cofounders and I launched Quantumize AI, and something unexpected happened. The businesses around our office started asking questions. The café downstairs, Caffé Lounge 1188. The business center that runs our building, the 1188 Union Business Center. They had heard AI could change everything, and they wanted in. But everything aimed at them was either overblown promises or enterprise pricing.

The overblown promises are the real problem. AI gets sold to small businesses as a miracle cure: buy the tool, transform overnight. That's not how it works. One of the best short-term returns on AI is the boring work. The weekly and monthly tasks nobody wants to do. And most businesses can't even get there, because their tools aren't ready. You don't go from zero AI to "AI native" in a few months. But you don't need to wait years either. You need a sequence.

So I started doing for my neighbours what I used to do for publicly traded companies. We mapped the business center's month-end and built a portal that saves them more than 20 hours a month. With two employees, that's real capacity back. I built the café a website they update themselves (caffelounge1188.com), synced with their point of sale: a price change no longer means calling a developer. Then something better happened: they asked me to show them how. Today they produce their own marketing content without going back through their agency, because AI works better as a colleague you work with than as a search engine you query.

They started referring me to the businesses around them. That's when I understood this deserved a real practice.

Here's what I do now, alongside Quantumize AI. I run problem-mapping workshops that surface the manual work burying your teams. I teach AI literacy to teams of two to two hundred people. I help companies without in-house IT choose technology that will still make sense in five years. And I build 90-day roadmaps that start with the "boring" wins and compound from there. In French or in English, whichever your team speaks.

I won't sell you a transformation program. I won't recommend a tool before I understand your work. And when something serious needs building, I have an AI studio behind me.

Start with the boring stuff. It pays.