WEBVTT
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(placid music)
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What does coffee
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have to do with artificial intelligence?
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In this episode, Mattias Ulbrich will talk to us
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about how Porsche is driving a cultural
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innovation and digital transformation
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with AI across all functions in the company.
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Welcome to Me, Myself, and AI,
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a podcast on artificial intelligence in business.
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Each week, we introduce you to someone innovating with AI.
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I'm Sam Ransbotham,
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professor of information systems at Boston College,
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and I'm also the guest editor
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for the AI and Business Strategy Big Idea Program
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at MIT Sloan Management Review.
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And I'm Shervin Khodabandeh,
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senior partner with BCG,
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and I co-lead BCG's AI practice in North America.
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And together, BCG and MIT SMR have been researching AI
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for four years,
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interviewing hundreds of practitioners,
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and surveying thousands of companies
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on what it takes to build and deploy
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and scale AI capabilities
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and really transform the way organizations operate.
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Hi, Mattias, welcome to the podcast.
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We're really excited that you could join us today.
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How are you?
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It's a pleasure for me to be with you.
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Thank you for your time.
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Can you tell us a little bit about yourself
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and your role at Porsche?
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My name is Mattias, and I'm the CIO of Porsche,
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and at the same time, the CEO of Porsche Digital,
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a subsidiary of Porsche
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where we really focus on the new stuff.
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For example, for AI, for cloud technology,
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for other like blockchain,
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and where we are, let's say, more international
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and that means we are in the United States,
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like Atlanta and Silicon Valley,
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but we as well in China, for example,
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we are in Tel Aviv, and of course in Germany.
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So, this is a company where we really look
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for digital projects that we can really support
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within the Porsche organization.
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But at the same time,
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we are looking at new ideas in the market
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and looking for new business ideas
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and business models within this organization.
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You know that I'm actually from Atlanta?
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Oh, I didn't know that, no.
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Yes.
But you know
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that the Porsche headquarters's there, right?
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That's the reason why we're there,
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because we have a very close collaboration
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with their headquarters in the United States and Atlanta,
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where we're really looking to find the best solution
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for the US market.
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I like the phrase "new stuff."
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Tell us a little bit about how you yourself
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got interested in "new stuff."
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You mentioned a fair number of new technologies
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as "new stuff."
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Yeah, I'm personally very interested
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in new technology.
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Well, I studied electronics when I started my career.
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I worked with Hewlett Packard.
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That was a very innovative, at that time,
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it was very innovative organization.
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So, I really like to understand
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what are the advantages of new technology,
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and what can it bring to the business
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to bring really technology and business together.
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This is what drives me and where I'm interested
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in finding the best solutions.
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So, tell us how that background started.
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Like how you got from Hewlett Packard
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or from electronics to Porsche.
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I started my career
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at Hewlett Packard a long time ago in the 90s.
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Then I had the opportunity to join Audi at the time
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in Neckarsulm, this is a plant
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where the A8, for example, is built,
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and there was a lot of new stuff between the car,
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the IT, and the production side.
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So, it was very interesting to see all the electronics
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and the software in the car
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and how you can handle that in the production process.
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And so, I learned a lot about car business, IT business,
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and production at that time.
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And after that, I had the opportunity to move to SEAT.
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That is, as well, part of the Volkswagen group, as Audi is.
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And I had a great time in Barcelona,
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where I was the CIO of SEAT at that time.
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After that, I had six years in Wolfsburg,
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the headquarters of the Volkswagen group.
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I was responsible for the IT services worldwide,
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and after that I became the CIO of Audi.
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I took this position for six years,
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and then I decided to get, let's say, a wrap-up
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of the new technology.
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So, I moved to MIT in St. Gallen here in Switzerland
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to learn more about the term of, let's say, transformation,
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digital transformation about new technology.
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And after that, I came to my role here at Porsche
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to drive the transformation of Porsche, and as well,
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getting all this stuff as well
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from this Porsche digital organization
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into the Porsche organization.
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You mentioned your interest in AI,
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you mentioned your interest in technology and innovation.
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Why cars?
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Yeah, that's a good question.
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Because I thought when I started with Hewlett Packard,
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it was really interesting,
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and I saw a lot of different branches
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and companies starting from trucks and going to ships,
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for example, as well for other things like chemical stuff.
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And then, I had those projects with Audi,
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and it was so interesting
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to see what technology really did to the car.
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So, it was a time at the end of the 90s
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where there were so many different electronic devices
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in the car,
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and it was a huge challenge to manage that,
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because in the beginning,
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they didn't communicate well with each other.
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And so, there was a huge technology challenge,
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and this was really interesting for me
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to understanding this challenge
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and bringing together production knowledge,
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IT knowledge, and the car technology together.
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And so, that was the moment when I changed
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to the automotive company.
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What kinds of new stuff
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are you interested in right now, particularly at Porsche?
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Yeah, the main focus is really AI, to be honest.
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Of course we are looking how we can organize as well
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with, let's say, agile working,
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what can we do as well in our development centers
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for the car business.
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And of course, software in the car
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is a very important thing as well,
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because this is changing the world of driving.
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But what we are focusing on as well
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is to look how we can use AI, for example,
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to improve our internal processes,
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how can we use AI to get a better contact
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and a better understanding of our customers.
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That is very important for us as well.
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And of course, yeah,
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to look what can we do in our product
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and really increase as well our product portfolio
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to have digital products
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that we can deliver for B2C markets.
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Like we started, for example, with some ideas
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in the direction of supporting sustainability as well,
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like Porsche Impact,
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where we developed a solution
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on how a customer can compensate, for example,
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his CO2 footprint that he drive with the car,
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and how we can really increase our product portfolio
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for our customers and make driving more attractive.
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So, is there some particular example
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of how AI has made a big difference
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in a way that other technologies would not have
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that you're particularly fired up about?
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Yeah, what we really found out, for example,
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in production that we can really use AI to predict,
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of course, a lot of things, for example,
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in the auto management systems,
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how we can predict for order to deliver markets
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to have the right cars in the dealerships.
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That is not easy, for example, in China.
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So, sometimes it's really a challenge to understand
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what kind of cars they would buy in three months.
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So, it's very important to get feedback from the market
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and understand what is the most important driver for that.
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But as well, to understand what can we do
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in the production process, for example.
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These are some very, very great examples
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of use of AI.
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You mentioned a whole bunch on the marketing side,
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a whole bunch on the supply side and supply chain,
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and also production.
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Are there also some examples where roles or functions
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that are typically more like engineer driven,
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like design or some of the trade-offs
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in terms of performance, et cetera, are aided by use of AI?
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Because we see that in other industries,
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which is sort of a new thing,
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because are typically things
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that engineers have a strong sort of discipline
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and playbook for.
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Is that something you can also comment on?
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It's very interesting
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that you mentioned design, for example.
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So, because I was in the design studio here
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in Weissach two weeks ago,
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and we had a long discussion on how they use AI
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to really support their design ideas.
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And they have a program that they're using for new cars,
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for example, they can use different models
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and really reshape the digital model
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before they go into the first physical model.
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And they are using AI to supporting the designer,
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for example, but as well, we have, of course,
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in other areas of engineering,
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as well support tools that really use AI
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to improve technical development.
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When you guys implement these AI tools
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in these areas where a strong collaboration
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between man and machine is needed,
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do you find there is a fair amount
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of sort of culture change needed or re-skilling
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or change management needed
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for the human to become friends with AI?
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I think the collaboration between the machine,
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the IT machine or the AI machine, and the person
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is very important.
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And you need to understand how you can use that.
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And it's totally different if you go to production,
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for example, or to engineering,
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because sometimes it's a supporting tool,
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and it supports the worker,
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for example, in the production line,
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in the other area in engineering,
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they're like friends, like colleagues
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that work on the same issue.
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And it's very important to understand
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that AI is further developed by the business area.
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So, it's not an IT task anymore.
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It's really a collaboration between the AI expert,
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but as well, the business expert and the machine,
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and you have to manage that.
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So, this is a huge difference to normal IT projects.
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So, we have a totally different approach
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to go together on such pilot projects.
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We have, for example,
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an acoustic anomaly assistant that works really
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with an engineer to understand anomalies that we have,
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for example, in the door and how he can really understand
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what is the reason for the noise.
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And this support, it's only possible
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if you worked really together for months
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to understand all the noises,
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and then you can adapt it to a different part of the car.
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But it takes a while and you learn,
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you have a learning curve like this,
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and it's only possible
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if you have a very good collaboration.
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When you implemented this system,
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what were people's reactions?
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Did they say, "Oh my gosh, this is taking my job"?
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Did they say, "Oh my gosh,
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this is a great way to do this"?
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How do people react the day that you turn the system on?
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Like always, people react totally different.
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As of course, we have a lot of very technical oriented guys
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that see the opportunity and see really
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how this can help them in doing their tasks better,
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doing their work better.
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You mentioned a little bit about the fears
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that some people have about when you say AI in general,
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and they think a machine will rule versus machine vision
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to help understand the right label
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or the acoustic anomaly system on the doors.
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How did you come up with these sorts of,
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like acoustic anomaly system, for example?
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I wouldn't have thought of listening to a door.
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I would've thought of looking at a door.
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How do you get someone to think about listening to a door?
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My doors haven't said anything, as far as I know,
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but maybe I'm not listening.
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Listening to a door is already a task
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that has been done in Porsche and at Volkswagen,
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for example, and the whole industry for years.
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But the idea to use AI to really improve
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that noise that a door makes,
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that came really from drinking coffee.
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Now, it was in the lab that we have in Berlin and I,
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as a specialist, wrote an application
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that could listen to the coffee machine
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and know what kind of coffee is done.
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So, he knows this was a cappuccino,
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this was an espresso, for example.
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And then there was the idea, what can we do with that,
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with this acoustic system?
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And then we had some discussion with our R&D people,
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and they said, "Well,
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we are listening to our door all the day,
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but we can't listen tonight, for example,
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because we are not there.
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And sometimes there is a noise,
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and we're coming back in the morning."
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So, together we started this project then,
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and it was the beginning of a success story,
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because right now we are doing this
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in several places in R&D and it was a good example
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how it can work.
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Yes, for sure,
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and the step to really educate, I'm sure it really,
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really helps too, because as you said,
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lack of information could create a lot of anxiety.
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And so, how widespread are these programs?
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Is it tens of people or hundreds of people
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or thousands of people?
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00:14:24.220 --> 00:14:25.730
Yeah, how many people in the organization need
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to know about these technologies, everyone?
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I think, yeah.
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I started to train really every IT person
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in my organization,
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so that we have a broader view on that
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and a common view on that as well.
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But of course, the AI program is,
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let's say, it's 200 person that really work on that
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in the Porsche organization
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and that are connected of course to others,
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but you need a very small, core team
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that is driving this change
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and bringing the best use cases in place.
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And we tell about that.
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And so, we have a good mixture of business people
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that drive that,
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and some people came as well to the IT organization
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that was new. They moved from engineering
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to the IT to drive this as a platform.
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We have an AI platform,
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and they had really fun to drive this program together
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with the IT people.
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And so, we have a good mixture of business and IT people
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that are driving this program.
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That's great, thanks for clarifying that.
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And then another question I had is you mentioned 200 people.
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How many of these people are new people or re-skilled people
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because of course, some of this training is to educate,
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but some of it could also be to really build new skills?
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Do you find that you have to bring talent
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that may not exist a massive amount
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of that talent from outside?
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Or do you find that it's more
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about re-skilling and training the existing workforce?
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It's different in the different business areas.
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For example, in engineering
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you have a lot of good, trained people
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that understand technology
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and that really understand as well
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the possibilities of AI.
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In finance, for example, you have totally different
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approach, and you have to bring more external people
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to really drive those ideas
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because there's no technology knowledge
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on the business side.
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So, if I look at my organization,
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we have maybe 40% new people
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and 60% that were already in the organization,
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but some are of course really focused in the past
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as well to new technology.
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So, they are very adaptive and they would like to learn.
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So for us, it's a very good mixture
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because you need really to understand the business process
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to find the best solutions, that is very important as well.
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00:16:58.490 --> 00:17:00.170
It's all about continuous
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and ongoing learning at all levels, right?
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For people, for the algorithms, for everybody.
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00:17:06.160 --> 00:17:07.510
Yeah, that's right.
368
00:17:07.510 --> 00:17:11.680
And creating a culture that is really open for that,
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that is very important as well.
370
00:17:13.050 --> 00:17:16.590
Mm-hmm, so what are you excited about?
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You've mentioned new stuff,
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the new stuff from the beginnings of Hewlett Packard days
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00:17:21.340 --> 00:17:23.850
to new stuff now, what's the next new stuff
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00:17:23.850 --> 00:17:25.770
that you're excited about?
375
00:17:25.770 --> 00:17:28.290
Yeah, the most thing I'm excited about
376
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is to work together with great people, to be very honest.
377
00:17:31.610 --> 00:17:35.430
I like technology of course, but to work with people,
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00:17:35.430 --> 00:17:37.980
it's the best thing that you can do
379
00:17:37.980 --> 00:17:40.360
and really to learn together as well.
380
00:17:40.360 --> 00:17:42.980
So, we are just in the beginning of AI,
381
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so there's a long way to go
382
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because we have done some really great project,
383
00:17:47.390 --> 00:17:51.137
but we are still having a lot of things to do with AI.
384
00:17:51.137 --> 00:17:55.230
And I believe really that AI is the biggest challenge
385
00:17:55.230 --> 00:17:57.970
that we are facing right now still.
386
00:17:57.970 --> 00:18:02.970
And all the other things are not such a game changer like AI
387
00:18:03.060 --> 00:18:05.561
is right now in the technology field.
388
00:18:05.561 --> 00:18:10.561
And I believe that AI and as well, other technologies,
389
00:18:11.420 --> 00:18:14.360
but AI in this very special situation
390
00:18:14.360 --> 00:18:15.920
can really drive the change
391
00:18:15.920 --> 00:18:18.650
that we have right now in society,
392
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all the challenges that we have in society,
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for example, sustainability.
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And I believe that digitalization can really help
395
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in those very important fields.
396
00:18:29.630 --> 00:18:32.710
And if you look, Porsche is doing a lot
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00:18:32.710 --> 00:18:35.540
to getting better cars on the road,
398
00:18:35.540 --> 00:18:39.490
like the Taycan, for example, to have better cars
399
00:18:39.490 --> 00:18:43.490
that really helps as well as the environment,
400
00:18:43.490 --> 00:18:46.830
and doing a lot in terms of sustainability.
401
00:18:46.830 --> 00:18:49.290
And I think those things are driving me
402
00:18:49.290 --> 00:18:51.960
to really creating a better world
403
00:18:51.960 --> 00:18:55.510
and looking what can technology really do for that.
404
00:18:55.510 --> 00:18:58.160
And of course, supporting people
405
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to really focus on the most important things in life,
406
00:19:01.840 --> 00:19:03.820
but as well, helping the world
407
00:19:03.820 --> 00:19:05.380
to getting a little bit better.
408
00:19:05.380 --> 00:19:08.840
And I think this is a great motivation for me,
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and this is very helpful for me.
410
00:19:11.560 --> 00:19:13.550
Mattias, thank you for joining us today.
411
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We appreciate all the time you spent
412
00:19:15.010 --> 00:19:16.860
and the interesting conversation.
413
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It was great with you, thank you for your time.
414
00:19:19.720 --> 00:19:21.264
Thank you very much, yes.
415
00:19:21.264 --> 00:19:24.010
(placid music)
416
00:19:24.010 --> 00:19:25.440
That was great.
417
00:19:25.440 --> 00:19:27.170
Mattias was really interesting.
418
00:19:27.170 --> 00:19:30.630
Mattias hit a lot of very key points
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00:19:30.630 --> 00:19:34.600
that we see in our work, we see in the survey this year,
420
00:19:34.600 --> 00:19:35.740
we saw it last year.
421
00:19:35.740 --> 00:19:39.250
He made the point around culture of innovation
422
00:19:39.250 --> 00:19:41.580
and creation of new ideas.
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00:19:41.580 --> 00:19:45.100
He made the point around, it's not tech,
424
00:19:45.100 --> 00:19:49.310
it's about business and technology coming together.
425
00:19:49.310 --> 00:19:51.580
He also underscored the importance
426
00:19:51.580 --> 00:19:55.100
of just by being who he is and the role he has,
427
00:19:55.100 --> 00:19:59.310
the importance of this being a senior executive role.
428
00:19:59.310 --> 00:20:01.060
And then, the other thing I really liked
429
00:20:01.060 --> 00:20:06.060
is the point he made about different roles of AI.
430
00:20:08.230 --> 00:20:09.740
We say that in our report that there
431
00:20:09.740 --> 00:20:11.120
are at least five different modes,
432
00:20:11.120 --> 00:20:12.230
and then he talked about it.
433
00:20:12.230 --> 00:20:15.470
He talked about vision and automation of that.
434
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Then he talked about design where AI gives some ideas
435
00:20:19.490 --> 00:20:21.770
to the engineer or to the designer.
436
00:20:21.770 --> 00:20:25.170
He talked about AI as generator of insights
437
00:20:25.170 --> 00:20:27.630
for supply chain or for the marketer
438
00:20:27.630 --> 00:20:30.710
or for the product designer.
439
00:20:30.710 --> 00:20:33.100
I really feel like Porsche is getting it.
440
00:20:33.100 --> 00:20:35.750
It's really, really impressive.
441
00:20:35.750 --> 00:20:37.540
Yeah, Mattias had a nice combination
442
00:20:37.540 --> 00:20:40.080
of excitement about technology
443
00:20:40.080 --> 00:20:43.000
but also a methodological approach towards it,
444
00:20:43.000 --> 00:20:44.980
which I thought was a nice combination.
445
00:20:44.980 --> 00:20:48.760
He both exuded excitement about technology,
446
00:20:48.760 --> 00:20:49.593
but at the same time,
447
00:20:49.593 --> 00:20:52.830
a very realistic view about how to implement it.
448
00:20:52.830 --> 00:20:54.800
Yeah, the other thing I was quite impressed
449
00:20:54.800 --> 00:20:59.800
is the extent of culture change that Porsche is undertaking
450
00:21:03.090 --> 00:21:07.550
that he underscored with training,
451
00:21:07.550 --> 00:21:09.960
with education, with re-skilling,
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00:21:09.960 --> 00:21:13.390
with getting a base level education
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00:21:13.390 --> 00:21:15.550
for everybody in his organization.
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00:21:15.550 --> 00:21:19.890
And it's hundreds of people, not a handful of people.
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00:21:19.890 --> 00:21:20.723
Right, and they have to
456
00:21:20.723 --> 00:21:23.575
because of the number of applications that they're,
457
00:21:23.575 --> 00:21:26.820
he listed off dozens of places that they're using it.
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00:21:26.820 --> 00:21:28.570
And they were just all across the organization.
459
00:21:28.570 --> 00:21:29.640
Yeah.
When he was talking
460
00:21:29.640 --> 00:21:30.657
about introducing it, he said,
461
00:21:30.657 --> 00:21:33.947
"Oh yeah, we don't talk about bringing in AI as a big,
462
00:21:33.947 --> 00:21:36.417
scary general thing that's abstract
463
00:21:36.417 --> 00:21:39.420
and who knows what baggage it's bringing with it."
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He said, "We're bringing in this application."
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He was very specific and very narrow.
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And then people can get their mind around what that means
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versus bringing in a lot of baggage from every sci-fi movie
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that anyone's ever seen.
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Yeah, the other thing I really liked
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is the coffee example.
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It was a great question you asked, why sound?
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And I didn't think he's gonna go to coffee.
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I thought there's a million other places he could go.
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But that's a great example,
475
00:22:05.990 --> 00:22:07.310
and I think we also heard
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00:22:07.310 --> 00:22:10.670
that from some of our other interviewees
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that a success story or an anecdote
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00:22:14.110 --> 00:22:16.630
or an example somewhere else translates
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into a completely new idea in a completely different field.
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Yeah, that's a very human role
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of still that creativity of recognizing
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that this situation isn't exactly
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where we saw that technology before,
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00:22:30.900 --> 00:22:33.070
but it's a great place we can use the technology now,
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and that's still a very decidedly human creative part,
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but it still requires expertise.
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And he was very clear about that,
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that you had to have that expertise as a baseline,
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and it sounds like they're working very hard to make sure
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that most people in the organization
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have some expertise in there.
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When I asked him about new technologies he's excited about,
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00:22:52.950 --> 00:22:55.210
he didn't bite it all in the rattling off the latest,
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00:22:55.210 --> 00:22:57.120
greatest new AI thing.
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00:22:57.120 --> 00:23:00.450
He's all about getting the people excited about it.
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That that was not where I thought he would go there.
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Yes, that's right.
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(placid music)
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We really enjoyed speaking with Mattias today.
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He really combined an enthusiasm for technology
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with an enthusiasm for business,
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and that was a fun combination.
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Join us next time when we talk
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with Arti Zeighami, head of AI at H&M.
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Thanks for listening to Me, Myself, and AI.
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00:23:27.250 --> 00:23:28.860
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507
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