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Better Together: Mattias Ulbrich on Combining Coffee, Business, and Technology at Porsche

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Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

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Mattias Ulbrich has always been interested in new technology. As CIO of Porsche and CEO of Porsche Digital, he runs a subsidiary focused on the “new stuff” — new ideas, new models, and new opportunities. That means implementing innovations in AI, cloud technology, and blockchain in local markets around the world, and instilling a culture of continuous learning within his own cross-functional workforce.

In this episode, Mattias shares examples of how AI is accelerating innovation at Porsche — by enhancing product design and the driving experience, improving production and sustainability efforts, and better managing the global supply chain. He has also connected some unlikely dots from other spaces — for example, by using the sound of an espresso machine to inform car component design.

Read more about our show and follow along with the series.

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Transcript

Sam Ransbotham: What does coffee have to do with artificial intelligence?

Shervin Khodabandeh: In this episode, Mattias Ulbrich will talk to us about how Porsche is driving a culture of innovation and digital transformation with AI across all functions in the company.

Sam Ransbotham: Welcome to Me, Myself and AI, a podcast on artificial intelligence and business. Each week, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of information systems at Boston College. And I’m also the guest editor for the AI in Business Strategy Big Ideas program at MIT Sloan Management Review.

Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG, and I co-lead BCG’s AI practice in North America. And together, BCG and MIT SMR have been researching AI for four years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and deploy and scale AI capabilities and really transform the way organizations operate.

Hi, Mattias. Welcome to the podcast. We’re really excited that you could join us today. How are you?

Mattias Ulbrich: It’s a pleasure for me to be with you. Thank you for your time.

Sam Ransbotham: Can you tell us a little bit about yourself and your role at Porsche?

Mattias Ulbrich: My name is Mattias, and I’m the CIO of Porsche and, at the same time, the CEO of Porsche Digital, a subsidiary of Porsche where we really focus on the “new stuff” — for example, for AI, for cloud technology, for … blockchain, and where we are, let’s say, more international. And that means we are in the United States, like Atlanta and Silicon Valley, but we [are] as well in China, for example; we are in Tel Aviv, and, of course, in Germany. So this is a company where we really look for digital projects that we can really support within the Porsche organization, but at the same time, we are looking at new ideas in the market and looking for new business ideas and business models within this organization.

Sam Ransbotham: Do you know that I’m actually from Atlanta?

Mattias Ulbrich: Oh, I didn’t know that. No.

Sam Ransbotham: Yes.

Mattias Ulbrich: But you know that the Porsche headquarters is there, right? That’s the reason why we’re there — because we have a very close collaboration with the headquarters in the United States, in Atlanta, where we’re really looking to find the best solution for the U.S. market.

Sam Ransbotham: I liked the phrase “new stuff.” Tell us a little bit about how you yourself got interested in new stuff. You mentioned a fair number of new technologies as new stuff.

Mattias Ulbrich: I’m personally very interested in new technology. Well, I started electronics when I started my career; I worked with Hewlett-Packard — that was very innovative. At that time, it was a very innovative organization. So I really like to understand, what are the advantages of new technology and what can it bring to the business to bring, really, technology and business together? This is what drives me and where I’m interested in finding the best solutions.

Sam Ransbotham: So tell us how that background started, how you got from Hewlett-Packard — or from electronics — to Porsche.

Mattias Ulbrich: I started my career at Hewlett-Packard a long time ago, in the ’90s. Then, I had the opportunity to join Audi, at the time in Neckarsulm. This is a plant where the A8, for example, is built, and there was a lot of new stuff between the car, the IT, and the production side. So it was very interesting to see all the electronics and the software in the car and how you can handle that in the production process. And so I learned a lot about [the] car business, IT business, and production for that at that time. And after that, I had the opportunity to move to SEAT, that is as well part of the Volkswagen Group, as Audi is. And I had a great time in Barcelona, where I was the CIO of SEAT at that time.

After that, I had six years in Wolfsburg, the headquarters of Volkswagen Group. I was responsible for the IT services worldwide, and after that, I became the CIO of Audi. I took this position for six years, and then I decided to get, let’s say, a wrap-up of the new technology. So I moved to MIT in St. Gallen here in Switzerland to learn more about the term of, let’s say, transformation, digital transformation, about new technology. And after that, I came to my role here at Porsche to drive the transformation of Porsche and … getting all the stuff as well from this Porsche digital organization into the Porsche organization.

Shervin Khodabandeh: You mentioned your interest in AI, you mentioned your interest in technology and innovation. … Why cars?

Mattias Ulbrich: Yeah, that’s a good question, because I thought when I started with Hewlett-Packard, it was really interesting, and I saw a lot of different branches and companies starting from trucks and going to ships, for example, as well for other things like chemical stuff. And then I had those projects with Audi, and it was so interesting to see what technology really did to the car. So it was a time at the end of the ’90s, where there were so many different electronic devices in the car. And it was a huge challenge to manage that, because in the beginning, they didn’t communicate well with each other. And so there was a huge technology challenge. And this was really interesting for me to understand this challenge, and bringing together production knowledge, IT knowledge, and the car technology together. And so that was the moment when I changed to the automotive company.

Sam Ransbotham: What kinds of new stuff are you interested in right now, particularly with Porsche?

Mattias Ulbrich: The main focus is really AI, just to be honest. Of course, we are looking [at] how we can organize as well with, let’s say, agile working — what can we do as well in our development centers for the car business? And of course, software in the car is a very important thing as well, because this is changing the world of driving. But what we are focusing [on] as well is to look [at] how we can use AI, for example, to improve our internal processes — how can we use AI to get a better contact and a better understanding of our customers? That is very important for us as well. And of course, yeah, to look [at] what can we do in our product and really increase as well our product portfolio to have digital products that we can deliver for B2C markets. We started, for example, with some ideas in the direction of supporting sustainability as well, like Porsche Impact, where we developed a solution [for] how a customer can compensate, for example, his CO2 footprint that he would drive with the car, and how we can really increase our product portfolio for our customers and make driving more attractive.

Sam Ransbotham: So, is there some particular example of how AI has made a big difference in a way that other technologies would not have that you’re particularly fired up about?

Mattias Ulbrich: Yeah, what we really found out, for example, in production [is] that we can really use AI to predict, of course, a lot of things. For example, in the order management systems, how we can predict for orders to deliver markets, to have the right cars in the dealerships. That is not easy, for example, in China, so sometimes it’s really a challenge to understand what kind of cars they would buy in three months. So it’s very important to get back feedback from the market and understand what is the most important driver for that, but as well to understand what can we do in the production process, for example.

Shervin Khodabandeh: These are some very, very great examples of [Porsche’s] use of AI. You mentioned a whole bunch on the marketing side, a whole bunch on the supply side, and supply chain, and also production. Are there also some examples where roles or functions that are typically more engineer-driven, like design, or some of the trade-offs in terms of performance, etc., are aided by [the] use of AI? Because we see that in other industries, which is sort of a new thing, because these are typically things that engineers have a strong sort of discipline and playbook for. Is that something you can also comment on?

Mattias Ulbrich: It’s very interesting that you mentioned design, for example. So … I was in the design studio here in Weissach two weeks ago, and we had a long discussion on how they use AI to really support their design ideas. And they have a program that they’re using for new cars, for example; they can use different models and reshape the digital model before they go into the first physical model. And they are using AI to support the designer, for example, but as well, we have — of course, in other areas of engineering as well — support tools that really use AI to improve technical development.

Shervin Khodabandeh: When you guys implement these AI tools in these areas where a strong collaboration between man and machine is needed, do you find there is a fair amount of, sort of, culture change needed, or reskilling or change management needed for the human to become friends with AI?

Mattias Ulbrich: I think the collaboration between the machine — the IT machine or the AI machine — and the person is very important, and you need to understand how you can use that. And it’s totally different if you go to production, for example, or to engineering, because sometimes it’s a supporting tool and it supports the worker, for example, in the production line. In the other area, in engineering, they are like friends, like colleagues that work on the same issue. And it’s very important to understand that AI is further developed by the business area. So it’s not an IT task anymore; it’s really a collaboration between the AI expert but, as well, the business expert and the machine, and you have to manage that. So this is a huge difference [from] normal IT projects, so we have a totally different approach to go together on such pilot projects. We have, for example, an acoustic anomaly assistant that works really with an engineer to understand anomalies that we have, for example, in the door and how he can understand what is the reason for the noise. And this support, it’s only possible if you worked really together for months to understand all the noises and then you can adapt it to a different part of the car. But it takes a while and you learn — you have a learning curve like this, and it’s only possible if you have a very good collaboration.

Sam Ransbotham: When you implemented this system, what were people’s reactions? Did they say, “Oh my gosh, this is taking my job”? Did they say, “Oh my gosh, this is a great way to do this”? How do people react that day that you turn the system on?

Mattias Ulbrich: Yeah. Like always, people react totally different. Of course, we have a lot of very technical-oriented guys that see the opportunity and see really how this can help them in doing their tasks better and doing their work better.

Sam Ransbotham: You mentioned a little bit about the fears that some people have about when you say AI in general, and they think the machine will rule, versus machine vision to help understand the right label or the acoustic anomaly system on the doors. How did you come up with these sorts of — like the acoustic anomaly system, for example? I wouldn’t have thought of listening to a door. I would’ve thought of looking at a door. How did you get someone to think about listening to a door? My doors haven’t said anything as far as I know, but maybe I’m not listening.

Mattias Ulbrich: Listening to a door is already a task that has been done in Porsche [and] at Volkswagen, for example, and the whole industry for years, but the idea to use AI to really improve that noise that a door makes, that came really from drinking coffee. Now, it was in the lab that we have in Berlin. An AI specialist wrote an application that could listen to the coffee machine and know what kind of coffee is done. So he knows this was a cappuccino, this was an espresso, for example. And then there was the idea, what can we do with that — with this acoustic system? And then we had some discussion with our R&D people and they said, “Well, we are listening to our door all day, but we can’t listen at night, for example, because we are not there. And sometimes there is a noise and we’re coming back in the morning.” So together we started this project, then, and it was the beginning of a success story, because right now we are doing this in several places in R&D, and it was a good example [of] how it can work.

Shervin Khodabandeh: Yes, for sure. And the step to really educate — I’m sure it really, really helps too, because, as you said, lack of information could create a lot of anxiety. And so how widespread are these programs? Is it tens of people, or hundreds of people, or thousands of people?

Sam Ransbotham: Yeah, how many people in the organization need to know about these technologies? Everyone?

Mattias Ulbrich: I think, yeah. I started to train really every IT person in my organization so that we have a broader view on that and a common view on that as well. But of course the AI program is, let’s say it’s 200 [people] that really work on that in the Porsche organization and that are connected of course, to others, but you need a very small core team that is driving this change and bringing the best use cases in place and [talking] about that. And so we have a good mixture of businesspeople that drive that, and some people came as well to the IT organization that was new. They moved from engineering to IT to drive this as a platform. We have an AI platform, and they had [a lot of] fun to drive this program together with the IT people. And so we have a good mixture of business and IT people that are driving this program.

Shervin Khodabandeh: That’s great. Thanks for clarifying that. And then another question I had is, you mentioned 200 people — how many of these people are new people or reskilled people? Because of course some of this training too is to educate, but some of it could also be to really build new skills. Do you find that you have to bring talent that may not exist — a massive amount of that talent — from outside, or do you find that it’s more about reskilling and training the existing workforce?

Mattias Ulbrich: It’s different in different business areas. For example, in engineering, you have a lot of good, trained people that understand technology and that really understand as well the possibilities of AI. In finance, for example, you have [a] totally different approach, and you have to bring more external people to really drive those ideas, because there’s no technology knowledge on the business side. So if I look at my organization, we have maybe 40% new people and 60% that were already in the organization, but some are, of course, really focused in the path as well to new technologies. So they are very adaptive and they would like to learn. So for us, it’s a very good mixture, because you need really to understand the business process to find the best solutions. That is very important as well.

Shervin Khodabandeh: It’s all about continuous and ongoing learning at all levels, right? For people, for the algorithms, for everybody.

Mattias Ulbrich: Yeah. That’s right. And creating a culture that is really open for that. That is very important as well.

Sam Ransbotham: Mm-hmm. What are you excited about? I mean, you’ve mentioned new stuff, the new stuff from the beginnings of [your] Hewlett-Packard days to new stuff now; what’s the next new stuff that you’re excited about?

Mattias Ulbrich: The thing I’m [most] excited about is to work together with great people, to be very honest. I like technology, of course, but to work with people, it’s the best thing that you can do, and really to learn together as well. So we are just in the beginning of AI, so there’s a long way to go, because we have done some really great projects, but we are still having a lot of things to do with AI. And I believe really that AI is the biggest challenge that we are facing right now, still, and all the other things are not such a game changer like AI is right now in the technology field.

I believe that AI, and as well other technologies, but AI in this very special situation can really drive the change that we have right now in society, all the challenges that we have in society — for example, sustainability. … And I believe that digitalization can really help in those very important fields. And if you look, Porsche is doing a lot to get better cars on the road like, the Taycan, for example — to have better cars that really help as well the environment, and doing a lot in terms of sustainability. And I think those things are driving me to really create a better world, and looking what can technology really do for that. And of course, supporting people to really focus on the most important things in life, but as well, helping the world to get a little bit better. And I think this is a great motivation for me, and this is very helpful for me.

Sam Ransbotham: Mattias, thank you for joining us today. We appreciate all the time you spent and the interesting conversation.

Mattias Ulbrich: It was great [to be] with you. Thank you for your time.

Shervin Khodabandeh: Thank you very much, yes.

Sam Ransbotham: That was great. Mattias was really interesting.

Shervin Khodabandeh: Mattias had hit a lot of very key points that we see in our work, that we see in our in the survey this year; we saw it last year. … He made the point around culture of innovation and creation of new ideas. He made the point around, it’s not tech, it’s about business and technology coming together. He also underscored the importance of — just by being who he is and the role he has — the importance of this being a senior executive role. And then the other thing I really liked is the point he made about [the] different roles of AI. We say that in our report that there are at least five different modes, and then he talked about it. He talked about vision and automation of that, then he talked about design, where AI gives some ideas to the engineer or to the designer. He talked about AI as [a] generator of insights for [a] supply chain or for the marketer, or for the product designer. I really feel like Porsche is getting it. It’s really, really, really impressive.

Sam Ransbotham: Mattias had a nice combination of excitement about technology, but also a methodological approach toward it, which I thought was a nice combination. He both exuded excitement about technology, but at the same time, a very realistic view about how to implement it.

Shervin Khodabandeh: The other thing I was quite impressed [with] is the extent of culture change that Porsche is undertaking that he underscored with training, with education, with reskilling, with getting a base-level education for everybody in his organization — and it’s hundreds of people, not a handful of people.

Sam Ransbotham: Right. And they have to, because of the number of applications that they’re — I mean, he listed off dozens of places that they’re using it, and they were just all across the organization.

Shervin Khodabandeh: Yeah.

Sam Ransbotham: When he was talking about introducing it, he said, “Oh yeah, we don’t talk about bringing in AI as a big, scary, general thing that is abstract, and who knows what baggage it’s bringing with it?” He said, “We’re bringing in this application.” He was very specific and very narrow, and then people can get their mind around what that means versus bringing in a lot of baggage from every sci-fi movie that anyone’s ever seen.

Shervin Khodabandeh: Yeah. The other thing I really liked is the coffee example. It was a great question you asked, you know — why sound? And I didn’t think he’s going to go to coffee. I thought there [were] a million other places he could go, but that’s a great example. And I think we also heard that from some of our other interviewees that a success story or an anecdote or an example somewhere else translates into a completely new idea in a completely different field.

Sam Ransbotham: Yeah. That’s a very human role of, still, that creativity — of recognizing that this situation … isn’t exactly where we saw that technology before, but it’s a great place we can use the technology now. That’s still a very decidedly human-creative part, but it still requires expertise. And he was very clear about that — that you had to have that expertise as a baseline, and it sounds like they’re working very hard to make sure that most people in the organization have some expertise in there. You’re right; I asked him about new technologies he’s excited about, and he didn’t bite on rattling off the latest, greatest new AI thing. He’s all about getting the people excited about it. That was not where I thought he would go there.

Shervin Khodabandeh: Yes, that’s right.

Sam Ransbotham: We really enjoyed speaking with Mattias today. He really combined an enthusiasm for technology with an enthusiasm for business, and that was a fun combination.

Shervin Khodabandeh: Join us next time when we talk with Arti Zeighami, head of AI at H&M.

Allison Ryder: Thanks for listening to Me, Myself, and AI. If you’re enjoying the show, take a minute to write us a review. If you send us a screenshot, we’ll send you a collection of MIT SMR’s best articles on artificial intelligence, free for a limited time. Send your review screenshot to smrfeedback@mit.edu.

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