Toronto Talks
Toronto Talks is the podcast from the Toronto Region Board of Trade. Each episode features prominent business leaders from across the Toronto region talking about some of the biggest challenges facing our economy - from productivity to congestion and beyond.
Toronto Talks
Making AI Work: Olivier Blais on Turning Technology Into Business Results
AI doesn’t create value until people use it.
Olivier Blais, co-founder and Vice President of AI at Moov AI, shares how businesses can turn artificial intelligence into real results. From grocery forecasting that cuts waste to financial tools that save hours of manual work, he explains how to build trust, boost productivity, and make AI part of everyday operations.
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The World Trade Centre Toronto’s AI Business Catalyst (AIBC) helps small and mid-sized businesses in southern Ontario explore how artificial intelligence can support smarter decision-making and drive growth. Through targeted learning and expert advisory, AIBC helps businesses identify where AI fits, understand available tools, and build a plan to implement them—regardless of industry.
I'm starting to see organizations waking up. They're starting to wake up. I'm very excited to see how the business from here will leverage AI. Canada helped invent artificial intelligence, but we still struggle to put it to work. Olivier Blais, co-founder of Move AI, has built his career turning research into real results. In this episode of Toronto Talks, Olivier digs into why AI adoption still lags, how to build trust around it, and what it takes to make innovation truly useful. Here's Olivier in conversation with the board's senior vice president of communications and marketing, Jennifer van der Vaak. So, founded in 2018, operating out of Montreal, you were named the best startup employer in Canada by Forbes in 2024, and now you're opening a Toronto office. Yes. So, Move AI is moving fast. So, really, it's really exciting because you're also gaining traction and recognition in the tech sector, and you're being placed along international giants like CGI, Deloitte, Accenture, other global leaders. So with that in mind, maybe you can start off by telling our listeners a little bit about Move.ai. Yeah, definitely. And it's intimidating the way it's been said because we don't see us this way. We're still a, I would say, a boutique firm where our goal is to democratize AI. So we've been, so the October 1st, 2018, we started Move.ai. So it's been seven years. Our goal was to, first, we tried to understand the gap between the research because there was a lot of research. There's a lot of research in AI in Toronto. There's a lot of research in Montreal. But nothing was happening in businesses. They were just not adopting any of the research, any of the tools that were built. And we tried to bridge the gap. So we said, you know what, let's democratize AI. Let's make sure this technology is great technology. I'm a big believer it's a technology that is useful. And we have proof and proof and proof of it. But if it's not adopted, then it's useless. So we wanted to change the game. And I think it's a great thing, the fact that we're recognized. It means that our message is working. People, they feel like they could do more. They could be a little bit more organized. They could implement a little bit more technology their day-to-day operation to be a little bit more productive. I love that, democratize AI. So really making it more accessible, making it more practical, and helping people move from something that is, in theory, going to come and take over their business to something that they're utilizing in their business. It's really about practical application, right? Exactly. Even move AI, move, it's exactly what you said. It's to move the perception that this is like a technology that's not accessible into something that's practical and adopted. And do you feel that you're unique in that approach? I know there are some other players that do it, but I don't think they have the same mindset. We're really into helping organizations. Like just the fact that we can help organizations that already have an AI team or a business intelligence team and bring them a little bit more autonomy in this sense. This is not something that is usually done. Usually people, they say, you know what, just move, move, we'll do it. And then you'll be stuck with us for the next decade. Right. Our goal is to say, you know what, if we could build something that works, and then they're autonomous, they have a strong team because they learned along the way. I think we have won, and they have won. And I mean, even Canada has won because we need more productivity. We need more of these examples in Canada. Productivity is the name of the game for the Board of Trade. It's really our mantra right now. How do you improve productivity? How do we catch up with that GDP gap between us and the U.S.? And AI has got a huge opportunity here. So I like that. It's really a teach them how to fish approach. Yes, exactly. I love that. Okay, well, so let's talk about you a little bit then. You're an incredibly well-respected figure in this space and the whole AI ecosystem. You've just been appointed as co-chair of the Government of Canada's Advisory Council on Artificial Intelligence. Tell us about your career journey. Was this tech and intelligence, was this space always a passion for you? How did you get here? By accident, in fact. I studied in marketing initially. And when I started, so I started my career at Bratton Whitney, Canada. It's an aircraft engine manufacturer. And they are collecting a lot of data, but they were not doing a lot of things. They were not using it as much as they could. So I was seeing so many opportunities. And it was inspiring. And I said, you know what? let's try to pivot and let's try to understand what else you can do with data. And this is when I started to self-teaching and self-learning how to build more intelligence using data. And this is how I found, you know, machine learning, artificial intelligence. So we're talking probably 13, 14 years ago. And along the way, I've been lucky enough to find very nice people, knowledgeable people, mentors, and different types of opportunities in retail. So I've been working with L'Oreal, for instance, in the tech world with a company called WorkLeap. And then at WorkLeap, I was leading the AI team and had a lot of colleagues. And all together, we started to talk about this future of AI. And we were trying to implement AI technology in this tech company. And it was hard. So in a tech first, in a digital company, it was very hard to implement artificial intelligence. And we said, you know what? If it's hard for this business, imagine mechanics. Imagine construction businesses. For sure, there's a big opportunity. So a lot of my colleagues left with me, and we decided to fund Move AI. But because I don't have a scientific background, I learned by myself. I made a lot of mistakes, and I realized how simple it is to bullshit. Sorry about that. I know it's the key, but I mean, it's so easy to improve, to claim that you're a data scientist, to claim that you know the technology, and building things that are suboptimal, that are unsafe, that are not working. And this is when I started to focus on, okay, how to make sure what we do are things that are working, are systems that are working. So I started to talk about standardization. So I started to integrate in the ISO standards. So since 2016, I think, there's ISO standards for the standardization of AI. So I started to play in this field. And then I became the chair of the ISO standards. And then when you start in ISO, then you start talking to ICED, and you hear that they're building a bill for the regulation of AI and data, so the AIDA. And then you say, you know what? I could maybe work with you. And then I'm talking to ICED. And then, you know, it's just a matter of networking when you have good ideas. Snowballs, didn't it? Hey, snowballs. And then now we're co-chair of the Advisory Console. And since last week, I'm one of the co-leaders of the AI task force that have been announced by Minister Solomon, working on the adoption of AI across industries and government. Yeah, we were really lucky. We had Minister Solomon here at the board when we announced our AI adoption program with the World Trade Center, which is about helping small and medium-sized businesses adopt practically AI. But it sounds like the fact that you have that background and you didn't actually come at this from a tech perspective, You came at it from a data insights and how do I apply it perspective is probably exactly what your customers appreciate about the way you come in and help them solve business problems. Probably, yeah. So sometimes that something not having that tech edge is actually the edge. But I agree. Like, for instance, you don't like if you're a hammer or if you have a hammer, everything looks like an L. Yeah. I mean, and usually that's how tech people see the world. It's like, oh, AI can just solve every problem. That's not how we should look at it. It's like, oh, there are definitely problems that require AI. And there are other problems, other opportunities that AI will just, you know, they defocus you, it will probably be, it won't work. So having the courage to focus on what is useful and let go things that are not useful, I think it's something that's very important and also focusing on business problems and business opportunities. It's so easy to fall into this, you know, the techie, geeky journey and just trying to implement technology. But that's not the right thing to do. So it's not productivity for the sake of productivity. It's really applying AI to solve business problems, which is probably really welcome. And so that's a good chance for us to maybe make a little bit of a leap now. So the Canadian AI market is projected to reach 28.2 billion in 2028. Like that's significant. And yet we are hearing that Canadian adoption, Canadian organizations are slow on the uptake compared to other jurisdictions around the world. And it's funny because we have this incredible world-class AI ecosystem here in Canada, here in Toronto especially as well. And you would think that would translate to this untapped competitive edge globally. So why do you think it is that we aren't uptaking and adopting and applying AI the way we should, given that we should have an advantage? I have maybe two hypotheses why. One is because there's a big difference between invention and innovation. We're good at inventing new algorithms, new tools. Okay. But we're not particularly good at innovating in businesses because we're afraid of making mistakes. That's OCDE. They did a study and they were comparing different traits and considerations between countries. And Canada is way more afraid than the U.S. of making mistakes. Interesting. We're very risk averse. We're risk averse. So if you have the-- so you have any business, any organization, you have a lot of different opportunities, a lot of different things that you could focus on, a lot of different elements. And what resource are you going to allocate to every one of these projects? then your fear of failing will probably make you deprioritize these big tech, these risky projects. Or sometimes they will invest in a pool of money that's called R&D. Yep. And it just, oh, good. I've tested, checked, so I can say to the board that I've tested some technologies. But the risk of bringing up this technology into your day-to-day operation,"Ah, I don't want to take it." So you park this technology, you park these projects. So that's one of the area. We almost have this cultural comfort with the status quo. It's very Canadian of us. We did a project for a school. It's a system that automatically correct written essays for primary school and high school students. Okay. It works very well. And at the beginning, the teachers, they were afraid of this project. They were against the project. But you know, at the end of the day, when you think about it, teachers, they correct these essays night and weekend. This is painful. My mother-in-law, she's a teacher. I'm married to a teacher, so I hear you. So, like, Sunday morning, they need to open the laptop, look at the different copies, and do this extra work. Now, this is a problem for them, but this is a problem they know, and they can anticipate, and they already live with it. The unknown is seen as something that's maybe bigger. And I'm starting to talk a little bit more and more about this anxiety of AI. AI is generating a lot of anxiety for people. do you really want to risk having this, you know, I don't know, this AI solution potentially challenging you at your job? This is something people are not comfortable with. So if you put this and the risk of failing together, it's not very compelling for AI and for companies like us. And this is why we need to double down. We need to educate a little bit more. It's all about education. So let's talk a little bit about unlocking ROI and competitive edge. So obviously you've done really well. You've got some clients and customers that are doing some great things. Are you able to share any examples of Canadian companies that have successfully turned some of these AI experiments and strategies into tangible ROI? What can we learn from what they're doing? Yeah, no, definitely. So one of the companies, it's Metro. It's a grocery store chain. So it operates in Quebec and Ontario and in other provinces as well. So we have worked with them. So one of the big problems they had is the fresh produce. You know, food, vegetables, meat. It's a short window. You can sell, I don't know, like an apple for a month because then it goes back. So that's a big challenge because people, they want more and more fresh produce. But the fresh produce, if you don't know exactly how much of every fruit, vegetable type of meat and all these different skews, what's going to happen is either you won't order enough of their product because you don't want to lose it or you lose some of these you'll waste you lose you'll waste products so they were looking around they were not able to find any traditional solution to forecast so what we did is we work with them on predicting the demand for fresh produce based on weather based on geography, based on seasonality. So for instance, if you're in May and it's supposed to be sunny outside, you'll sell more barbecue meats. It's just clear. But if you don't leverage this data, you won't know it, so you won't be prepared. Right. And also we've been able to analyze the effect of promotions. Promotion is a a big driver for sales, but also it prevents other products from selling as much. So if you have a big promotion on some kind of food, then you won't sell the other ones, because people will pick apple instead of oranges. Yeah, that makes sense. Yeah, of course. I wouldn't think of that. Everything makes sense. But the thing is, how to integrate all of this In a forecasting solution, it's not clear. Usually, it was up to the store managers to do it. The thing is, store managers, it used to be, you know, these people with a lot of experience, they have 20, 30 years of experience. It's not that anymore. People, they retire. The new generation, they stay for a year, three years, three years, and then they're gone. So you lose all of this, all of the information. So then they decided to leverage AI. So we build an AI solution integrating all of these different factors, these different signals. And we've been able to get great results, ROI in a few months. So at first, we did it for 10 stores. And they realized very quickly it was a winner. Then it's been deployed all over Canada. And they have deployed it for a lot of other departments in their stores. So that's a great solution that helps reduce waste while increasing revenues. I love that example. It's such a specific situation where you're optimizing inventory. You're solving a specific problem. You're deploying the AI tool to show how it can succeed. And then you're able to see the results, which de-risks, which reduces anxiety, which actually then addresses all of those sort of base problems that you talked about. So that's an example, another one that we've done more recently. I am not allowed to talk about the client yet, but it's a big client in wealth management. And so they had a problem. So they have thousands of advisors, advisors, you know, in like financial services. It's very hard to find. There's so much documentation, so many forms. So the processes, they're crazy. Like everything's complex. It's a complex industry and companies are complex or big. So they needed help to be a little bit more efficient, spend a little bit more time with their customers, which is good. Yeah, more hands-on time. Exactly. So we built for them a tool. So it's agentic. I don't want to necessarily go into this, the different type of technologies. But at the end of the day, now we have the capability of, based on natural language, we are able to do so many different things. So based on simple requests by advisor, we were able to search their customer database, find the different customers who might be interesting, search in different forms, being able to explain how to fill out some forms, what are the different news for these forms, and even filled out some of the forms. And what's interesting with Agentec is that you can also combine these different operations. So I'll give you an example. An advisor could say, you know what? Can you pre-populate all the required forms for all my clients who are retiring in 2025? And then, so what the platform does, it first searches for all the clients who are retiring. Then it searches for all the forms that need to be filled out. and then it fills out these forms with the contact information from the initial search. It's a huge time saving. It's a huge time savings. It also saves. And we were surprised to see how supportive the advisor would be because we thought first, you know what, they're experts in their field, So they might not be open to use these technologies. So you've given some really great practical advice for how organizations can start thinking about de-risking, how they can start considering what are the problems that I'm trying to solve. And all of this very pragmatic thinking and application is probably why you've grown as fast as you have with Move.ai. And now you're going to be looking at opening offices in Toronto. Tell me a bit about that move. In fact, and we're very excited about it. We're excited to have you. In fact, we have sold AI to Publicis Group in April, and it's great since we joined. So now we're part of the Publicis ecosystem, and we're very excited to build the different AI solution for Publicis. Our goal is to become the North American hub for the AI delivery and for also all the different publicist clients. So it's great. They have great clients. They have a great network of agencies. And we're fortunate to be one of these agencies that will support the implementation of AI for all the different clients of the group. So let's close with this. So clearly, AI is here. It's not going away. Neither are you. And this is all a great thing. So what are you most excited about if you envision how we could be using AI five years from now in everyday business? It's not going to be a very creative answer, but I think I'm starting to see organizations waking up. They're starting to wake up. I'm very excited to see how the business from here will leverage AI. And you'll see also in the AI industry, because right now we're talking about all the other industries or all the companies in other industries leveraging AI. But because they don't leverage AI that much, there's a small AI industry because we don't have a lot of clients regionally to make a big AI industry. We heard about Cohere, but what I'll see, I'll see more of these companies, more of these startups having some great success in Canada so you don't have to sell systems elsewhere to be successful. So we'll build more Canadian solutions with Canadian talent. We have a lot of talent. We need to use it to build the solution from here. And we'll eat our own dog food. So we'll be able to use these capabilities to be better, we'll be more competitive. I think it will be a great solution also for all the retirements that we're currently seeing. You know, the previous generation retiring. It leaves big gaps in organizations. And sometimes we're so focused on not wanting to lose our job, we forget that a lot of people are just leaving. And there's nobody to replace them. So I think this is how we'll be able to stay competitive on the long term and keep the knowledge internally, despite the fact that a lot of people will leave organizations. So yeah, I'm very excited about this. I think the conversation will just change. I think it will evolve everything. I think we'll also be able to have solutions that are a little bit more business to business because now business, we're not responding. So people, organizations, they just started to go directly to the consumer with JetGPT and it's a huge success. Why? Because people, they're buying more and they're adopting more AI than businesses, which is not normal. So we'll then, I think we'll be able to get a little bit more support by the business. We'll build business first AI solutions. That's exciting. So we're waking up and there's a whole new world out there. And thank you so much for coming in and talking to us today. And we hope we'll see more of you. Thank you very much. Thank you very much. Take care. Bye-bye. If you're a business leader in the Toronto region looking to put AI to work, the AI Business Catalyst program is a practical next step. Visit bot.com slash AI Business Catalyst to take the readiness assessment and start turning potential into performance. We'll include a link in the show notes as well. Thanks for listening to this episode of Toronto Talks, the voice of Toronto's business community.