Continuous Learning With AI: Aflac’s Shelia Anderson
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Today, we're airing an episode produced by our friends at the Modern CTO Podcast, who were kind enough to have me on recently as a guest. We talked about the rise of generative AI, what it means to be successful with technology, and some considerations for leaders to think about as they shepherd technology implementation efforts. Find the Modern CTO Podcast on Apple Podcast, Spotify, or wherever you get your podcast. AI-based automation means less human manual work.

What opportunities does that unlock for one insurance provider? Find out on today's episode. I'm Sheila Anderson from Aflac, and you're listening to Me, Myself, and AI. Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI. I'm Sam Ransbotham, professor of analytics at Boston College. I'm also the AI and business strategy guest editor at MIT Sloan Management Review.

And I'm Sherwin Korubande, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

Our guest today is Sheila Anderson, Senior Vice President and Chief Information Officer at AFLAC. Sheila, thanks for taking the time to join us from my beautiful home state of Georgia. Welcome. Hi, Sheila. Thank you for making time. Thank you. Good to be here. Okay, let's get started. AFLAC is a really well-known company. I think it's the largest provider of supplemental insurance in the United States. Let's start there. Sheila, maybe tell us a bit about your current role. What do you do at AFLAC?

I actually joined AFLAC in July of 22, and you previously stated as Chief Information Officer, I have responsibility for overseeing all of AFLAC's Digital Services Division in the U.S., and basically driving the technology strategy in support of all of our company's U.S. business, aligning the work that we do to our business objectives. I'll start with sharing a bit about AFLAC, the company itself. AFLAC specializes in supplemental benefits and

Accident, cancer, critical illness, hospitalization, and life insurance. And we provide value to our policyholders by basically helping them to bridge the gap for unexpected costs that your standard medical insurance may not cover.

Aflac was founded in 1955, actually as a cancer insurance company. And today we're a $26 billion company operating out of both the U.S. and Japan, covering over 50 million customers. You know us best, most likely, by the Aflac Duck, which for 20 years has been our international brand icon. And one of the things that I've really enjoyed learning about coming into Aflac is the My Special Aflac Duck, which is an innovative robotic device

for Children with Cancer or Sickle Cell Disease. And it's a cornerstone of the AFLAC Childhood Cancer Campaign that spans over 28 years and more than $165 million in contributions. That program launched in 2018, and to date we've distributed more than 21,000 of these robotic ducks

across the U.S., Puerto Rico, Northern Ireland, and Japan. So that's one of those things where Aflac has taken the brand and is doing a really great thing by serving children who are going through a challenging time. And it's just one way that we can give back to the community that we're serving. Tell us a little bit about the role of artificial intelligence at Aflac.

AFLAC's approach to AI, like many other companies, really came out of a necessity to innovate. There was a major push, of course, that we all had during the pandemic. And in lieu of those face-to-face interactions, the digital enablement to support our customer experience had to come to the forefront of a lot of the business. AFLAC included in that. The contraction in the market also meant that companies had to learn to become more efficient and

And so for us, we were looking to artificial intelligence and machine learning to do both of those things to help to improve our customer experience and also to help us streamline our operations. Maybe give us an example of that. What's a place you've used AI?

The area that we focus largely on has been in the claims processing space. Our claims process, as in many insurers, is a complex and time-consuming task. It involves tremendous expertise, and it also is subject to various regulations. We had about 46 of our claims that were fully automated, or what we call straight-through processing, primarily wellness claims.

We saw these as an opportunity to automate because we don't require additional proof of loss for these, and we can accept the answers to the claim form questions along with attestation from the policyholder to serve as proof. Also, 49% of our manual claims result in payouts less than, for example, $200.

We spend a great deal of effort, whether they're large or small, manually managing these claims. So for that, we had a significant opportunity to automate in the space. AI gave us the opportunity to do that. And our goal, of course, is to ensure a frictionless experience, paying our claims for our insureds as quickly as possible so that they see the value and then they persist and stay with AFLAG longer as a policyholder and as a customer. I'd assume it's making...

decisions that would normally be quite judgment-based, having to pull together lots of different information. So this is a common theme actually we're seeing in a lot of insurance and financial institutions, companies. Maybe if you could share with our listeners a bit about the challenges to stand up a use case like that and scale it. I think that would be really helpful.

Because it's something that's top of mind, I'd say, for a lot of folks. So any of your learnings that you think would be valuable? I think in the beginning for most organizations, of course, I would focus on finding the right talent. That's a huge challenge. So focus on understanding the talent that you need to adopt the technologies and who have a good understanding actually of how to leverage those technologies.

And then we also had learnings around, if you look at the initial cost and investment, that's a little bit of a research, so to speak, to understand how much is it going to take. So you have that initial cost of deploying the models, and you can minimize that through more of a test and learn approach by taking more small targeted use cases that would prove out the value. And then you can seek to scale that across your organization.

Sheila, you started with talent, which I'm sure resonates with a lot of people. Tell us more about the challenges there and the importance of finding the right talent. What is the right talent?

From my perspective, the right talent in any engineering discipline is, first of all, an individual who's going to have the inquisitive nature, really seeking to be an innovator and understanding your business. You're really looking for that engineer who can solve both.

Jump in, have a great understanding of the business and the opportunities that you're seeking to solve for, and then understanding how to translate that technology lingo against those business opportunities. I noticed you started with engineering talent, but you didn't go into engineering.

technical, depth, not that it's not important, but your starting point was around innovation, inquisitive mind, curiosity. You mentioned test and learn earlier. So experimentation, learning. So these are somewhat unique individuals, right? Because they have the technical know-how and depth and experience, but they also have to be curious and to some extent artists, right? We call them data scientists, but maybe they should be data artists.

What is the value proposition for this kind of talent and how are you going about cultivating and recruiting that special breed of technical talent that you're talking about?

For us at Aflac, of course, our brand is so important to us and truly focusing on serving our customer base. We have a bit of a unique space, supplemental insurance. So there's that tie to the service side of what we do. So that's often an attraction for people as well. One of the things that you can always do is share a real life scenario from a day in the life of one of our policyholders so that they can understand how the work that they're doing is tying in.

to something that actually matters because I do think that a lot of individuals that are looking to join a company today are also looking for that connectivity to service-minded companies that have not you're not just serving a business but you're actually serving people in a community at the end of the day as well and then of course if you're in the engineering space engineers

data scientists, you're really seeking to be able to have the opportunity to solve unique problems. At the end of the day in technology and here at Aflac, we're focused on our company mission, which is care on purpose. I love that. It was a big draw for me to Aflac as a company, focusing on that experience for our insureds and for our employees alike. And one of the things you mentioned was

I think, was it 46 or 49 percent of your claims are these simple claims? 46 were fully automated. About 49 percent of our manual claims result in the less complex claims. Historically, you've had people processing those claims, right? What happens organizationally when you come in and say, all right, we're going to have this claim.

gee whiz, newfangled computer system come in and do that. Are they distressed? Are they thrilled that they're not doing a bunch of small claims? Where are their emotional reactions to this?

I'm sure that people start thinking, here we go again, another computer starting to take over my job. But the philosophy here at AFLAC is very much focused on providing a higher level of customer service. It actually frees up the time spent on some of these claims to spend more quality time on the higher and more complex claims. And oftentimes with AFLAC, if our customer service rep is on the phone with an individual based upon the types of

policies that we have, that can be a very stressful time for those people on the end of the phone. So it's very important for those individuals to be able to spend that time serving our customers. Yeah, that resonates with, I think, a lot of what Shervin and I are hearing. I mean, we've almost never heard stories where people come in and say, all right, we're

We're going to put this in and suddenly people are not doing anything anymore. And we're all overwhelmed with everything we've got to do. And most people are just thrilled to have some help in this process. Right. And think of that as in the old days, the change management aspect. But that communication truly is key. All right. So how do you do it? Tell us how. Yeah.

There's no secret sauce. More than anything, it's understanding, first of all, the business areas that you're serving and what their primary needs are. And secondly, bringing them along with you as part of this. So that would be...

My recommendation as far as keys to success, don't throw it at them in the end. Bring them along so they're understanding a bit about your journey. And usually many of these individuals are helping to contribute to the training of your models as well. So looking at your data, your data quality, and the training of the models over time.

Yeah, the bringing them along, I think, is something that resonates a lot. And Sheila, you've been a leader in technology and engineering and digital for a long time. And it used to be that you would not bring them along because there wasn't anything to bring them along at, like you had to build it. Sure, and I think you're onto something there because it's a different feeling in terms of, hey, there's a system, we're going to automate it.

Does it do the same thing that the previous system did or the previous way of doing it? That's a very different approach than this. All right, we don't exactly know how this should work. We're going to figure more of that out as we go. We're going to figure out what this data means as we go. It strikes me as just a very different, as Shervin put it, a very different way of pursuing these projects.

It is. And for any organization, you're going to benefit if you have that mindset of innovation across your entire organization, because we are all going to benefit the more that you can, as I say, unleash the hearts and minds of your organization to be able to deliver great value. Whether it's an innovative idea that comes out of your business, they don't have to know how to deliver it. They just know how to say, here's a great idea or here's an opportunity that I see that I can improve our business operations.

and then bring that forward and partner across the organization to make that innovation happen. We also have a formal innovation team here at Aflac called Hatch. So we do have another arm that we put some other ideas through to do a bit of innovation and test and learn as well. So that team can focus on not only AI and ML solutions, but any other type of innovation solution as well. Sheila, you've been a leader in innovation

technology, digital engineering space for a while. Tell us a bit about what got you interested in engineering and technology and what was your journey like? I'm from a very small town in South Louisiana, population 2,500. Neither of my parents had actually had the opportunity to go to university. So for me, that was something that they instilled in me very early on, that love of learning and a focus on education. So I chose

to focus on engineering in the beginning. My dad gave me strong advice that basically said, this is going to be the field of the future. I had no idea at the time what that choice would mean, though, in my career, it's just been such an excellent choice. So I started in the engineering discipline, computer science engineering, which was a bit more on the operating system side at the time dealing with

mainframe technologies. And I was one of the very few women in my class at the time. It's certainly been wonderful to see more women choosing the field as I matured through that journey. My first job was with Ross Perot's first company, Electronic Data Systems, and had the opportunity to go through that systems engineering development program. So during that phase, I really learned the rigor and the focus of how to do things with

a practice with procedures, and then also just to focus on continuous learning in your career as an engineer. And as a leader, you have to focus on learning continuously. So that was a big takeaway. I had the opportunity to do consulting, technology consulting very early in my career, in and out of a lot of different companies. I worked with American Airlines during 9-11. That was probably one of the most interesting times of my career. And

And then migrated into the financial services with insurance. And I've been in insurance for the last 15 years or so of my career and have absolutely loved it. Many people would not see insurance as a place where you can see a lot of automation and a lot of innovation, but it's an industry that's prime for that. So there's a lot of opportunity in the industry and that's why I'm still here.

Yes. And the interesting thing about insurance, I do a fair bit of work in insurance as well, is there is a lot of science and technology in it. What I see is there is a lot of interesting use cases in all kinds of insurance, property, casualty, health insurance.

commercial, which is why I asked about talent. How do you get the young folks out of college to know all the cool stuff that is happening in that space? Yes. I also think that, for example, doing these podcasts and part of what you have to do as a leader in any organization today is to make sure that up and coming talent understand that

some of the interesting work that you are doing inside of the walls of your organization. So that's key as well. We'll spend a little bit more time focusing on sharing some of the interesting work that we're doing that would also attract talent into the organization. Sheila, we have a segment where we ask you a bunch of rapid fire questions. So just answer the first thing that comes to your mind. What have you been proudest of in terms of artificial intelligence within Half-Life? What's the moment that resonates?

The fact that we have already started this journey and we're seeing success already. So for me, it's having the opportunity to expand and grow on that in our company. We think about bias. We think about ethical issues. But is there anything else that worries you about artificial intelligence?

Those two, of course, the other one that I hear a lot of is focused around privacy, of course. And for AFLAC, we absolutely focus on safeguarding our information. That's key. So you will hear those concerns and we're committed to the privacy of our individuals or protection of data. So we have that significant focus in-house here. And I believe that over time, there's so much positive opportunity with AI machine learning, assuming we can get to

adhere to some standard of use that's acceptable broadly, there'll always be great positive outcomes for AI. But there will be some downsides that we have to watch for as well. I think with that many upsides from AI and ML, we're going to have to put up with a few more downsides. What's your favorite activity that involves no technology? I love traveling and trying new things. So I would say a little bit of an adventure traveler.

Okay. What was the first career that you wanted when you were a kid? What did you want to be when you grew up? When I was a kid, I was a concert pianist very early on and did college classes when I was in high school. So I wanted to be a concert pianist, classical concert pianist. Okay. Well, that's a first for us. What's your greatest wish for artificial intelligence in the future? If you can dream, what's it going to do for us?

Oh, dreamy. Well, I'll talk about for Aflac, of course, I want to see us expanding those models to include more benefits for both Aflac as a company, as well as our customers driving things like intelligent automation and building out in our other lines of business, frankly. So I think we have a huge opportunity. And then in general, I

I think in society as a whole, there's a lot of meaningful value that can come out of the leverage of AI and machine learning. Once again, it goes back to that leveraging AI for good.

Sheila, thanks for taking the time to talk with us. I think you've got such background in so many different organizations. And one thing I enjoyed, too, is this recognition that a lot of the things that we want and dream are maybe easy to say, but hard, really hard to do. And I think that's the story of lots of technology is just while it may sound good to say some of these things, doing them every day and progressing them is difficult. Thanks for taking the time to share your experiences, good and bad with us. Thank you.

It's hard to believe, but that's the end of Season 6 for Me, Myself, and AI. We're back in two weeks with Season 7. Please join us. Thanks for listening to Me, Myself, and AI. We believe, like you, that the conversation about AI implementation doesn't start and stop with this podcast. That's why we've created a group on LinkedIn specifically for listeners like you. It's called AI for Leaders, and if you join us, you can chat with show creators and hosts, ask your own questions, share your insights,

and gain access to valuable resources about AI implementation from MIT SMR and BCG, you can access it by visiting mitsmr.com forward slash AI for Leaders. We'll put that link in the show notes and we hope to see you there.