The HEINEKEN Company is one of the world’s pioneering global brewers. Founded in 1864, it continues to innovate as it pursues its ambition to become the world’s best‑connected brewer. For the Season 7 premiere of Smart Talks with IBM, Malcolm Gladwell sits down with Surajeet Ghosh, HEINEKEN’s Chief AI Officer, in front of a live audience at SXSW to explore how the company is using data and AI to transform its operations.
This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies or opinions.
Visit us at https://www.ibm.com/think/podcasts/smart-talks
See omnystudio.com/listener for privacy information.
Listen
Watch
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It's
a new season of the Smart Talks with IBM podcast series.
Speaker 2 (00:09):
This season on Smart Talks with IBM, Malcolm Gladwell is back,
and this time he's taking the show on the road.
Malcolm is stepping outside the studio to explore how IBM
clients are using artificial intelligence to solve real world challenges
and transform the way they do business.
Speaker 1 (00:25):
From accelerating scientific breakthroughs to reimagining education. It's a fresh
look at innovation in action, where big ideas meet cutting
edge solutions.
Speaker 2 (00:34):
You'll hear from industry leaders, creative thinkers, and of course
Malcolm Gladwell himself as he guides you through each story.
Speaker 1 (00:41):
New episodes of Smart Talks with IBM drop every month
on the iHeartRadio app, Apple Podcasts, or wherever you get
your podcasts. Learn more at IBM dot com slash smart Talks.
This is a paid advertisement from IBM.
Speaker 3 (00:57):
H'm Malcolm Gladwell. Welcome to Season seven of Smart Talks
with IBM. This year, we're exploring new stories about how
companies are using the latest advancements in AI and quantum
computing to create smarter business. For the first episode of
the season, I flew to Austin, Texas to join Sergei
Ghosh on stage at south By Southwest. Sergei is Chief
(01:20):
AI Officer at Heineken, the world's pioneering beer company. Founded
in eighteen sixty four. Heineken is deep roots, but it
continues to push the boundaries of innovation today. In twenty twenty,
the company came up with a goal to become the
world's best connected brewer. Surge plays a key role in
leading that transformation, and I sat down with him in
(01:42):
front of a live audience to understand what that journey
looks like and what it takes to reinvent a global
company from the inside out. And before we get to
the question of what you do in your job. So
I'm really interested in people who have jobs it didn't
exist for most of their life, and I'm curious how
(02:03):
you got there.
Speaker 4 (02:03):
Yeah, first of all, thanks for having me here. Yeah,
actually it did exist, and people sometimes don't realize AI
is not new. It's been there for seventy five years,
since nineteen fifty.
Speaker 5 (02:15):
It just changed over time.
Speaker 4 (02:16):
How the application is happening, right, So one thing to
keep up with is as AI became more popular and
more embedded in business, how do we upscal ourselves to
stay at par with the technology trends. So the preparation
for me personally started actually a long time ago, so
when I was in grad school in US, and I
also to live in US by the way, for a
long time.
Speaker 3 (02:35):
You're Indian.
Speaker 4 (02:37):
I'm an Indian originally, but it's news. I did my
grad school here and there. Actually I started taking courses
in newer networks and at fission diagents back in two
thousand and two, and it wasn't popular back then.
Speaker 5 (02:49):
I was just curious, what is it? Maybe it's the
next big thing.
Speaker 4 (02:52):
And I'm so glad I did that because that sort
of helped me build that foundation.
Speaker 3 (02:56):
What was it you said you were? You were curious
about it? You're curious about it? Why what caught your
eye about it?
Speaker 4 (03:03):
Was very different because the main difference was before that
I was an engineer by professions.
Speaker 5 (03:08):
I went to engineering college.
Speaker 4 (03:10):
Everything is rule based, Everything is based on a formula,
a physical equation. AI is something different because based on
data and statistics, it never gives you a clear answer.
It gives you a probability, and I just thought this
is very interesting because if you're trying to solve a problem,
you don't know exactly how to solve it. There is
no equation. How do you get around that? I think
(03:30):
that's where AI comes in. It finds those patterns within
data and comes up with some prediction that intrigued me.
So this is what year that you start I started dabbling,
let's call it dabbling in AI was two thousand and two.
Speaker 5 (03:44):
It was almost twenty four years ago.
Speaker 3 (03:45):
Four years so put what you were playing with in
two thousand and two was an extremely primitive version of what.
Speaker 5 (03:52):
We have now.
Speaker 4 (03:53):
I think it was very relevant because the way I
see it, should I have skipped all the foundations that
I learned over the years and just gone to the
current state. Maybe, But when I look back, I think
that foundation really helped me because back then, and surprisingly,
by the way, new on networks. When I talk about that,
it's still very ralliant and relevant within AI. The entire
(04:15):
foundation is new on networks. So I think that foundation
really helped. Yeah, and I still find it very relevant
and I apply it day to day.
Speaker 3 (04:22):
Yeah, imagine in having a conversation with you twenty years
ago and I say what are you up to? And
you say, I'm playing with this thing neural networks. Early version,
would you have used the term artificial intelligence?
Speaker 5 (04:37):
Probably not.
Speaker 4 (04:37):
I probably would have used something is called statistics, which
is everyone is aware of. Back then, it was more statistical,
so you don't have these big algorithms at that point.
But then something happened. I don't know if you heard
of this company called Cago. They used to host these
sort of data science competitions and anyone can participate and
if you do really well, you get a price. That
(04:58):
was a good motivation, just to see, okay, learned something,
let me apply it and see how good I am
I'm getting at it. So I think that was my
first entry point where I really got hands on into AI,
and that probably stayed with me for a while. I
think that was back in two thousand and six. That's
what I started getting hands on. And the funny thing
is when you look at these Caggle competitions, the use
cases they used to give actual industry applications, so you
(05:21):
were really dealing with business problems applying AI to solve it,
and then you know, wait a minute, a medical company
is using it. A manufacturing company is using it, a
banking is using it. And this is two thousand and six,
so it already started and then it just yeah today,
it's a different ballgame now.
Speaker 3 (05:38):
So you you came to Heineken.
Speaker 4 (05:40):
When twenty twenty, twenty twenty right middle of COVID, right.
Speaker 3 (05:46):
And where you brought in to be the chief AI officer?
Speaker 4 (05:49):
Was you explore it and the title changed? But yes
I was the global leader?
Speaker 3 (05:52):
Yeah yeah, And what made you want to take the job?
Speaker 4 (05:56):
I was actually working for Amazon at that point, but
when I looked at High and I thought, okay, this
is a legacy, traditional company, right, and AI was not
a capability embedded.
Speaker 5 (06:07):
At that point of time.
Speaker 4 (06:08):
So it's a great opportunity if I can start something
from scratch, really build it across the entire valuation of Heineken.
I mean, that's probably the best job anyone can even
ask for. Yes, it's of course a lot of responsibility
that you have to make sure that you really build
the right products and right capability, But that also happened,
so I look back, it's quite fulfilling.
Speaker 3 (06:29):
But it's also if I might playable was advocate for
a moment, you're also taking a risk going into an
established how long has Heineken been around one hundred and
sixty two years to be specific, eighteen sixty four. It's
a very different proposition walking into one hundred and sixty
year old company and saying I want to bring the
future to the way you operate. Then it is with
the startup.
Speaker 5 (06:49):
That is true, but it's also a challenge.
Speaker 4 (06:51):
It's a good challenge, and also that Heineken is also
looking externally. There are companies that are picking up speed
and embedding and adopting AI, so should be all behind
not really, so we also need to pick it up.
So I thought it was a good challenge because the
use cases were there, the opportunity I put sense the
business really was having the appetite.
Speaker 5 (07:11):
Let's do something different.
Speaker 4 (07:12):
When we apply AI in a corporate setting like this,
it's super important to understand how the business actually works.
What's the value chain looking like, what are the nuances?
Where can I And once you get an understanding. It
took me some time, by the way, to understand the
full business and the complexities, but once you cross that
threshold you figure out what's feasible what's.
Speaker 5 (07:30):
Not, then it opens up.
Speaker 4 (07:33):
Wait a minute, within the valuetion, I see ten areas
I can optimize.
Speaker 3 (07:36):
You say, once you want to see in the business,
describe the business. What the Heineken puzzle?
Speaker 5 (07:41):
So well, puzzle, Let's see if it's puzzle.
Speaker 4 (07:43):
After I explained it's actually you start with the procurement
where you get the glasses, cans and all the raw materials.
Then it comes to the brewery where the magic happens.
That's what behinneqn beer is produced. Then it goes to
the distributors. Basically a supply chain takes over. Then it
goes to the customers. What we refer to as customers
are the bars, restaurants, retail stores, moment pop stores, convenience stores,
(08:06):
and that's where actually consumers then come and actually consume
the product.
Speaker 5 (08:11):
So that's actually the value chain.
Speaker 4 (08:12):
It's actually pretty linear when you think of it, but
there are nuances depending on the country and the market.
There are some specific rules and guard rails that you
have to be aware of.
Speaker 3 (08:21):
So you have that process going on all around the
world and across multiple brands.
Speaker 4 (08:27):
Multiple brands, multiple countries, multiple operating companies.
Speaker 3 (08:30):
Yeah, from your perspective as someone who is the chief
AI officer, what are the tasks in front of you?
What's your opportunity?
Speaker 4 (08:38):
There any process that you think that is maybe not
digitized or maybe not data driven, you can optimize. I
look at it like a pendulum. So one side of
the pendulum you have complete gut based decision making. The
other extreme is completely data driven. So the idea is,
can we swing this pendulum it will be towards data
driven from where we are.
Speaker 3 (09:00):
Give me a specific example of a problem you set
out solve or address.
Speaker 4 (09:06):
There are quite a few, but if I want to
pick one of the most fun one fun one may be.
Speaker 5 (09:11):
The most most complex one. Let's bring that one. I think.
Speaker 4 (09:14):
So we spend quite a bit on advertising, and Heineken
is a largely a lot of it marketing company, and
we really care about our brands.
Speaker 5 (09:22):
And products, you know, we're almost obsessed with it. Let's
take an example.
Speaker 4 (09:26):
Let's say you have X million dollars as your budget
and you have two brands, let's say Heineken and Tosa Kiss.
I think the crowd audienceale will be familiar with that.
And then you have three touch points TV, YouTube, Instagram,
and I want to optimize my advertising budget between different
brand and touch points, so heiniken on Instagram.
Speaker 5 (09:46):
How much should I spend? It's a very easy question to.
Speaker 4 (09:49):
Ask, but to actually solve this you have to study
historically how these performed and then create a model and
then predict if I allocate my budget in this way
that's probab more optimal. Before it was more like somebody
to con gun based decisions saying okay, here goes xpillion,
here goes five million, and we say no, no, no, that's
not the right proportion.
Speaker 3 (10:08):
What did the AI tell you about the accuracy of
those spending decisions?
Speaker 4 (10:14):
In the past, we looked at the return on investment
from this advertising, so how much incremental volume or volume
of brre reselling or revenue are recreating and we find
out can we improve that. It's a moment to apply AI.
And when we look at that this significant improvement. In
some cases we have thirty percent uplift, thirty percent, thirty percent,
three zero not everywhere. In some places we got but
(10:37):
it ranges between ten to thirty percent uplift depending on
the type of AI product you're building.
Speaker 5 (10:41):
And that impacts the top line.
Speaker 4 (10:43):
So it's very easy to also realize that value.
Speaker 5 (10:46):
People get to see.
Speaker 3 (10:46):
It, so you say, oh, we we can do a
way better job if we spend x more or x
less in this particular area. Give me another example of
a if.
Speaker 4 (10:54):
Another one would be we have a very big large
salesforce within Ainiken. So this sales reap. What they do
they go to the outlets, the bars and restaurants, and
they maintain that human to human relationships with these our
customers is super important to maintain that, and they go
solve the customer problems. Let's say someone is out of stock,
someone is about to churn, or their surprise mismatch, something
(11:17):
like this, and before they us to go like this,
let's say a sales rep on a day to day
job has to visit five places ab CD E, five
different outlets, and he used to go ABCDE. Turns out,
the model tells you on any given day, if you
optimize taking into account the traffic conditions, instead of going
from that linear route, you go to D first and
(11:40):
then to B, then to C, then to E and
then to A. And the reason for doing that is
the model tells you, if you visit customer D first,
he has the biggest problem that needs the most amount
of time to be solved, and that's how it optimize.
Speaker 5 (11:52):
And also the sales reps now.
Speaker 4 (11:54):
They are becoming so educated with some of these AI models,
they are now becoming a business advisors. So they are
no longer just solving little problems. They are having the
time to say what else can I do for you
as the customer? So that I think it was a
big change within Heineken because it impacted a lot of
people that were using that.
Speaker 3 (12:13):
In an instance, it requires not just building a model
that can be smarter about how people should spend their
time and what they should say, but you have to
obviously educate your salesforce to believe in what the selling
tell me about that piece. Is that a piece that
you that you're a part of or is someone else?
Speaker 4 (12:31):
Yeah, that's also part of because that is super important.
I think we can build the best models. Best algorithm's
highest accuracy doesn't mean anything if it's not used the
right way. So what we do we have within our
company a pretty big upskilling program. So bring everyone along
in common understanding, basic understanding of what AI does. Not
everyone needs to understand new networks or algorithms right, But
(12:54):
what we do is give them a handheld device and
an app which is driven by AI. Play with it,
have fun, see how it changes your life. And once
you start liking the product, liking the UIUX, then you
start getting more. And the insights also tell you the
story because once you start getting the value, I am
not having to pitch my models anymore. The sales reps
(13:17):
and the markets they are pitching on behalf of us.
Speaker 5 (13:19):
And that's such a good place to.
Speaker 3 (13:21):
Be, is it. It's interesting? So in that instance where
you're designing a more efficient form of interaction and fruitful
for of interaction between sales reps and customers, I could
see a version of that where it is really clear
looking up from a high level that things are working better,
but it might not be clear to the salesperson. Is
(13:43):
the salesperson who's now following the direction of the AI
aware that they are more efficient?
Speaker 5 (13:48):
They are?
Speaker 3 (13:50):
How are they aware the market?
Speaker 4 (13:51):
They realize few things that they were visiting customers just
because they had to visit because it was in the schedule.
Now they go there and they find out, wait a minute,
I never tackled this big problem that was not being addressed,
and they solved it. And the customer feedback also comes
back saying we are really happy. So for all these products,
we get the feedback not just from the sales reps,
(14:12):
but also for the customers. Do you really like the
recommendations we are giving you? And that's the best validation
you can think of, because it's four stand or feedback.
Speaker 3 (14:20):
When the AI is doing this ranking, it wants you
to focus on the customer with the biggest problem first
or is it much more complex than that.
Speaker 5 (14:29):
It's a little bit more complex than that.
Speaker 4 (14:30):
Yeah, but usually it's a rank pordert in terms of
which one is the biggest problem that needs the most
amount of time.
Speaker 5 (14:37):
That's how it's ranked order.
Speaker 4 (14:38):
But sometimes you can also override the model, right, We
also give options to people. You don't have to all
the time one hundred percent follow the recommendation. If you
have some urgent priority, you can overwrite that.
Speaker 3 (14:48):
That's also possible with something like that. Is there a
next level you can go to? So you design this
system and you say, oh, I can make our sales
staff a lot more effective and the way they operate
with their customers, and then you see that it works,
and then it comes back and then you say, okay,
what's two point oh? Is there a two point oh.
Speaker 5 (15:09):
It could be.
Speaker 4 (15:09):
So it's always about innovation. Then you think, okay, today
we go and solve the problems that have already happened.
What if we solve the problems that are likely to happen,
that will be the next step. So this customer hasn't
been very active for a while, there's a high chance
that that customer might churn out of Heineken. So what
actions can I actually recommend to make sure And we
(15:31):
do this, by the way, we also gather a lot
of customer feedback and complaints and feedback, and we use
LLLMS to extract and glean information. Okay, what are the
real pain points? What's the theme and the topic that
needs to be addressed. And once you do that, then
also you can prepare ahead of time. We're already there,
by the way, when I say two point zero, we're
already testing it. You solve problems or you'll try to
(15:52):
solve problems before daven occur. So I think that's a
little bit of a two point zero. Then we have
to see what else we can do with it.
Speaker 3 (15:59):
Tell me you've had this partnership at Heineken with IBM
for since twenty thirteen, twenty thirteen, So you came in
and there was already a strong working relationship. Tell me
about how that relationship started and what does it mean
on a practical basis. You're building all these tools, how
does the interaction with IBM work.
Speaker 4 (16:20):
Yeah, so I think good to give a little bit
of context. Heineken started this digital transformation journey in twenty
twenty formally, but the tech was already there. We had
our systems, platforms, data, everything was there. So all the
IT four ID systems is where IBM was partnering with
us from the get go, from twenty thirteen, and it's
a very long standing partnership because as we found the
(16:43):
tech is evolving, our partnership also kept evolving because we
need to keep up up to the speed. So it
was more about IT for IT systems, cybersecurity platform, data,
incident management, service level, you name it.
Speaker 5 (16:57):
All of that.
Speaker 4 (16:57):
IBM was supporting us both in terms of hands and
also in terms.
Speaker 5 (17:01):
Of strategy to create together.
Speaker 4 (17:03):
But that also evolved, like I said, when we went
into this digital transformation journey in twenty twenty, then we
started building this digital core, which is the central nervous
system software system of finikin that's where IBM is really
partnering with us and helping us not just shape the
whole thing in terms of building it hands on, but
how do we strategize that so that it lands well.
(17:25):
So that's yeah, it's a long trusted partnership. I think
we are going to go a long way together.
Speaker 3 (17:30):
Yeah, Heineken Space just sells out of Amsterdam.
Speaker 5 (17:34):
The head office is absolutely so the IBN.
Speaker 3 (17:36):
People who work with you are they on site?
Speaker 4 (17:38):
There are some on site and there are some teams
in India some times spread across the globe. But for
for tech, I think the location doesn't matter. But you
still need people on site to actually talk with the
business and really understand what the problem for.
Speaker 5 (17:51):
Those interactions are also very important.
Speaker 3 (17:53):
When you said you wanted to when you got there,
you wanted to build a digital nervous system, what does
that mean?
Speaker 5 (17:58):
Maybe good to give you an example.
Speaker 4 (18:00):
Let's say iPhone, right, it's a central platform, but you
can download thousands of apps there and all of them
once you download seamlessly integrates with the system and you
don't see any difference. This is the same thing. So
what we want to build within Heineken is a central
software system which the old school way of saying it
is the ERP enterprise resource planning. It removes the fragmentation
(18:22):
of different platforms, it brings it all together. It makes
sure all the business applications within supply chain, commerce, finance
HR all in one place and coordinates them everything orchestrates them.
The benefit of doing that is two one across the
value channing of one way of doing business because everything
is standardized, but we also have multiple markets globally. Across
(18:44):
the multiple markets also it becomes one way of doing business,
so it's both ways. And once you standardize it, we
can embed new apps which will seamlessly integrate, and then
it just keeps scaling further. Can we scaled very quick
and having that digital core really help us scale because
the value from AI and insights is not just building
(19:04):
one product in one place. It's how quickly can you
scale it?
Speaker 3 (19:08):
And are you still building it or is it an
ongoing thing or.
Speaker 4 (19:10):
It's ongoing thing because there are nuances in markets, There
are nuances in tax systems and currency systems, so it
takes a little bit of as much as we want
to standardize, you also have to bake in some of
the nuances otherwise people cannot use it. So those sort
of outliers we have to also bake in.
Speaker 3 (19:28):
You must learn something when you suddenly suddenly, but when
you standardize a bunch of things that have not been
standardized before, presumably you have a basis for comparisons you
couldn't make before.
Speaker 5 (19:39):
That's correct.
Speaker 4 (19:40):
So we also get a lot of external inspiration. So
sometimes these large projects we don't start by over whom,
so we get inspiration from partners like IBM or someone else.
How have they done it in somewhere else and where
it's really working. So then you get those ideas, the learnings,
and you start building that way, and while doing that,
you feel out that, wait a minute, we might have
(20:01):
done something different and maybe it's even better than what
others have done.
Speaker 5 (20:05):
Yeah, so it also creates creativity.
Speaker 3 (20:07):
Yeah, I'm just curious whether there was an insight that
you learned from that process that comes to mind A
big one.
Speaker 4 (20:16):
I think we don't look at tech for the sake
of tech and embedding it, you know, just just as
one would say it's one single core, one single platform,
everything coordinated. What's the big deal. The big deal is
bringing people along to actually believe that there is a
benefit of doing one way of business, and that actually
means the entire company, not just the leadership team.
Speaker 5 (20:37):
So to bring.
Speaker 4 (20:38):
Everyone on board and say, tell us how this platform
should look like, what are the components we should build?
It's a pretty big task. Yeah, that's where the chain
management comes in.
Speaker 3 (20:47):
Yeah, what was hard about that? Did you have bumps
or we did?
Speaker 4 (20:52):
I think it's about convincing people the benefit of doing this.
Why do we say, if you standardize something, we can
go at high speed in scaling. It's not very easy
to visualize that at first. But what you do is
you show some proof of concepts. And that's I won't
call it a trick. It's almost bread and butter of
what we do. Show a small proof of concept, show
that it works, show that we can scale, and then
(21:13):
automatically if people start having the faith, and then well
say okay, I see it.
Speaker 5 (21:17):
Yeah it makes sense.
Speaker 3 (21:19):
Sergery, at least half of what you've talked about is
not about the check itself, but about being a kind
of evangelist for the check. It is half the right percentage.
How much of your time is spent convincing an organization
and people in the organization to see the value in
what you're doing as opposed to building the thing that
has value.
Speaker 4 (21:36):
Yeah, I think that proportion changed over time. When I
first joined, I was very much into the products itself.
I was to review codes myself, let me check what's
going on. And over time, of course, then you focus
on somewhere else. You realize, like I said, best codes,
best models, I use this, if not use the right way,
then I said, okay, now my time is to actually
inspire and show people the value of it. What I
(21:57):
realized is explaining in very simple language really goes a
long way because you take away that that anxiety that
a new product is coming in and we humans a
little bit have this. I don't know if it's the
right thing to say, but it's inertia of rest. We
like status, poke, we don't sometimes like change that stops our.
(22:18):
So every time you build a new product that will
change our way of working. Yeah, there's inherent little bit
of anxiety. Yeah, take that away. Yeah, a job becomes
a lot easier.
Speaker 3 (22:26):
Are you a good evangelist?
Speaker 5 (22:29):
So far it's working?
Speaker 4 (22:30):
I think I can do better, for sure, because it's
about understanding what's the reason people sometimes might be reluctant
to actually onboard or adopt a new technology. And once
you sort of understand that, then that anxiety goes away,
it becomes easier.
Speaker 3 (22:46):
How many people work for a hidekinde.
Speaker 5 (22:48):
About eighty five thousand, ninety thousand.
Speaker 3 (22:50):
Globally, so you have essentially a city pretty much. And
if you look at that universe of eighty five thousand,
is there anyone in that universe who is not what
you're doing?
Speaker 4 (23:02):
The way we do it is we prioritize based on
the size of the market and the potential opportunity. Yes,
if I had infinite resources, I would go everywhere within
the high Naken company and do everything, But we cannot.
Speaker 5 (23:15):
We don't have infinite resources.
Speaker 4 (23:17):
So we say, let's be a little bit picky and
choosy where the biggest opportunities are. But it's a matter
of time. Right today we touch upon the big market's
biggest scope. Over time, it's going to be pervasive through
the company. But the appetite is already there, so people
are really even if they have not really embedded some product,
they're asking for it, which is a fantastic place to be.
Speaker 3 (23:38):
Yeah, yeah, what's been your biggest disappointment?
Speaker 5 (23:41):
So far.
Speaker 4 (23:42):
So far, it's been very fulfilling, I must say, but
I think what I would look back. Can we do
things a little bit quicker? Can we go a little
bit at high speed? And that's why this whole concept
of digital backbone. Can we standardized everything? If we can
speed that up, if we can really scale quickly, I
think that will be the best. Because today have a
very good problem. People are asking for products. Sometimes I say, yeah,
(24:03):
I need to put it on a timeline in a roadmap,
because I cannot just cater to it immediately.
Speaker 3 (24:09):
Presumably that's one of the things that the people you're
working with at IBM can tell you. They can give
you a sense of how quickly others have adopted some
of these technology.
Speaker 4 (24:19):
That's correct, and that's actually one of the bench funds
that you're referring to. We see I'll be losing pace,
and which other things can we go forward? And in
some case when you look at the digital core and
the backbone, maybe specific areas we can speed up because
those are the areas that are maximum potential value, and
some of them we can deprioritize a little bit.
Speaker 5 (24:36):
Yeah, that we do all the time. Yeah, just a
pragmatic approach.
Speaker 3 (24:39):
To I'm curious about a highly specific question, which is,
so here you have a legacy brewer bas in the Netherlands,
one hundred and sixty years old. If I were to say,
I want you to take an entirely new job, I
want you to do what you're doing, but I want
you to do it for an American company in a
(25:02):
completely different industry that's thirty years old, maybe a company
that makes the vacuum cleaners thirty years old in America.
How much of what you're doing. I guess what I'm
trying to say is, are there's things that are particular
to Heineken that have made your job sort of challenging
or interesting or that just wouldn't be an issue in
(25:23):
another environment.
Speaker 4 (25:25):
So it's a good question, and thanks for the enticing offer,
but I will pollite.
Speaker 3 (25:31):
I tried to make it as an average exactly.
Speaker 4 (25:33):
But I won't polite to reject offer. But I'll tell
you why i'll reject.
Speaker 3 (25:36):
You're going to be in Nebraska. They're making just one
kind of vacuum cleaner.
Speaker 4 (25:40):
What I went to school in Iowas, Yes, exactly, So
I'm quite a bit. I think there's a cultural difference
we're all very passionate about our brands and products, and
there's a lot of it is connection based in the
sense we create these connections with our customers sometimes consumers,
and it's all about maintaining that. And once you get
a feel of it, you feel part of the family.
(26:01):
That's a very good feeling to have. And the fact
that today where I am, if I look back, I
probably will happy to say very fortunate to have probably
one of the best jobs in the world in the
current times. And there is no end to innovation, by
the way, and even within Heineken, yes it's a traditional
company who is stopping innovation. There's a lot more too,
so I'll be very busy for the next few years.
Speaker 3 (26:21):
What are the Dutch like? This is one of the
oldest and most successful commercial cultures in the world, A
tiny country that's been solidly successful, that's for it. I'm
curious about innovating in that kind of environment. How is
that different from innovating in a huge country like the
United States or in a different kind of national culture.
Speaker 4 (26:41):
I think it's a question of opportunity because within Netherlands,
by the way, Netherlands has one of the most highest
number of startups within Europe, if not the highest. So
there is this culture of innovation that's already embedded in there.
It's happening all the time. Companies like Phillips, ASML some
of the very big players already there. So it could
(27:01):
be a little bit different. I think in Netherlands we
want to make sure what we are doing really is
going to work, so there's a little bit of discussion alignment.
It's more structured, but also agile in a way we
do things, and us was more like let's do let's
go quick, experiment, learn fail. So I think there's pros
and cons on both sides, but so far it's quite good.
Speaker 3 (27:22):
Give me a sense of what your what's a day
in a life like for you? What does it look
like to have the job that you have in a
place a cande.
Speaker 4 (27:32):
First of all, it starts with the calendar and the
number of meetings I have, which is usually filled for
forty hours longer in the week. So that's the starting point,
and then I have to pick and choose which meetings
I need to prepare for what, and usually these meetings
are mostly about where are we with the product.
Speaker 5 (27:48):
What are the challenges?
Speaker 4 (27:49):
How can I help and solve it, and then sometimes
also pitching new products or convincing something, and also sometimes
chain management. I'd also do sessions where I present internally
quite quite often go to different places, because it always
helps to be in front of the audience when you're
presenting something. We also started something recently which we call
AI boot Camp, which is you use JANEAI as an
(28:11):
interface for all these big AI models and people can
interact in a very fun way. That's our new way
of really convincing the rest of the company that hey,
this is fun to play with and let's go.
Speaker 3 (28:24):
So yeah, it's how many people would you cycle through
that kind of book camp anyone?
Speaker 4 (28:30):
Usually we keep it a small group just to make
sure everyone is doing something hands on and nobody's just listening.
So usually a twenty to thirty people max. And then
go from one place to other and it's all hands on.
You cannot sit and watch. You have to participate.
Speaker 3 (28:44):
Are you directly involved at all in the design or
creation of any of these tools?
Speaker 4 (28:49):
So I review it and then I used to review
also the codes before, and now I'm mostly like trying
to get the feedback from the people that are using it,
because that's my best validation point.
Speaker 5 (28:59):
I high net promoter score on these products.
Speaker 4 (29:02):
I know the job is well done, but I do
check accuracy of model. Some of the basic things you'll
check in AI. Is the model drifting over time? What's
the accuracy?
Speaker 5 (29:10):
You know?
Speaker 4 (29:10):
How is it hosted on a platform? These things I check.
But we also have mechanisms on those, so it's not
like every time you have a DiPT deep and look
into everything.
Speaker 5 (29:18):
Yeah.
Speaker 4 (29:18):
So once you have these mechanisms in place, then these
sort of tasks become easier.
Speaker 3 (29:23):
You've used the phrase that you want to make honey
in the Best Connected Brewer. What does that phrase mean?
Speaker 5 (29:28):
Yeah?
Speaker 4 (29:29):
So I think it started with the ambition in twenty
twenty when we said we're going to digital transform. Remember
the pendulum I was talking about from gut page to
all the way to data driven And in today's world,
when you think of digital transmission, there are a few components,
cybersecurity being one of them, the digital core, like I
was saying, is one of them. Simplification and automation of
systems is one of them. Our breweries, how can we simplify?
(29:50):
And then comes data and AI, which is the really
one of the biggest components. And when you think of
best connected Brewer, the idea is we have been serving
our consumers and customers for one hundred and sixty two years.
What's different If you leverage tech into Bay's world. I
think you can really enhance the experience the customers have.
The example I was giving you earlier about the salesforce
(30:12):
going in different places and optimizing the rout that's a
good example why the relation is maintained just simply by
data driven insights. So if you can connect all the
different applications, all the platforms, remove fragmentation, scale very quick,
make sure your company is CyberSecure, things are simple and automated.
That's what we call the best connected brower. That's the ambition.
Speaker 3 (30:34):
Actually, how do you measure this success of what you're doing?
Is do you expect that your efforts will have a
measurable and tangible effect on the bottom line of the company?
And can you actually figure out what the impact of
your efforts is?
Speaker 5 (30:53):
Yeah, we do.
Speaker 4 (30:54):
I think that is super important to measure because the
first one I was referring to proof of value and
I'm embedding some model, does it really work? So we
do a B testing, which is basically you keep aside
some sample and you actually launch the product on a
different sample, and you see the difference between the two.
The assumption is those that had the product and those
that didn't have the product, both of them went through
(31:16):
the same experiences because of market seasonal, etc. That's one
good way of doing it. And if you cannot have
the luxury sometimes of doing a B testing because everyone
is having high appetite, give me the product I don't
want to sit aside. Then you do some sort of
causal models like we say, so you kind of look
at what would have happened if the model was not there,
(31:36):
and then you predict that and since the model was there,
something else happened. The difference between the two is the
incremental value the model is creating. A B testing is
more accurate the causal models. The other one, like you said,
which called time series model, a little bit less accurate,
but directionally both give you the sense that yes, it's working.
Speaker 3 (31:53):
What happens if you do a B testing or a
new idea and you don't see a difference.
Speaker 4 (31:59):
In that case, we will move on to something else
because it means it's already optimal. Then we said, good,
check that now let's move on to something else. But
we need to just make sure that the process is
still running optimally.
Speaker 5 (32:11):
So time to time, you keep.
Speaker 4 (32:12):
Doing every testing anyway, every six months or whatever the
timeframe is. Yeah, just to make sure that it's still
still relevant.
Speaker 3 (32:18):
But what if this is we're getting out on a
little bit of a digression here, but it's something I've
often thought about. What if the value that is being
created is not measurable? So I'll give you a dumb example.
When you were talking earlier about the salesman and giving
them a new, you know, better instructions about how to
basically spend their day, what if you tested that, discovered
(32:42):
it it didn't have any effect on the bottom line.
But in fact, what was happening was that the salesmen
were a lot happier with their jobs. And we're satisfied
and we're excited to come to work. Do you measure
something like that? Something intolerable?
Speaker 5 (32:57):
Measure?
Speaker 4 (32:58):
One way is NPS four, which I said promoter score.
Are you really happy with the product? Has it changed
your life? That gives you a good indication then, And
by the way, it's a numeric output, so it gives
you a score between minus hundred to plus hundred, and
sometimes it's not even tangible. Let's say we do something
for corporate affairs because they want to get external signals
of consumer insights and then just lean some information. Maybe
(33:21):
we act on it, maybe we don't, but this is
for a good cause. Sometimes you just want to study
the market. There's no immediate value if you don't create
a product out of it, or something to do with
legal If there's a reputational risk for Heineken, can I
extract some insight that will prevent us or create the
best briefing or summary or external briefing that using AI
that will help us protect ourselves. That's also reputational damage.
Speaker 3 (33:46):
Last question before we go to questions. I'm curious when
you look at the very beginning you talked about this
linear value chain we're in that along that chain are
you having the most and where are you having the
least impact. I'm more interested in the second half of that.
Speaker 4 (34:04):
Yeah, I think we covered few things, but one area
I think we can do more is really.
Speaker 5 (34:09):
Understanding consumer sentiments.
Speaker 4 (34:12):
And the reason for that is Heineken is people go
to the bars and outlets and you're not really leading
your first hand data there right, you're enjoying a beer,
then you walk away. I don't know exactly what you did.
I can get some aggregated data to make some sense
out of it. But if we can really get consumer
insights as to what the consumers like and dislike, what
sort of ad you like? How should I design my
(34:34):
Hanneken campaign so it resonates with a cluster of individuals.
That would be a little bit of holy grail as
the next step, like you were talking about two point zero,
and to get consumer insights first party data.
Speaker 5 (34:46):
It's not super easy.
Speaker 4 (34:48):
So what we are trying to do is create digital
twins of consumers. So at an aggregate level, they give
you a sense of Also with agent Kei, which is
also you hear a lot about to get a sense
of how consumers might react to certain campaign or certain product,
and that should give us quite a bit of insights
that right now we don't have access to. I think
that's one of the areas we could really do.
Speaker 5 (35:09):
A lot more. Yeah.
Speaker 3 (35:10):
Yeah, So if I said last question, if I sat
down with you, it's twenty twenty six. Now, we did
this over five years from now twenty thirty one, we're
sitting in this chair, tell me what the kind of
what's going to be the next big score.
Speaker 4 (35:26):
I think one area will be how we make our
lives as employees are finding a lot easier. So the repetitive,
boring task, manual task. Can we automate those things and
just use the time to do something more creative and
think big about the business itself. That will be one area,
most on the productivity side. But the other area would
be Indeed, when we look at gen Z and this
(35:47):
is fact, I'm not saying something my own opinion, there's
a trend of distinct trend of alcohol as a beverage.
The consumption is on a decline. So then what's the
next best thing for the new generation? What will resonate?
Those are the pockets we need to find, and I
think that's where we will transition very quickly over the
(36:07):
next five years.
Speaker 5 (36:07):
And if you get there, I think that will be
big success.
Speaker 3 (36:10):
So you think that your specific department responsibility can help
the company in discovering what the answer to that question
is about.
Speaker 4 (36:19):
Definitely, that's the ambition, that's what we're trying to do.
That's what we are really trying to get this one
over insights. I think that's the last mile. That's the
one part that is left.
Speaker 3 (36:28):
Sergey, this has been fascinating. Thank you so much. I
should say thank you for a question, sir. My uncle
was a Heineken salesman in Jamaica. He was the local
distributor and I have so many child memories of going
to Jamaica and he would show up in his Heineken truck.
So we're resonating deep in my mimory with this conversation.
(36:50):
He would come and he would have a Heineken right
there on the on the table and would drink it
at the end of the day. But we have we
have a few moments for questions. They're all the screen
and I don't have my glasses. Can you read them?
Speaker 5 (37:02):
Yeah, I can, I can read them. Then they should
be going order to the first one.
Speaker 3 (37:06):
Yeah no, no, no, no no, no, Rookie air, never
do that. Okay, read the first four and pick the
one you want to answer.
Speaker 5 (37:14):
Okay, go tip.
Speaker 4 (37:16):
But I gave it away already, so I'm going to
now do what I said. No, I think the first
one is quite relevant. So it's a question for both
of us. If you were advising a twenty year old,
what three skills. Would you tell them to start developing
right now to stay relevant in an AI driven world?
Speaker 3 (37:34):
Oh well, well you don't you have a twenty year
old or a near twenty year old. You have a
fifteen year old?
Speaker 5 (37:39):
You told me I have a fifteen year old?
Speaker 3 (37:41):
All right, what do you tell your son?
Speaker 5 (37:42):
I thought you were going to answer them this first,
but my.
Speaker 3 (37:45):
Kids are two and four. I tell them to put
away their toys. You this is more, you start more?
Do you have a is your fifteen year old son
or daughter?
Speaker 5 (37:55):
His son?
Speaker 4 (37:56):
And he's already thinker with AI. He's doing his own Python, etc.
Which I couldn't imagine when I was fifteen. So I
think I'll give a high level answer to be actually
successful depending on whether your hands on within AI, building
models yourself or not. There are three things I think
is super important. One is having that tech background, having
(38:17):
a common understanding of what AI really is.
Speaker 5 (38:19):
It always helps.
Speaker 4 (38:20):
Not everyone needs to have the details and algorithms and
how models work, not needed, but having that basic understanding
always is good. Then you know exactly how to gauge
what AI is really doing. And I think the other
thing is if you are in a corporate setting and
you are doing something for the business, work backwards from
the business and understand whatever you're building should actually touch
(38:41):
the business and make it beneficial for them. It's not
AI and modeling for the sake of it. That's for
a separate research and development. If you're in a corporate world,
try to build something beneficial for business. And I third
one I think which myself I learned quite a bit
in my in last six years.
Speaker 5 (38:56):
It's communication. Talking about AI.
Speaker 4 (38:59):
If you used a lot of tech jargon and mathematics,
sometimes people lose you. It's about how you really narrate
the story in a very simple way so people can
relate to it. I think if the combination of these
three has worked very well for me, so I can
say that anything you want to add.
Speaker 3 (39:17):
It's funny because I I met this guy who's the
headmaster at a Jesuit school in Manhattan. We've been chatting
and I want to do a little program at his school,
and it's all about asking questions because we're now into
the era of asking questions right.
Speaker 5 (39:36):
That's correct.
Speaker 3 (39:36):
AI is this incredibly good tool, but you have to
ask the right questions. But this is not just true
of AI, but it's also true of the world we're
living in is a world that's so interconnected and everything
involves so many different people that your distinguishing feature in
many context is not where they're not the answers you have,
(39:56):
but the quality of the questions that you ask.
Speaker 5 (39:58):
That's fantastic what you said. I fully avery.
Speaker 4 (40:00):
I think it's about asking the right questions that really
tells you, you know, you're looking for that that unique
thing that that you're missing.
Speaker 5 (40:08):
Yeah, I fully agree.
Speaker 3 (40:10):
Maybe I'll advite you to this class and like you
have that kind of time on your hands, if you
brought if you brought up you know, Heineken for all
the kids in school, that would that would really.
Speaker 5 (40:22):
Yeah, surely we have to build a special product for that.
Well let's see, all right, next question, let's.
Speaker 4 (40:29):
Go to this one or Malcolm, is there a particular
AI capability you are each excited to explore?
Speaker 3 (40:37):
That's for you, my friend.
Speaker 4 (40:39):
I think in the in the short term, I'm really
looking forward to Agent K. I is hearing a lot
of noise and hype and there are a lot of
feedback that I'm getting from a lot of companies. Have
you really embedded Agent KI within your systems? There is
a very mixed feedback. Some say yes, some say no.
I think the potential of agent k I when we
look at this task we do day to day. Let
(41:02):
me gives an example, invoice management or transactional finance or
very repetitive task. If you can really automate augment those
things with agent KI, I think it's going to be
a game changer. If you free up thirty percent of
our time just by embedding these things, then I can
really think big. Everyone can think big. What's next? Then
the creativity comes in. Otherwise all day you are stuck
(41:23):
with the repetitive task. So I think that's what I'm
really looking forward to. And this is very short term.
Within the next few years.
Speaker 3 (41:30):
Yeah, we have I think, what time for one more question,
Sergey go for let's see this is it's got to
be the last one always has to be the best one.
Speaker 4 (41:40):
Certain let for people hearing the phrase for the first time,
what is the real example that shows Heineken being the
best connected broer? Basically you're asking for a proof point.
Are we really becoming the best connective brower when we
look at our markets. Heinek in Mexico is a very
good example across how value chain if you work backwards
(42:01):
from consumers, customers and so on. We have advertising optimization
for consumers. For customers, we have next best action. Actually
for customers we are pricing and promotion optimized. For the salesforce,
we have next best action. For the breweries, we have
connected brewery. We are getting signals from these machines and
optimizing them. I think it covers a significant portion of
(42:23):
a value chain that's fully automated end to end. So
that would be a good example where we really saw
the benefit of taking it to the next level when
it comes to automation. So Mexico Heineken Mexico is a
good example.
Speaker 3 (42:35):
Thank you so much for joining us. Thank you to
all of you who came to listen.
Speaker 5 (42:40):
Thanks for it. Asper, Thank you very much.
Speaker 3 (42:43):
Yeah, that's it for the first episode of season seven
of Smart Talks with IBM, But stay tuned. There's so
much more to come this season as we die further
into how AI and Quantum Computing are creating smarter business.
Smart Talks with IBM is produced by Matt Ramano, Amy
Gains McQuaid, and Jake Harper. Engineering by Ninabird Lawrence, Mastering
(43:08):
by Sarah Buguer music by Gramoscope, Strategy by Cassidy Meyer
and Sophia Derlin. Special thanks to Sergei Ghosh and Michelle Ganji.
Post from the Heineken Company. Smart Talks with IBM is
a production of Pushkin Industries and Ruby Studio at iHeartMedia.
To find more Pushkin podcasts, listen on the iHeartRadio app,
(43:30):
Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell.
This is a paid advertisement from IBM. The conversations on
this podcast don't necessarily represent IBM's positions, strategies, or opinions.
Hey everyone, it's Robert and Joe here. Today we've got
something a little bit different to share with you. It's
a new season of the Smart Talks with IBM podcast series.
Speaker 2 (00:09):
This season on Smart Talks with IBM, Malcolm Gladwell is back,
and this time he's taking the show on the road.
Malcolm is stepping outside the studio to explore how IBM
clients are using artificial intelligence to solve real world challenges
and transform the way they do business.
Speaker 1 (00:25):
From accelerating scientific breakthroughs to reimagining education. It's a fresh
look at innovation in action, where big ideas meet cutting
edge solutions.
Speaker 2 (00:34):
You'll hear from industry leaders, creative thinkers, and of course
Malcolm Gladwell himself as he guides you through each story.
Speaker 1 (00:41):
New episodes of Smart Talks with IBM drop every month
on the iHeartRadio app, Apple Podcasts, or wherever you get
your podcasts. Learn more at IBM dot com slash smart Talks.
This is a paid advertisement from IBM.
Speaker 3 (00:57):
H'm Malcolm Gladwell. Welcome to Season seven of Smart Talks
with IBM. This year, we're exploring new stories about how
companies are using the latest advancements in AI and quantum
computing to create smarter business. For the first episode of
the season, I flew to Austin, Texas to join Sergei
Ghosh on stage at south By Southwest. Sergei is Chief
(01:20):
AI Officer at Heineken, the world's pioneering beer company. Founded
in eighteen sixty four. Heineken is deep roots, but it
continues to push the boundaries of innovation today. In twenty twenty,
the company came up with a goal to become the
world's best connected brewer. Surge plays a key role in
leading that transformation, and I sat down with him in
(01:42):
front of a live audience to understand what that journey
looks like and what it takes to reinvent a global
company from the inside out. And before we get to
the question of what you do in your job. So
I'm really interested in people who have jobs it didn't
exist for most of their life, and I'm curious how
(02:03):
you got there.
Speaker 4 (02:03):
Yeah, first of all, thanks for having me here. Yeah,
actually it did exist, and people sometimes don't realize AI
is not new. It's been there for seventy five years,
since nineteen fifty.
Speaker 5 (02:15):
It just changed over time.
Speaker 4 (02:16):
How the application is happening, right, So one thing to
keep up with is as AI became more popular and
more embedded in business, how do we upscal ourselves to
stay at par with the technology trends. So the preparation
for me personally started actually a long time ago, so
when I was in grad school in US, and I
also to live in US by the way, for a
long time.
Speaker 3 (02:35):
You're Indian.
Speaker 4 (02:37):
I'm an Indian originally, but it's news. I did my
grad school here and there. Actually I started taking courses
in newer networks and at fission diagents back in two
thousand and two, and it wasn't popular back then.
Speaker 5 (02:49):
I was just curious, what is it? Maybe it's the
next big thing.
Speaker 4 (02:52):
And I'm so glad I did that because that sort
of helped me build that foundation.
Speaker 3 (02:56):
What was it you said you were? You were curious
about it? You're curious about it? Why what caught your
eye about it?
Speaker 4 (03:03):
Was very different because the main difference was before that
I was an engineer by professions.
Speaker 5 (03:08):
I went to engineering college.
Speaker 4 (03:10):
Everything is rule based, Everything is based on a formula,
a physical equation. AI is something different because based on
data and statistics, it never gives you a clear answer.
It gives you a probability, and I just thought this
is very interesting because if you're trying to solve a problem,
you don't know exactly how to solve it. There is
no equation. How do you get around that? I think
(03:30):
that's where AI comes in. It finds those patterns within
data and comes up with some prediction that intrigued me.
So this is what year that you start I started dabbling,
let's call it dabbling in AI was two thousand and two.
Speaker 5 (03:44):
It was almost twenty four years ago.
Speaker 3 (03:45):
Four years so put what you were playing with in
two thousand and two was an extremely primitive version of what.
Speaker 5 (03:52):
We have now.
Speaker 4 (03:53):
I think it was very relevant because the way I
see it, should I have skipped all the foundations that
I learned over the years and just gone to the
current state. Maybe, But when I look back, I think
that foundation really helped me because back then, and surprisingly,
by the way, new on networks. When I talk about that,
it's still very ralliant and relevant within AI. The entire
(04:15):
foundation is new on networks. So I think that foundation
really helped. Yeah, and I still find it very relevant
and I apply it day to day.
Speaker 3 (04:22):
Yeah, imagine in having a conversation with you twenty years
ago and I say what are you up to? And
you say, I'm playing with this thing neural networks. Early version,
would you have used the term artificial intelligence?
Speaker 5 (04:37):
Probably not.
Speaker 4 (04:37):
I probably would have used something is called statistics, which
is everyone is aware of. Back then, it was more statistical,
so you don't have these big algorithms at that point.
But then something happened. I don't know if you heard
of this company called Cago. They used to host these
sort of data science competitions and anyone can participate and
if you do really well, you get a price. That
(04:58):
was a good motivation, just to see, okay, learned something,
let me apply it and see how good I am
I'm getting at it. So I think that was my
first entry point where I really got hands on into AI,
and that probably stayed with me for a while. I
think that was back in two thousand and six. That's
what I started getting hands on. And the funny thing
is when you look at these Caggle competitions, the use
cases they used to give actual industry applications, so you
(05:21):
were really dealing with business problems applying AI to solve it,
and then you know, wait a minute, a medical company
is using it. A manufacturing company is using it, a
banking is using it. And this is two thousand and six,
so it already started and then it just yeah today,
it's a different ballgame now.
Speaker 3 (05:38):
So you you came to Heineken.
Speaker 4 (05:40):
When twenty twenty, twenty twenty right middle of COVID, right.
Speaker 3 (05:46):
And where you brought in to be the chief AI officer?
Speaker 4 (05:49):
Was you explore it and the title changed? But yes
I was the global leader?
Speaker 3 (05:52):
Yeah yeah, And what made you want to take the job?
Speaker 4 (05:56):
I was actually working for Amazon at that point, but
when I looked at High and I thought, okay, this
is a legacy, traditional company, right, and AI was not
a capability embedded.
Speaker 5 (06:07):
At that point of time.
Speaker 4 (06:08):
So it's a great opportunity if I can start something
from scratch, really build it across the entire valuation of Heineken.
I mean, that's probably the best job anyone can even
ask for. Yes, it's of course a lot of responsibility
that you have to make sure that you really build
the right products and right capability, But that also happened,
so I look back, it's quite fulfilling.
Speaker 3 (06:29):
But it's also if I might playable was advocate for
a moment, you're also taking a risk going into an
established how long has Heineken been around one hundred and
sixty two years to be specific, eighteen sixty four. It's
a very different proposition walking into one hundred and sixty
year old company and saying I want to bring the
future to the way you operate. Then it is with
the startup.
Speaker 5 (06:49):
That is true, but it's also a challenge.
Speaker 4 (06:51):
It's a good challenge, and also that Heineken is also
looking externally. There are companies that are picking up speed
and embedding and adopting AI, so should be all behind
not really, so we also need to pick it up.
So I thought it was a good challenge because the
use cases were there, the opportunity I put sense the
business really was having the appetite.
Speaker 5 (07:11):
Let's do something different.
Speaker 4 (07:12):
When we apply AI in a corporate setting like this,
it's super important to understand how the business actually works.
What's the value chain looking like, what are the nuances?
Where can I And once you get an understanding. It
took me some time, by the way, to understand the
full business and the complexities, but once you cross that
threshold you figure out what's feasible what's.
Speaker 5 (07:30):
Not, then it opens up.
Speaker 4 (07:33):
Wait a minute, within the valuetion, I see ten areas
I can optimize.
Speaker 3 (07:36):
You say, once you want to see in the business,
describe the business. What the Heineken puzzle?
Speaker 5 (07:41):
So well, puzzle, Let's see if it's puzzle.
Speaker 4 (07:43):
After I explained it's actually you start with the procurement
where you get the glasses, cans and all the raw materials.
Then it comes to the brewery where the magic happens.
That's what behinneqn beer is produced. Then it goes to
the distributors. Basically a supply chain takes over. Then it
goes to the customers. What we refer to as customers
are the bars, restaurants, retail stores, moment pop stores, convenience stores,
(08:06):
and that's where actually consumers then come and actually consume
the product.
Speaker 5 (08:11):
So that's actually the value chain.
Speaker 4 (08:12):
It's actually pretty linear when you think of it, but
there are nuances depending on the country and the market.
There are some specific rules and guard rails that you
have to be aware of.
Speaker 3 (08:21):
So you have that process going on all around the
world and across multiple brands.
Speaker 4 (08:27):
Multiple brands, multiple countries, multiple operating companies.
Speaker 3 (08:30):
Yeah, from your perspective as someone who is the chief
AI officer, what are the tasks in front of you?
What's your opportunity?
Speaker 4 (08:38):
There any process that you think that is maybe not
digitized or maybe not data driven, you can optimize. I
look at it like a pendulum. So one side of
the pendulum you have complete gut based decision making. The
other extreme is completely data driven. So the idea is,
can we swing this pendulum it will be towards data
driven from where we are.
Speaker 3 (09:00):
Give me a specific example of a problem you set
out solve or address.
Speaker 4 (09:06):
There are quite a few, but if I want to
pick one of the most fun one fun one may be.
Speaker 5 (09:11):
The most most complex one. Let's bring that one. I think.
Speaker 4 (09:14):
So we spend quite a bit on advertising, and Heineken
is a largely a lot of it marketing company, and
we really care about our brands.
Speaker 5 (09:22):
And products, you know, we're almost obsessed with it. Let's
take an example.
Speaker 4 (09:26):
Let's say you have X million dollars as your budget
and you have two brands, let's say Heineken and Tosa Kiss.
I think the crowd audienceale will be familiar with that.
And then you have three touch points TV, YouTube, Instagram,
and I want to optimize my advertising budget between different
brand and touch points, so heiniken on Instagram.
Speaker 5 (09:46):
How much should I spend? It's a very easy question to.
Speaker 4 (09:49):
Ask, but to actually solve this you have to study
historically how these performed and then create a model and
then predict if I allocate my budget in this way
that's probab more optimal. Before it was more like somebody
to con gun based decisions saying okay, here goes xpillion,
here goes five million, and we say no, no, no, that's
not the right proportion.
Speaker 3 (10:08):
What did the AI tell you about the accuracy of
those spending decisions?
Speaker 4 (10:14):
In the past, we looked at the return on investment
from this advertising, so how much incremental volume or volume
of brre reselling or revenue are recreating and we find
out can we improve that. It's a moment to apply AI.
And when we look at that this significant improvement. In
some cases we have thirty percent uplift, thirty percent, thirty percent,
three zero not everywhere. In some places we got but
(10:37):
it ranges between ten to thirty percent uplift depending on
the type of AI product you're building.
Speaker 5 (10:41):
And that impacts the top line.
Speaker 4 (10:43):
So it's very easy to also realize that value.
Speaker 5 (10:46):
People get to see.
Speaker 3 (10:46):
It, so you say, oh, we we can do a
way better job if we spend x more or x
less in this particular area. Give me another example of
a if.
Speaker 4 (10:54):
Another one would be we have a very big large
salesforce within Ainiken. So this sales reap. What they do
they go to the outlets, the bars and restaurants, and
they maintain that human to human relationships with these our
customers is super important to maintain that, and they go
solve the customer problems. Let's say someone is out of stock,
someone is about to churn, or their surprise mismatch, something
(11:17):
like this, and before they us to go like this,
let's say a sales rep on a day to day
job has to visit five places ab CD E, five
different outlets, and he used to go ABCDE. Turns out,
the model tells you on any given day, if you
optimize taking into account the traffic conditions, instead of going
from that linear route, you go to D first and
(11:40):
then to B, then to C, then to E and
then to A. And the reason for doing that is
the model tells you, if you visit customer D first,
he has the biggest problem that needs the most amount
of time to be solved, and that's how it optimize.
Speaker 5 (11:52):
And also the sales reps now.
Speaker 4 (11:54):
They are becoming so educated with some of these AI models,
they are now becoming a business advisors. So they are
no longer just solving little problems. They are having the
time to say what else can I do for you
as the customer? So that I think it was a
big change within Heineken because it impacted a lot of
people that were using that.
Speaker 3 (12:13):
In an instance, it requires not just building a model
that can be smarter about how people should spend their
time and what they should say, but you have to
obviously educate your salesforce to believe in what the selling
tell me about that piece. Is that a piece that
you that you're a part of or is someone else?
Speaker 4 (12:31):
Yeah, that's also part of because that is super important.
I think we can build the best models. Best algorithm's
highest accuracy doesn't mean anything if it's not used the
right way. So what we do we have within our
company a pretty big upskilling program. So bring everyone along
in common understanding, basic understanding of what AI does. Not
everyone needs to understand new networks or algorithms right, But
(12:54):
what we do is give them a handheld device and
an app which is driven by AI. Play with it,
have fun, see how it changes your life. And once
you start liking the product, liking the UIUX, then you
start getting more. And the insights also tell you the
story because once you start getting the value, I am
not having to pitch my models anymore. The sales reps
(13:17):
and the markets they are pitching on behalf of us.
Speaker 5 (13:19):
And that's such a good place to.
Speaker 3 (13:21):
Be, is it. It's interesting? So in that instance where
you're designing a more efficient form of interaction and fruitful
for of interaction between sales reps and customers, I could
see a version of that where it is really clear
looking up from a high level that things are working better,
but it might not be clear to the salesperson. Is
(13:43):
the salesperson who's now following the direction of the AI
aware that they are more efficient?
Speaker 5 (13:48):
They are?
Speaker 3 (13:50):
How are they aware the market?
Speaker 4 (13:51):
They realize few things that they were visiting customers just
because they had to visit because it was in the schedule.
Now they go there and they find out, wait a minute,
I never tackled this big problem that was not being addressed,
and they solved it. And the customer feedback also comes
back saying we are really happy. So for all these products,
we get the feedback not just from the sales reps,
(14:12):
but also for the customers. Do you really like the
recommendations we are giving you? And that's the best validation
you can think of, because it's four stand or feedback.
Speaker 3 (14:20):
When the AI is doing this ranking, it wants you
to focus on the customer with the biggest problem first
or is it much more complex than that.
Speaker 5 (14:29):
It's a little bit more complex than that.
Speaker 4 (14:30):
Yeah, but usually it's a rank pordert in terms of
which one is the biggest problem that needs the most
amount of time.
Speaker 5 (14:37):
That's how it's ranked order.
Speaker 4 (14:38):
But sometimes you can also override the model, right, We
also give options to people. You don't have to all
the time one hundred percent follow the recommendation. If you
have some urgent priority, you can overwrite that.
Speaker 3 (14:48):
That's also possible with something like that. Is there a
next level you can go to? So you design this
system and you say, oh, I can make our sales
staff a lot more effective and the way they operate
with their customers, and then you see that it works,
and then it comes back and then you say, okay,
what's two point oh? Is there a two point oh.
Speaker 5 (15:09):
It could be.
Speaker 4 (15:09):
So it's always about innovation. Then you think, okay, today
we go and solve the problems that have already happened.
What if we solve the problems that are likely to happen,
that will be the next step. So this customer hasn't
been very active for a while, there's a high chance
that that customer might churn out of Heineken. So what
actions can I actually recommend to make sure And we
(15:31):
do this, by the way, we also gather a lot
of customer feedback and complaints and feedback, and we use
LLLMS to extract and glean information. Okay, what are the
real pain points? What's the theme and the topic that
needs to be addressed. And once you do that, then
also you can prepare ahead of time. We're already there,
by the way, when I say two point zero, we're
already testing it. You solve problems or you'll try to
(15:52):
solve problems before daven occur. So I think that's a
little bit of a two point zero. Then we have
to see what else we can do with it.
Speaker 3 (15:59):
Tell me you've had this partnership at Heineken with IBM
for since twenty thirteen, twenty thirteen, So you came in
and there was already a strong working relationship. Tell me
about how that relationship started and what does it mean
on a practical basis. You're building all these tools, how
does the interaction with IBM work.
Speaker 4 (16:20):
Yeah, so I think good to give a little bit
of context. Heineken started this digital transformation journey in twenty
twenty formally, but the tech was already there. We had
our systems, platforms, data, everything was there. So all the
IT four ID systems is where IBM was partnering with
us from the get go, from twenty thirteen, and it's
a very long standing partnership because as we found the
(16:43):
tech is evolving, our partnership also kept evolving because we
need to keep up up to the speed. So it
was more about IT for IT systems, cybersecurity platform, data,
incident management, service level, you name it.
Speaker 5 (16:57):
All of that.
Speaker 4 (16:57):
IBM was supporting us both in terms of hands and
also in terms.
Speaker 5 (17:01):
Of strategy to create together.
Speaker 4 (17:03):
But that also evolved, like I said, when we went
into this digital transformation journey in twenty twenty, then we
started building this digital core, which is the central nervous
system software system of finikin that's where IBM is really
partnering with us and helping us not just shape the
whole thing in terms of building it hands on, but
how do we strategize that so that it lands well.
(17:25):
So that's yeah, it's a long trusted partnership. I think
we are going to go a long way together.
Speaker 3 (17:30):
Yeah, Heineken Space just sells out of Amsterdam.
Speaker 5 (17:34):
The head office is absolutely so the IBN.
Speaker 3 (17:36):
People who work with you are they on site?
Speaker 4 (17:38):
There are some on site and there are some teams
in India some times spread across the globe. But for
for tech, I think the location doesn't matter. But you
still need people on site to actually talk with the
business and really understand what the problem for.
Speaker 5 (17:51):
Those interactions are also very important.
Speaker 3 (17:53):
When you said you wanted to when you got there,
you wanted to build a digital nervous system, what does
that mean?
Speaker 5 (17:58):
Maybe good to give you an example.
Speaker 4 (18:00):
Let's say iPhone, right, it's a central platform, but you
can download thousands of apps there and all of them
once you download seamlessly integrates with the system and you
don't see any difference. This is the same thing. So
what we want to build within Heineken is a central
software system which the old school way of saying it
is the ERP enterprise resource planning. It removes the fragmentation
(18:22):
of different platforms, it brings it all together. It makes
sure all the business applications within supply chain, commerce, finance
HR all in one place and coordinates them everything orchestrates them.
The benefit of doing that is two one across the
value channing of one way of doing business because everything
is standardized, but we also have multiple markets globally. Across
(18:44):
the multiple markets also it becomes one way of doing business,
so it's both ways. And once you standardize it, we
can embed new apps which will seamlessly integrate, and then
it just keeps scaling further. Can we scaled very quick
and having that digital core really help us scale because
the value from AI and insights is not just building
(19:04):
one product in one place. It's how quickly can you
scale it?
Speaker 3 (19:08):
And are you still building it or is it an
ongoing thing or.
Speaker 4 (19:10):
It's ongoing thing because there are nuances in markets, There
are nuances in tax systems and currency systems, so it
takes a little bit of as much as we want
to standardize, you also have to bake in some of
the nuances otherwise people cannot use it. So those sort
of outliers we have to also bake in.
Speaker 3 (19:28):
You must learn something when you suddenly suddenly, but when
you standardize a bunch of things that have not been
standardized before, presumably you have a basis for comparisons you
couldn't make before.
Speaker 5 (19:39):
That's correct.
Speaker 4 (19:40):
So we also get a lot of external inspiration. So
sometimes these large projects we don't start by over whom,
so we get inspiration from partners like IBM or someone else.
How have they done it in somewhere else and where
it's really working. So then you get those ideas, the learnings,
and you start building that way, and while doing that,
you feel out that, wait a minute, we might have
(20:01):
done something different and maybe it's even better than what
others have done.
Speaker 5 (20:05):
Yeah, so it also creates creativity.
Speaker 3 (20:07):
Yeah, I'm just curious whether there was an insight that
you learned from that process that comes to mind A
big one.
Speaker 4 (20:16):
I think we don't look at tech for the sake
of tech and embedding it, you know, just just as
one would say it's one single core, one single platform,
everything coordinated. What's the big deal. The big deal is
bringing people along to actually believe that there is a
benefit of doing one way of business, and that actually
means the entire company, not just the leadership team.
Speaker 5 (20:37):
So to bring.
Speaker 4 (20:38):
Everyone on board and say, tell us how this platform
should look like, what are the components we should build?
It's a pretty big task. Yeah, that's where the chain
management comes in.
Speaker 3 (20:47):
Yeah, what was hard about that? Did you have bumps
or we did?
Speaker 4 (20:52):
I think it's about convincing people the benefit of doing this.
Why do we say, if you standardize something, we can
go at high speed in scaling. It's not very easy
to visualize that at first. But what you do is
you show some proof of concepts. And that's I won't
call it a trick. It's almost bread and butter of
what we do. Show a small proof of concept, show
that it works, show that we can scale, and then
(21:13):
automatically if people start having the faith, and then well
say okay, I see it.
Speaker 5 (21:17):
Yeah it makes sense.
Speaker 3 (21:19):
Sergery, at least half of what you've talked about is
not about the check itself, but about being a kind
of evangelist for the check. It is half the right percentage.
How much of your time is spent convincing an organization
and people in the organization to see the value in
what you're doing as opposed to building the thing that
has value.
Speaker 4 (21:36):
Yeah, I think that proportion changed over time. When I
first joined, I was very much into the products itself.
I was to review codes myself, let me check what's
going on. And over time, of course, then you focus
on somewhere else. You realize, like I said, best codes,
best models, I use this, if not use the right way,
then I said, okay, now my time is to actually
inspire and show people the value of it. What I
(21:57):
realized is explaining in very simple language really goes a
long way because you take away that that anxiety that
a new product is coming in and we humans a
little bit have this. I don't know if it's the
right thing to say, but it's inertia of rest. We
like status, poke, we don't sometimes like change that stops our.
(22:18):
So every time you build a new product that will
change our way of working. Yeah, there's inherent little bit
of anxiety. Yeah, take that away. Yeah, a job becomes
a lot easier.
Speaker 3 (22:26):
Are you a good evangelist?
Speaker 5 (22:29):
So far it's working?
Speaker 4 (22:30):
I think I can do better, for sure, because it's
about understanding what's the reason people sometimes might be reluctant
to actually onboard or adopt a new technology. And once
you sort of understand that, then that anxiety goes away,
it becomes easier.
Speaker 3 (22:46):
How many people work for a hidekinde.
Speaker 5 (22:48):
About eighty five thousand, ninety thousand.
Speaker 3 (22:50):
Globally, so you have essentially a city pretty much. And
if you look at that universe of eighty five thousand,
is there anyone in that universe who is not what
you're doing?
Speaker 4 (23:02):
The way we do it is we prioritize based on
the size of the market and the potential opportunity. Yes,
if I had infinite resources, I would go everywhere within
the high Naken company and do everything, But we cannot.
Speaker 5 (23:15):
We don't have infinite resources.
Speaker 4 (23:17):
So we say, let's be a little bit picky and
choosy where the biggest opportunities are. But it's a matter
of time. Right today we touch upon the big market's
biggest scope. Over time, it's going to be pervasive through
the company. But the appetite is already there, so people
are really even if they have not really embedded some product,
they're asking for it, which is a fantastic place to be.
Speaker 3 (23:38):
Yeah, yeah, what's been your biggest disappointment?
Speaker 5 (23:41):
So far.
Speaker 4 (23:42):
So far, it's been very fulfilling, I must say, but
I think what I would look back. Can we do
things a little bit quicker? Can we go a little
bit at high speed? And that's why this whole concept
of digital backbone. Can we standardized everything? If we can
speed that up, if we can really scale quickly, I
think that will be the best. Because today have a
very good problem. People are asking for products. Sometimes I say, yeah,
(24:03):
I need to put it on a timeline in a roadmap,
because I cannot just cater to it immediately.
Speaker 3 (24:09):
Presumably that's one of the things that the people you're
working with at IBM can tell you. They can give
you a sense of how quickly others have adopted some
of these technology.
Speaker 4 (24:19):
That's correct, and that's actually one of the bench funds
that you're referring to. We see I'll be losing pace,
and which other things can we go forward? And in
some case when you look at the digital core and
the backbone, maybe specific areas we can speed up because
those are the areas that are maximum potential value, and
some of them we can deprioritize a little bit.
Speaker 5 (24:36):
Yeah, that we do all the time. Yeah, just a
pragmatic approach.
Speaker 3 (24:39):
To I'm curious about a highly specific question, which is,
so here you have a legacy brewer bas in the Netherlands,
one hundred and sixty years old. If I were to say,
I want you to take an entirely new job, I
want you to do what you're doing, but I want
you to do it for an American company in a
(25:02):
completely different industry that's thirty years old, maybe a company
that makes the vacuum cleaners thirty years old in America.
How much of what you're doing. I guess what I'm
trying to say is, are there's things that are particular
to Heineken that have made your job sort of challenging
or interesting or that just wouldn't be an issue in
(25:23):
another environment.
Speaker 4 (25:25):
So it's a good question, and thanks for the enticing offer,
but I will pollite.
Speaker 3 (25:31):
I tried to make it as an average exactly.
Speaker 4 (25:33):
But I won't polite to reject offer. But I'll tell
you why i'll reject.
Speaker 3 (25:36):
You're going to be in Nebraska. They're making just one
kind of vacuum cleaner.
Speaker 4 (25:40):
What I went to school in Iowas, Yes, exactly, So
I'm quite a bit. I think there's a cultural difference
we're all very passionate about our brands and products, and
there's a lot of it is connection based in the
sense we create these connections with our customers sometimes consumers,
and it's all about maintaining that. And once you get
a feel of it, you feel part of the family.
(26:01):
That's a very good feeling to have. And the fact
that today where I am, if I look back, I
probably will happy to say very fortunate to have probably
one of the best jobs in the world in the
current times. And there is no end to innovation, by
the way, and even within Heineken, yes it's a traditional
company who is stopping innovation. There's a lot more too,
so I'll be very busy for the next few years.
Speaker 3 (26:21):
What are the Dutch like? This is one of the
oldest and most successful commercial cultures in the world, A
tiny country that's been solidly successful, that's for it. I'm
curious about innovating in that kind of environment. How is
that different from innovating in a huge country like the
United States or in a different kind of national culture.
Speaker 4 (26:41):
I think it's a question of opportunity because within Netherlands,
by the way, Netherlands has one of the most highest
number of startups within Europe, if not the highest. So
there is this culture of innovation that's already embedded in there.
It's happening all the time. Companies like Phillips, ASML some
of the very big players already there. So it could
(27:01):
be a little bit different. I think in Netherlands we
want to make sure what we are doing really is
going to work, so there's a little bit of discussion alignment.
It's more structured, but also agile in a way we
do things, and us was more like let's do let's
go quick, experiment, learn fail. So I think there's pros
and cons on both sides, but so far it's quite good.
Speaker 3 (27:22):
Give me a sense of what your what's a day
in a life like for you? What does it look
like to have the job that you have in a
place a cande.
Speaker 4 (27:32):
First of all, it starts with the calendar and the
number of meetings I have, which is usually filled for
forty hours longer in the week. So that's the starting point,
and then I have to pick and choose which meetings
I need to prepare for what, and usually these meetings
are mostly about where are we with the product.
Speaker 5 (27:48):
What are the challenges?
Speaker 4 (27:49):
How can I help and solve it, and then sometimes
also pitching new products or convincing something, and also sometimes
chain management. I'd also do sessions where I present internally
quite quite often go to different places, because it always
helps to be in front of the audience when you're
presenting something. We also started something recently which we call
AI boot Camp, which is you use JANEAI as an
(28:11):
interface for all these big AI models and people can
interact in a very fun way. That's our new way
of really convincing the rest of the company that hey,
this is fun to play with and let's go.
Speaker 3 (28:24):
So yeah, it's how many people would you cycle through
that kind of book camp anyone?
Speaker 4 (28:30):
Usually we keep it a small group just to make
sure everyone is doing something hands on and nobody's just listening.
So usually a twenty to thirty people max. And then
go from one place to other and it's all hands on.
You cannot sit and watch. You have to participate.
Speaker 3 (28:44):
Are you directly involved at all in the design or
creation of any of these tools?
Speaker 4 (28:49):
So I review it and then I used to review
also the codes before, and now I'm mostly like trying
to get the feedback from the people that are using it,
because that's my best validation point.
Speaker 5 (28:59):
I high net promoter score on these products.
Speaker 4 (29:02):
I know the job is well done, but I do
check accuracy of model. Some of the basic things you'll
check in AI. Is the model drifting over time? What's
the accuracy?
Speaker 5 (29:10):
You know?
Speaker 4 (29:10):
How is it hosted on a platform? These things I check.
But we also have mechanisms on those, so it's not
like every time you have a DiPT deep and look
into everything.
Speaker 5 (29:18):
Yeah.
Speaker 4 (29:18):
So once you have these mechanisms in place, then these
sort of tasks become easier.
Speaker 3 (29:23):
You've used the phrase that you want to make honey
in the Best Connected Brewer. What does that phrase mean?
Speaker 5 (29:28):
Yeah?
Speaker 4 (29:29):
So I think it started with the ambition in twenty
twenty when we said we're going to digital transform. Remember
the pendulum I was talking about from gut page to
all the way to data driven And in today's world,
when you think of digital transmission, there are a few components,
cybersecurity being one of them, the digital core, like I
was saying, is one of them. Simplification and automation of
systems is one of them. Our breweries, how can we simplify?
(29:50):
And then comes data and AI, which is the really
one of the biggest components. And when you think of
best connected Brewer, the idea is we have been serving
our consumers and customers for one hundred and sixty two years.
What's different If you leverage tech into Bay's world. I
think you can really enhance the experience the customers have.
The example I was giving you earlier about the salesforce
(30:12):
going in different places and optimizing the rout that's a
good example why the relation is maintained just simply by
data driven insights. So if you can connect all the
different applications, all the platforms, remove fragmentation, scale very quick,
make sure your company is CyberSecure, things are simple and automated.
That's what we call the best connected brower. That's the ambition.
Speaker 3 (30:34):
Actually, how do you measure this success of what you're doing?
Is do you expect that your efforts will have a
measurable and tangible effect on the bottom line of the company?
And can you actually figure out what the impact of
your efforts is?
Speaker 5 (30:53):
Yeah, we do.
Speaker 4 (30:54):
I think that is super important to measure because the
first one I was referring to proof of value and
I'm embedding some model, does it really work? So we
do a B testing, which is basically you keep aside
some sample and you actually launch the product on a
different sample, and you see the difference between the two.
The assumption is those that had the product and those
that didn't have the product, both of them went through
(31:16):
the same experiences because of market seasonal, etc. That's one
good way of doing it. And if you cannot have
the luxury sometimes of doing a B testing because everyone
is having high appetite, give me the product I don't
want to sit aside. Then you do some sort of
causal models like we say, so you kind of look
at what would have happened if the model was not there,
(31:36):
and then you predict that and since the model was there,
something else happened. The difference between the two is the
incremental value the model is creating. A B testing is
more accurate the causal models. The other one, like you said,
which called time series model, a little bit less accurate,
but directionally both give you the sense that yes, it's working.
Speaker 3 (31:53):
What happens if you do a B testing or a
new idea and you don't see a difference.
Speaker 4 (31:59):
In that case, we will move on to something else
because it means it's already optimal. Then we said, good,
check that now let's move on to something else. But
we need to just make sure that the process is
still running optimally.
Speaker 5 (32:11):
So time to time, you keep.
Speaker 4 (32:12):
Doing every testing anyway, every six months or whatever the
timeframe is. Yeah, just to make sure that it's still
still relevant.
Speaker 3 (32:18):
But what if this is we're getting out on a
little bit of a digression here, but it's something I've
often thought about. What if the value that is being
created is not measurable? So I'll give you a dumb example.
When you were talking earlier about the salesman and giving
them a new, you know, better instructions about how to
basically spend their day, what if you tested that, discovered
(32:42):
it it didn't have any effect on the bottom line.
But in fact, what was happening was that the salesmen
were a lot happier with their jobs. And we're satisfied
and we're excited to come to work. Do you measure
something like that? Something intolerable?
Speaker 5 (32:57):
Measure?
Speaker 4 (32:58):
One way is NPS four, which I said promoter score.
Are you really happy with the product? Has it changed
your life? That gives you a good indication then, And
by the way, it's a numeric output, so it gives
you a score between minus hundred to plus hundred, and
sometimes it's not even tangible. Let's say we do something
for corporate affairs because they want to get external signals
of consumer insights and then just lean some information. Maybe
(33:21):
we act on it, maybe we don't, but this is
for a good cause. Sometimes you just want to study
the market. There's no immediate value if you don't create
a product out of it, or something to do with
legal If there's a reputational risk for Heineken, can I
extract some insight that will prevent us or create the
best briefing or summary or external briefing that using AI
that will help us protect ourselves. That's also reputational damage.
Speaker 3 (33:46):
Last question before we go to questions. I'm curious when
you look at the very beginning you talked about this
linear value chain we're in that along that chain are
you having the most and where are you having the
least impact. I'm more interested in the second half of that.
Speaker 4 (34:04):
Yeah, I think we covered few things, but one area
I think we can do more is really.
Speaker 5 (34:09):
Understanding consumer sentiments.
Speaker 4 (34:12):
And the reason for that is Heineken is people go
to the bars and outlets and you're not really leading
your first hand data there right, you're enjoying a beer,
then you walk away. I don't know exactly what you did.
I can get some aggregated data to make some sense
out of it. But if we can really get consumer
insights as to what the consumers like and dislike, what
sort of ad you like? How should I design my
(34:34):
Hanneken campaign so it resonates with a cluster of individuals.
That would be a little bit of holy grail as
the next step, like you were talking about two point zero,
and to get consumer insights first party data.
Speaker 5 (34:46):
It's not super easy.
Speaker 4 (34:48):
So what we are trying to do is create digital
twins of consumers. So at an aggregate level, they give
you a sense of Also with agent Kei, which is
also you hear a lot about to get a sense
of how consumers might react to certain campaign or certain product,
and that should give us quite a bit of insights
that right now we don't have access to. I think
that's one of the areas we could really do.
Speaker 5 (35:09):
A lot more. Yeah.
Speaker 3 (35:10):
Yeah, So if I said last question, if I sat
down with you, it's twenty twenty six. Now, we did
this over five years from now twenty thirty one, we're
sitting in this chair, tell me what the kind of
what's going to be the next big score.
Speaker 4 (35:26):
I think one area will be how we make our
lives as employees are finding a lot easier. So the repetitive,
boring task, manual task. Can we automate those things and
just use the time to do something more creative and
think big about the business itself. That will be one area,
most on the productivity side. But the other area would
be Indeed, when we look at gen Z and this
(35:47):
is fact, I'm not saying something my own opinion, there's
a trend of distinct trend of alcohol as a beverage.
The consumption is on a decline. So then what's the
next best thing for the new generation? What will resonate?
Those are the pockets we need to find, and I
think that's where we will transition very quickly over the
(36:07):
next five years.
Speaker 5 (36:07):
And if you get there, I think that will be
big success.
Speaker 3 (36:10):
So you think that your specific department responsibility can help
the company in discovering what the answer to that question
is about.
Speaker 4 (36:19):
Definitely, that's the ambition, that's what we're trying to do.
That's what we are really trying to get this one
over insights. I think that's the last mile. That's the
one part that is left.
Speaker 3 (36:28):
Sergey, this has been fascinating. Thank you so much. I
should say thank you for a question, sir. My uncle
was a Heineken salesman in Jamaica. He was the local
distributor and I have so many child memories of going
to Jamaica and he would show up in his Heineken truck.
So we're resonating deep in my mimory with this conversation.
(36:50):
He would come and he would have a Heineken right
there on the on the table and would drink it
at the end of the day. But we have we
have a few moments for questions. They're all the screen
and I don't have my glasses. Can you read them?
Speaker 5 (37:02):
Yeah, I can, I can read them. Then they should
be going order to the first one.
Speaker 3 (37:06):
Yeah no, no, no, no no, no, Rookie air, never
do that. Okay, read the first four and pick the
one you want to answer.
Speaker 5 (37:14):
Okay, go tip.
Speaker 4 (37:16):
But I gave it away already, so I'm going to
now do what I said. No, I think the first
one is quite relevant. So it's a question for both
of us. If you were advising a twenty year old,
what three skills. Would you tell them to start developing
right now to stay relevant in an AI driven world?
Speaker 3 (37:34):
Oh well, well you don't you have a twenty year
old or a near twenty year old. You have a
fifteen year old?
Speaker 5 (37:39):
You told me I have a fifteen year old?
Speaker 3 (37:41):
All right, what do you tell your son?
Speaker 5 (37:42):
I thought you were going to answer them this first,
but my.
Speaker 3 (37:45):
Kids are two and four. I tell them to put
away their toys. You this is more, you start more?
Do you have a is your fifteen year old son
or daughter?
Speaker 5 (37:55):
His son?
Speaker 4 (37:56):
And he's already thinker with AI. He's doing his own Python, etc.
Which I couldn't imagine when I was fifteen. So I
think I'll give a high level answer to be actually
successful depending on whether your hands on within AI, building
models yourself or not. There are three things I think
is super important. One is having that tech background, having
(38:17):
a common understanding of what AI really is.
Speaker 5 (38:19):
It always helps.
Speaker 4 (38:20):
Not everyone needs to have the details and algorithms and
how models work, not needed, but having that basic understanding
always is good. Then you know exactly how to gauge
what AI is really doing. And I think the other
thing is if you are in a corporate setting and
you are doing something for the business, work backwards from
the business and understand whatever you're building should actually touch
(38:41):
the business and make it beneficial for them. It's not
AI and modeling for the sake of it. That's for
a separate research and development. If you're in a corporate world,
try to build something beneficial for business. And I third
one I think which myself I learned quite a bit
in my in last six years.
Speaker 5 (38:56):
It's communication. Talking about AI.
Speaker 4 (38:59):
If you used a lot of tech jargon and mathematics,
sometimes people lose you. It's about how you really narrate
the story in a very simple way so people can
relate to it. I think if the combination of these
three has worked very well for me, so I can
say that anything you want to add.
Speaker 3 (39:17):
It's funny because I I met this guy who's the
headmaster at a Jesuit school in Manhattan. We've been chatting
and I want to do a little program at his school,
and it's all about asking questions because we're now into
the era of asking questions right.
Speaker 5 (39:36):
That's correct.
Speaker 3 (39:36):
AI is this incredibly good tool, but you have to
ask the right questions. But this is not just true
of AI, but it's also true of the world we're
living in is a world that's so interconnected and everything
involves so many different people that your distinguishing feature in
many context is not where they're not the answers you have,
(39:56):
but the quality of the questions that you ask.
Speaker 5 (39:58):
That's fantastic what you said. I fully avery.
Speaker 4 (40:00):
I think it's about asking the right questions that really
tells you, you know, you're looking for that that unique
thing that that you're missing.
Speaker 5 (40:08):
Yeah, I fully agree.
Speaker 3 (40:10):
Maybe I'll advite you to this class and like you
have that kind of time on your hands, if you
brought if you brought up you know, Heineken for all
the kids in school, that would that would really.
Speaker 5 (40:22):
Yeah, surely we have to build a special product for that.
Well let's see, all right, next question, let's.
Speaker 4 (40:29):
Go to this one or Malcolm, is there a particular
AI capability you are each excited to explore?
Speaker 3 (40:37):
That's for you, my friend.
Speaker 4 (40:39):
I think in the in the short term, I'm really
looking forward to Agent K. I is hearing a lot
of noise and hype and there are a lot of
feedback that I'm getting from a lot of companies. Have
you really embedded Agent KI within your systems? There is
a very mixed feedback. Some say yes, some say no.
I think the potential of agent k I when we
look at this task we do day to day. Let
(41:02):
me gives an example, invoice management or transactional finance or
very repetitive task. If you can really automate augment those
things with agent KI, I think it's going to be
a game changer. If you free up thirty percent of
our time just by embedding these things, then I can
really think big. Everyone can think big. What's next? Then
the creativity comes in. Otherwise all day you are stuck
(41:23):
with the repetitive task. So I think that's what I'm
really looking forward to. And this is very short term.
Within the next few years.
Speaker 3 (41:30):
Yeah, we have I think, what time for one more question,
Sergey go for let's see this is it's got to
be the last one always has to be the best one.
Speaker 4 (41:40):
Certain let for people hearing the phrase for the first time,
what is the real example that shows Heineken being the
best connected broer? Basically you're asking for a proof point.
Are we really becoming the best connective brower when we
look at our markets. Heinek in Mexico is a very
good example across how value chain if you work backwards
(42:01):
from consumers, customers and so on. We have advertising optimization
for consumers. For customers, we have next best action. Actually
for customers we are pricing and promotion optimized. For the salesforce,
we have next best action. For the breweries, we have
connected brewery. We are getting signals from these machines and
optimizing them. I think it covers a significant portion of
(42:23):
a value chain that's fully automated end to end. So
that would be a good example where we really saw
the benefit of taking it to the next level when
it comes to automation. So Mexico Heineken Mexico is a
good example.
Speaker 3 (42:35):
Thank you so much for joining us. Thank you to
all of you who came to listen.
Speaker 5 (42:40):
Thanks for it. Asper, Thank you very much.
Speaker 3 (42:43):
Yeah, that's it for the first episode of season seven
of Smart Talks with IBM, But stay tuned. There's so
much more to come this season as we die further
into how AI and Quantum Computing are creating smarter business.
Smart Talks with IBM is produced by Matt Ramano, Amy
Gains McQuaid, and Jake Harper. Engineering by Ninabird Lawrence, Mastering
(43:08):
by Sarah Buguer music by Gramoscope, Strategy by Cassidy Meyer
and Sophia Derlin. Special thanks to Sergei Ghosh and Michelle Ganji.
Post from the Heineken Company. Smart Talks with IBM is
a production of Pushkin Industries and Ruby Studio at iHeartMedia.
To find more Pushkin podcasts, listen on the iHeartRadio app,
(43:30):
Apple Podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell.
This is a paid advertisement from IBM. The conversations on
this podcast don't necessarily represent IBM's positions, strategies, or opinions.
Stuff To Blow Your Mind News
Hosts And Creators
Robert Lamb
Joe McCormick
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
Kingdom of Fraud
It’s the unlikeliest of criminal partnerships: a devout polygamist from an insular Utah sect joining forces with a shadowy Armenian tycoon from LA. The result - a billion dollar fraud conspiracy. In Kingdom of Fraud, investigative reporter Michele McPhee traces the origins of the extraordinary alliance between Jacob Kingston and Levon Termendzhyan. Together, the two men trigger the largest tax investigation in American history and weave around themselves a web of dirty cops, influential political relationships and transnational money laundering. All this is set against the backdrop of Jacob Kingston’s clan – The Order. A powerful and secretive polygamist organization in Salt Lake City. To whom Jacob is desperate to prove his worth. Kingdom of Fraud is produced by Novel for iHeart Podcasts. For more from Novel, visit https://novel.audio/. You can listen to new episodes of Kingdom of Fraud completely ad-free and 1 week early with an iHeart True Crime+ subscription, available exclusively on Apple Podcasts. Open your Apple Podcasts app, search for “iHeart True Crime+, and subscribe today!