Who is Maverick?
Preetha Sekharan, VP, Digital Incubator, Applied AI & Transformation joins us as we delve into the challenges and successes of establishing a digital strategy practice in a well-established company, along with insights on AI implementation in the insurance industry.
In this episode you will learn how strategies are tailored, technology readiness is assessed, and what kind of leadership mindset goes into successful digital transformations. We also dipped into the gap between strategy and execution, and the importance of actionable strategies and effective communication. Preetha shares how AI is operationalized, the value of involving operational experts, and the journey from skepticism to trust. Tune in for practical insights on technology adoption and adaptation in the corporate world. This episode has some great lessons from the corporate trenches.
Vandana: Hello, hello. Welcome to the Fail Faster podcast. Preetha, how are you today?
Preetha: I’m good. How are you?
Vandana: I am very well. Thank you so much. Well, we would love to dive into your personal and career journey with you now. So let’s begin by bringing a little bit about your background, Preetha. You can go as far back as your childhood, tell the audience how you were shaped all along this journey to be in the role that you are at today at
Preetha: Absolutely. I would love to. I am originally from India, so I did my schooling and everything there, and then got into the tech world. Surprise, surprise, right? But that is really where I honed a lot of my leadership skills, my technical skills, and so on. I did that for a good amount of time. And then I’m a person who gets bored very quickly. And so it started getting very comfortable. And while I love technology, and I still do, but what I like more is what it solves for business. And so I decided to take a little bit of a detour with my job, did my MBA program, and then decided I wanted to be in consulting for some time.
I did that for a good bit of time. And finally, you know, my latest job kind of came knocking when I was a little tired of all the travel that consulting brings with it. And it was a really good opportunity. Yeah, they were kind of establishing a new team. And it was, you know, I felt it kind of brought in the best of what I had to offer to the company from a technical leadership standpoint, as well as understanding of business and business problems. I took up that job. And so here I am.
Vandana: Awesome. Awesome. You summed it up very quickly. Like normally people take a lot of time to go. Now to know a little bit about you from the personal angle, tell us a little bit about, you know, your family construct and how do you manage all that with the job?
Preetha: Oh, yeah, I have a one son, and he has just completed his undergrad in computer science. So, you know, it kind of, it’s, it’s a good stage to be in where you’re finally French like, you know, the bird has left the nest and you’re a little bit more comfortable there. My husband works for consulting as well. So he has, I mean, of course, things have changed a lot with COVID. I think we had, you know, there was a time when I was traveling pretty extensively, he was traveling, we rarely saw each other other than weekends. But through COVID, you know, our house saw more people through the entire two years than we’ve ever been together. Now things are, you know, things are slowly getting back to getting back to what it was pre COVID with, yeah, with travel coming. Yes, everything picking up right to pre COVID.
Vandana: Absolutely. Let’s dig in a little bit into some successes and failures, Preetha. So let’s begin with successes. Could you at Unum, it’s a it’s, you know, it’s a major player in the insurance industry. I would love to know what were some of the significant pivots or significant journeys, or, you know, kind of instances where you were able to provide the kind of expertise that you are bringing in this role? Do you have any examples? And you could go back to any other stint as well, not just Unum.
Preetha: Yeah, absolutely. I think when I mentioned when Unum came knocking at my door, you know, the role was to kind of establish a digital strategy kind of practice at Unum, which did not exist before, right. And so it was a pretty interesting journey, because you can define digital strategy in a zillion ways, right? And it can mean something on paper, it can totally translate into something totally different. And so my view of digital strategy was really, how can you enable a business, right?
Because we were still very much an insurance company. And so a big thing that I did was to, you know, first learn the business, this was not a business that I had worked in before. And so that was a lot of learning. And then also gauge where various parts of the business was in terms of their acceptance of technology, their, you know, readiness or being bold in terms of their vision, and the numerous pivots on the way, right. So we found that some areas were a lot more open, not so open in some other areas. And so we had to kind of pick and choose. And we’ve kind of shaped, you know, that digital strategy for the organization almost that way, right.
So we didn’t say it was a big, bold, you know, kind of a grand vision for everybody. But we made it extremely tailored to kind of enable different parts of the businesses. And we really kind of focused on businesses that were a lot more open to kind of doing as a starting point. Because once you have some successes under your belt, it is very easy for others to kind of see like, okay, this is what they mean when they say digital strategy, right. We’re talking about, you know, a company that has been in existence for a long time, but then people have not always seen what technology could do, right.
So you are influencing them, you’re kind of showing them a world that they have not seen within their own walls, right. And so how do you show it, unless there is enough evidence and that you can kind of share, right. So that’s, that’s kind of the approach that we, but it has been like a lot of, a lot of pivot. So you, you kind of try one thing that doesn’t work, you try something else. And that’s how you kind of get going.
Vandana: Awesome. And yes, and like being in a new organization, you know, like getting the buy-ins is so much like, why should people listen to you in the first place?
Vandana: Yeah. Awesome. Yeah. Could you also share some of the hiccups and challenges that came along the way, some things that did not go as well as you planned?
Preetha: Yes, it did, right. And so we had embarked on a platform modernization kind of effort, right. And so we put the strategy together, right. And that was kind of, I would say my biggest learning, like as a consultant, you’re very happy when you give the recommendation and you expect customers to do it, right. But like, I feel like there is a chasm between defining a strategy and seeing that strategy getting executed, right. And it is very difficult to kind of, I feel like that is probably where most strategies kind of fall to, right. Because how do you translate that strategy into something that is actionable and people can actually act upon, that is always a challenge. And so one of the failures, like I said, we recommended a platform modernization for a part of our business.
And, you know, the execution team was a different team. And the approach that they had taken from an execution standpoint was a more big bang kind of an approach, right. But that, unfortunately, didn’t pan out the way it had to, but a lot of learnings for me in the process, right. One, don’t assume whatever you put on PowerPoint is what people kind of get, right. Like there’s interpretations and interpretations when you start increasing the circle of who you are bringing in right into the projects and efforts, you have to kind of make sure that vision kind of carries through, right. So that was a big one. I am now a firm believer that, you know, less is more a lot of times, right. Sometimes when you have initiatives that are way bigger, it works in some cases, but where you need nimbleness, it is very difficult to move a ship, right. Like it takes a lot of effort to move a ship, whereas it’s much easier to manoeuvre, right.
And so the lesson for me was like, always keep thinking like, what is it that you can do with a leaner team, because you’ll go faster and that sometimes that fast and that flexibility to pivot is not more valuable than having a lot of people tied to kind of a network where it is, there’s a lot of overhead in managing that. Yeah. In fact, I think I, I don’t know if I mentioned this to you, but I have also responsibilities for what is called as an incubator, right. The concept of that incubator came from that failure, because it is not enough to just say, this is the, this is a strategy, right. To test out a few of the strategies so that people have like some kind of a prototype that they can, they can go back to, right. That was kind of the genesis of that.
Vandana: Awesome. And yeah, that is like, it’s almost like you’re fostering a culture of innovation, right. With these kinds of strategies and then executing them step by step, showing folks how it is really done. Yeah, I, that’s what I was going to ask you and you already brought that up. So love that you are able to do that with the incubator lab. Now, another very interesting thing that you brought up was the applicable AI, when we spoke earlier, I would love to know and get deeper into maybe show us an example or talk about an example of how you have really implemented AI to make some kind of an impact.
Preetha: I don’t know how significant or insignificant, but like some kind of movement in that right direction, maybe in improving customer experiences or anything else in the insurance tools that you guys work with day in and day out. Yeah, absolutely Vandana. And so we had, you know, we are probably three, four years into our journey with AI. And the first area that we, so if you think about any insurance business, the claims processing and underwriting, those are two areas that are extremely data intensive and pretty crucial part of, of the insurance business. Right. And so when we had to pick a use case for AI, we had a choice of picking a pretty, you know, safe use case, which would work.
Right. But you also knew that, you know, at that point, like I’m talking about four years ago, when we started that journey, if you don’t pick one that is solving a real burning issue with business, you’re not going to get the visibility that you need. And we were very, very, you know, we were just starting the AI journey. So we picked the claims area and we were trying to see how we, you know, could kind of help with the claims solution. That’s kind of the use case that we, that we picked. And we started with a quick POC because again, four years ago, I think AI as such is at a very different place today in 2023 than what it was four years ago. And, and for, for an insurance company, you know, like that is risk averse, you know, by, I mean, we are in the business of risk.
It is different, you know, they look at it as, okay, I, we see this shiny object. We think there is a potential value we don’t know. Right. So I would say a lot of skepticism and, and some curious stakeholders as well. Right. Some, some folks were genuinely interested in seeing how this could be applied. And so the POC really shows that it’s a solvable problem. It was a, you know, we kind of did some rapid POCs and to see how good the results are. And, you know, we at least proved that this is solvable. The real challenge was not based on that success of the POC, we got the funding. And then the question was, okay, how do you now operationalize this? Right. You begin talking about four years ago, other than the big tech companies, right. We didn’t have a lot of, especially in the insurance space, not a lot of examples of how it has been done, how, and a lot of companies do struggle with AI not moving out of the lab.
Yeah. We didn’t want that to kind of happen. So once we got the funding, you know, it was, I would say it’s been a long time since I implemented a project, not based on my instincts from my experience, but literally first principles thinking, right. Like you are in the weeds, in the work, you’re looking constantly at what you are seeing and how should I change my next day, you know, based on what I’m learning. And we did that on, on, on multiple fronts, right? How do you get business adoption?
You know that people will trust it only if they are involved in it and they actually see it, right. And so getting business and getting people who are kind of on the floor, I’ll be part of this designing process right from the beginning. That was very, very crucial. And we did, we saw the light bulb going, right. Which was, it’s an extremely rewarding experience where we would do these sessions, which is called false positive renewal, and where we bring in the operational SMEs and we are looking at why the AI made a decision one way and why, you know, the, the humans made the decision a different way. And in the beginning, when we were teaching the machine, a lot of times, you know, it was most, most of the time it was, Hey, you know, the machine didn’t understand this. And so then that was continuously fed into the learnings of the machine.
Yeah. It reached a point like maybe in two, three months where the SMEs would basically say, and they started seeing it, right. That was the specialist or the human that was making a mistake and not she, right. And once we started seeing enough of those, their default was, Oh, we know the AI is right. Let’s look at why the specialist made this decision. And that was, that was a very important, a very pivotal, slipping moment where initially it was like, why, why is the machine doing that? The burden of proof was on the machine. And then we got to a place where the burden of proof was really on people. But yeah, I mean, these are all, and again, you know, sometimes when you are, when you’re just looking at the AI was performing at almost 97% accuracy, but that meant 3% was inaccurate.
Right. And so these SMEs were only looking at that 3%. They’re not looking at the 97% that was accurate because that was, that was accurate. So we were not getting them to review it. Right. And, and we realized that because they were only looking at where the machine was wrong, they did not understand that 97%, right. Like you have to kind of put things in perspective. It’s like, as you start seeing people, how they are interacting, we would then say, okay, maybe then we need to start these sessions by describing that, Hey, this is the overall result. 97% is accurate. Now you are only reviewing the 3%. Right.
And things like these are very small things, but that journey of getting skeptics and people who were non-believers to start believing in the machine and what the machine produces, you, all these started to kind of add up and, and, you know, how you communicate, how you bring them along, what is the message, right? What do they see? How do you put that in the context? Right. They are seeing the trees and you are looking at the forest. Right. How do you bring the forest to them so that they understand that context? Right. Those are all extremely important parts of that journey.
Vandana: Super cool. Wow. I am so excited that you were able to do all this and yet the timeframe seems like very short to be able to learn so much, right. From all the stuff that your machines were generating. So congratulations on that. And I hope that it keeps on, you know, advancing to the levels that you guys need it to.
Preetha: Oh, yeah, we are. I mean, the whole organization is super pumped. I think that was one initiative that really put the spot right on AI. And then of course, you know, with Gen AI and everything that’s happening these days, you know, it has only helped further. Absolutely. It’s almost like the next level of, you know, the chat GPT that is going to be coming up where everything is going to be super personalized like it could be generating an email in exactly the way that, you know, the bot learns from what you how you interact with.
Vandana: Yeah. Yeah. That’s amazing. And with all of these, you know, good things, there’s also challenges. So what are some of the challenges that you face, especially delving into the incubator labs, delving into Gen AI, like you seems like you’ve done a lot of new things in a very short amount of time at Unum. And so what were some of the things that you can share with leaders in your position today?
Preetha: I think one of the biggest challenges and, you know, I feel like when people don’t understand the onus is as much on us, right. And so I would say never underestimate the need for education and bringing people along. I think that has been very, very big because you’re, you know, this role requires you to be kind of fairly technical, and you will be talking language to business users that may not necessarily understand that language, right? How do you break that barrier, translate it in a way that matters to them and the way that it makes sense to them? How do you bring them along in the process?
I would really say a big part of trusting the machine is how you have thought about change management and bringing business along in this job. So that, you know, it is a challenge, but it is a challenge that is totally worth solving. You don’t want to reach the other end and find that people are not adopting what you, what you have actually built for them. And how do you, what are some of the tactics that you’ve used to bring them along? You talked about some workshops, like what are some other things that you haven’t touched upon? They were a core part of the team. Okay. Wow. So that was, you know, like I said, it was a cross-functional kind of a team. Like, you know, it had team members from my organization, but then there was like a core part of the business and operational SMEs at different levels. So not just the SMEs, but their managers, their directors, their VPs, like across the board, right? And we used the project and the way we kind of did it almost as a way to educate.
So I mean, that team now is so much like, we now have a few folks on the ditch, on the operations who are the managers for the machine, right? And so they call them because they know how the machine works, right? And so we basically humanize the machine to some degree where we say, Hey, look, just like you have specialists here, we have a name for this. And it was called Maverick. And so we said, Maverick, think of Maverick just as another specialist, you know, set of specialists that you have. Right. And so just like you would think about people when the performance is not good, what do you, if you have a conversation, what they need to learn, right? Think about Maverick exactly the same, right? And that has really, this was something that the second thing that I was saying, like we didn’t plan for this, but it just happened. People started calling it the Maverick buddy. So as we started scaling it, people were, you know, like the folks on the operation side, they would call it my buddy, because they did not perceive that to be, you know, something that would take their jobs, right? So that is an, like, we need to understand what are the concerns people have, where are they coming from?
And how do you then either allay the fear or like, you know, kind of, you know, react to that and solve that, right? That is going to be very, very, so understanding the why behind some of push backs and things like that, like that, takes the time, because that is what is going to be important.
Vandana: Awesome. Awesome. That’s, I love it. How amazing, you know, humanizing the machine. I just love that concept. Yeah. So what’s up next? Like, what are you excited about next? I know this year is almost over. So is there something that you’re working towards as the next year rolls in?
Preetha: Yeah, I mean, I know the whole world has been taken by storm with Gen AI. So a lot, you know, I always do that. It’s very rarely that technology catches you by surprise, but things are happening at such a pace that I feel like a lot of folks are overwhelmed, like things are changing every day, you know, kind of living in a world where you can never say 100% right, like, so you are living in an uncertain kind of a space. And that is challenging, but also something that if you are able to master that, the potentials are in, and we are slowly, you know, kind of trying to build a muscle around there. And I definitely see a lot of opportunities going forward. But that’s, that’s what 2024 looks like, another tool in our toolbox, and a lot more opportunities to kind of create business impact.
Vandana: Awesome, awesome. Well, I really wish you all the good love, luck and lessons with all my love. And I know this is all festive season is coming around the corner. So I am super excited for what’s about to come for you, Preetha, and all the stuff that you’ve been able to bring at Unum. So yeah, more power to you, my dear leader, you’re doing such an amazing job.
Preetha: Thank you for having me. And this was wonderful.
Vandana: And we might bring you back, you know, as you are progressing into things. Please let us know if you are ready to share about something else because a lot of insurance companies, not only insurance, but I feel like there’s lots of good leaps that you guys are taking that others could learn from.