(soft music)
Shervin: Is there really such a thing as an AI unicorn?
There might not be,
but for sure there are horses for courses.
Find out more when we talk with Colin Lenaghan,
Global Senior Vice President
of Net Revenue Management at PepsiCo.
Sam: Welcome to "Me, Myself, and AI,"
a podcast on artificial intelligence in business.
Each episode we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Information Systems
at Boston College.
I'm also the guest editor
for the AI and Business Strategy Big Idea program
at MIT Sloan Management Review.
-: And I'm Shervin Khodabandeh, Senior Partner with BCG
and I co-lead BCG's AI practice in North America.
And together MIT SMR and BCG have been researching AI
for five years, interviewing hundreds of practitioners
and surveying thousands of companies on what it takes
to build and to deploy and scale AI capabilities
across the organization
and really transform the way organizations operate.
Sam: Today, we're talking with Colin Lenaghan.
Colin is the Global Senior Vice President
of Net Revenue Management at PepsiCo.
Colin, thanks for dialing in today from the UK, welcome.
Colin: It's my pleasure, Sam.
It's an honor to be with you guys today.
I'm excited to be in such esteemed company,
really looking forward to this session.
Sam: Yeah, we'll definitely keep the esteemed comment
in there for sure.
Colin, can you tell us a little bit
about your current role at PepsiCo?
Colin: My role, globally, very much
is around building capabilities
that help our business units win in the future.
There's three major legs that I try to push,
or that I am pushing.
One is solidifying our foundation,
that's like, have we got the right talent in the right roles
at the right seniority to integrate
all the different elements that have to come together
for revenue management.
The other leg then is are we building for future growth?
So, if you take the potential of our brands
and how we can position them with the consumer
as it relates to pricing and promotion, et cetera,
that's a huge unlock for PepsiCo.
And then this whole third leg is this digital transformation
that we're calling it.
And that goes from,
everything from standardizing the analytics
that we want all our businesses to be looking at
across the world to speed up diagnostic,
to speed up decision-making, and solutioning,
right through to this more advanced AI agenda,
and we call that our AI program.
So, it's those major three areas.
And clearly we sort of orient it
with what the business need is
across the world and what are the capabilities
that we need to help and support to deploy
and clearly work to implement those
and hopefully extract the value out of them.
Sam: What is the scope of this that you're working on?
Colin: The scope is right across the full spectrum
of the PepsiCo portfolio, everything from beverages,
to snacks, of course dairy.
This is a classic capability that can help
to solve some of the problems that we're facing.
So I think the categories,
we're almost agnostic on the category.
If there's a really clear use case of where this capability
can help us address a business problem,
it really then can go across wherever we want.
And I would see this applying as much to Quaker in the US
as our Quaker business in China, or our beverage business
in Latin America to the UK.
It's everywhere.
Sam: I think you've been at PepsiCo for 23 years
if I remember right?
Colin: I'm 23 odd years at PepsiCo,
and I actually started what you might call
a net revenue monitoring transformation in the UK eons ago
it seems like now.
And at that time we were building capabilities on systems
but very much on the low, very much linear.
I would say literally in the last 18 months,
what we've had to adapt to has been very rapid indeed.
PepsiCo clearly sees advanced analytics,
AI, as a core to how we're going to have to operate
in the future.
Sam: So Colin, if you can, give us one specific example
of a place you've used artificial intelligence.
What's one project you're excited about?
Colin: I think the example is very much related to
pricing around how do we take very high level,
very average pricing insights and transform that
from you know, if I could give the example,
operating with 60 elasticities that help you understand
where your pricing opportunity is to 40,000.
I mean, that's the sort of scale that you're getting to.
And then the decomposition of that elasticity
around what it drags and draws from across your portfolio,
across the portfolio in one retailer versus another retailer
really is sort of mind-blowing around what that can do.
And that's a real life example of a product
that we're scaling up as we speak.
Capabilities like that give us insight
around what the right bets to make are
and how we can journey towards an end state
around what that portfolio price architecture
could look like.
I think if we didn't have this type of capability,
you would be flying quite blind.
I think you'd be spending a lot of time with consumers
trying to get answers out of them
that they're maybe not equipped to give.
And I think you'd be taking bets,
and you'd be taking some risks,
because as we know, pricing is the most powerful lever
in a PnL without doubt, stronger than volumes,
stronger than cogs, whatever.
You get it right, it's amazing.
If you get it wrong, it's devastating. (laughs)
So across all the things we're trying to do,
this capability on pricing for me
is by far the most exciting.
If we can get that deployed, let's say the 15 markets
in a short period of time,
I think that becomes really powerful
in our ability to understand the marketplace
and to candidly make sure we're giving the consumers
what they want.
Sam: Yeah, it makes sense.
But going from something like 60 to 40,000,
that seems daunting,
but there must be a pretty big carrot out there
for you to want to do that.
Colin: I think once you begin to see---
and of course the 40,000 means you take up certain pack,
to a certain customer, to a certain geography, right?
It's just a full deaveraging almost of your pricing.
So that's how you get that level of granularity.
I mean my mind has now moved on to this is now what
is just going to happen everywhere,
because why wouldn't we have this?
If my partners in the data analytics team can tell me
that we are industrializing this as we speak,
you're going to be able to spin this off
and build it in weeks versus months.
It becomes a bit of a no-brainer
as to why we shouldn't be considering that.
Shervin: I wanted to ask Colin,
what's the best part of your job?
Colin: The best part of my job,
it's almost a little bit why we're doing this podcast.
I honestly learn every day, and that's not a cliche
or thing that I'm just saying.
I mean, I keep saying to people,
"I still get the butterflies every Monday morning,
where I think, crikey, what's going to happen this week,
(laughing)
and what am I going to have to learn?"
And also, candidly, we're building a capability
with PepsiCo that's relatively young.
And I take great hope that there's a legacy there
that I could be proud of
and then I've made a small contribution
to helping the business.
That's what really excites me about this role
and as ever with PepsiCo.
Sam: I'm going to give you a chance to redirect though,
from crikey, what do I have to learn,
to crikey, what do I get to learn?
Colin: No, honestly, it's absolutely true.
It's absolutely true,
because these things are coming at me now, right?
I have to embrace them. I have to learn.
I have to be super uncomfortable.
And honestly, I just go with it saying,
"Look, this is making me uncomfortable."
And that's probably a good sign rather than a bad sign.
I am going through a personal journey with AI,
and it's real, and it's eventful, shall we say.
Sam: So how are you doing that learning?
I'm sure you're a little further on the maturity curve
than you're admitting here.
I think you're being a bit modest with your background.
Colin: I think there's a couple of levels here.
One, PepsiCo's very much an organization and a culture
that learns by doing, so I think we're very targeted
on key use cases where we see value
for this type of capability.
I think we're operating very collaboratively with agility
across how we get those use cases developed,
how we prove them out and then how we begin to scale them.
But then broader, I would say PepsiCo
is making quite an investment in just bringing literacy
of advanced analytics up across the broader community
starting with the senior management.
This is clearly something
that has to be driven from the top.
It needs cultural change.
And so we are starting to do a lot of work
in elevating that literacy, really getting the understanding
of what this is, what do I need to know
that is enough for me to embrace and leverage the capability
versus be the expert, right?
I mean, obviously we're hiring a lot of amazing people
to help us do that and partnering
with a lot of great parties outside of PepsiCo
to help us do that.
I would say that's a big initiative right now
as we start to build more and more use cases
and start to build more products as we call them,
then how we operate with that,
and candidly, how we learned to live with that
is a big opportunity.
And I would say we're all learning.
I can speak for myself for sure.
Sam: The problem with all learning
is that everybody has to learn.
I mean, you mentioned that the senior management,
how do you get, for example, those people
to learn something that they too didn't grow up knowing?
Colin: I think there's a lot of factors
that come to that,
one being very overt about it
and taking time to make it important.
The tone from the top, I think is very important in PepsiCo.
We're all in on this one.
I think this is one where it's kind of table stakes.
(laughs) There's no debate.
You have to be really leaning in heavily here
if you're going to want to compete
in the future CPG industry and broader.
So, I think it's a combination of factors,
and I don't foresee it being an easy exercise
or something that we're just going to walk up
and do a course
and hey, we're all sort of literate.
I feel this is going to be such a cultural process,
even how we're working and doing things is much more fluid,
much more a process and an evolution.
You have to learn to really go in a very fluid way here,
and there's a lot of zigzag left and right.
There's a lot of two steps forward, one step back
as you're experimenting and trying to get this capability
to do what you want it to do.
So I have to say personally,
I feel quite uncomfortable at times.
I feel I'm not in control, but then I've just got to look at
and sort of engage with the team that we're working with,
and they kind of know what they're doing,
so (laughs) you have to have a lot of trust
in this thing around that.
It's something that we're building for the long term.
And I think that long-term perspective
is also very important.
I think another point related to how we're going to learn,
I think seeing this not as a very short-term fix,
that it solves an immediate problem in 2021.
This is going to just be capability that morphs
into helping us solve lots of problems in the longer term.
And so I think that's why we view it
as a very strategic capability,
helping us to solve strategic problems
that hopefully over time, we improve, we enrich,
we get better, and we strengthen the capability
and hopefully the culture at the same time.
Shervin: Colin, I want to build on a point you made
about the process not being set in stone
and sort of the process itself is ever changing,
and sort of the new way of working between all the parties
that are involved.
What do you think the role of yourself as a leader
and other senior management is
in giving comfort and stability in a situation
like as you said yourself,
like you yourself aren't always comfortable with it.
How do you guys deal with that kind of a,
I would say a culture shift?
Colin: Like many large organizations, proving stuff out
and very overtly socializing that clearly builds confidence,
builds trust, and helps you to move
from one step to the next.
Another aspect of what we're trying to do with AI
is the whole promotional optimization piece.
If you take the results that we're starting to see
from these capabilities,
it immediately inspires trust and credibility.
And therefore that's a really important process for us,
because you're starting small, you're building confidence.
And I think that's going to be important because
if you really took on quite a large, big bang approach
with this thing, I just think it's overwhelming.
I'm acclimatized or acclimated as you guys in the US say,
but I think we're very much into prove things out,
embrace of course, and be very aggressive
in what we're trying to do, but make sure we take the steps
to take the organization with us, to build trust
and evolve the culture in that way.
Shervin: This sort of process that's not necessarily
always set in stone, you know,
adaptability is part and parcel
of getting these things to scale, right?
Colin: For sure, this capability needs to trickle down
into the hands and minds of people
who do the day-to-day operations.
That's where the real hard work is.
So as we put task forces together to get,
for example, an MVP up and running,
that's quite exciting, right, because everyone's there.
It's a project, but then you go, "Okay, so, wow,
that MVP worked really well."
Now we're scaling this thing.
We're taking it broad-based, maybe to a category
within a market or a customer or whatever the case may be.
You very rapidly get from a very excited project team
to saying, "Hey, I got to use this thing."
And then I think you're into the classic potential areas
where you run the risk of it becoming tactical
versus it being incredibly strategic.
Do people really understand their role,
how they're supposed to embrace it?
Those, I think for me and my agenda
that I have within PepsiCo,
those are areas that are very top-of-my-mind right now
by okay, how are we really going to operationalize this
day to day?
Sam: Well, let's build on that.
You mentioned more of the strategic nature of this.
Some of these things seem like small changes.
How do you keep this collection of small changes
from ending up in a place
that isn't where your overall strategic focus should be?
I guess, how do you balance that set of small changes
and making incremental improvements with not just ending up
at a place that's slightly better,
but wanting to end up at a place
that is strategically better?
Colin: Yeah, that's a great question,
because you can very much in the day-to-day
of these projects and what you're trying to do,
get lost in that, right?
I think the use cases though
that we're trying to drive at and in PepsiCo
in my space, those are very much around big levers
for how we accelerate the top-line growth
and improve the quality of our growth.
So pricing is super strategic.
That's not going to go tactical anytime soon.
Promotional investment and using promotions
to underpin category strategy and what we're trying to do
with the joint value creation with our retailers,
that is super strategic,
and that's not going to go away anytime soon.
And ultimately when you think about from the user,
is it always going to be efficient for these capabilities
to be in verticals and in silos
or at some point, are they all going to come together?
So the revenue monitor in the field has all these integrated
at his or her fingertips that allows them
to plan strategically, leveraging this capability.
And that's a little bit of the vision
of where we might want to be going
in terms of these capabilities.
So this is very long term.
I think it's a little bit of, you know, the Irish saying
that we have here around "horses for courses",
and I think we're quite pragmatic about that.
But when you see the potential of it
and what I'm fully expecting is that more and more parties
begin to embrace this, it then becomes almost an expectation
on both sides that we're going to have to operate
with these types of capabilities.
But if I take how we're trying to set these things up,
what we want them to achieve,
could there be some longer-term vision
of a more integrated solution underpinned
with this connectivity and AI, which it can do.
That keeps certainly what I'm trying to drive,
super strategic, I think.
Shervin: As you think about talent,
I'm assuming those are probably
the three most important dimensions, technical,
organizational, and commercial.
You're finding that there is sort of unicorns out there
that have enough of all three
or you're finding that it's required for everybody
to have some of all three, but some would spike in some
or others, or could it work where you've got talent
that's commercially and organizationally savvy on one side,
and then talent that's technically savvy on the other,
and combined they sort of cover everything.
I'm trying to get a sense
of how much an individual contributor has to partake
of all those three main components.
Colin: It's a great question.
I think it also depends where you sit in the organization.
So if you take globally, candidly,
I need more distinction in the capability set.
So I need a classic strategy type individual
to drive that strategy agenda.
I need a capability expert,
and then I need a digital tech expert,
because we're building capabilities.
The more you go down in the organization,
the more you see the need for the blend to come together
in the operating markets.
And you don't see too many unicorns,
and I'm not sure you need to have the unicorn,
because ultimately in the business,
it's around how do you use this to commercialize
and drive the business impact?
Sam: Well, that's good to hear,
because I think it's tough to find those people
who can do everything.
And it sounds great on paper to find someone
who does all those things,
but the reality is we call them unicorns for a reason.
Shervin: It seems like Colin's saying
you could build a unicorn.
(laughing)
by having the parts.
Yeah, you've got the parts.
Assemble them internally together
and create the organizational unicorn
rather than individual unicorn.
Colin: That's right.
Shervin: Is there a specific example of an area
where you guys put a solution in place
and the organization went through that learning
and that understanding that you can talk about?
Colin: Yeah, we're live in a particular market
with one of those products that we have,
and this capability is very much
around how we predict a different shape
of promotional calendar against the shared objectives
between a retailer and ourselves.
So it's very much a strategic capability.
So for example, we might want to agree
that given the profile of the shoppers
and the agenda of the retailer,
they want to accelerate their top-line growth.
In another retailer that could be the need
to manage their margins a bit more.
And that's taking a full category view
of how this capability can help them to do that.
And it's not what you might refer to
as common, backward looking tools.
This is very much around forward-looking capability.
Against this objective we and the retailer have set,
this is the shape of the calendar that needs to change
for us to achieve that.
I remember we went through a great process
through the MVP of, you know, we probably took a month
in virtual rooms or whatever,
bringing all sorts of all their third-party data
like panel data, like ROI data
to validate this crazy hypothesis
that this algorithm was suggesting.
Like, there's no way that would work
on this particular part of the category.
And we managed to get confidence
that what the capability was suggesting
for execution could work.
And low and behold, when we put it into the market,
it was in the stores, hundreds and hundreds of stores.
It worked.
And this is a little bit about what we talked about before,
around proving that use case site,
showing how it can predict different scenarios
that maybe we would be comfortable with.
And that then builds confidence for us to keep this
and use this as a more strategic capability.
Sam: Well Colin, many thanks for your time today.
You've brought out a lot of things
that many managers are going to feel like are urgent
and are important really, for everyone,
not just revenue management, not just snacks,
not just sodas, but I think managers everywhere.
Thanks for taking the time to talk with us today.
Colin: Thank you very much.
(bright music)
Sam: Shervin, Colin covered quite a few points.
What struck you as particularly interesting?
Shervin: I liked his comment about horses for courses,
talking about how the new reality that it creates
in terms of how people will use it,
how the big network of retailers and the whole ecosystem
is adapting to these new ways of working
and how even internally within PepsiCo,
teams are doing things in different ways
in more innovative, less static, more dynamic,
and test-and-learn ways.
I liked how he talked about this is going to be our future.
We all know that, and it's not going to be a one-time thing.
It's horses for courses,
and that's how the new course is going to be.
Sam: Definitely, horses for courses makes sense,
because there isn't just one horse
that's excellent at every racetrack.
Similarly, I'm sure that companies would love
to find an AI unicorn who was somehow magically good
at everything AI-related, but it just seems unlikely.
You know, they're not going to find someone
who are ML coding experts, who can talk fluently
with business managers and can communicate
and present perfectly, and still somehow willing to work
at entry-level salary.
Instead, what Colin was talking about,
and like others are saying,
that success is a team composition
where different people bring different skills.
And even if some companies
somehow could find a magical AI unicorn today,
even that unicorn would have to learn
as technologies change so quickly.
Shervin: Yeah, the other thing I really liked
that he brought into the dialogue,
well first of all, the best part of my job
is waking up every morning and saying,
"What am I going learn today?
And I'm looking forward to it,
having been at PepsiCo for 24, 25 years."
And I thought that's really, really encouraging
and energizing coming from a leader.
Sam: That was a bit of a contrast too.
In some of our other episodes,
like with Walmart and Mehrotra,
like these people have gone through multiple jobs
and relatively quickly, moving from one technology space
to another technology space, applying job learnings
from different areas to their current role.
Colin was a very different story.
His own story about how he got to be in his role
was quite different.
He's learned while he's been there,
not brought learning from other places.
Shervin: Yep, and I think that's actually quite valuable
in a place like PepsiCo, and it fits quite well
to the point he made about what it takes
to get these things to scale.
It is about culture change, organizational change,
and also commercial focus for value.
And having been in an organization long enough
to know what it takes to create that change,
I think really works for him.
I could have imagined if he came from outside
with series of very, very successful stents
in highly digital companies,
it might've been a very different trajectory for them.
Sam: So the other part there too,
is that with that ecosystem,
Colin mentioned the butterflies that he got every day.
But there's a lot of butterflies
in a lot of different people there
and getting that trust, so people are comfortable
with those butterflies, hard job and not a technical job.
Shervin: No, it is a hard job.
(bright music)
Sam: Thanks for joining us.
Next time, in our last episode of season two,
we'll talk with Elizabeth Renieris, Founding Director
of the IBM Notre Dame Technology Ethics Lab.
Please join us.
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