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Artificial Intelligence and Business Strategy
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Arti Zeighami’s interest in artificial intelligence started when he read science fiction as a teen. Yet as head of advanced analytics and AI for global retailer H&M Group, his leadership style focuses on reality: first building a business case and a proof of concept, and then undergoing an agile process of iteration and scaling, failure and success, measurement and improvement.
In this episode, Arti talks about weaving AI into the value chain in the fashion industry — specifically around personalization, pricing, merchandising and forecasting. He has coined the term amplified intelligence — where humans and machines work together — and in this episode shares stories and practical tips on how teams can get started and scale successfully.
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Transcript
Sam Ransbotham: When you’re putting a new tire on your car, you don’t want to tighten one bolt all the way and then tighten the rest. You want to tighten them all a little bit and continuously tighten. What does that have to do with artificial intelligence and fashion? Find out today when we talk to Arti Zeighami from H&M.
Sam Ransbotham: Welcome to Me, Myself and AI, a podcast on artificial intelligence in 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.
Sam Ransbotham: Today, we’re talking with Arti Zeighami. He leads artificial intelligence at H&M. Arti’s joining us from Stockholm. Welcome, Arti.
Arti Zeighami: Thank you. Thank you very much for having me here.
Sam Ransbotham: Really, we’d like to hear a little bit about your background. Why don’t we start there? How’d you get interested in artificial intelligence? And, for our podcast today, I’m actually wearing a nice shirt because we’re talking with a fashion person. Even though this is audio, I can assure everyone that I look fabulous. How did you get interested in artificial intelligence though?
Arti Zeighami: I think my interest in the area started many years ago as a young teenager, when, like a lot of people, I was studying math and physics and loved all [that] stuff. And I read these books by Isaac Asimov, the science fiction author. I was reading about this theory that he had about psychohistory, [as] he called it, which was about how you can start [to] somehow predict the future by looking at the past and applying mathematical models on top of that.
I was so intrigued by that when I was 15, 16, or 17. I was like, “This is something amazing. This guy made it up by himself. What if this can be true? What if I can work with something like this in the future?” It took me a long time to get there because [in] between, school came, and I started working as a consultant. I was doing programming, I was in the architecture, I was a business developer, I was a strategist, I did a ton of different startups, and all that. And then I ended up in a fashion retailer.
Then I got this opportunity to start looking at advanced analytics and AI as a capability. I don’t have a formal data science background. I do have an engineering background from engineering school. I even started at business school parallel to that. And then life brought me here. Somehow it’s like the universe brought me to artificial intelligence. Somebody made fun of me [and said], “Yeah, your name’s Arti. That’s arti [for] artificial.” Probably that’s why.
Sam Ransbotham: Oh, yeah.
Shervin Khodabandeh: That’s pretty funny.
Arti Zeighami: Yeah, I always joke about that.
Shervin Khodabandeh: Joking aside, I would say, what you described as a diverse and colorful background and experiences — architect, engineer, strategist — how does it help you now, that diversity, versus if you’d been all focused? Do you have a point of view on that?
Arti Zeighami: Yeah, absolutely. I think it has helped me tremendously. I think one of the major parts of working with introducing a new capability such as AI into an old industry like a retailer, which [has] done certain things a certain way for so many years, is about shift of mindset. It’s about transforming people’s way of thinking. It’s very little AI. It’s very little tech. I usually refer it as 10% AI, 20% tech, and then 70% people and processes. So you try to shift people into thinking differently, to ask different questions, to look at the world differently, and working as a consultant, I think that was one of the biggest helps I ever got, because as a consultant you always make sure that it’s not about you shining. It’s about your clients shining. And it’s about making them the hero of the day, and it’s always been about that. And I even, internally with my colleagues and my peers, always said, “Listen — let’s make sure that we are almost like internal consultants, because it’s about helping our colleagues to achieve their goals.”
Ultimately, if you’re talking about transforming people’s mindset, it’s about the rhetoric. It’s how you make them understand what you’re trying to do, and how you make them understand what you’re trying to help them with. So this goes back to the Greek rhetoric [of] ethos, pathos, [and] logos.
Sam Ransbotham: That’s a long way … from Greece to Sweden here, I guess. Is there something specific that AI has been able to help with at H&M?
Arti Zeighami: Absolutely. We, as a company — it’s always been analytical. If you go back to how the company was brought up, [it was] a family that started it — the Persson family. The third generation is now chairman of the company; Erling [Persson] started this back in 1947. It was very analytical on its way already, back in those days. There [are] stories about something they called “following the bags.” When he was trying to enter a new city, he sent out people with pens and papers, walking the street, looking at the bags of the people, where they were going, and if they were crossing intersection on this side or that side, and then they [understood at] what sort of intersection they should build the store.
We started to look at artificial intelligence back in 2016 and tried to understand what it entails for us as retailer. We entered a very small area and made a proof of concept in that area within personalization to understand how we can enhance the communication, the personalization, the offering to our customer in a way utilizing AI analytics. And we saw that based upon the amount of data that we have, the vast information that we have of our product, of our sales, of our customer, we can be really precise on that.
That by itself was not pivotal. It was more of understanding how you change the mindset, as I said. You want people to come in Monday morning and ask different questions. And to do that, you need to get them [to be] more analytical. In order to penetrate that into the entire organization, we took an approach that was a little bit different, because a lot of people asked me back in those days, “How do you pick your AI use cases?” And I said, “I don’t have any AI use cases. I have business challenges that my colleagues have.” I looked at the portfolio of our projects and [saw] we had a lot of problems. We had a lot of challenges. And then those challenges have been tailored to projects. They’re going to change and fix those things, and here I can come in and help in a very small part of it. And I think that was also a very important part — how you infuse something new in your organization. Because a lot of people, again, take one part, a very small part, and then they do deep dives on that small part, and they create a sexy app or cool customer-facing stuff. That’s fine. But then you make one part of your organization to become very, very good at that, and then the rest are still lagging behind. I believe you need to elevate everybody a little bit.
It’s almost like putting a tire on a car. You don’t screw one bolt really hard and then do the next one. You just do every bolt a little bit, and then tighten everything up. And I think that has been a really good approach for us to do that to everybody. I’m enhancing stuff in the beginning of the value chain with fashion forecasting, with quantification, how we quantify how many pieces you buy, to how you allocate the garments throughout the whole value chain, to how to put set prices on them, and maybe also working with personalization and all [that] fancy customer-facing stuff as well.
And for us, AI has not meant artificial intelligence. We have always talked about amplified intelligence, because we’re amplifying an existing knowledge and competence of our colleagues. So it doesn’t have to be the AI that does the decisions. It could be [a] combination. And we see that when we do the combination of AI and human, the gut feeling and the data, the art and science — that’s when we get the most out of it. I see a lot of things that we do today [are] that mixture.
Shervin Khodabandeh: Mm-hmm. I want to pick up on what you referred to as amplified intelligence. I think it’s very elegant. And it also underlies the theme you started this conversation with, around organization, people — 70% is the people in an organization. And it also ties very well with the research we just did, which is all, again, talks about the role of humans, and sometimes the misunderstood or understated role of humans. Comment more on that, and particularly different ways that humans and AI can interact across these different business problems.
Arti Zeighami: When we did our first pilot, a test of utilizing AI and advanced analytics in end-of-season sales (and that was my first attempt to try to use an actual use case and an actual business challenge), we saw that very early that the AI could actually enhance — [it] was much better than the human in putting the prices in [the] end-of-season sales results. And the important part of that journey was to make sure that the teams that were actually applying it — not my team, not the AI team, but actually the people that were working the merchandise online on a very small selected markets — I let them just actually calculate what the outcome was. So they both were responsible for the test: for setting up the test, putting all the constraints they want to meet, and making sure my algo was not getting anything else than merchandise they were getting on a daily basis. And then they try to understand how [much] better it is by actually [calculating it] themselves. And they said, “Perfect. It helps to enhance our job. Let’s do a next test for the next season end-of-season sales, or midseason sales.” So a couple months later we added the test and we made it a little bit larger, and we added another country, another market, two warehouses … same amount of products. And we tested that.
And then we saw results. And we wanted to add a little more complexity to this, but one thing we also wanted to do was add a third bucket. So we’re still going to have a few products to look at, but we want to divide it not in two buckets, but in three buckets, where one bucket is the algo putting the price on, one bucket is the merchandising, and one bucket is the algo putting the price on and then merchandising coming in and tweaking those prices. Because we saw there are some things that the algo isn’t good at. And we found that very interesting and we said, “Absolutely, let’s do this.” And then we did the midseason test, and the outcome of that was even more interesting because the algo was, again, a few percentage points better than the human, but the algo, in combination with the human, was twice as good as the algo itself. And actually it was then we started talking about amplified intelligence. We realized the machine by itself won’t help us. It’s a combination of the human and machine, the gut feeling and the data.
Sam Ransbotham: May have to change your name from Arti to Ampfi.
Arti Zeighami: My mom won’t be happy.
Sam Ransbotham: Yeah, your mom might not be happy there. You described a process where you went further in depth into a pricing process, but earlier you were talking about a process of saying, “Well, we need to do lots of things in different areas.” How do you balance that tightening all the bolts versus tightening this one bolt harder? Because it sounds like in that example you were doing some more tightening of one bolt. How do you balance those out?
Arti Zeighami: Well, you didn’t hear the whole story.
Sam Ransbotham: Ah, there’s more. OK.
Arti Zeighami: Already from first tightening of the first bolt, the first test that I got, which was [a] good result, but it was not enough — already then, I was happy about the result, and I took that result and went to another part of the business and said, “Listen. We did this with these guys. They’re already happy about the result. We said that we were x amount percent better on net sales by using an algo, which we created in four and a half weeks. And it has a huge impact on the business. Do you want us to help you to look at this area? Because I know you have a problem here.” And then we brought in data scientists, and we put experiments around that.
And then, when we started that discussion of that conversation for that specific project, it was connected to another part of the business, and they said, “Hey, if you’re going to do change there, maybe we should do change here as well. Arti, do you want to help us in this case?” “Yes, please. Let me help.” And then we started doing that, and then that follows.
And by the end of the year, suddenly we have those eight, seven, whatever, use cases that we had. And then we saw that we’re actually applying this throughout the whole value chain. And then meanwhile, you start tightening each of the bolts a little bit more.
Shervin Khodabandeh: It’s like a pit crew.
Arti Zeighami: [Laughs.] Exactly. This is the whole idea of being agile, right? You start small. You dream big, you start small, and then you scale fast. So you start small with something here, and then you start the next wave, and the next wave, and the next wave. And all these waves have a cycle of starting small, testing a little bit larger, failing, pivoting, and testing more, failing, pivoting, learning, and then [it] goes on and goes on. Organizations such as ours — we’re huge: 5,000 stores, 70-plus countries, 180,000 people. So there’s [a] huge amount of things when you start to industrialize that. And AI doesn’t mean anything if you don’t industrialize it.
Shervin Khodabandeh: And Arti, as you were talking about industrializing this and getting real scale out of it, in the context of amplified intelligence, you know, [the] machine is easy to get to play with humans because they don’t have a choice. How do you get the human to play nice with the machine? And what’s the kind of pushback that you would expect or you get, and how do you deal with that?
Arti Zeighami: It’s different from different levels of people in the organization. One thing I found out is that maybe it’s also about the culture of the organization as well. We come from a company with a culture of always being entrepreneurial, always seeking for improving ourselves, and that has been part of the organization. It’s in our DNA somehow to always try to be better. That’s why also we talked about the combination — that we’re just taking the small part of the work and we’re actually internal consultants. So when they own that, it also makes them feel pride about what they’re doing.
We really went for this DevOps model where we put people into teams, where you have use [a] case lead, and then you have business experts, and you have machine learning engineers, data engineers, software engineers, UX designers, all working together on a daily basis in a very agile way by sitting together in the same office, having morning stand-ups. So they were part of the whole development team. My job [is] to make sure that my other colleagues also understand the impact, and then utilize this to realize their value in their part of the company, their part of the organization: making our company to continue to thrive and become even better.
Shervin Khodabandeh: I think there’s an important lesson in there for everyone as well, including for CEOs and heads of businesses. You’re underscoring the importance of focus on value versus a single-minded tech-centric focus on building capabilities, and building more and more capabilities, and the need for the business and the users and the people to come in from the beginning to design that.
Arti Zeighami: Yes.
Shervin Khodabandeh: Arti, thank you so much for making time.
Sam Ransbotham: Yes, thanks for taking the time today.
Arti Zeighami: Thank you, guys.
Shervin Khodabandeh: Sam, I thought that was such a stimulating conversation with Arti. Let’s quickly recap.
Sam Ransbotham: Sounds good. Shervin, it’s interesting — when we talked with Porsche, we never even talked about tires and hubcaps and tightening bolts. But when we turned to fashion, he talked about the importance of doing that, and his analogy was very auto-related.
Shervin Khodabandeh: That’s right. And when we talked with Porsche’s Mattias Ulbrich, we talked about coffee.
Sam Ransbotham: That’s right.
Shervin Khodabandeh: Joking aside, I think the common theme we’re hearing is the importance of archetypal problems and translating or transferring these learnings across problems, which interestingly enough is a topic in AI itself, like transfer learning. … I also thought it was quite interesting how, from the beginning, Arti said, “Look, it’s about changing the mindset of the people, and it’s about the organization.” … He talked about it as amplified intelligence — bringing humans and AI together rather than all one or the other.
Sam Ransbotham: I think if we pulled out the words, he said “learning” more than anything else.
Shervin Khodabandeh: That’s right. The other important point he made, which I think might be lost on many, is that you can’t just start with technology and capabilities. And that reminds me of Field of Dreams: If you build it, they will come.
Sam Ransbotham: It’s the opposite.
Shervin Khodabandeh: He said exactly the opposite. You can’t build it and wait for them to come. You actually have to build it together. You need to get them first, and you’ve got to build it together. I think it’s an important lesson here.
Sam Ransbotham: Yeah, he emphasized value first and then structure, and that was important. There’s an element, too, of weakest-link thinking that came through. He talked about lots of different places that they were using artificial intelligence. He didn’t actually use this phrase, but part of the idea was that it doesn’t do any good to strengthen one area extensively but not another one. And so that kind of speaks to the tension with value versus structure. He wanted it kind of [at] the same time progressing. He wanted different parts of the business to be progressing at the same time as well, not too deep in one area, not just completely shallow everywhere either. So almost everything seemed to be about a balance.
Shervin Khodabandeh: You don’t start by saying, “I’ve got to move everybody all into one center of excellence. Then I’m going to go build a baseball field and then everybody will come and play.” You know?
Sam Ransbotham: Right. The other part of that was he said that he didn’t have AI use cases. And that sort of structure first would lend you toward thinking of AI use cases first. He said not AI use cases. He said business challenges that we sometimes and often solve with AI.
Shervin Khodabandeh: That’s right. That’s right.
Sam Ransbotham: Well, that’s all the time we have for today. Join us next time with our last episode for this season. We’ll be talking with Kay Firth-Butterfield from the World Economic Forum. We’ll see you next time, Shervin.
Shervin Khodabandeh: You too.
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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