Jonathan, good to talk to you. It's been, it's been a while. How you been?
It's been great, likewise. Happy to talk to you again.
Yeah. For sure. You know, Jonathan, I have watched you and heard you speak at multiple LIMRA and LOMA events and I've been super jazzed to talk to you about, AI in relation to all things customers and customer experience for the simple reason because you have been advising the industry — and guiding the industry — on all things CX from a digital lens. Right? From a digital transformation lens. So maybe we should focus our conversation today on not just CX, but more importantly, what artificial intelligence can do for our customers present and the customers of tomorrow.
Yeah. Right. Too kind. Too kind. Happy to, really excited to join this discussion today.
That's great, Jonathan. So, you know, you've been evangelizing, talking about all things digital customer experience for the past several years. You know, AI has obviously captured hearts and minds, burst on the scene. Lots of organizations — including Nationwide — are leveraging artificial intelligence across the value chain, but specifically for all things customer. Right? Customer engagement, customer understanding, customer intimacy, and all of those things. How do you think, Jonathan, how can AI improve the customer experience for our policyholders?
Yeah. So artificial intelligence, you know, both the gen AI, as well as the traditional predictive AI is truly making a meaningful difference in the customer experience, like you said. And at Nationwide, where I work, we believe in a bionic model where humans and AI can really work together throughout the customer journey, starting from planning for the purchase, planning for the future, applying and going through underwriting, managing policy after the policy is issued, and through all the way through accessing benefits, throughout the claims process. And in my role — responsible for marketing and customer experience for the life insurance company at Nationwide — we're always thinking about use cases where AI can truly help consumers, find the right protection because life insurance can be fairly complex. And, also, not only that, but also keep their policies after they become members. And I'm happy to share some practical and realistic use cases, today.
Oh, I would love and I know our audience would love to hear those use cases because I know Nationwide has been, investing, in doing what you just described. Before we talk about that, Jonathan, maybe just to orient ourselves. Right? You know, we've talked about — and I know don't cringe, because I know executives always cringe when we say this — but, the hallowed Amazonian experience. Right? Make my insurance offering bespoke to me. So maybe talk to us about your thoughts on how artificial intelligence can use to hyper personalize the insurance offerings based on individual customer needs.
That's a really good point. And, I think the consumer's expectation is very, very high just given their experience with you talked about Amazon, but also, recently with ChatGPT. And I can react to your question in two ways. I think, first, carriers or life insurance companies can personalize the buying experience, using predictive AI. And you talked about Amazon, but similar to how online shopping offers recommendations based on the customer profile, life insurers can also offer guidance to simplify that purchase experience. And we have an application process that personalizes offerings by analyzing applicants' data, leveraging data from the past, and also matching them with the best suited life insurance policy. And the second part is, you know, some carriers even offer dynamic policy adjustments, even after the issue. So, for example, an AI based algorithm that can be coupled with a wearable device can detect changes in lifestyle or health habits and recommend changes in their policy even after the policy is issued. So, I think those two are some trends that we're seeing in our industry.
No. For sure, Jonathan. You and I have talked about this in the past, and it's going to be so important. Right? Our industry — for the longest time, — we've been talking about how to customize our offerings to millennials. Right? And my general response is if somebody tells you, oh, they're, they're a child of the eighties, and my response is they're like, dude, you're forty years old. Right? So, while we've been focused on all things millennials, we have to look for Gen Z. I mean, this is going to be the bulk of our purchasing consumer in the next several years. Right? I mean, they've had smartphones since elementary school. They think very differently. They're wired very differently.
So I completely agree with you that we need to rethink the way we've been engaging with our customers , going forward. The IoT example was a great one. Right? So, if your IoT sensor from — I think where you were guiding us to was kind of like a continual underwriting for a lack of better word. Right? You know, if your IoT device detects you dropped 15,000 feet in a few seconds, you've either gone skydiving or maybe there's a there's a life claim policy that needs to be ready.
Jonathan, you talked about some of the use cases that you had in mind. What are those use cases that you've been experimenting with?
Yeah. So, some of those ones are specific to the life insurance operation. So, for the routine and repetitive tasks, you know, we try to, delegate to machines so that us humans can focus on tasks that require either intuition or empathy, which we humans are better at. And I have some two examples, that I can share today. So, the first one is we have an AI powered platform that enables efficient processing and analysis of big amounts of medical records. So, you know, you may have heard about medical records summarization.
Yeah.
So we use, a solution that handles both conventional as well as electronic health records. And the benefits to this is, you know, underwriting application experience can be much, much faster, and more accurate.
The second one is, we are a leader in long term care — hybrid long term care — product. And we've streamlined our underwriting process with a new three minute online cognitive screening assessment, for LTC, customers, that are over sixty. And this has been shortened from a twenty minute phone call much, much quicker, much, much more streamlined, much, much faster, and much better experience for the consumer. So those are two examples of use cases that we've, implemented in recent years.
That's great, Jonathan. And I think, you know, not only are these use cases that you talked about helping to streamline operations internally, ergo implicitly reduce costs, right, of operation, but they're also directly benefiting the end consumer. You know, we talked about Gen Z just a just a second ago. I mean, Gen Z is going to be reticent to wait four to six weeks for a life insurance policy to be adjudicated. Right? I mean, I have I have two kids, eighteen and fourteen. If the Amazon Prime order doesn't get here in two days, they believe we're in, like, the dark ages. You know? So I completely concur. It's so important for us to be able to invest in doing these things, in order to provide the next generation of consumer, expeditious financial protection.
Jonathan, I want to pull on a thread that you just talked about. Right? You know, life insurance annuities, we've been told many times, too confusing, too complex to understand, especially with a generation that, I've said this to you before. Right? We're trying to get a generation. I mean, think about the life insurance contract and, of course, some of these things are regulatory in nature. Right? But we're trying to get a generation on TikTok to try and get them to read Moby Dick.
Right? Just not going to happen. Not going to happen. Right? So, life insurance contracts can be, or policies can be too confusing, too complex. How can organizations leverage AI to simplify the consumer decision making and increase transparency along the way?
Yeah. You bring up a really good point. I think life insurance, and insurance in general, is hard for Gen Z's, but also for any generation. Right? So, I think the industry needs to do a better job of simplifying the life insurance solution. So, I would approach it in two different ways. First one, as we think about some of the things — new capabilities that AI has brought us — the first one is natural language processing capabilities — NLP. So, we're using NLP to synthesize and analyze vast amounts of voice to customer, especially those unstructured data. Right? Those things like, think about customer calls or comments on social media platforms. Those are really valuable customer input. But in the past, we weren't able to process it in meaningful ways and leverage it. But now that we have these capabilities, we are using NLP to really digest customer feedback, especially those Gen Z customers and identify patterns in consumer needs and behaviors so that we can make a life insurance solution much more simpler. So that's one way, that we're using AI. Another, aspect of things is so for certain demographic, or country, language barrier is something is a hurdle to getting access to quality life insurance solutions for some nonnative English speakers.
Right?
Yeah.
So, we've been able to use a generative AI solution to translate materials, educational materials, as well as product materials for, those individuals, that needs that would prefer tr read things in Spanish or whatever language that they're familiar with. And we've been able to reduce the cost of translation to about 10-15% of what we used to spend, which means that now we're able to translate the piece in ten more languages or ten other pieces of information within the same language. So those are some practical and realistic use cases of AI that's been really helping to protect more families that need that protection that they deserve.
That's great to hear, Jonathan. Now, you know, the LIMRA and LOMA AI Governance Group — which Nationwide is a proud contributor to — we are now up to over 85 business and technology executives representing close to, actually over 50 firms, across the industry. Right? And we're co creating tools, frameworks, benchmarks, turnkey things that organizations can leverage to benefit everybody.
One of the things that we realized in our journey thus far through 2024, most organizations, including Nationwide, there are a plethora of use cases in the ideation pipeline. Right? And my message to the industry has been, if you've got 100 use cases, you know, one of them might be successful. We need to get comfortable with the ones, the 99 ones that aren't successful. Right? And the one that is successful could be potentially transformational for an organization. So, it's the iterative development mindset. It's the test and learn mindset.
So, Jonathan, from a test and learn mindset perspective, can you talk to us about, you know, one use case where you tested it and you're like, you know what? Yeah. That's not working out for us and just move forward.
Yeah. Like you said, there's a lot of activities going on. So this example, isn't specific to our company, but a good example that comes to mind first, because it applies to — it gave a good lesson to a lot of the, our company as well as many others in their industry — which is chatbots. Right? But chatbots for highly complex customer queries. So chatbot is something that can be easily implemented because there is large language models and there's NLP.
But then I think most companies are deprioritizing this specific use case until the technology matures further. So, some companies that tested AI powered chatbots to handle not just basic customer inquiries, but also these complex customer service tasks like policy adjustments, claims management, and providing personalized financial planning advice, have not really seen success.
So the goal of these use cases were to, you know, automate a significant portion of customer interactions because they get a lot of calls, and offer faster responses 24/7. But these pilot tests have not proven to be successful quite yet. I think we're eventually get there, but I think the technology is not quite there quite yet, because oftentimes the responses were either not 100% accurate or it fell short of conveying the empathy that humans can show, for example, during a claims process.
However, companies, I think, are actively developing chatbots for internal use that can aid associates who are helping customers. And the associates can help their customers with speed and accuracy, by using internal chatbots that can be powered by AI. So those were some things that we're seeing. Like you said, there's a plethora of test cases that's currently happening and not all of them will come to fruition, but I think there are some ones that are really making a difference in the customer's experience.
A hundred percent agree with you, Jonathan.
So, Jonathan, as we as we round the corner, right, until I have the privilege of speaking with you again, let's whip out our crystal ball. How can AI help organizations like Nationwide predict future trends in the next generation of consumer needs and behaviors? And we'll wrap up with that and we'll revisit how accurate your predictions have been the next time you and I have a conversation.
Yeah.
So, you know, like, we don't have a crystal ball, but probably the closest thing to having a crystal ball is being humble and being able to really listen to what the customers are saying. So as — I think I already talked about this — but, I think we can use AI to really value and leverage and capitalize on what the customers are saying using those NLP techniques as well as being able to capture customer feedback more frequently and being able to identify meaningful patterns and trends in consumer needs and behaviors. And the output of that can be used to build better products, simpler products, build a better experience for consumers as well as the producers who are helping those consumers. So, I would say, as a marketer, as someone who is responsible for customer experience, I think, that one comes to mind first.
That's great, Jonathan. As always, I feel smarter at the end of every conversation with you than when I did going in. Thank you for spending time with us today, and I look forward to seeing you again very shortly.
You bet. Thanks, Kartik.
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