Sam: 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 with Arti Zeighami from H&M.
(upbeat music)
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 and Business Strategy Big Ideas Program,
at MIT Sloan Management Review.
Shervin: And I'm Shervin Khodabandeh,
senior partner with BCG,
and I colead 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.
(ethereal music)
Sam: Today we're talking with Arti Zeighami.
He leads artificial intelligence at H&M.
Arti is joining us from Stockholm, welcome Arti.
Arti: Thank you, thank you very much for having me here.
Sam: Really we'd like to hear
a little bit about your background.
Why don't we start there?
How did you, 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: I think my interest in the area
started many years ago as a, you know, a young teenager,
when the, like a lot of the people I was, you know,
studying math and physics and loved all those stuff,
and I read these books by Isaac Asimov, you know,
the science fiction author. And I was reading about this,
this theory that he had about psycho history, they call it,
which was about how you can start somehow predict the future
by looking at the past,
and apply mathematical models on top of that.
And 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,
and you know, what if this can be for true?
What if I could work with something like this in the future?
And, you know, it took me a long time to get there
because there between school came,
and then I started working as a consultant,
and I was doing programming, I was in architecture,
I was a business developer, I was a strategist.
I did, you know, 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 as an 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 business school parallel to that, you know,
and then life brought me here.
Somehow it's like the universe brought me
to artificial intelligence and somebody made fun of this.
"Yeah, your name is Arti, that's already Arti, artificial.
It's probably that's why," so, yeah.
Shervin: Oh yeah--
Sam: That's pretty funny.
Arti: Yeah, I always joke about that.
Shervin: Joking aside, I would say,
like what you described as a diverse and colorful background
and experiences, you know, 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: Yeah, absolutely.
I think it has helped me tremendously.
I think one of the major, you know,
parts of working with introducing a new capability,
such as AI, into a, you know, old industry, like in retail,
which has done certain things in a certain way
for so many years, is about shift of mindset.
It's about transforming people's way of thinking it's,
you know, it's very little AI, it's very little tech.
I usually refer to that 10% AI, 20% tech,
but it's 70% people and processes.
So you try to shift people into thinking differently,
to ask different questions, to, you know,
look at the world differently and working as a consultant,
I think that was one of the biggest help 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 kind of sort of even internally would,
you know, 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's,
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, you know, it goes back to the Greeks rhetoric,
ethos, pathos, logos.
Shervin: That's a long way from the ancient Greeks to,
from Greece to Sweden here, I guess.
Is there something specific
that AI has been able to help with at H&M?
Arti: Absolutely, you know, we as a company
have always been analytical.
If you go back to how the company was brought up, even,
you know, the company is a family,
that started as this, the personal family,
the third generation is now chairman of the company.
Erling who started this back in 1947.
It was very analytical on the,
on this way already back those days,
you know, there's a story is about,
something they called "Following the bags,"
you know, when he was trying to enter a new city,
he send out people with pens and papers
and then will walk in the street,
looking at the bags of the people or where they were going,
and if they were crossing an intersection,
or this side or that side, and they didn't understand,
"Okay, what sort of intersection
that should be on the store?"
We start to look at this artificial intelligence,
back in 2016 and try to understand what it entails for us
as a retailer, and we entered a very small area
and we did a proof of concept in that area,
within the personalization to understand, you know,
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,
you know, 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 to set, you know,
you want people to come in Monday morning
and ask different questions.
And to do that, you need to get them more analytical,
and 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,
you know, "How did you pick your AI use cases?"
And I said, "I don't have AI use cases.
I have like business challenges that my colleagues have.
These are, I look at the portfolio of our project and see,
we have a lot of problems, we have lot of challenges,
and then those challenges are have entailed into 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 an organization,
because a lot of people, again, take one part,
a very small part,
and then they do deep dive on that small part,
and they create that, you know, a sexy app
or a cool customer facing stuff, and 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, you know, lagging behind.
I believe you need to elevate everybody a little bit.
So instead of, it's almost like putting a tire on a car,
right, you don't screw one bolt really hard
and then do the next one. You just do every by 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,
and I'm enhancing stuff in the beginning of the value chain
with fashion forecasting, with quantification,
how you quantify, how many pieces you buy, to
how you allocate the garments throughout
the whole value chain, to how that puts prices on them,
and maybe also working with personalization
and all those 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 decision,
it could be combination,
and we see that when we do the combination of AI and machine
and human, the gut feeling of 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 is that mixture.
Shervin: I want to pick up on,
what do you referred to as amplified intelligence?
I think it's very elegant,
and it sort of also underlies the theme you started
this conversation with around organization,
people, you know, 70% is the people and organizations.
Arti: Yes.
Shervin: And it also ties very well
with the research we just did, which is all again,
talks about the role of human
and sometimes the misunderstood
or understated role of human.
Comment more on that, and particularly,
different ways that human and AI can interact.
You know, across these different business problems.
Arti: Right, right. When we did our first pilot,
a test of utilizing AI and advanced analytics,
and end-of-season sales, and that was my first attempt
to try to use it on an actual use case
and an actual business challenge. We saw that very early,
that the AI could actually enhance
what was much better than the human on putting the prices.
I mean, end-of-season sales result.
And important part of that journey was to make sure
that the teams, they were actually applying it,
not my team, not the AI team,
but actually the people that are working the merchandise
online on a very small selected markets,
I let them 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 me,
and making sure that my algo was not getting anything else
than the merchandise that we're getting on a daily basis,
and then they'd try to understand,
how better it is by actually calculating it themself.
This is perfect, you know, it helps us to enhance our job.
Let's do a next test, for the next season,
mid-season sales, so a couple of months later,
we added the test and we made it a little bit larger.
We added another country, another market, two warehouses,
sell them on the products, and we tested that.
I want to add a little more complexity to this,
But one thing that we want to do is also 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 three buckets, where one bucket is that
algo putting the price on,
one bucket is the merchandising,
and one bucket is the algo putting the price on
and the merchandise merchandising coming in
and tweak those prices.
Because we saw there some things that algo isn't good at.
And we found that very interesting, and said,
"Let's absolutely, let's do this."
And then, we did the mid-season test,
and the outcome of that was even more interesting.
Because the algo was again,
you know, a few percentage better than human,
but the algo in combination with human,
was twice as good as the algo itself.
And actually it was, then we,
we start talking about amplified intelligence
because we realized the machine by itself won't help us.
It's a combination of the human and the machine,
the gut feeling and the data.
Sam: May have to change your name
from Arti to Amfi.
(Arti laughs)
Arti: Yeah, oh my mom won't be happy.
Sam: 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."
Arti: Yes.
Sam: How do you balance that,
tightening all the bolts
versus tightening this one bolt harder?
Because it sounded like in that example,
you were doing some more tightening of one bolt.
How do you balance those out?
Arti: Well, you didn't hear the whole story.
Sam: Oh, there's more, okay.
Arti: Already from their first tighten up
of the first bolt, so the first test that I got,
which was good result, but it was not enough,
already then I was happy about those results,
and I took that result and went another part of the business
and said, "Listen, we did this with these guys.
They were really happy about the result.
We saw 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 to us to help you to look at this area?
Because I know you have a problem here."
And then we brought in the data scientists,
and we put the experiments around that.
And then when we started that discussion
and that conversation for that specific project,
it was connected to another part of their business,
and they say, "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 had those eight,
seven, whatever use cases that we had,
and then we saw that we're actually applying this
throughout the whole value chain.
So, and then, meanwhile you start finding each of the bolts
little bit more and more.
Shervin: It's like a pitch crew.
Arti: Yeah (chuckles) exactly.
And it's the whole line of being agile, right?
You start small, you dream big,
you start small and you scale fast.
So, you start small with something here,
and then you start the next wave and the next wave,
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 goes on and goes on, and you know,
organizations such as ours, we are huge like 5,000 stores,
70 plus countries, 180,000 people, you know.
So there's huge amount of things when you start
to industrialize that.
And AI doesn't mean anything if you don't industrialize it.
Shervin: And Arti, as you were talking about
industrializing AI and getting real scale out of it,
in the context of amplified intelligence,
you know. Arti: Yes.
Shervin: Machine is easy to get them to play with human
because they don't have 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: So, it's different from, you know,
different levels of people in the organization.
Shervin: Mm-hmm.
Arti: One thing I found out is that,
maybe it's also about the culture in the organization as
well, right? So we come from a company with a culture of,
always being entrepreneurial, always seeking for,
you know, improving ourselves.
And that has been part of the organization,
it's in our DNA somehow to always try to be better.
And that's why also we talked about,
you know, the combination that we're just taking
a 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 a use case lead,
and then you have business experts,
and you have machinery 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 the whole development team,
and my job to make sure that,
my other colleagues also understand the impact,
and then utilize this to, you know, realize their value
in their part of the company,
they're part of our organization,
making our company continue to thrive
and become even better.
Shervin: I think there's an important lesson in there
for everyone as well, including for CEO's
and heads of businesses.
We are underscoring the importance of focus on value versus
a single sort of 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, thank you so much for making time.
Sam: Yes, thanks for taking the time today.
Arti: Thank you guys.
(ethereal music)
Shervin: Sam, I thought that was such a stimulating
conversation with Arti, let's quickly recap.
Sam: Sounds good. Shervin, it's interesting, you know,
we talked with Porsche,
we never even talked about tires and hubcaps and tightening,
(chuckles) tightening bolts, but when we turn to fashion,
he talked about the importance of doing that,
and his analogy was very ultra-related.
Shervin: That's right, and when we talked with Porsche,
if you're talking about coffee,
Sam: (chuckles) Exactly.
Shervin: Everyone is, you know, joking aside,
I think the common thing 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.
This is not exactly mean what we're talking about here.
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, and it's about."
You know, he talked about it as amplified intelligence,
you know, bringing human and AI together
rather than all one or the other.
Sam: I think if we pulled out the words,
he said learning more than anything else.
Shervin: That's right, 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, you know,
reminds me of "Field of Dreams,"
you know, "If you build it, they will come."
I think this is-- Sam: He said the opposite.
Shervin: 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 got to build it
together. I think it's an important lesson here.
Sam: Yeah, he emphasized the value first
and then structure and that was important.
There was an element too of,
of a weakest link thinking that came through that,
he talked about lots of different places
that they were using artificial intelligence,
and 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, you know, that kind of speaks to his,
the tension with value versus structure,
he wanted to kind of 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
and so, almost everything seemed to be about a balance.
Shervin: 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 this baseball field,
and then everybody will come and play," you know.
Sam: Right, the other part of that was that he said,
they didn't have AI use cases.
And that sort of structure first
would lend you towards a 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: That's right, that's right.
Sam: 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.
See you next time Shervin.
Shervin: You too.
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