Fueling Interdisciplinary Innovation With AI: Volvo’s Anders Sjögren
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How does one carmaker use AI to bring together all of the complex systems required to engineer a safe and high-performing vehicle? Find out on today's episode. I'm Anders Sjögren from Volvo Cars, and you're listening to Me, Myself, and AI. Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of analytics at Boston College.

I'm also the AI and business strategy guest editor at MIT Sloan Management Review.

And I'm Shervin Kodubande, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

Today, Sam and I are delighted to be joined by Anders Sjogren, Senior Technical Leader, Data Analytics and AI-enabled Engineering at Volvo Cars. Let's get started. Anders, welcome to the show. Thank you. So tell us a little bit about your role at Volvo Cars. I'm a Senior Technical Leader in Data Analytics and AI in the engineering part. So that's the R&D. So essentially...

What comes in is a wish for a car and what comes out is drawings and code, essentially. In that area, there is, of course, a lot of possibilities for data analytics and AI, both during the development process and also as part of the actual functions in the car. So intelligent functions and personalized functions and so on.

So my purpose here is really to make sure that we get the value that we can through data analytics and AI.

Is it the AI and data analytics that goes into the car, like the sensors and all kinds of intelligent devices in the car that makes driving safer and more interactive and things like that? Or is it also customer acquisition and dealer network and all kinds of data analytics to run a business of making and selling cars? Yes. So, I mean, all of those apply to Volvo cars, but the part that I'm really active in

is more in the part of the making of the car. I think that's a great point because when most people think about cars and artificial intelligence, I think they immediately jump to this idea of

fully self-driving cars. And what you're pointing out is how much other stuff that there is going on. And even in the production of cars that can benefit from artificial intelligence, you know, I think perhaps everyone's frustrated that we don't have fully automated cars now, but there's so much going on behind the scenes that people don't get a chance to see. What are some examples of the ways that you're using artificial intelligence in that production process?

If we take a little bit of a step back, the goal of Volvo Cars is really to give people the freedom to move in a personal, sustainable and safe way. If we start with personal, then it's really critical that we understand you and make you feel special as a customer or as someone in the car.

And there, of course, it has to do with starting with the sensors and then really interpreting those values. That can be cameras, different types of steering input. Another area is definitely sustainability.

AI is being used there to make sure that we have as lightweight parts as possible. So using AI, one can, for example, get mechanical parts with the same strength and those kind of properties.

but with much lower weight and less material that's being used. In the design process you're talking about, right? As engineers consider all the different permutations of parts, yep. In the design process, yes, exactly. That's really the essence of AI, I think, in lots of engineering activities is that we go from

manually deciding first what we want to do, but then actually performing all the different steps. That's the current way of doing it.

While in the AI era, it's much more about deciding and describing what are the aspects I want to reach to, what are the things I want to optimize, and what are maybe the boundary conditions. And then the AI helps you get there. So in the context of the mechanical parts, you might say, these are the attachment points, these are the strength and stiffness properties I want.

give me the part with those properties, but be it as light as possible and also, of course, possible to produce. That could be one area. And also, it's super important for sustainability that we use as little material as possible and also have as low weight as possible. And then, of course, the third point was about safe. And of course, we have autonomous cars.

But also, even before that, there is other types of functionality being used. For example, understanding the driver. Is the driver aware or not? Should we maybe nudge him to take a cup of coffee or something if he seems to be tired?

In the later versions of our cars, we understand if there are some pets or children left in the car, maybe on a hot day, and then preventing them to get hurt in such a situation and so on. I'd say that those are some of the areas where AI can really be a core technology in bringing us towards our purpose. That's really interesting because lots of times organizations tell us they start with a problem they're trying to solve and then find a technology to solve it.

And that makes sense because otherwise you're trying to find a problem to fit a solution, which seems backwards. You mentioned that your cars can sense breathing to assess if a person or a pet is accidentally left in the car. How did Volvo make the decision to focus on that particular problem to solve?

A lot of what we do is really driven by real-world safety, in that we actually see what are the actual causes of people getting injured. If we take the analogy with crashes and that type of safety, we have teams that go out to sites when there has been a crash to really see what actually happened in reality, and not just on certification, that type of crash tests and so on.

And going back to this example, I mean, if we look here, pets and kids do get hurt. Hopefully not in a Volvo car, but that's reality, right? But what you mentioned there is really super interesting because then it also goes the other way around. Like now that we have these sensors, what are the other things?

really valuable functionality that we can provide our customers with through this. Of course, taking privacy into account and so on. This is quite intriguing because Volvo, I remember as a child that my uncle used to say, you want a safe car, you get a Volvo. And it's always been synonymous with safety. And it's really amazing to step back and think about for a company who has put one of its main goals for safe experience,

Now, with the availability of this amount of data and all of this

massive amount of processing, I could imagine there are so many use cases that to Sam's point are being thought about. So that's really, really encouraging. What is sort of the roadmap here look like? I mean, is this a constant sort of innovation ideation approach going on to say, what else could we do in these three pillars of personalization?

personal, safe, and sustainable. What's the process for coming up with these ideas and picking up the good ones and pursuing them or not?

I think that those ideas can either come from the technology side and be really inspired by that, or it could come from the kind of needs side. And often it's when the new technology and the really customer-centric needs, where they meet, and also, of course, where we have the ability and the organization to execute on it. That's really where we have something that's really fruitful.

It's typically a mix of different sources of these innovations and new directions, I would say. And there's a mechanism to create this interdisciplinary inspiration in the company? Yeah, I would say so. I would say that there are both formal mechanisms, but also informal mechanisms.

To be a car company, Volvo Cars is not super big. Of course, there are also smaller ones. But I think it's also an advantage that, I mean, pretty much all the different steps from product strategy, design, the R&D, engineering, and then the later stages. The headquarters is in Gothenburg, within walking distance. The US department is literally...

50 meters from where I'm sitting right now, while 50 meters in the other direction, there is the trash test facilities and the safety center. So what I want to say with that is that it's rather easy to get connections and to create this kind of informal innovation system.

Let's do this. This is where relatively smaller size and co-location really, really helps to have teams that close to each other. It's funny to talk about it being small because it's certainly not a small company. Everything's relative. Everything's relative, I guess.

As we're chatting, I'm thinking about some of the people we've talked to before. And one of the recurring themes that people have mentioned is, oh, this idea of starting with a business problem. You don't have AI and find a place to use it. You start with a business problem and then solve it. But this is kind of a nice mix on that, that it sounds like there's a lot that starts with a business problem. But then interestingly enough,

Once these processes are in place and once these technologies are in place, then there becomes a grassroots innovation to say, all right, how can we use that? And that's an interesting perspective that I think hasn't come through as strongly. Or maybe I'm forgetting something, but it seems like that hasn't come through as strongly. And this is a nice mix of that that maybe works in this size organization, maybe this co-located organization.

Yeah, and not least in the prototyping and ideation stages. But then, of course, before it actually goes into the product, it needs to go through a more thorough review and so on.

There's been quite a few investments and acquisitions of smaller AI startups and firms by Volvo. Tell us a bit about the overall ecosystem of internal and vendor and partner companies that come together to bring to life some of these AI-enabled ideas that you're talking about. Is it internal? Is it external? Is it a mix? How do you think about the ecosystem?

I would definitely say that it's a mix. Some of the things where we need to do it ourselves to get the full understanding or where we really want to be in the forefront. In some other areas, we definitely want to partner with other companies that are strong in those areas. Traditionally speaking, a car is a super complex product.

It has hundreds or thousands of different parts that all need to come together. And of course, it is a space where there is traditionally a lot of suppliers supplying different parts. Lately, we are moving more towards bringing software implementation in-house to increase the speed and agility in the development process.

That seems particularly complicated in the auto manufacturing because, you know, if I think about how cars got started, there were independent systems. There was a braking system and a powertrain system and an air condition and an infotainment system. And all these were separate. And that's kind of nice because we have a certain different standard that we would have for the infotainment system than we would have for the braking system. Or at least I hope that there would be. But what you're pointing out is...

Each one of these may be using sensors that come from a different area and how the whole car has become more complicated independently, but it's also become more complicated cohesively, trying to connect all these parts and have them work together. And that seems, on the one hand, an opportunity for artificial intelligence, but at the same time, a challenge. Yeah, it's 100% true. It's both a really big opportunity

But that also means that's really one of the core challenges. I mean, how to build the cohesive understanding of both the inside and the outside of the car. We speak about the customer digital twin and vehicle digital twin and so on. And in some sense, those aspects can mean a lot of different things. But of course, these different systems that you speak about, they are traditionally in different parts of the company. So that also means that there is a lot of, you know,

cross-functional collaboration that is needed. But we really need to bridge those kind of organizational borders.

I think that that's really one of the key points that is in order to get successful adoption of the analytics and AI, it really means that different parts of the company need to work together to make it happen. Because otherwise, they will just be those kind of silos and will not really have the benefits of it.

You know, it's very true. It's a common theme. And in our work at BCG, we have this rule of thumb, the 10-20-70, where we say like 10% is the data and algorithms and 20% is the technology and the digital platform, but 70% of it is the business integration and implementation and bringing different parts of the organization together. This perhaps is

nowhere more true than a car company where you have, as you said, Sam, seemingly disparate systems that are coming together to create a bigger system. But each one of these units have been perfected individually, and now you want the collective perfection as well. Anders, we talked a lot about Volvo. How did you personally get interested in

Artificial intelligence in data, in technology and analytics. What's your origin story? I think I've always been interested in computers. My father was really an Apple addict, so I kind of grew up with that. I started out as an engineering student, a master's in computer science, and then started off as a research engineer in the medical area at the University Hospital in Gothenburg.

Then found out fairly quickly that in order to really make use of data, conclusions and so on come from data. So I then went into the area of mathematical statistics. So did a PhD in that, then went back to software product development. After some time, I went back to academia for a couple of years, did a postdoc there.

And then, yeah, I was offered a good opportunity at Volvo Cars. So I've essentially been here for seven years now. That's a bit of my history. Quite inspiring. So Anders, we also want to ask you a few rapid-fire questions. And the idea is just to answer it as quickly as you can think of. These are not particularly Volvo questions. What have you been proudest of that you've done with artificial intelligence? The problem is that most of the things I can't speak about. So it's...

That's a great answer, too. You have to wait and see. Okay, well, what worries you about artificial intelligence? The worries? Oh, I think, of course, for one, it's the longer term, the kind of singularity things. But I think a bit closer to now, so to speak, is we definitely see that this super fast progression of large language models and what they can do, and also the kind of systems that

don't just take one prompt and give one answer, but can really produce a series of steps in sequence. And that is a super powerful technology. But a super powerful technology can be used both for good and for bad. That's both something that makes me feel super excited, but also a little bit worried. And what will the world look like in 20 years?

What's your favorite activity that does not involve technology? Motorcycling, but that's obviously using technology, but it's more of a, yeah. Technology involved, everything's involved in technology in some way. Yeah, pretty much. So what's the first career you wanted when you were a kid? What did you want to be when you grew up? Medical doctor.

And that ties with your first career in working in a medical company then? Yeah, I think so. But then I found mathematics and those things to be super exciting. So I went into that area. So what's your greatest wish for artificial intelligence in the future? What do you hope that we can gain from the advent of these technologies? If we say the greatest wish...

I think it is that we find a way to use it in a way for our common good. We need to find a way to integrate it into society. I think that that is really my biggest wish for it.

In a relatively short time, we learned so much about various uses of data, AI and technology in just what it takes to build a car in new ways and all the different ways that AI and tech are helping and serving the people who are driving them. It's been really enlightening, Anders. Thank you for joining. Thanks for having me.

Thanks for listening. On our next episode, Sam and I speak with Shilpa Prasad, entrepreneur in residence at LG Nova. Please join us.

Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders. And if you join us, you can chat with show creators and hosts, ask your own questions, share your insights,

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