We Build AI, Now It’s Time to Use It with Greta Cutulenco

Canada is a leader in AI research, but are we applying that technology to boost our own industries? Greta Cutulenco, CEO and co-founder of Acerta Analytics, joins us to discuss how we need to do a better job using AI to transform this country’s manufacturing sector. This episode is being released shortly after International Women’s Day, highlighting a trailblazer shaping our economy. As a female founder in a traditionally male-dominated industry, Greta shares her perspective on leadership, breaking barriers, and building a global tech company. 

Transcript

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From the Toronto Region Board of Trade, this is Toronto Talks. Toronto, let's talk AI. Canada has a chance to lead, and not just in exporting our technology, but in applying it within our own industries to boost our own productivity. And it's up to us as business leaders to accelerate the adoption of technologies like AI that are currently giving those advantages to other nations. That's Greta Kudalenko, CEO and co-founder of Aserta Analytics, addressing the board's annual dinner. Greta spoke passionately about Canada's world-class talent, cutting-edge research and game-changing technologies. But she also highlighted a problem. not using any of these for a full potential. So how do we shift gears and adapt to a world, yes, even the world of manufacturing, that's driven more and more by artificial intelligence? Greta expands on her annual dinner speech in a conversation with the board's VP of Communications and Marketing, Jennifer Vandervoak. Here's that conversation. Let's get our listeners to get to know you a little bit and just tell us a little bit about yourself and your background. Yeah, so myself, I'm a graduate of University of Waterloo, so started my career there. Was in engineering for a while. After finishing, was very much excited about automation, robotics, and all of the promise of autonomous vehicles at the time. It was a big and exciting topic. And so when I graduated, decided to go and start working at Magna. And Magna is one of the largest tier one manufacturers here in Canada, and they were doing a lot of work on a variety of advanced driver assistance systems and electronics within the vehicle. So went there, was working with Magna for a while, got inspired through some of the work with the University of Waterloo and professors were doing their NAI and decided, well, hey, what if we actually take that innovative and entrepreneurship mindset? You know, they teach us at Waterloo to pursue and actually try to start a company. And so in 2017, decided, you know what, let's do this. Let's start a company. And yeah, started that, founded Assurda, and it's been quite a journey ever since. So I've been running the company since then. We're venture-backed, working with major tier one manufacturers globally. And with our team, really are doing quite a lot of great work across the world that I'm happy to talk about. But that's a little bit about myself. No, that's great. I mean, Waterloo is such an esteemed university and academic institution and research institution, especially in just the advanced technology and areas like AI. I think there's really a global reputation for Waterloo graduates. Oh, for sure. Especially when going all through the program, we did a lot of co-op placements. And so that took me also all across the world, the Valley, all across US, even went to China at one point. Really? Yeah. Wow. And that was through your program? Yeah. Wow. So quite adventurous. And they were very supportive of us testing out new things. And so that, to your point, I think Waterloo engineers are really world-renowned as a result and in high demand quite a lot of the time. Well, we talk about it a lot at the board, just the quality and the world-class level of the talent that we grow and nurture here in the Toronto region. It's pretty impressive. So why don't you give us the Asserta Analytics elevator pitch? So not everyone knows exactly what Asserta does. And if you were-- explain it to my grandmother. For sure. So today, whenever, let's say, a manufacturer like Magna is creating components for the car, All of those parts, they're building them across production lines, putting them together, assembling hundreds and thousands of parts to make that final vehicle. So what Asserta does is we actually track data that's being generated all across that process. So if you have certain-- your wheels, what are their dimensions, diameters? If you're putting in and torquing some parts of your engine or transmission, what are those data points that are being collected when that's happening. So we're looking at all of that and helping the manufacturers use that data much faster to make better decisions about the quality of the cars. So any time that they're producing these parts, we're looking at the data, analyzing it, predicting if there's going to be defects, helping them analyze in just a fraction of the time where the problems are coming in, and really then helping the engineers avoid problems, avoid warranties, avoid recalls, and catch them as soon as possible within their production facilities. It's really quite amazing, but I love something that you said too about it's capturing that data that was probably not being utilized properly before or effectively, but also giving it back into the hands of the workers and those who are building these cars and planning and engineering and really bringing all of the the manufacturing facility and the work to life. Yeah. And that's real value back into the hands of the workforce, right? Oh, 100%. Because if you think about it today, really the tools we're still faced with, and especially when I was at Magnet, that was still the reality, you use Excel. Yeah. So basically you're producing all of this, and we're talking about millions and hundreds of thousands of different parts and products that we're creating. And if something goes wrong, you're really trying to go back, find any amount of data you can, load it into Excel, try to figure out what's happening. It's imagine the stress someone must feel when you have a huge recall on your head and you're trying to trace back where is this coming from. And so really, you know, we're leveraging the power of automation, of AI, of all of this technology that's already available in bringing it into the production line to help the engineers, the operators, make their decisions much faster and really not have to run around, you know, trying to figure out and guess where the problems are happening, but help them through the data identify in a matter of seconds and minutes what's happening and what they need to do. Wow. Well, one less Excel spreadsheet. It's going to hurt anyone. Yes. So I love this line that I read from an automotive news article that cited you as one of the key Canadians to watch. And the quote was, "It wasn't that Greta Kudalenko came around to cars as much as cars came around to her." Can you tell me more? Because based on what you studied and where you were academically, to end up in the auto manufacturing, just tell me a little more about that? Yeah, so when I was a big, you know, we were software engineer, so really interested in just how software systems were created. But one of the things really fascinated me was the interaction of software with real things, whether it's robots, whether it's machines. And vehicles or cars, basically, when I started looking at it, they were basically like becoming like big computers on wheels. Yes, there are still mechanical components, but there's more and more electronics. There's more and more software. So it became really exciting to see just how much information can be leveraged to manipulate the vehicles at the time. And that's what initially brought me into the industry. But when I started designing and developing some of these new capabilities, really what struck me is that we were putting very advanced technology into the vehicles, but all of the processes that we as engineers were leveraging to create them were still very archaic. So I would actually have to think through and sit down and think through what is every possible way this new product I'm creating for the vehicle can fail. And I sat back at one point and thought, well, why am I just imagining these? We have the data to start to actually very deterministically figure out how can this vehicle fail, how can this new system fail. And so that's kind of what led me into a slight variation, not just in-car technology, but actually looking at the whole process of making every part and component of the vehicle. And they are computers on wheel. I mean, we've just gone from a 2013 vehicle to a new vehicle, and it is night and day, just the technology, everything is plug-in, everything is automated. It's pretty incredible. We really needed a new car. So when it comes to AI, how do you envision AI and machine learning really transforming that manufacturing floor over the next decade? It sounds like those huge strides have already been taken in that putting that data into the hands. But do you have thoughts about where it's going to go next? Yeah, so we're seeing more and more automation make its way into the factories. Initially, it's been just to try to get some degree of data to look at productivity throughput in production. But now, more and more, we're seeing camera-based systems penetrate the production floor. So inspection and quality checks are being automated more and more. We're seeing more closed-loop control systems, where the machines are self-regulating themselves in some cases. I'm sure you've seen a lot of the Tesla robotics make its way to the news. So humanoid robotics are something a lot of manufacturers are considering next, especially as we deal with shortages in labor, who can actually work within the production lines. So what we're seeing basically is more and more digital systems are making their way into our manufacturing facilities, which in its turn creates even more data from production that we can understand what's happening and how things are being created since everything's becoming more and more digitalized. And on its way, it's creating even more capabilities that we can bring in with AI, where today, we really-- I often think about it as a co-pilot, right? You have a lot of the engineers in production, a lot of operators, and your goal is to help them make decisions faster and leverage that data to do that. And more and more, as we have more automation, we can go towards autopilot-like systems, where we're controlling everything and observing everything continuously and still supporting the engineers. Because at the end of the day, when problems happen in production, it's the engineers who have to figure out what's going on. But we're seeing more and more of that digitalization helps create even more data for them to do that. You also touched upon a shortage of labor, and we hear that across so many sectors, skilled trades especially, but just this reflection of the fact that the trades are, the skills that you need are going to and are already so different than they were, you know, a decade ago, even maybe five years ago. Can you tell me a little bit more about that and that shortage and the implications of that? Yeah, so a lot of the times from the manufacturers welding machines. And if they require welders or welding specialists who are able to operate these machines, there's just a shortage of labor who has been trained in those very hands-on capabilities. The same with many other operators of very advanced and specialized equipment that you see within these manufacturing environments. And outside of that, if you think of typical assembly, it's very repetitive tasks that, yes, you have labor to support it, but more and more, I'd say we as a society are moving towards more interesting work, and so less folks probably want to be doing that. And so what we're seeing manufacturers really try to find is how do they operate this equipment with less reliance on some of that skilled labor, but also how to de-risk their operations in case they cannot find that skilled labor. And especially because today, you know, decades ago, you would have engineers that spent 20, 30 years within the same facility running the same lines. Today we hear it's engineers are there two, three years and they move on. Two, three years. Wow. So it's really changing even the tenure of how long some of these folks are staying within the facilities. So it's really forcing that change of how do we become more robust in the operations of the factory without having to rely on that, you know, you yourself would be there for 30 years to support that one factory. Yes. How do you capture and transition that knowledge from, you know, one generation to the next? worker, I mean, two, three years is not really a generation. So that definitely would change the game. Yeah. And that's where the data comes in play. Because the more data you have about how you've been operating, what has worked in the past, what hasn't worked in the past, how have you dealt with it, the more you can create that knowledge base and really then support so that someone in the job for three months can operate almost as quickly and efficiently as someone who might in the past have needed 10 plus years to get there. So it's creating a really big shift in how quickly we can ramp up and become productive. So you can't talk about auto and the future without talking about EVs and the electric vehicle, the very ambitious transition that we're trying to achieve here across Canada. What are your thoughts on how Asserta is shaping that space, especially when it comes to EVs? Yeah, so of course EV has been a major push, and I think it's great. It's creating a much more sustainable future for us from a vehicle perspective, and the technology is just fantastic as well in some of these vehicles. The one thing we, from our perspective, have seen is that manufacturers have created engine-powered vehicles for a century now. So they know kind of what it takes to build those products. They've done it for a very long time, made it efficient and very kind of repeatable. With these new electric vehicles, a lot of the systems within the car have changed. Everything's powered through the battery. So that means there's much more motors, different ways the transmissions, gearboxes are working. Basically, all of the internals of the vehicle are different. And so what we're seeing is that manufacturers are facing a lot more issues in production because they're, again, they just haven't done this long enough to create good quality products first time around. So within the manufacturing facilities, there's a lot of rework, a lot of scrap, a lot of waste that's created that they have to deal with. And then when they take these vehicles also to the field, to consumers, there's been a lot more recalls, a lot more warranties that are happening. So what our goal at Asserta has been is to help them ramp up production faster and get to those good quality production metrics without having to go through all of that waste in the meantime and do that through data and AI. And I suppose the end result of that would just be a higher level of consumer confidence in EVs. And I think there's a lot of people who think, oh, I like the idea. I'm not sure it's for me yet. I want to wait and see. So that will actually help to accelerate the transition. I think so. And another thing it would help is with the prices of those vehicles. Right. Exactly. Because today they have to somehow offset some of the costs that are being created because of the waste, because of the new issues. And the more we can offset that to make it more efficient for the manufacturers to run that production, I think it just also impacts that final price for the consumers. So I want to bring it back to you a little bit, because I mean, it really is a, it's such a great, incredible success story. And as a female founder and a CEO in a pretty traditionally male-dominated industry. How have your personal experiences shaped the way that you've approached this very innovative sector in business and just leadership in general? That's a great question and something I'm still figuring out continuously. I'd say even though the industry is very traditional, I'd say the technologies that we as a company have been creating are very innovative within that industry. And I think kind of as a female founder, bringing something so different to the industry kind of continues to help us stand out so that, you know, we're not kind of being treated the same as any parts supplier. Right. But we're really being treated as like an innovative technology company. And from that perspective, our team comes from all of our best Canadian universities. Some of the research and capabilities we've built are extremely promising and have been proven in many production facilities. So I really kind of rely back on that experience, knowledge, and competence that we've built up to drive my conversations with many manufacturers. Of course, it still means that we have to prove ourselves, but that's the reality for any small business in a way. But the approach I take with my team is that, again, I think as a female and as a leader, we strive for success like any young company would. but we do it with a set of cultural values that I think are based on, again, collaboration, transparency, things like that that I can, I think, have a more unique perspective on and a unique way to drive within our organization that I think other leaders might be able to. So that innovation, that mindset of innovation, that culture that you're leading and building, in and of itself sort of creates the path and creates the space for you to be successful. I'd say so, yeah. So based on that, is there any specific advice that you would give to emerging tech leaders here in Canada? Is there a secret sauce? Well, one of the things my talk is going to be about is daring to be bold in how we're doing things. I think in Canada, a lot of the times, we kind of restrict ourselves in what we could potentially accomplish. We try to do things locally. where there's kind of that slowness and desire to make steady progress rather than kind of how US is approaching it. Like, go head on and just try to do it. And when I was creating Asserta, really, we wanted to kind of change the status quo. We didn't want to create a local Canadian tech company. We really wanted to create a global tech company where we're leading in our space and are competitive with any other organization in the world who is providing quality-based software, AI-based software within manufacturing. So I'd say my advice would be just, especially in the age of AI and technology, is thinking boldly and thinking globally.- Yeah, be bold, think big. Which is something that we know here in Canada. We have this incredible entrepreneurial ecosystem. We have these great tech innovators, but we don't always think as big as some of our global competitors do. And then there's this sort of ceiling that maybe perhaps we're in some ways creating for ourselves across the whole system. So I'm going to steal something from you right now. So you are, as we said, going to join us as one of our esteemed Toronto Talks speakers at our 2025 annual dinner, which we're really excited about. And thank you. So I'm going to quote you because I got a peek at your remarks and ask you to answer one of your own questions that you're going to be posing to our audience. And what you said is that we have the technology, we have the talent, we have the research. So why aren't we leveraging it to boost our own productivity and make Canada more globally competitive? How do you answer that question? It's a great question. Yeah, it's something I think that I think as leaders in Canada, and especially if we are choosing to lead, We need to be thinking more of, I think there is a healthy skepticism often within Canada, within Canadian businesses in terms of these new technologies. And, you know, it's a healthy skepticism. The approach is often, well, let's have the rest of the world prove it out. And once it's proven out, we'll implement it ourselves in a way. So it is a funny conundrum where we are creating the innovation that then is being exported to the rest of the world to test out, validate. And then we're kind of then implementing it once it's done and we're comfortable with that. And I think, again, if we are to boost our productivity as a country, and I think if we want to have businesses that lead, we need to go beyond just creating the technology and really focusing on the execution so that we're incorporating it in our businesses. And what it means is all of these AI tools we hear today about, from chat GPC to GPT to GitHub and Microsoft co-pilots, leveraging that to boost efficiency of our own workforces as an example and being open-minded to that. Simple things like that, but I think it's really moving away from that skepticism and kind of being the first movers sometimes in terms of actually testing it within our businesses. Even if it doesn't work out, I think the learnings and the iteration will help us be more competitive. We tend to play it a little bit safe. So maybe it's nurturing that appetite for a bit more risk and willingness to test and fail and pilot and learn through that rather than waiting for others to do it. Yeah. Yeah, absolutely. So I can't have this conversation without mentioning tariffs. So tariffs and the U.S. relationship, of course, it's on everyone's mind. And with that, it comes back to that competitiveness on that global stage and that reliance that we've on the US as a partner, as an economic partner. So are there any specific things you think we could be doing right now to help Canada become more competitive and less vulnerable than maybe we had left ourselves? So again, within the automotive industry, and I'm also on the board of APMA, which is the Automotive Parts Manufacturing Association, and that's constantly part of our discussion. How do we, as a supply base within Canada, support each other, create a united front, create even more capability that we can leverage within our own networks, but then also then go and leverage outside of the United States as well. So I think there's a couple of ways. One is really how we have this bi-Canadian mentality now in terms of our wine, some of our produce. I think within the supply base, it's really also how can we support maybe instead of buying certain major providers, software solutions from the U.S. and installing those inside of our own factories, how can we leverage technology that we're building here within Canada and installing that within our own factories to boost our own economy as one example. So it's really creating that economy within the country. But I think outside of that is really diversifying and seeing how can we work with the rest of the world and become competitive for the rest of the world, not just for the United States.- Yeah, it's a big market out there. And I think like we said, with that talent, that innovation, the bold thinking and entrepreneurship that's happening, I'm sure the market is out there and looking for us to play a bigger part. So, Assurda was founded in 2017. And how big are you today? So we're between 40 to 50 people right now operating still from a technology perspective within Canada, but also have offices and people within the United States, since we have a lot of customers there, but also within Europe. We're really deployed today from our product perspective across 12 countries globally and supporting over 350 production lines globally as well. So from that perspective, a small team in Canada really can create leading technology for the world. And then we, of course, create the support systems in key geographical areas to support our customers. Wow. It's really less than 10 years, eight years, really, for that level of global growth. It's really quite phenomenal. Yeah. Thank you. So if there were one key takeaway that you would want to leave with our listeners-- we have the Toronto Region Business Community, our listeners are growing. Is there one key takeaway that you would want to leave with those that are listening? Yeah, I'd say, again, let's be bold. Let's leverage the technologies we're creating to boost our own businesses, and let's lead globally, I think would be my takeaway. I feel like we, again, create our own barriers for that sometimes. But I think there's a lot of capability within Canada that if we band together, create competitive products, we can really lead all across the world in the technology and innovation we bring forward. Excellent. Well, I completely agree. And now just to close, because everyone loves a good recommendation, what are you listening to or reading right now? Lots of things. But in terms of if it's a book, I'd say the one that has been very helpful for me recently is A Hard Thing About Hard Things. It's by Mark Adresen and Ben Horowitz. I think it's a classic and really talks through all of the crazy things that could help with a small business as you're growing. And it's something that I lean on sometimes. But outside of that, I listen to a lot of different podcasts, everything from all-in podcasts, so VC ecosystems to 20 VC. So basically, a lot of information on just what's working across the global in terms of innovation and startups. Where is this ecosystem heading? And what's that next set of innovation that's coming in AI, whether it's agentic AI or some of the robotics capabilities that are coming to market soon.

KAREN FOLEY: 28:19

Excellent. The hard thing about hard things. I have not read that one. Well, Greta, thank you so very much for being with us today and sharing your story and your key takeaways and where you're going. It's really exciting. And we wish you the best of luck. And thank you again. Yeah. Thank you so much for having me. Appreciate it. That's all for this episode. Thanks for listening to Toronto Talks. Make sure you subscribe on Apple Podcasts, Spotify, or wherever you listen. And don't forget to keep talking Toronto. Our voice drives meaningful change.