Manish Shah: The Intelligent Core — How AI Is Redefining Insurance from the Inside Out

Chapter 1: The Promise of Protection in the AI Era (00:00)

Sabine VanderLinden: Did you know that risk transfer sectors, such as insurance, are built on a promise? A promise of protection, trust, and certainty in moments that matter most. Yet, today, that promise is being tested. Customer expectations are shifting. Legacy systems are creaking. And AI is no longer a future concept. Indeed, it is actually reshaping our systems and processes across corporations and industries are designed, delivered, and experienced. That is why I am delighted to welcome on the Scouting for Growth podcast, today's guest, Manish Shah, President and Chief Product Officer at Majesco. Manish sits at the intersection of technology, product strategy, and deep insurance expertise. He is one of the industry leaders, not just talking about AI, but embedding intelligence directly into the core of insurance operations. From the rise of agentic AI to the shift from automation to genuine autonomy, with humans firmly in control, Manish is helping insurers rethink how work gets done, how trust is earned, and how growth is unlocked. In this conversation, we'll explore what happens when insurance moves from reactive to intelligence, from process-led to human-led, and from legacy-bound to future-ready. We will talk about fear, trust, and hope, the three emotions that sit at the very heart of insurance, actually, and what the next three to five years will demand from leaders who want to stay relevant. So, if you are asking how regulated industry, like finance and insurance, will really be redesigned, not in theory, but in practice, this is a conversation you won't want to miss. Manish, welcome to Scouting for Growth.

Chapter 2: 30 Years of Transformation: Meet Manish Shah (02:45)

Sabine VanderLinden: Manish, thank you very much for joining me today. It's so good to see you.

Manish Shah: Thank you so much, Sabine. It's really, really my pleasure to see you, and really honored to exchange some thoughts here.

Sabine VanderLinden: Yes, absolutely. So, to get started, I know you for quite a while. You're a Majesco guru. I've worked with colleagues of yours, and it would be wonderful if you could introduce yourself to help everybody understand what you do every day, right, as the Chief Product Officer of Majesco, and let us know where you actually see the platform and the work you are doing every day moving into 2026.

Manish Shah: Yeah, sure. I mean, just as a little bit of a background, I've been in insurance for over 30 years now, and quite a shift that we have been witnessing it over 30 years in terms of how the insurance was bought, sold, serviced, and all the entire end-to-end life cycle that we have done. I'm very, very glad to be a part of this journey. We came quite a bit far from where we were 30 years ago, and I think even specifically from last three to four years has been with an introduction of AI, it's been even more, more exciting times. There are so many possibilities, there are so many avenues to execute some of the really, really bold ideas with an introduction of an AI and the speed at which the AI is advancing. So that's where majority of my mind share goes in terms of how can we actually transform the industry, how can we shift it from more of a protection to prevention and several other aspects that we will be happy to discuss during this call. But that's really where we go. So very simply speaking, as Chief Product Officer at Majesco, I carry a lot of responsibility in terms of evolving the product that will help our customers and overall insurance market for the future.

Chapter 3: The $1.8 Trillion Protection Gap: A Design Failure? (05:31)

Sabine VanderLinden: Absolutely, and you already touched upon a big theme, so I would like to start with the protection gap, the cost of inaction. The current problem we are facing in our industry today, I think setting the scene for this upcoming year, $1.8 trillion of protection gap, a number we've seen so many times, which has been quoted by Swiss Re. A million of people remaining uninsured, not being protected today, and I think the protection gap is often misunderstood, actually. So from your perspective, Manish, is this primarily a consumer education problem? Because I hear that when I think about the wildfire resilience and protection themes, which as we have seen in 2025, you know, building stronger homes so that they can be insured, or is it a product and experience design failure that we need to drive or address today?

Manish Shah: Yeah, that's a great question. And it's a simple answer. From my perspective, you know, it is primarily a product and experience design issue, and not necessarily just an education gap. If our customers don't understand or see the value in the product, then it is a design problem. It's not really a, you know, customer problem or, you know, education problem. Frankly, our products are quite complex in insurance. They're rigid, they're inside out, things are designed around policy and actuarial constructs, so to speak, in terms of, you know, historical pricing and, you know, how the rate modeling works. Instead of thinking in terms of a life event, instead of thinking in terms of the customer's risk, appetites and goals, adding more to that distribution and the advisory function is also a bit of a misaligned at the moment with what the modern behavior that we are seeing with the consumers, right? They're like digital first customers, like they want the, you know, more intuitive, they want embedded experiences with the other products that they may be buying. They don't want to deal with large forms with jargons of exclusions and inclusions and disclosures and so forth. Like, you know, it's a bit of a misalignment here. But I think that we have a nice opportunity as an industry with an AI and intelligent cores that we can sort of turn this complexity into simplicity for our customers, like allowing them to ask more of a what-if questions or simulate a what-if scenarios, give them a sort of a real life recommendations that's specifically tailored for individual rather than just a cohort of, you know, people alike, because people alike is probably a worse way of putting somebody together and saying that, well, you're just like that person. We don't think, I think there is a lot of individualities that insurance needs to tackle that we're not. And frankly, there has been a lot of technological constraints that not necessarily true anymore. But bottom line is, I would say that we gotta, you know, if you want to close the protection gap, then I think we need to move from selling policies to orchestrating protection. And we really need to, you know, shift like from sort of one-time transactional events to a more of a continuous data-driven engagement that would sort of adopt to people's lives, you know, changes that they go through. I think that will change how consumers view about insurance, understands about insurance, and also how they will engage with the insurance. So I think that's a very important question, and I think it's a product and experience design issue.

Chapter 4: The Fear of Inaction: Legacy as a Behavioral Debt (10:15)

Sabine VanderLinden: Indeed. And I think that this is also where we see a lot of fear, right? And I think that fear is a very interesting emotion, especially when we talk about innovation. Because on one side, you have the fear of moving too fast, right? And then you have the fear of moving too slowly. And I think that in our industry, we have a lot of fear of moving too fast. And I think that this is where we see a lot of legacy systems, right? And I think that legacy systems are not just about technology. It’s also about the way we think, the way we operate, the way we make decisions. So from your perspective, Manish, what should leaders be more afraid of over the next few years? Moving too fast with AI, or moving too slowly while customer expectations race ahead?

Manish Shah: Yeah, that’s a great question. And I think that the fear of moving too slowly is a much bigger fear than the fear of moving too fast. Because the customer expectations are changing so rapidly. And if we don’t keep up with that, then we are going to be left behind. And I think that the legacy systems are not just a technology debt. It’s a behavioral debt. We are so locked into how we serve the customer, what process that we follow, how exactly that has to happen. And if it is working, if no customer is complaining about it, then it must be good. So why change something that’s not broken kind of a mindset. And I think that that is a fundamental one of the one of the big reason why people basically are doing that. Obviously, culturally, we are a risk averse industry, we will have an incrementalism in terms of, you know, any decisions that we will make, we’re also organized as an industry quite a bit into silos in terms of, and then those silos has created data silos too, which has actually blinded us from seeing a bigger picture in terms of what our customer want. And you know, how can we actually service them better, so that we will develop more trust, we will develop, you know, more better relationship with our customers and so forth. So there’s a there’s a there’s a lack of information at the end of the day, because of the legacy silos that has been protected, because of the behavioral debt, that is actually causing a lot of tentativeness, a lot of hesitation, in terms of making decisions, like business is always feared for their fear, or they’re sort of hesitated because of the disruptions, that what’s going to happen or to an existing revenue, I’m going to cannibalize this thing, I’m going to unsettle my distribution channel, things like that, it obviously has its own sort of hesitations in terms of going to a newer tech stacks, and so forth and so on.

Chapter 5: Beyond Cloud: The Leap to the Intelligent Core (15:00)

Manish Shah: I think that that’s where that’s where the legacy thinking is, where we are is, is also an opportunity. And this is a great opportunity to say that, let’s not just go into two steps, like, again, we’re still have a legacy thinking kind of thing. When we when we speak with customers, they say, when we talk about AI, for example, they’ll say, yeah, but why don’t we just first convert that into a cloud system? And then we will talk about AI, like, why would you think of it? It’s a two step process. It’s a great opportunity for you to actually rethink your workflows, rethink your business processes, rethink even your business products that you’re selling it, as you are implementing this thing into a new, new modern core systems might as well just use an intelligent core systems, because it will actually trigger you to rethink everything from ground up and from outside in. So I think that across PNC, live group benefits, at the end of the day, let’s really start from an outside in, let’s modernize, whether it’s a legacy, or as, you know, we call it modern legacy, the legacy that was implemented 10 years ago, with a legacy mindset, forget the technology, I think we get all messed up with saying that COBOL is a legacy and Java is the modern, that’s not what I mean by that. The system that is implemented, customized, configured with a legacy mindset, to me, on a latest tech stack, it is still a legacy system. So if you think of it from that lens, and you said that, okay, here is a chance for us to actually help impact the customer experience, help impact our product effectiveness to our customers. Here is an opportunity to build trust with our customer. Let’s just think of it from that perspective. Now that we have technologies with an intelligent core, agentic AI, that we can actually put those technologies to work so that we can think of it for customer first, and that will actually help improve and iterate our business model, which also is, you know, quite dated, in a way saying that with a few interactions versus a constant touch and constant relationship. So I think it’s a great opportunity within AI, and we see that there are some, there are some leaders who has that kind of progressive thinking, while there are, but I would see unfortunately, majority of the people are still thinking in a very traditional way of saying, let’s just implement the core systems, it will take us three, four years to do that. Or here is the reason I cannot do AI, because I have this data that is not good in four different systems. These are all valid, legitimate problem, I get that. But tackling them one after another and stacking them before that you can actually put an AI to work is a big blunder in my view.

Chapter 6: The Velocity of Change: From Velocity 5 to Velocity 9 (20:00)

Manish Shah: You know, I don’t know, you know, but I heard this thing from Hugh MacArthur, who is a vice chairman of Bain Capital, and he gave a very profound statement just now. And he said that, well, if you’re doing a POC with AI, you are already late, you should not be at the POC stage at this point, you need to be actually putting it into your productions, you need to be thinking about scaling things, you need to be thinking about refining this. And I think that for that, I don’t think that we can just find more excuses to say how do we delay this thing? I think we just have to say, how can I help my transformation, leveraging AI, instead of saying AI comes, you know, as a second stage.

Sabine VanderLinden: You know, I want to echo a few statements you’ve made, which I think are critical. First, speed of change, velocity, we are at a time where the change we are going through is fastest that we have ever experienced. So things we do, we used to do in 12 months, we need to do them in three months. So it is very important for every careers out there to really understand. Velocity, change in speed, is at the optimum. I remember reviewing a presentation from one of the executives from Microsoft a few months ago, actually in the summer last year. What he was saying is people realize that maybe a year or two, when ChagPT came out, we had Velocity 5. Today, we have Velocity 9. Remember, the speed at which we are going through is unprecedented. So first point. Second, to actually transform, you have, as you said, it’s a behavioral change, so therefore, we need to have the right people, and we need to manage that fear, that fear of losing a job. We need to manage that fear of trying a new system. We need to actually have the right culture and the right leadership to facilitate that. It’s why I think often we end up working with the fewer leaders and the transformation leaders which are there, because they know that if their company needs to be a Fortune 500, or they need to be in the Fortune 1000 or 2000, they need to get through that change.

Chapter 7: Agentic AI vs. RPA: Empowering the Human Operator (22:45)

Sabine VanderLinden: Let’s go into AI, autonomy, the world which are going to affect us in 2026, starting with some of the things I’ve recently learned around AI systems, versus, I guess, AI as teammate, versus AI as operators. We are moving from AI using some of the tools which are there, where we put the data, the prompts, to a world where the system is going to help us do our job better, so that we focus on the most complex tasks. When we start looking at the shift from AI, co-pilot, co-workers, companion, all the world we have already in 2025, to an agentic future, what does that mean, Manish? At what point does autonomy becomes empowering from individual? Because I think a lot of people are going to ask that. But also, how do we differentiate what we do with the employee vis-a-vis what we do with the customer, and also with the regulator? How are we addressing all of this? And the change? Well, and how do you do it? Remember, Jessica?

Manish Shah: Yeah, that’s a great point. And AI usage from automation, or assistant rather, to an autonomy, is rapidly shifting. But that’s also creating a lot of doubt, fear, uncertainties in terms of what the future is going to look like, as you said. I think that, I totally agree, that AI assistant is like a no-brainer, so to speak, if you’ve got the right capabilities that can be installed. But AI assistant, before I go into autonomy, I’ll just say a few words on that, is that I don’t think that you should see AI assistant as a standalone separate thing, in my view. There are use cases for that. But I think that the place where you want an assistant is where you need the assistant. So if you are writing a policy, or if you are processing a payment, or if you are adjudicating a claim, anything that you are doing, if you need an assistance, it needs to be right there and then. It needs to be embedded into those point-of-sales and point-of-services platforms. It is not a separate thing. It is like, again, AI, even if it’s assistant, let alone the autonomy, is not a bolt-on experience. It’s an integrated experience. Because at the end of the day, you wanna provide a very data-rich, intelligent suggestions as an assistant to the person or to the human being who’s actually you are an assistant. And it’s not possible if it is not well-embedded within those systems, because having a lot of POCs where we’re just gonna do an integrated, or we’re just gonna do a standalone enterprise AI, and we’re just gonna bring in all the disputed data sources and all those things, that’s great.

Chapter 8: Graduated Autonomy: Keeping Humans in the Loop (28:30)

Manish Shah: So, for example, an underwriter, their job is to underwrite. They are going through various different sources, filling in data, a lot of repetitive tasks. Those things AI can do for you autonomously before you even get to see that, what am I underwriting it? It is fueling you with that synthesized information. It is providing you recommendations. It brings everything ready, but we still want an underwriter to make the decision. So we have a word in Majesco, what we use it, we call it a graduated autonomy. And what graduated autonomy is that we just start with a decision support rather than just building a decision. And then a supervised execution. So from a decision support, you go to a supervised execution. And then you go to select cases, which are very well understood, which are very well, very low risk to an individual customer or your own business. There you can achieve a selective autonomy. So that’s the path. Do the decision support, do the supervised autonomy, and then do a selective full autonomy. It’s a journey. It’s not a broad umbrella thing. You go with use case by use cases. We’ll never, and I would say right now, we’ll never recommend anybody, neither we will build a software to support a full autonomy. Like for example, claims denial. I mean, that impacts someone’s life, okay? And it’s important that human decision comes in a play. It’s important that even empathy factor comes into the play. It is not just an algorithmic decision. Let them have an algorithmic decision come in, but then let a human who can actually understand the life events, who can understand, who can actually relate or exert an empathy to come and make those decisions. Because as an insurance company, if all we are gonna do is an algorithmic thing, then I don’t think that you are building a relationship or trust. So there are certain areas where we’ll just not do, like just give you that as an example. But as I said, graduated autonomy is a way to go about it.

Sabine VanderLinden: Love it. And I would like to pick on the auditability. So auditability, transparency, explainability, right? We need to be able to really be clear if we are going to share the information regulators to really understand where things are coming from and whether it’s in Europe or United States, right? The NAIC, Principle of AI. We have the AI Act here in Europe as well. Regulation will actually define, I think, the way for our regulated industry, which is insurance, how we use artificial intelligence, how we are going to define, but also the reputation of an organization. So that is critical. The same part around control. And I think, as you said, the human in the loop has probably been overused because you said, okay, people, you know, you’re not going to be replaced. You are going to have a job and you are going to be part of that framework. But it’s also about when you understand what in the loop means. It could be in the loop, it could be over the loop, partly when we start looking at autonomy and partly looking at some of the steps you have mentioned, whether it’s supervised or we are actually able to go to the next level of leveraging artificial intelligence within the process. So it’s much more complex than saying in the loop.

Chapter 9: Turnkey AI: Impacting the Expense Ratio in 2026 (32:25)

Sabine VanderLinden: And I think it’s a very important statement to make. So looking at the insurance industry, right? And the decision insurance organization needs to make, you know, delegating is important, as you said, and it’s about really understanding your business, understand the customer to ensure that we are not going to get into denial situation. So how do we actually leverage advanced technology like yours to become a truly agentic firm? How do we, you know, are they case study? Are they use cases you think are, you know, ready right for innovation today to actually help the insurers out there, to actually say, you know what, I need to have my Intelligent Core. This Intelligent Core already is agentic. And by the way, I can actually start delivering value, right, we heard about this 95 to 88% of AI-filled projects so that I can deliver value to my business in 2026.

Manish Shah: That’s really a great question. And that actually, you know, that just the heart of the whole AI value proposition that what can I actually impact the business with in 2026, not something that I can show it in a POC that it works, and then it will give me five more years to get there. I think that the way we do it is, and I’ll give you examples, but with the Majesco Intelligent Core, our approach is that it has to be a turnkey because it’s an embedded intelligence, which means that if our core systems, for examples, is doing the policy administrations or billing functions, or, you know, whole claim processing, we want to embed the assistant first so that it can provide a decision support and faster customer service and reduced new staff onboarding cost. I think these are very, very important because we will be hiring new people and telling them to learn insurance, talk about learning this new system. It’s a big lift. Having an embedded system that is aware of every single data that is there into a core system, and then contextually, based on the intent, it can actually find an answer for you. So it’s not making a decision for you, but it can find an answer for you. As simple as it looks, it’s extremely impactful. Provide a faster customer service to sort of simplify the insurance per se. But moving from an assistant to an agentic part is where we see, and what we did is that we said, listen, we’re not going to start with use cases, for example, that will impact irregulatory requirements or any kind of scrutiny. So for example, we’re not gonna make an arrival to pricing at the moment through an AI. We’re not going to go and say, okay, here are the product filing that we can take an exception from it from this perspective, or going outside the process of what the company’s claims adjudication guidelines are or underwriting guidelines are. So we said we need to actually leverage those things. So first of all, all our agents, while they come out of the box, they will not be turned on until you feed them the knowledge sources of the company’s guidelines. So if it’s an underwriting-related agent, you have to feed the underwriting guideline. And underfeed meaning very simple, just upload a PDF or a Word document or pointing to a SharePoint site or whatever, but it’s that as straightforward as that, but the agent has to be aware of you as a company and saying what guardrail in which that you operate. And I will make sure that every decisions that I make or every automation that I trigger is within that guardrail. So that’s a number one aspect of it from our perspective that we do. But what we saw is then first and foremost, we said, how can we impact the operational efficiency starting with an operational efficiency? So we started out on an operational efficiency and the reason being is simple. The operational efficiency can help impact the expense ratios and improve the customer services, both in terms of speed of customer service. service, but the quality of customer service as well. So we will take a use cases, I’ll give you a great example, speaking with a customer, their point is that they are able to actually respond or a quote 10% of the submissions that actually walks their inbox. So that’s an interesting thing to think about it, right? That you have a lot more, you know, demand, but you are supply constraint. Many economics will teach you that when you are not demand constrained and you are supply constrained, then it is something that you can do about it. What we see is simply because they have do a very specialized business. They have a very limited staff of underwriters. And what we found is that many of those underwriters who are inundated with a lot of maniacal, you know, busy work. And we said, if we have the agent that can actually help a prioritize the submission based on what we see is different ways to prioritize what’s winnable, what’s within your underwriting appetite, you know, what’s within your segments of focus, things like that. And then while those underwriters are sleeping overnight or throughout the day, 24 by seven times 365 or 366, if it’s a leap year, they just process all those applications, prepare the code with the underwriting notes, with the guidelines and the research that is cited by them. So that underwriters are spending time in making a quality decision and they can make a lot more quality decisions with that. So that was the one situation. Another situation came, which is like something, something small enough, but it’s like it adds up big time is the claim side of it on a claim adjudication side of it. An OL has been triggered and by the customer, and then there is a huge process that goes on before it get even assigned to a claim adjuster. They basically identify that should this have a special investigation, should you flag this claim for special investigation unit for this, again, based on the company’s, whatever the criteria that they are using it, what kind of complexity of this claim is, what kind of severity of this claim is, what is the litigation risk on this claim is. Based on all those things, which they call it in a simple word called claim triaging, then they identify what’s the right people that, you know, or right adjuster where it should go to. Today, it takes somewhere around three to five days to do a triage, elapsed time. That triage now gets done silently overnight without any supervision and then gets assigned. So again, you’re making the claim settlement faster. It helps you. You’re making, you know, avoiding some of the mistakes that can lead you to a higher litigation cost. So there’s a benefit for everybody. So there are so many of such use cases, which initially as like everybody kind of jumped the wagon in the entire world, that AI equal to less hours, equal to less people, equal to more profit. Well, while that is a hardcore ROI, maybe it’s true for somebody, but I think I’m missing a big picture if you’re just focusing on efficiency only.

Chapter 10: Operationalizing Trust: AI Working For, Not On, the Customer (40:40)

Manish Shah: I think what you’ve got to focus is on effectiveness. And I think what AI brings is an effectiveness. I think what AI brings in terms of intangible, which is what people kind of ignore it, is the relationship with the customer, the buildup of a trust with the customer, and the loyalty that it brings with the customer. And that in terms, it brings more business, it brings more long, you know, renewals, all those capabilities that makes you stand out as an insurance company that is a very different from what a typical insurance company is. And I think that that’s where the huge value is. And we saw that, that, yeah, sure, you can reduce some hours by doing a claim triaging automating, but that’s not the real benefit. What the real benefit is, is that you’re expediting the claims process. Anybody filing a claim, you know, we know we all have filed claims in our life. And what is the one thing we want? We want this process to be as painless as possible and as quickly as possible. And if somebody gives me that experience and they charge me a little bit more premium, I’m going with them. I’ll tell you that. And I think that that’s really where the effectiveness comes into the picture and AI can bring it. So those are the use cases, how we are actually thinking about it.

Sabine VanderLinden: Yeah. It’s interesting because when you mentioned the word effectiveness, what came to my mind is the word capacity. I think what technology gives you is the ability to have more capacity and therefore not focus on the cost saving, but focus more on the growth. And the only way you can drive growth is, as you said, customer experience, customer relationship, dealing with the moment of truth, which takes us to the truth statement, right? How do we operationalize trust with the customer, partly within our industry, which is often, you know, struggling with reputational risk, right? So when you think about an AI world, right? And the AI-enabled insurance business model. How is that trust earned? Therefore, when we look at the new world, right, underwriting claims, service, as you mentioned, and how can we maybe demonstrate it if we can already?

Manish Shah: Yeah, I mean, look, you know, first and foremost, trust is built in small moments, not in any marketing material or strategic tech, okay? What it means is that how fast you respond, how transparent you are in your responses, how consistent your responses are, and how fair and understandable, you know, your responses are. So if you look at, as an insurance company, if you’re doing an underwriting, be super clear about what data you used. Don’t hide it, the how did I arrive at this thing? Just be clear about it. And saying that this is what I found. I mean, if you’re using social media to find out somebody’s behavior, say it. I don’t think it’s anything wrong about it. Be transparent and explain them that how it influenced pricing, that because of this, because even if they become your customer or don’t become your customer, you are certainly becoming a trusted partner for them who say that, oh, so that’s how insurance company, if I want to reduce my costs, maybe I should change my behavior or if I should have this healthy habit or I should do this. Like you’re giving them a pointer.

Chapter 11: The 5-Year Vision: Insurance as a Proactive Safety Net (45:30)

Manish Shah: An and then the third thing is recourse and having an ability for the decisions to be escalated to the humans so that they can be appeal. And I think that all those things, if you summarize, is trust will be built with an AI if the customer feel AI is working for them and not on them, and the trust will grow.

Sabine VanderLinden: Yeah, that’s very well said. Working for them rather than against them, which is critical because that is, I think, partly where the fear we’re using artificial intelligence stem from when we think either about employee or customers. You have actually mentioned that future, the future which is need to be experience-driven, but also empathy-driven. And platform is here to enable us to be more successful at doing what we do every day, solving customer problems. At the end, that’s why we are in business. So looking at three years, five years from now, and I know we have so much work to do this year in 2026, but I think that’s the future. So I think that’s the future. six, right? But looking, let’s say three to five years from now, what do you feel is going to be really the characteristic of those insurers embracing an intelligent core with agile workflows? Because then they become much more enabled to serve the customer better. And how that customer should feel working with those really great insurers who are already focused on them with the right people in the team.

Manish Shah: Yeah. I mean, listen, Sabine. So if we can actually execute well as an industry, and if we willing to have a bold leadership to actually do things, this is how I think the insurance should feel. This is what the successful look like in next three to five years from a customer point of view. First of all, insurance should feel more like proactive safety net than a just reactive payment mechanism. I think it would help me prevent some of the losses, some of the bad events that can happen. Of course, you can’t prevent everything. I know if it happens, it’s there to give me some kind of redemption. But if it is going to help me prevent, it’s a safety net for me. It’s not something saying, I’ll come to the picture once you fall. I want the insurance to actually be a safety net preventative. That’s like a big point. The second point is onboarding becomes almost invisible. Right now, I don’t know, try to switch a carrier and insurance and the amount of paperwork, amount of data that you have to fill in, amount of calls that you have to go through. It’s crazy. And I think that having all the data automatically done, having an instant risk assessment done, having a understanding me as Manish and offering me a tailored coverages and getting rid of a lot of friction that are unnecessarily there in today’s technology landscape that don’t deserve to be there, but still there. We take away those frictions and just make it a very, very frictionless, almost instant experience for an onboarding, which I think customers should feel that way if we get this thing right. I think one interesting thing is that the services or interactions with the insurance company should become continuous and not episodic. That’s how it will feel. I think that the coverage should be automatically optimized based on life events, right? It’s not like just stay like that for a year and on renewal, we’ll talk about it, or you call me and I’ll do an endorsement kind of a thing. There should be some kind of coach that looks at your data and gives me a coaching like, okay, you can save something by adjusting this, or you are underinsured here. Things like that. If I have a personal trainer and if I’m working out, he’s looking at my data and he will shift my plan, right? It’s not like, okay, see you next month kind of a thing. It doesn’t work. I think from a claims perspective, as we said, it should get much faster, much simple, much frictionless, avoid a lot of errors, avoid a lot of complexity that happens. Eventually, I think a customer perspective, and I’ll tell you what I observe people, I’m one of them, but calling insurance company, if there was a monitor, it will tell you, you are anxious. Especially if you’re filing a claim, you don’t know what’s going to happen. You don’t know how painful it’s going to be. Will it even work? I think the true measure of success is that the emotional tone will shift from anxiety to confidence. I think that is going to be very good. It’s a confidence that it’s my active partner. I know that they are interested in my safety. They’re interested in my health. They’re interested in my financial resilience. I think it just becomes a true partner rather than just somebody who says, our interests are diagonally placed because if you pay me, I win, you lose. If you don’t pay me, you win, I lose. I don’t think it needs to be that way. I think it’s really important that it becomes an active partner and that’s what customers should feel.

Chapter 12: Collaborative Intelligence: Sprinkling AI Across the Enterprise (51:50)

Sabine VanderLinden: Absolutely right. That term around, which came to mind is that trusted advisor, right? Really proactively thinking about the customer. I think we are moving to a world where we are going to manage portfolio. The insurer as well, if they want more of our risk, they need to look at us as, as you said, moment of truth and different events and therefore looking at managing a portfolio, whether it’s life, health, home, motor, you know, talking about the simple product. But then when you look at commercial, it’s even more sophisticated. And so therefore I think customers are looking for that relationship, which is a portfolio based relationship and then the trusted advice as well. And actually that proactive thinking around, by the way, you know, are you making more money? or should we ensure a bigger top line, or is that being reduced as well, going both ways? That is trust. It’s like, okay, we’re here to support you as you are growing your business, but also to be really there for you. So you can tell us both sides, and we are going to actually make the right adjustment. It goes both ways, because that is where you drive retention, I think, with your customers. When you look at your work, with those amazing insurers you are working with, any tips you could actually help other insurers think about when they think about where AI can unlock new, better product or services, better relevance for the customer, we just talked about portfolio, or where this human-centric insurance model is going to shift. Is there any business model you are seeing with an agentic workflow and an intelligent core that can facilitate the entry in your markets, maybe?

Manish Shah: Yeah, I mean, I think we need to adapt to what I call it collaborative intelligence. I think that go look at every single processes, question, should it exist? Number one, I’m not a believer in automating the irrelevant or non-value-add stuff. So question that, and when you question that, question it from that perspective, is it really for customer or is it something for us? And if the answer is it’s not for customer, I don’t think it should exist. Then go through every single thing. I think leverage it, start now. Don’t worry about building a big enterprise capability, try applying it at individual scale. Because the way I see AI, and we ourself are leveraging AI within our own organization on our own operations, is AI is going to be sprinkled, not layered. What it means is that if you have a process and if you can infuse an intelligence on it, do it. There is not going to be one tool, there’s not gonna be a one approach, there’s not gonna be a one model on which that you’re going to use this thing. Go now, don’t do POCs, try to find an embedded courses AI, not something that is you’re building it from scratch or you’re thinking of it something as a siloed AI enterprise. You think that how do you improve the intelligent quotient of your current enterprise by embedding an AI in every process, in every system that you currently have. And speed is an essence. And the reason why speed is an essence is there is a lot to be learned as a company. And no matter how many POCs you do, no matter how many internal service you do, you don’t learn this thing unless you actually roll it out into your customers. Until your customer experience it, you don’t get a real feedback. And until you get a real feedback, you are just basically not gonna be able to evolve the thing to a meaningful level. So it’s an opportunity. People sometimes say it’s an AI race and I don’t like to use that word race, but it is some way in a way that not that who runs faster, but in a way that who can evolve faster. But in order to evolve it, but in order to evolve it, you have to start doing and start rolling it out. And the best way to do it is find things that has AI intelligence built into it. That’s an AI. Find something where if you think you’re augmented intelligence by building something that is very proprietary for you, that’s okay too. But don’t try to look for a unicorn answer.

Chapter 13: The Brave Ones: Who Will Redesign Insurance First? (55:00)

Sabine VanderLinden: I agree. It’s not about the unicorn. It’s about actually, as you said, it’s about the implementation deployment, right? The exploitation, not about the proof of concept. It’s about making sure that the human, whether it’s a customer or an employee feel they are part of the transformation, which is a behavioral and people transformation first. So I’m going to ask you for your last word of wisdom, Manish. We’ve moved from protection, right? To prevent participation, peace of mind. So for you, who are going to be the brave one to go first through that transformation? And you see some of them already. Tell us.

Manish Shah: Well, I’m going to start by saying that it’s going to be the brave ones. And the way I define the brave ones, are the brave ones, those who are willing to rethink their business model and not just tech stack. And I think it’s kind of important to understand that are they willing to ask this question of how do we help our customer live safer, healthier? That they really, instead of just saying, how can we process the current business model with faster and more profitable? I think the brave ones are going to be able to ask those questions and execute on that. I think we’re already seeing some pioneers emerge across the lines of businesses. I think carriers are building prevention services into health and benefit side of it. I think we see PNC insurance. integrating some of the IOTs, telematics, some of the risk coaching capabilities, embedded AI, like as part of our intelligent core, and adopting that thing and, you know, rolling it out into the market for a quick experimentation and learning from it to evolve it, then doing all those things. But I think many of these insurers who have adopted an intelligent, you know, core and working with us, you know, we may not hear a lot from them, but they’re quietly leading the shift. And, you know, they use all these capabilities, but that’s as an enabler, that’s as an execution mechanism for a bold vision that they’re ready to invest into it. So like, I don’t think we’re going to get a one Superman or hero company that will say that did all of it. I think it’s going to be about more of those brave, bold community of leaders. I think they are the one who’s going to make a difference. It’s not like going to say, here is one company that just changed the insurance forever. I think we’re going to have a community of leaders, the vanguards of insurance in a real sense are the people who’s going to be actually challenging the business model, who’s going to say, how can I leverage AI so that I can address. So bottom line, and the message from my side is that, listen, the tools are here. I think the need is clear. I think the only real question is, is there a leadership courage? And that is, I think, what is going to differentiate, if you don’t mind me using, you know, some of those management framework, the leaders from the lab arts, right. And we are, I think, going to see that quickly within 2026, moving into 2027.

Chapter 14: Becoming a Frontier Firm: Sabine’s Final Take (59:20)

Sabine VanderLinden: Manish, if an insurer wants to talk to you, DM you, right? Do it anytime. And then I’m thinking as well about the scale-ups, I always said to the scale-ups, you know, why don’t you think about the platform with cloud-native, intelligent core, agentic workflows? They can’t do everything, right? And they need your help. So what do you want to tell them as well?

Manish Shah: I would say, talk to me, talk to us. I think let’s have more frequent communication. Let’s exchange ideas. I mean, as much as I would like to share what I have learned, I’m always look out for learning from other leaders. So let’s communicate. We can even share what we are seeing in the industry, what other customers, who are the bold leaders are doing and what are their early results. I think that will be more surprised that how the new way of thinking actually challenges the status quo of traditional transformation concept. And I think that talk to us, we can explain to you that you don’t have to succumb to the tyranny of war, but you can actually harness the power of end.

Sabine VanderLinden: Absolutely. Manish, I’m so grateful for you to come here to share, you know, your vision around Majesco, intelligent core and agentic workflows, because I think we are going to see a lot of transformation happening this year in 2026. Thank you for the opportunity to exchange our thoughts. And it’s always a pleasure to speak to you, Sabine. And thank you for doing such a great work for the industry. I think, you know, this thought leadership really helps. This is a wrap. And as shared by Manish here, if insurance is no longer just about protection, but about prevention, participation and peace of mind, then the real questions become who is brave enough to redesign it first. And I would add, you know, who is already doing it, whether it’s within a regulated industry like finance and insurance or any adjacent sectors. And now I will add, if you enjoy this podcast discussion, please subscribe to it, share it with a few friends, DM me on LinkedIn and let me know what you want me to dive into next. This year has to be about how you become a frontier, a frontier firm, a frontier insurer, a frontier corporation or enterprise. So please help me. Also, please leave a five star rating. Your rating and comments are so valuable. I review all of them and my team helps me adapt content to meet your needs. Finally, please connect with me on my preferred channels. I’m a B2B growth expert and venture client architect. You will find me on LinkedIn, Instagram, X.com, mostly nowadays, when I engage with my audience as much as I can. Right. We also have to work during the day, but where I combine over 170,000 followers. All information available below on the podcast description summary. Until next, keep scouting for growth.