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‘The First Day Is the Worst Day’: DHL’s Gina Chung on How AI Improves Over Time

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Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

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As vice president of innovation at logistics company DHL, Gina Chung oversees a 28,000-square-foot innovation facility in Chicago. Fascinated with supply chains since college (“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,” she explains), she spearheads AI and robotics projects focused on front-line operations — like automated pallet inspection and stacking, delivery route optimization, and aircraft utilization.

Gina notes that “the first day for AI is the worst day”: The technology improves with human input over time, achieving accuracy to a level where people trust and embrace it. She describes how success requires closely collaborating with key stakeholders, integrating change management, bringing teams along when introducing new technology, and designing solutions with the end user in mind.

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For Further Reading
Gina Chung shares insights about human-machine collaboration at DHL with Wired: “Companies Are Rushing to Use AI — but Few See a Payoff.”

Transcript

Sam Ransbotham: Of course, managers can change processes to use AI, but how does adopting AI change organizations?

Shervin Khodabandeh: AI is a force of change, but change is not easy, and it’s got to be a learning process.

Sam Ransbotham: In this episode, Gina Chung of DHL relates how adopting AI can shift a corporate culture to embrace innovation.

Sam Ransbotham: Welcome to Me, Myself and AI, a podcast on artificial intelligence in business. Each week, we introduce you to someone innovating with AI. I’m Sam Ransbotham, professor of information systems at Boston College. And I’m also the guest editor for the AI in Business Strategy Big Ideas program at MIT Sloan Management Review.

Shervin Khodabandeh: And I’m Shervin Khodabandeh, senior partner with BCG, and I colead BCG’s AI practice in North America. And together, BCG and MIT SMR have been researching AI for four years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and deploy and scale AI capabilities and really transform the way organizations operate.

Sam Ransbotham: So in the last couple of episodes, we’ve talked with Walmart, we talked with Humana. I’m pretty excited. Today, we’re talking with Gina Chung from DHL.

Shervin Khodabandeh: Hi, Gina. Welcome to the show. Can you take a minute and introduce yourself and tell us a little bit about your role?

Gina Chung: Hi, I’m Gina Chung. I head up innovation for DHL in the Americas region, and as 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, [and] wearables in our global operations.

Sam Ransbotham: So how did you get there? How did you end up at that position?

Gina Chung: 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 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 Ransbotham: Actually, I think we can end right there. 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 Chung: One project that we’ve completed using AI and computer vision is to use it to automate the inspection of pallets in our work. 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 is 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 Ransbotham: So, who uses this system?

Gina Chung: It’s our operations, people on the shop floor that are helping to load our aircraft. 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’s 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 would have to be trained on [looking 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. And now we can actually automate that process using AI and computer vision.

Shervin Khodabandeh: This is a great example of how AI is taking an unnecessary human role away, probably even increasing the accuracy and precision, I would assume, of even picking things a human might’ve missed. Can you comment on the process that you guys go through to make that AI engine intelligent? I’m assuming there was a human involved as it was being designed. Can you comment on how that worked?

Gina Chung: Absolutely. I always say AI is a very broad term. You can use AI 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 a recommendation, right? That this pallet is not stackable. And it’s up to also our workers [whether] 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 Khodabandeh: That is a great point because, as we know, without that loop, it’s going to make mistakes and those mistakes will compound. So, [it’s] really interesting how that loop is made.

Sam Ransbotham: On day one, when you turned this system on, what kind of reaction did you get from people? How did people feel?

Gina Chung: We always like to say, “The first day for AI is the worst day.” And what we mean by that is, the algorithm 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 it ingests more and more data, [and] it also learns from our workers as well, the accuracy drastically increases. And then people start to really believe in it, and it 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 the AI spitting out recommendations that people know aren’t correct.

Shervin Khodabandeh: 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 [using] their own judgment. I could imagine if you guys did it differently, maybe a more 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’ve gotten. So it’s really great to hear that.

Gina Chung: Yeah. I think it’s very important to have that option available for the end users. 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. That’s something that we’ve always kept front of mind as we drive our AI agenda at DHL.

Sam Ransbotham: So, you’ve 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 Chung: 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. So our customers — 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 is at the price point that meets their needs. And the same goes for our operations, our end users of some of our AI tools; they just want to 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 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 [are] designed to make the lives of our workforce easier. So I think in the past, 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 [and] collaboration. That is always something that is top of mind in our innovation initiatives.

Shervin Khodabandeh: Can you share some other examples of use cases with AI?

Gina Chung: Yeah, so I have a couple of ones that are really exciting, actually, that tie in a bit to keeping humans in the loop. So we’re also working with a startup on implementing AI-driven route optimization in 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, who then actually get the recommendation on their kind of tablet or on their phones in the vehicle, they can either choose to follow that recommendation or they can actually choose to not follow the recommendation — because 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 [or] 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 Khodabandeh: That’s really a great example.

Sam Ransbotham: Somebody was making that routing before now, and then you’ve introduced an AI element to it. And I’m guessing a lot of that may have been automated before. 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 Chung: I always like to say [that] you cannot trivialize 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’s 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 does, 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 [them] that option to say, “Actually, 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 have people make their own choices as well.

Shervin Khodabandeh: Gina, I want to build on that comment — around 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 personality types that you’re finding your technical folks must have to be able to thrive in this kind of an environment?

Gina Chung: 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 no-no’s of corporate innovation is just sitting at an innovation center and being disconnected from 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 — 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, I would say, a mix of an individual that has a good technical background but can really clearly communicate these concepts [and] get buy-in, and [is] also down to earth [so] that they can work in our operations on some of these projects.

Sam Ransbotham: Just a quick follow-up. Who initiates these sorts of projects? Is your innovation team looking for them, scouring, trying to, like, “We’ve got some cool tools. Where can we use them?” Or do you have people approaching you saying, “We’ve got a problem”? Which directions are these flowing?

Gina Chung: I would say it’s pretty organic at DHL. Sometimes it might start with the use case. It might be our business units saying, “Hey, we have this specific challenge. 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, and then we can find a problem to solve there. So it comes in different directions. Sometimes it’s our team. Sometimes it’s our 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 Ransbotham: As a professor, we call that the “(e) All of the above” answer — 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 Chung: 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, some of the first robotics projects we did back in 2016. … It was one of the first handfuls of robots we were putting into our warehouses, and then, four years later, it’s one of the highest topics on the agenda of our business units. It’s all about, 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 … 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 [and] also with our partners, that always makes life exciting day to day.

Sam Ransbotham: I feel like some of that’s the curse of AI, 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 the next cool thing. I think if we went back to the 17th century and showed someone spellcheck, they’d think, “Oh, man, my quill will actually underline with red when I misspell a word?” I mean, that would be sorcery, but now, 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? What’s short term on the horizon that’s exciting?

Gina Chung: 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 Resilience360, which is very timely now because of everything that’s happened this year. So with Resilience360, 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 big data analytics lighthouse tool at DHL, but we never could really predict the risk and then 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 Ransbotham: Thank you for taking the time to talk with us today, Gina.

Shervin Khodabandeh: This was really great.

Gina Chung: Thanks, Sam. Thanks, Shervin. It was great to talk to you.

Sam Ransbotham: We really enjoyed talking with 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 Khodabandeh: 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 deploy it, we test it, we see how it goes.” And so I thought that was really, really elegant how she talked about [how] 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 Ransbotham: 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 Khodabandeh: 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 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 Ransbotham: Setting that expectation that it will get better from that worst day. And maybe that’s a good benchmark to start with — that we hope that it doesn’t get worse, [and] it’s going to get better after that first day. But the other part of that is just the idea that AI itself can change the organization. AI can be a force for change. Gina, 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 Khodabandeh: Yeah. I totally picked up on that as well on several dimensions. One is that the users of AI are, you ask them, “Ten years ago, how was it being done?” Well, it was being done manually. “And how were people going down those routes?” Well, 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. 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 Ransbotham: 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 Khodabandeh: I thought it was a great conversation.

Sam Ransbotham: We’re looking forward to our next episode with Mattias Ulbrich from Porsche. Please take the time to join us.

Allison Ryder: Thanks for listening to Me, Myself, and AI. If you’re enjoying the show, take a minute to write us a review. If you send us a screenshot, we’ll send you a collection of MIT SMR’s best articles on artificial intelligence, free for a limited time. Send your review screenshot to smrfeedback@mit.edu.

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