How is artificial intelligence reshaping the skills employers look for in the Canadian job market? That question sits at the heart of the white paper produced by Le Wagon Canada and Rhum.hr, and of the panel discussion held in Montreal to dig deeper into the findings with people working in the field.
Moderated by Pierre-Luc Labbée (Rhum.hr), the panel brought together Philippe Trépanier (CTO, Osedea), Rémi Dion (co-founder and partner, Explor.ai) and Marie-Gabrielle Ayoub (co-founder and general director, Le Wagon Canada), who shared what they’re seeing as work, hiring and skills evolve in the age of AI.
And the conversation quickly moved beyond technical skills alone.
Philippe illustrates the shift with how interviews have changed at Osedea. “Ten years ago, that made a lot of sense. Now, look, you can generate code. If the code runs, that’s not what interests me.” What interests him more: does the person understand the problem? Can they come up with a solution and iterate on it quickly?
As the conversation unfolded, a pattern emerged: as AI makes certain execution-heavy tasks easier, reasoning, learning, communication and adaptability emerged as central themes.
For Philippe, this shift comes down to one word: reasoning.
“In a world where it’s easier to execute, thinking needs to take up more space in people’s work.”
In practice, this means interviews focus less on someone’s ability to carry out a technical task and more on how they approach a problem: how they break it down, how they choose their tools and how they adapt their solution.
“To do well in an interview, you have to show that you can solve problems quickly. That’s really what it comes down to,” Philippe sums up.
Rémi builds on this with an important distinction: autonomy is not independence.
“Independence is ‘I got this.’ That’s a red flag.” An autonomous person, he explains, is someone who can analyze a problem, recognize their own limits and go find the tools or the people they need.
One question he likes to ask in interviews shows this approach well: how many leaves are on the tree outside? Nobody actually knows the answer.
“What interests me isn’t the answer, it’s how you’d go about figuring it out. Show me how you think.”
This capacity to reason goes hand in hand with another skill the panel spent a lot of time on: learning how to learn.
“That’s even more true now. Knowledge keeps changing, so you have to expect that skills will keep changing too,” Rémi explains.
For him, that also means a shift in organizational culture. After years of talking about an “innovation culture,” he argues companies should put more emphasis on a learning culture: what did you learn this week, this month, this year?
That shift extends to education as well.
“The big challenge has been updating the way we teach to integrate AI into the curriculum,” Marie-Gabrielle explains. At Le Wagon, the AI Software program no longer stops at web development: students also learn to integrate AI into their applications and use it as part of their work as developers.
But learning continuously doesn’t mean trying to master every new tool that comes out.
Toward the end of the discussion, Marie-Gabrielle flags the FOMO that today’s flood of AI tools can create: the constant feeling of falling behind because a new tool just launched.
The real question to ask, she says, is whether that tool will actually be useful for your work and help you reach your goals.
One of the paradoxes to come out of the discussion: a panel about AI ended up talking a lot about human skills.
Curiosity, communication, problem-solving, the ability to work as a team and to adapt: Marie-Gabrielle points out that these are also among the skills employers say they’re looking for.
Technical skill still matters, but it’s part of a bigger picture.
“If you know how to use a tool or how to code, but you don’t know how to communicate or work well with a team, are you as strong a candidate as someone who’s built up all of that expertise?” she asks.
Rémi also stresses the importance of communication. Being able to articulate an idea, to convince a colleague, a client or an investor, all comes down to structuring your thinking clearly.
Pierre-Luc picks up on this at the close of the panel: despite an event built around artificial intelligence, the conversation ended up touching very little on code or on deeply technical subjects.
“Everything we talked about was curiosity, a hunger to learn, adaptability, non-linear paths […] the ability to question yourself, humility.”
This mix of technical and human skills also highlights the value of non-linear career paths.
Marie-Gabrielle sees it in the people who come through Le Wagon: musicians who become developers, analysts who move into data science, and more broadly, people who arrive in the classroom after a first career elsewhere.
They’ve already worked with clients, colleagues or on projects before picking up new technical skills.
“They sometimes bring two or three layers of experience with them, on top of the technical skills they recently developed at Le Wagon.”
For Rémi, that diversity of backgrounds is valuable in itself, because it exposes teams to different ways of approaching new situations.
This connects to another concern raised during the discussion: people who already have years of experience worrying about becoming obsolete because of AI.
Philippe takes the opposite view.
“People with experience are almost at an advantage with AI. You’ve got a maturity that comes with it, and that’s not something that gets automated.”
According to him, someone who’s already built a career can draw on that experience even when they switch fields entirely. Tools and methods can be learned, but that professional maturity brings a different perspective.
The panelists’ optimism didn’t stop the conversation from touching on the risks of this shift.
During the Q&A, several attendees asked the panelists about job automation and about people who might struggle more to adapt to a world where learning, reasoning and the ability to evolve matter more and more.
Rémi acknowledges the limit:
“People who are vulnerable will keep being vulnerable in this world.”
He sums up his view this way: “AI doesn’t create problems, it makes existing ones worse.”
The discussion also pushed back on the idea that automating a task simply means replacing the person who does it. Several panelists pointed to the real complexity of automation, the new needs it can create, and the importance of staying involved in the process.
The panel doesn’t offer a simple answer to what AI means for jobs. What it does offer is a clear picture of the skills the panelists see as mattering most for navigating this environment.
Across the discussion, four themes kept coming back: reasoning through a problem, continuing to learn, knowing how to communicate, and being able to adapt.
Tools will keep evolving. And as Marie-Gabrielle points out, that doesn’t mean learning all of them: it also means knowing which ones are actually useful for your work and your goals.
That might be the best way to sum up this conversation about the future of work: the real challenge isn’t just knowing how to use AI, it’s knowing how to keep evolving alongside the technologies reshaping your field.
Skills keep changing, so do our programs. See how Le Wagon’s bootcamps bring together AI, problem-solving and hands-on learning.
Discover Le Wagon Canada’s programs →
Quotes have been translated from French and lightly edited for clarity.
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