Sam: Of course managers can change processes to use AI,
but how does adopting AI change organizations?
Shervin: AI is a force of change, but
change is not easy, and it's got to be a learning process.
Sam: In this episode, Gina Chung, DHL,
relates to how adopting AI can shift a corporate culture
to embrace innovation.
Welcome to Me, Myself, and AI,
a podcast on artificial intelligence and business.
Each week we introduce you to someone innovating with AI.
I'm Sam Ransbotham, professor of information systems
at Boston College,
and I'm also the guest editor for the AI
and Business Strategy Big Idea program
at MIT Sloan Management Review.
Shervin: And I'm Shervin Khodabandeh,
senior partner with BCG, and I co-lead BCG's AI practice
in North America.
And together, BCG and MIT SMR have been researching AI
for four years, interviewing hundreds of practitioners,
and surveying thousands of companies
on what it takes to build and deploy
and scale AI capabilities
and really transform the way organizations operate.
Sam: So in the last couple of episodes,
we've talked with Walmart, we've talked with Humana,
and I'm pretty excited today
we're talking with Gina Chung from DHL.
Hi, Gina, welcome to the show.
Can you take a minute and introduce yourself
and tell us a little bit about your role?
Gina: Hi, I'm Gina Chung.
I head up innovation for DHL in the Americas region,
and as a part of this role,
I also operate our innovation center out here in Chicago
that's focused on helping supply chain leaders
leverage technologies like AI, robotics,
wearables in our global operations.
Sam: So how did you get there?
How did you end up at that position?
Gina: I might actually answer this
by starting back in college.
So I started college wanting to be an investment banker
and very quickly figured out that's not for me.
But I took a supply chain course
and ended up becoming fascinated
by how things get manufactured
and how things get distributed.
I think it's something to do with the fact that
I'm from New Zealand and grew up in a pretty isolated part
of the world.
But anyway, after college I joined DHL
at their headquarters in Germany.
I've helped launch, eight years ago,
some of our very first projects working with startups
in our operations.
And a few years ago, they then asked me to have the pleasure
of launching our third innovation center
that serves the Americas region out here in Chicago.
Sam: Actually, I think we can end right there that way.
I love it when someone gets converted
from investment banker to supply chain and operations.
I think that's great.
So can you give us an example of a project
that your team has applied a technology like AI to?
Gina: Yeah, so one project that we've completed
using AI and computer vision is to use it to automate
the inspection of pallets in our world.
So currently today, our operators have to see whether
you can stack one pallet on top of another
and that might seem very trivial, but actually,
it's sometimes very difficult to identify
whether that bottom pallet's going to be damaged,
and you have to look for certain markers,
certain indications.
And through combining a camera vision system
with AI software, we're able to automate that process
and reduce potential damages
as well as also optimize utilization in our aircraft.
Sam: So who uses this system?
Gina: Who uses it, it's our operations.
So people on the shop floor that are helping
to load our aircrafts.
The pallets pass through our system,
it flags if the pallet can't be stacked,
and then our operators are able to see that
and then take that pallet out and give it the right marker
to say that it can't be stacked.
And then there are some other steps in that process
to deal with a pallet that can't be stacked.
Before somebody would have to be trained
on how to identify whether a pallet can be stacked or not.
So they have to be trained on look out for these
kinds of markers, these kinds of indentations,
and then each pallet as it comes through,
you'd have to kind of walk around it
and make a note and type it into the system.
But now, we can actually automate that process
using AI and computer vision.
Shervin: This is a great example of how AI
is taking human, unnecessary human roles away,
probably even increasing the accuracy and precision,
I would assume, of like even picking things
that humans might've missed.
Can you comment on how the process that you guys go through
to make that AI engine intelligent?
I'm assuming there was humans involved
as it was being designed.
Can you comment on how that worked?
Gina: Absolutely, so, you know, I always say AI
is a very broad term, so, you know, you can use AI
for in robotics.
You can use AI with computer vision.
You can use AI algorithmically.
For this particular use case, we worked with a partner,
a startup actually, and together with our operations
and a startup we developed the algorithm
for this particular use case,
and it was designed with a lot of images initially.
So just collecting images and images of pallets,
working with our operations to train that algorithm
to look out for these specific markers and indentations.
And then after it's deployed,
there's, you know, a recommendation, right,
that this pallet is not stackable,
and it's up to also our workers to trust in that.
If they think it's inaccurate they can make a marking,
and we'll look at it and see if the AI,
the algorithm, needs to be improved.
So we have that loop in the process
to continuously train the algorithm
because our shipments come in all different
shapes and sizes.
Shervin: That is a great point,
because as we know, without that loop,
it's going to make mistakes
and those mistakes will compound.
So really interesting how that loop is made.
Sam: So when on day one, when you turn the system on,
what kind of reaction did you get from people?
How did people feel?
Gina: Yeah, we always like to say the first day for AI
is the worst day.
And what we mean by that is, you know, the algorithm,
it only gets more accurate over time as you
ingest more and more data and more
and more different exceptions.
So when we turn on the AI, especially during a pilot,
the accuracy looks pretty low
and people start to question,
"Hey, I don't think that the AI can actually do this."
But then as we see the pilot go on week after week,
and ingest more and more data, it also learns from our
workers as well that the accuracy drastically increases.
And then people start to really believe in it
and that starts to make their lives easier.
So we try to focus on automating those activities
that are really tedious, repetitive.
We try to also build the accuracy to such a confidence level
that people trust it and embrace it rather than,
you know, the AI spitting out recommendations
that people know aren't correct, yeah.
Shervin: I think that's a great point, Gina.
The idea that it's not being forced as brute force,
but the users are involved from the beginning
in the design, and they actually see it better
their own judgment.
I could imagine if you guys did it differently,
maybe a more sort of old school way of saying,
"Well, this is the best thing because it's using
all the algorithms and all the signals
and it knows more than you.
And if you don't use that, we're going to take points off
of you or whatever,"
the kind of backlash you would have gotten.
So it's really great to hear that.
Gina: Yeah, I think it's very important
to have that option available for the end-users, right?
So that, you know, you have a lot of people
in your workforce that are experts at what they do,
and they've been doing it for years and years.
So the tools that we're introducing are there to aid
our workforce and our employees.
So that's something that we have always kept front of mind
as we drive our AI agenda at DHL.
Sam: So you mentioned the users,
how much do users need to understand that this is
AI versus just a computer program?
How do you get people to accept
some sort of recommendations?
Do you do a lot of training or how does that work?
Do they need to know if it's AI,
I guess is one way of phrasing that?
Gina: I think people are interested to know if it's AI,
but a lot of the time people just want
to get on with it, right?
So our customers are, you know, they ask us for a solution.
They don't want to understand in detail
how that model was developed,
how did we develop that algorithm,
what type of techniques were used.
They just want something that works,
that's reliable that, you know, is at the price point
that meets their needs.
And the same goes for our operations,
our kind of end-users of some of our AI tools.
They just want to use some, have something that's easy
to use that makes their lives easier
and that they can trust.
And if all those things are there they don't want to,
you know, dig into understanding what latest ML technique
was deployed to make that happen.
A key part of the success is the change management.
Many of the technologies that we're introducing
into our operations,
it's designed to make the lives of our workforce easier.
So I think in the past you know, change management,
yes, it's important.
Yes, we need to focus on culture, changing communications,
changing our processes,
but over time, I think we've learned as a company,
just how important and how critical change management is.
Especially when you're introducing cutting-edge AI,
cutting-edge robotics,
it's completely new forms of
human-machine interaction, collaboration.
So that is something that is always top of mind
in our innovation initiatives.
Sam: Could you share some other examples
of use cases with AI?
Gina: Yes, so I have a couple of ones
that are really exciting actually that ties in a bit
to keeping humans in the loop.
So we're also working with a startup
on implementing AI-driven, route optimization
and last-mile delivery and pickup.
So there, again, we look at leveraging the data,
looking into the route, looking into other external factors
to optimize the best path for pickup and delivery,
and these pickup and delivery requests
come throughout the day.
So it's constantly optimizing the route,
and our drivers within actually get the recommendation
on their kind of tablet or on their phones and the vehicle.
They can either choose to follow that recommendation,
or they can actually choose to not follow the recommendation
because, you know, they've driven these routes for years
and years, and some of them will just know the best way
for various different reasons.
So they're able to follow it, not follow it.
If they don't, we can then try to understand why,
and again, improve that algorithm for maybe a driver
that's brand new and doesn't have that tribal knowledge.
Shervin: That's really a great example, yep.
Sam: Somebody was making that routing before now,
and then you've introduced an AI element to it.
I'm guessing a lot of that may have been automated before,
but so how do people react when you say,
"All right now, I want you to follow what this computer
is telling you to do."
Are people thrilled?
Are they angry?
What's the reaction with people?
Gina: I always like to say, "You cannot trivialize,
you know, the people aspect."
I mean, the AI can make a recommendation,
but it's actually people
that are going to take the action, right?
That pallet is not going to just move by itself
somewhere now that it's come up with this recommendation.
So with a lot of these projects that our team do,
we try to make sure that we have the right people
at the table.
So it's not just the innovation team and the leadership,
but it's also people on the shop floor
that'll actually be using the AI as an end-user.
So we try to get their buy-in very early on,
and then we also give that option to say,
"Actually, you know, the recommendation is incorrect,
or I think this is the better way of doing it."
So we allow that option so that we're not forcing everybody
to follow that recommendation,
but we still give the freedom to, you know,
have people make their own choices as well.
Shervin: Gina, I want to build on that comment around,
you know, your customers want it to work.
They don't want to necessarily understand all the details
and then tie it to the teams
you have at your innovation hubs.
What are the kind of attributes or sort of personality types
that you're finding your technical folks
must have to be able to thrive
in this kind of an environment?
Gina: I always say for our innovation managers,
they're the ones that go into our operations
and work with customers and partners
to bring these projects to life.
There are three kinds of success factors.
One is that they are able to have a deep understanding
of technology.
I'm not a technical person myself,
so I always say that as a disclaimer,
but I really make an effort to keep up
with the pace of technology
and try to understand different concepts
and learn that pretty quickly.
The second part is understanding our operations.
So one of the big kind of no-nos of corporate innovation
is just sitting at an innovation center
and being disconnected with the realities of your business.
So we really make sure that we're out there
at the operations learning about some of the challenges,
talking to our people on the front line.
And the third one is being close to our customers,
to being able to communicate some of these complex ideas
and concepts in a way that our customers can digest them
and translate that into business value drivers
that our customers will embrace and want us to embark on
and work together with them on.
So it's kind of, I would say, a mix of an individual
that has a good technical background
but can really clearly communicate these concepts,
get buy-in, and also down to earth that they can work
in our operations on some of these projects.
Sam: So to quick followup,
who initiates these sorts of projects?
Is your innovation team looking for them,
scouring, trying to like, you know,
we've got some cool tools,
can we, where can we use them?
Or do you have people approaching you, saying,
"We've got a problem,"
which directions are these flowing?
Gina: I would say it's pretty organic at DHL.
So sometimes it might start with a use case, right?
So it might be a business unit saying,
"Hey, we have this specific challenge,
like what are some solutions to solve this?"
Other times, we work very deeply with a whole host
of different startups and there's a new one that comes by,
and we know that it makes things more efficient
then we can find a problem to solve there.
So it comes in different directions.
Sometimes it's our team,
sometimes it's itself business unit,
sometimes it's our customers,
and sometimes it's just a partner that's come up
with a really groundbreaking solution
that we know holds a lot of potential in our business.
Sam: As a professor, we call that the E,
all of the above answer,
(Gina and Sam laughs)
coming from everywhere.
So what's exciting about this to you?
What's fun?
What makes you dread getting up and going into a project?
What makes you excited about going into a project?
What's fun about it or exciting, if anything?
Gina: The exciting part of AI robotics
and some of these other topics that I work on
is, it is truly shaping the future of logistics.
So, you know, some of the first robotics projects
we did back in 2016, it was one of the first handfuls
of robots we're putting into our warehouses,
and then four years later, it's, you know,
one of the highest topics on the agenda
of our business units.
It's all about, you know, how can we leverage new automation
in our warehouses.
So I think that's always exciting that
some of these early proof of concepts and pilots we do,
they might be the first for the industry.
And then several years later, they become the norm
and it's just the way of doing business,
so that always keeps it really exciting.
And then of course, working with some brilliant individuals,
both within the company but also with our partners,
that always makes life exciting day to day.
Sam: I feel like some of that's the curse of AI
is because it's all shiny and new,
and then suddenly it's just what everybody's supposed
to be doing
and it's normal, and you always have to be searching out
for that next, that next cool thing.
Gina: Yeah.
Sam: I think if we went back to the 17th century
and showed someone spell check, they think,
"Oh man, my quill will actually check, you know,
underline with red when I misspell a word,
that would be sorcery."
But now, you know, if the paper doesn't practically
write itself we're bored with it,
and it doesn't really seem like cool technology.
Is there any cool technology coming
that you're fired up about,
or you think that you can apply to do something,
you know, what's short term on the horizon that's exciting?
Gina: When it comes to AI, I think the evolution
of some of our analytics services
evolving into more advanced AI will be really exciting.
So to give you one example, we developed some years ago
a supply chain risk management tool called Resilience 360,
which is very timely now because of everything
that's happened this year, right?
So with Resilience 360, it's a tool that alerts you
if there's a risk that's going to disrupt
your supply chain.
And it's a true kind of big data analytics lighthouse tool
at DHL, but we never could really predict the risk
and then kind of quantify what that risk will do
to your supply chain.
And there, we're now working on leveraging AI
and taking that to the next level.
So I think that's a really exciting space.
Sam: Thank you for taking the time
to talk with us today, Gina.
Shervin: This was really great.
Gina: Thanks Sam, thanks, Shervin,
and it was great to talk to you.
(gentle music)
Sam: We really enjoyed talking to Gina.
Shervin, let's recap a minute and talk about
what we learned.
One point is about AI being a big change.
It's not just a tech thing.
It's about change management, and she emphasized that.
Shervin: Yeah, I like that point,
and I'm going to borrow that quote from her
that the first day for AI is the worst day
because it very much talks to how difficult it can be.
And so yes, change management is critical,
but it's also going to be difficult.
And she talked about the process that she follows
or she's created that brings the users
and the operators into the design phase early on,
so that they're not surprised by what AI ends up creating
down the line,
but they're very integral to the creation of it,
they evolve it, they have the right expectations
that it's not going to be perfect.
We're working with this, we deployed, we tested,
we see how it goes.
And so I thought that was really, really elegant,
how she talked about it's got to be a learning process,
and it's got to be with the right expectation setting.
But more importantly, the users have to be involved
on an ongoing basis,
not just at the end when the technical folks
have built something and they're forcing it down
to the users.
Sam: I think it requires patience.
There's a learning process involved,
and if the organization is expecting things to run well
the first day
then there's going to be a lot of disappointment.
Shervin: Yeah, and I love that point
because without patience and without that interactive,
ongoing engagement of user,
the innovation center, and the evolution of AI,
there can't be a transformation.
But if that process is being done cohesively,
then you know, the sky's the limit.
That's when the innovation center can begin
to actually completely change processes
and create new ones and really change the way people work.
Sam: Setting that expectation that it will get better
from that worst day,
maybe that's a good benchmark to start with
that it's not, we hope that it doesn't get worse.
It's going to get better after that first day.
But the other part of that is the idea that AI itself
can change the organization.
AI can be a force for change. Gina was in the,
she's in the innovation group,
and their charge is not to put AI across the organization.
Their charge is to innovate,
and it sounds like AI has been really instrumental
in changing the culture around innovation at DHL.
Shervin: Yeah, no, I totally picked up on that as well
on several dimensions.
One is that the users of AI are,
you know, you ask them 10 years ago,
how was it being done while it was being done manually
and how were people going down those routes
while they were all deciding based on their own judgment.
Of course, today, they have the AI telling them something,
it's not being forced on them.
So, but they have the benefit of that signal
and that recommendation,
and if they think they could do better,
they will know whether they did better or not.
And if they did better, then AI will know
that it could do better,
and that process itself is introducing this change
you're talking about Sam.
So I thought that's really an interesting way
that they've set that up,
and they are scaling it across different use cases.
Sam: She used the word recommendation a lot,
and that was nice.
She didn't talk about the solution that the system offers.
She talked about recommendation,
which is a very collaborative, working together approach.
Shervin: I thought it was a great conversation.
(gentle music)
Sam: Looking forward to our next episode,
with Mattias Ulbrich from Porsche.
Please take the time to join us.
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