PegaWorld 2026: New Frontier in Action: Re‑Architecting Execution at the Intersection of Governance, AI, and Speed

Welcome everyone. My name is Brian Kim, client executive for MetLife. And you're here at MetLife breakout session following the earlier keynote. Please welcome Sadiq. Hi, everyone. Good afternoon. I'm Sadiq Mohammed. I am the director of Intelligent Automation at MetLife and I handle enablement, execution and implementation of intelligent automation solutions in Latam and EMEA. And today I'm I have two of my colleagues, Sridhar Mulpuru, who is the solution principal architect, and I have Venkat Esakkimuthu, who is a co lead for enterprise automation solutions at MetLife. So yes. We have spent almost a year and a half together building a solution, building a platform that is reusable. We have accelerated by creating marketplaces for these solutions and something we are generating, something that we are genuinely proud of. So I'm very excited to share this with you today. Today we will talk about our journey with Pega.

And it's not just Pega. So the idea was never to onboard a new tool to the existing tech stack. The idea was always to find a platform or an orchestrator that will complement our existing solutions of Intelligent Automation. So as a group, we have we built RPA solutions. We provide a platform for RPA solutions, workflows, e-signature, AI, document, OCR, and document management. So today we will talk about. We'll also touch upon our digital transformation journey about adopting Pega and how it became a part of our tool stack tech stack, enabling our customers across globally. So we'll also talk about the tension between governance and speed. So I'm sure each and everyone in this room wants to move fast, deploy faster, and automate more, and get to the value quicker. And AI makes that feel more approachable and more easily possible. But in a highly regulated industry like insurance, it's hard to achieve that because because of the regulatory framework, the speed and governance, it's the speed is not an asset.

It's actually a risk. So we so we want to share with you, how did we build a system that is fast but also governed and auditable. And there are two pieces of it. So what Nick mentioned in the keynote session today, There are two aspects that we're looking at. The Intelligent Automation orchestration and AI. One is to build top down and the other one is to bring bottom up. So in the top down we talk about how we enable our customers. And then bottom up we talk about how we enable our developers with citizen development or adoption of these key tools to basically enable our customers at speed and at speed. So before I get into the technology, I want to talk about the company that we work for. And that gave us this opportunity to be here. So MetLife has been around for 158 years, and we take huge pride in it.

And with MetLife, we were not built out of a boardroom. We were built during the Civil War. So with MetLife, we formed MetLife to basically support the soldiers and their families during the time of a national crisis. And that is our DNA. We are built on resilience and protection as we are in the industry of insurance. Our customers need us to show up at the critical times in their life, and we cannot discount that. And we supplement that with technology that has been our principal. Also, a couple of cool facts. How many of you know Empire State Building in New York and Rockefeller Center? Both of those national monuments have been funded by MetLife, and we are very proud to be associated with that. And at one time in the in the 20th century, we MetLife was running one of the biggest customers for USPS where we were sending out policy payments.

We were sending out managed claims and policy notices through these mail. So that comes with Legacy. So this process needs to be digitized in the era of AI and automation. And those are the problems. Those are the business problems that we are looking to solve and scale. Also, how many of you know Snoopy? See, it brought a smile to your faces. So Snoopy has been our mascot for over 31 years. And that was to showcase that Insurance has to be humane and approachable. And although we retired Snoopy for a couple of years, now he's back as our mascot or brand ambassador for a pet Insurance. So the point I'm trying to make is. For 158 years, this company has looked at adversity challenges. We have seen the Civil War, the digital revolution. But we stood up and faced all of this with a chin up.

And now we are in the AI era and we want to embrace this technology, move forward, be present for our customers. And that is exactly what we at MetLife as a team try to achieve. So now let's talk about scale. So that was our history. But where are we today? So we have over 100 million customers across individual and institutional customers across 40 countries. We have over 46,000 employees globally, and we operate across 60 plus countries in Latin America, Europe, Middle East, Asia-pac, and also North America. We are the top life insurer in United States, one of the three top insurers in the United States. And in 2010, we acquired Alico. And that doubled our presence in the international market. And now in Japan, we are the the largest player, largest top five player in Japan, and which is the second largest life insurance market in the world.

Why does this scale matter? To, because with this kind of scale, we're talking about different regions. And with different regions, there comes different regulators, different languages, different products, and different legacy systems that we have inherited during these acquisitions. And at the level of this complexity, manual processes are inefficient, not just inefficient. They are a liability. And intelligent orchestration is the optimal. At MetLife. It's how we keep our 158 year promise to our customers to show up for them when it matters the most. So let's take a look at where this journey actually started because it didn't start with. A boardroom decision or a top down mandate to pick up a new workflow tool or Pega. It started with one broken process, and it all started with the example that Nick shared in today's keynote session. It was Mexico underwriting. We had a policy renewal system workflow that was slow, fragmented and completely manual with no auditability and no visibility.

The kind of process where everyone knows it is painful, but everyone made the amends that we have to live with it. We did not. As a small team, we work with our business process re-engineering folks, and we had to prove that intelligent orchestration could replace this manual complexity with speed and with measurable outcomes. And our goal was, again, not to build a process for Mexico underwriting our process or our thinking was to build a platform, a reusable set of assets that can be replicated across globally, across regions, so that the delivery times and the time to market for other players reduces. So if you talk about building a reusable asset, a good example would be a document management framework wherein where you store your documents as an orchestrator from Pega, you can upload that to your document or content management platform. Now that can be reused across globally, be it in US, Japan or APAC.

So with our concentrated POC, it worked. The cycle times dropped, errors reduced. The business saw the results, and our tech leadership and business leadership believed in what we were building. Because, like I mentioned, the idea was never to solve a use case, but to build a platform that complemented our other intelligent automation capabilities or offerings like Idpf, which is intelligent document processing framework, our OCR solutions, our Rpas and workflow all together. From that point, we scaled this and we brought it here. So we have a couple of examples that we want to share with you today. Some lessons learned and also our thought process about building this digital transformation strategy. So it all started with Americas. Then it went to APAC uh, with different solutions of underwriting claims transformation. And now we couldn't have, we have standardized with regional flexibility. So the other key thing is you must have heard, uh, a solution called accelerator from MetLife.

So we built that solution for two different countries, but we didn't build it twice. We built it once. So with Pega localization, uh, offering, we build the solution in English, translated into Spanish from Mexico and then translated into Portuguese for Brazil. So that is a great example of how we have used reusability across and how we have enabled and empowered reusability. So as I'm not going to bore you with the same numbers that, uh, Nick shared during the keynote, but our not in good order numbers have been reduced by 40%, and overall visibility and auditability of scale of the claims have been really helpful to the business. Again, these are not projections. These are actual numbers from our production environment. So now Pega is the foundation. But the vision like I mentioned, was always bigger. What we built at at MetLife is a unified Intelligent Automation program that spans across major dimensions of our business.

And I want to be specific about what that means. So we have deployed conversational AI agents that handle millions of customer interactions every day, not just routing calls, actually resolving them with context and empathy. The key insight, and I want to be direct about this, is that the biggest unlock was never a single capability. It was committing to a unified platform strategy and building everything on top of it. And Pega is a foundation for all the complex workflow that we are building at MetLife. And moving on, I want to thank and be honest that we didn't get here alone in the early days when we were running a POC with uncertain outcomes and a lot of internal skepticism to manage, the Pega teams showed up and they facilitated some meaningful conversations, right? Ones that helped us build internal trust and also backed us at a stage when most of the vendors would have waited to see if we succeeded first.

And I cannot move forward without thanking our implementation partners, TCS throughout the pilot, the scaling across the regions, the kind of partnership doesn't just accelerate delivery, but it it it showed us what is possible. So I just want to end this with saying great technology in the hands of the right partners is a force multiplier. And we are fortunate to have both with Pega and TCS. Thank you. And. The mission hasn't changed. Like I mentioned, we are 158 year company and the mission hasn't changed. Just the tools have. So Intelligent Automation is how we honor that legacy at scale, making every interaction faster, smarter, and more human. The question I leave you with is, is simple. What is that one process in your organization that if automated, end to end would bring real value to the company? And to talk more about the digital transformation, I would like to invite my colleague Sri.

As Sadiq mentioned, that we didn't start this journey just to start with the Pega even prior. We choose Pega as a vendor. I mean, Pega is a tool to bring on our stack where we started our transformation journey, just onboarding the Pega into the Platform. It is about which is the right tool to onboard into our platform. For that, we did a journey of six months. We evaluated a paper evaluation of the different tools and also. we went through a journey consulting the Foster the Forrester Gartner report, and then we choose the top ten products that are in the market. And then from there we choose a few three products, which are like the market leaders and then which will fit for our MetLife organization. And then from there, we consider a lot of things as part of our journey as MetLife is a global company.

We choose, we look for a product or a, or a tool or a technology company which has a global presence, which can be deployable in multiple environments like on prem cloud, as well as the SaaS, and which has the capability to offer in those different regions. As, as MetLife has some stringent data restriction policies in those respective regions and Even before that. We looked at it as we started with an enterprise view. Do we need a new workflow tool in our stack or not? We have several tools that are already in stack, and our problem is not lack of the tools. Our problem was a fragmented workflow on a different tools. And then each tool will offer a different workflow patterns. Right. And the approach for integration is different for each of those tools. And then the legacy complexity that comes with the tools that we onboarded several years earlier.

So this move was not to onboard a new platform. It was to remove the structural complexity and also define a clear pathway for MetLife to define which tool to use based on the complexity of the workflows. And we evaluated three tools, and then we built a POC with the Mexico underwriting, as Sadiq stated. And then we collected the business feedback from as well as the architectural like run through the architectural decisioning and also the IT and then other areas of the MetLife. With all this, the Pega came out as. Pega is the one which aligned with our enterprise strategy and also other key items that we considered as the flexibility at the deployment layer and consistency at the architecture and then governance layers. Most important architecture principle was integration. In every every integration, we have to recreate the integration for different tools. It becomes complex.

Our prime motive is build once and then reuse for across the organization for different use cases for that. Pega cake layer is a perfect example that we used, and we able to build once and able to integrate with different capabilities within our organization, which will support our digital journey, as well as MetLife internal as well as externally hosted applications. And also, as Sadiq mentioned, able to integrate with the content management because most of the workflow tools that requires a content is the is the base requirement. And then we able to reuse the integrations that was created for in all those layers. The key to reusability or the strategy, what we use for reusability is to build a marketplace within Pega ecosystem. So what we did is we created several reusable assets and patterns within the Pega ecosystem, to name a few. We created a intelligent document processing connector for extracting data from the documents and summarizing information from the documents.

So the other thing, key thing is the document management framework, which we have created just to connect to different document management frameworks within MetLife. And what we have seen with this reusability aspect is once we build a connector, What happens is if you use in one particular business process, it can be reused across multiple business processes because it's the same document management framework, which is our system, which is sitting behind in most of the regions. So we have seen a considerable improvement in the speed to market by introducing these reusable assets. The other key thing is if you have seen like you have Apigee API connect or API management, we have something called API management for our gateways. So what we have done is we have created a reusable connector for API M as well. So for any interaction, mostly it's through APIs. So what we have built is like one time you build it, you reuse it for like N number of times.

So that has introduced like a lot of speed to market in terms of executing at scale across the enterprise. Yeah. And as part of our, the deployment, we choose Pega. SaaS as its, it supports our strategy, which like faster time to market and lowering the operational efficiencies and also reduces our footprint in order to support the platform. And also able to deliver our business outcomes faster. And also at the same time with the accelerator, like Pega, Blueprint, and AI, we are able to fast move faster without losing the governance. With this, we stop treating like a as an experiment. And then we started operationalize it as part of how the MetLife works. And as part of this Pega journey, what we felt is like it's just not as a platform modernization, Modernizing the existing legacy workflow platforms that what we have and migrate into the Pega.

This is an enterprise operating model for governance, transformation, and reusable architecture, and also includes the orchestration at scale. And also, how did we scale across the MetLife in the different global line of businesses? Without losing the control speed and also the discipline as scale doesn't come from just adopting the platform alone. It comes from building an enterprise operating model. And for that we use the four dimensions. First one what we use is an accelerate innovation and delivery Through scaled Pega capabilities as product comes with a lot of capabilities that supports the workflow and orchestration, as well as it supports the modern capabilities that can able to easily integrate and then go along with some of the fastest trends that are coming up in the market, both AI as well as content. Content management. And the second dimension is have a create a true partnership model across like, say, business, technology, operations, architecture, security, and an adaption actually.

And we built partnership with all these respective stakeholders is as our operating model. And third dimension is what we use is hub and spoke and COE With a Federation ownership as hub provides the standards and governance and shared services and guardrails, and then spoke provides the MetLife COE both control and then agility. Fourth dimension. What we used is scale enterprise transformation through Pega plus agent ICA. Our goal is to build the balanced value horizon, show a near-term impact in implementing the AI, and as long as and build that as a long enterprise capability and AI becomes credible only when it is embedded within the process, orchestration and within the governance workflows. And it has to ride with the right business outcome. And MetLife is building this governance model and then partnership model and then reusable model and then a ready enterprise operating model, right. And as we brought in Pega, we brought in just it's not as an, as a platform or a tool.

It's an, we built an enterprise operating model around it for governance, transformation, and a reusable capability model, and also an AI ready orchestration model. And to speak more about that, our business outcomes and our visible impact. I'd like to hand off to our CEO, Lee. The true measure of this journey is not effort, but outcomes and the ability to scale them. So when we started this journey, we focused on three critical priorities from business leaders feedback, speed control, trust. So we started off with the Mexican writing process. The whole key thing was how can we make faster underwriting decisions and reduce, for example, the claims time cycle, time for control. As you all know, Pega has an inbuilt auditability in terms of providing what really happens in a workflow. That was very key for an insurance industry. The third piece to it is we want to provide a consistent and a very transparent customer experience with Pega providing a very good UI experience across the board with Constellation.

So as you know, in insurance, speed without control introduces risk and obviously control with speed limits Value. Our focus has been to provide like speed with control to provide maximum business impact. What we delivered. We embedded all these key business priorities through our Global Center of Excellence, and also applied it across all our global lines of business. If you've noticed it, we have started with Mexico Underwriting. We scaled it across different lines of business for claims in Japan, in Latam, and then we have even gone to the extent of introducing acceleration in acceleration in Latam as well. So the key thing with respect to the claim transformation is we have built a composite AI platform with Pega as the orchestration layer for complex business process, and then inject AI wherever you need to have intelligence into the business process. So we have been building business process for maybe several decades now.

Everyone is building business processes for several decades now, stuff, right? The key differentiating factor is how can you involve or invoke AI into the overall business process. So we have introduced an adjudication feature which involves AI along with a well-built Pega orchestration layer which provides auditability, thereby reducing the claim cycle time by weeks to minutes or hours in certain cases. The other key thing is accelerating underwriting decisions, right? So what we have done is we have introduced a lot of reusable assets and patents. We have created those, put it into a mega marketplace and then started reusing them more often. Right. By doing this, what happened is the speed to the market. So we have Legacy workflow products in our tech stack today. I used to build those like ten years back. It used to take like eight months, nine months to build a particular process.

So with the existing Pega tech stack, we are able to build things in like 8 to 10 weeks. So that's the speed we are talking about now, right? So overall, the speed to market has improved by 40% by using Pega as the orchestration layer for complex business processes. And then on top of it we are introducing process efficiency. We have minimized delays in claims and underwriting process. We have overall improved consistency and throughput. Right. One of the key things I want to bring here is in today's keynote, Nick brought up what a responsible use of AI use case, right? Every company wants to use AI across the board. So you need to govern how we are using AI within MetLife. So over the last 18 months, before 18 months or maybe 12 months before, we had a very disjointed, fragmented process where different stakeholders were involved like legal compliance, risk and architecture.

So what happened is, if I'm a business use case owner who wants to experiment with AI, I have to go through all these teams to get an approval to even start working with AI. So I have no idea what's happening in the entire process because there's not a single unified platform which provides me details of what's happening with my use case. Right? So while brainstorming, the key thing is to provide a complex orchestration layer which can handle this interactions with multiple control partners. So we introduced Pega as a unified platform experience, which is going to integrate with all the key systems which these key control partners are using, and also providing a unified experience for the business use case. So that's what we are talking about. So before, when we start, start talking about a responsible use of AI. When a use case owner wants to start experimenting with AI, it takes probably few weeks to get an approval.

Now they exactly know how fast it is, where the bottlenecks are, which team is taking more time. It provides a full fledged view, like a pizza tracker. When you order a pizza tracker, you get a full view of what's happening with the pizza. That's what you get with this responsible use of AI. What we have built, and to come to further things like the overall, how we scaled, you have noticed it. We have not looking at a single like a particular use case. We are trying to scale capabilities. So that's the key part of how we brought in Pega, which Shri and Sadiq touched upon. Right. So the Pega marketplace which you have introduced in MetLife, it has a reusable patterns. It has common components which I mentioned earlier, like the document Management Framework Document Intelligence API. So all these accelerators we have built into Pega layer, which is speeding, speeding up our time to market.

The whole thing, I think most of you guys have used some kind of a business process diagrams before, right? So before what happens is you have to build a business process. You have to show it your business leaders to get that alignment. Okay. This is what we're building with Pega. Blueprint. What changed is you can build a business process on the fly and show them this is how it's going to look like. And then you can start showing them like an interaction and you can do changes along with them and then get the alignment. That's the speed at which we're talking about. Right. And then the composite AI. So Pega is the orchestration layer for the complex business process. But in order to inject air into your overall business process, like claims underwriting or notice of sale, any of those, you need a platforms, you need intelligence.

So what you're talking about is in MetLife, you're building something called a composite AI platform, which has Pega as the orchestration layer for complex business process. But we do have a platforms built which can plug and play into the overall ecosystem, right? So connecting Pega with AI platforms, we have embedded decisioning across the governance and auditability. We have embedded intelligence using the AI platforms into the complex business process. What it does is it introduces smarter decisions, consistency across regions, and then regulatory compliance and traceability. Why this works. You know, business leaders always wants two things. They want value measurable value and scalable solutions. Like everyone is talking about reusability. Now, so what we have done is the Pega marketplace provides a reusable pattern and reusable assets. And also it provides a scalable architecture, right? You have the Pega cake layer for scalable architecture. And also we are talking about a scalable composite AI platform.

So we are no longer delivering individual use cases. Whenever we start looking at any of the business process or business problems which comes to us. We are looking and scaling at Intelligent Automation level as an enterprise capability and delivering a overall composite platform with speed control and trust consistently across MetLife. So whenever you scale you, you run into challenges. So as we scaled Pega across the enterprise, we do have some several practical and repeatable lessons which can be used to drive transformation with Pega and AI. So always engage early, align upfront. So anyone who has been building workflows for so long would know that's very critical to understand how you want to build a workflow. So what we do now with a new Pega blueprint is we create a blueprint process, share with the business IT key stakeholders, make sure there's an alignment, and then we start building it.

So in our key programs, what we did is we leveraged Pega Blueprint to visualize processes and create a shared understanding across stakeholders before we begin building. So always alignment early is what enables speed and simplicity later reimagined before automating. So I think shift lift lifting and shifting can work sometimes. But most of the times what we have to do is reimagine, relook at your business processes along with your key business stakeholders, and then start the automation. So we have a business process re-engineering team specifically built for it to look at the overall business process for a claims process or underwriting, or a notice of sale or servicing a customer call center or whatever you call it. They have like a full fledged understanding of how the business process should work. And then you can apply AI and automation on top of it to deliver measurable value.

So we apply automation where it adds value, not where it adds complexity. Define always define the future state clearly. We explicitly define how people process technology and work together in the target state. This clarity on the desired outcome enables faster and predictable execution. Scale through reuse. Right. So we have focused enough on reusability. So few years back or maybe five, ten years back, what you used to do is we used to build workflows in silos. But in the last few years, what has changed within MetLife is we have built a marketplace where you can share your patterns and assets, and it becomes a hub for people to come and check for existing connectors rather than building their own connectors. That's the power we are talking about building your own marketplace, bringing all your reusable assets, bringing all your reusable patterns. That way it's much more faster to scale across the enterprise.

We are not building use cases. We are building our scaling capabilities. Right? So that's what matters the most. Obviously we are in the business of trust. So governance is a key factor for us. So we embed controls early through pipelines and control deployments. So governance is built into the system when we start. So that way it ensures compliance auditability and consistency in a regulatory environment. Build beyond the pilot. So always what I hear is let's start a pilot. Right. So it's built beyond the pilot. Always think through the future state or how you want to build it all. And then look for an enterprise scale. Underwriting is a common theme across multiple lines of business. Claims is a common theme across multiple lines of business. How can we build it once and reuse or scale it across the enterprise? That's the key in bringing enterprise wide transformation.

So if you cannot scale, it is not transformation, it's pilot. So Pega is the orchestration layer for our overall composite AI framework. We do have other capabilities which is ensuring the overall business process has the intelligence built into the overall auditability factor as well. So Pega plays the role of the orchestration layer for complex business process. We do have intelligent document processing framework for showing the document intelligence, thereby maximizing the Pega native capabilities, and then integrate with partner systems, including AI platforms, document intelligence, and conversational solutions to accelerate outcomes. So key takeaway from this 18 months of journey, what we had so far reimagined the process align early with the key stakeholders built for reuse our marketplace share with people across the enterprise. Governance by design and transformation is not about moving faster. It's all about moving smarter with alignment, scalability, and control built in from the start.

So what's next, right? So we have built the foundation. So what are we going to do next? We have already started modernizing our legacy processes. So we are planning to expand the reusable assets. And then we are also planning to scale it consistently across the regions. MetLife being a global company. Right. So again, this is not possible where we are today without the support from Pega, our partners, right. And also our implementation partners, TCS, my team here, the Dias. And there are a lot of people who are sitting in US offices right now and in India. I have to mention them as well because without them this was not possible. Thank you. Can you share any findings on the document processing, uh, component that you build that's reusable? Uh, what were maybe some points where you felt like you had some difficulty overcoming some hallucinations or, uh, sort of the context length of some documents, the technical challenges in that space.

Uh, sure. So document intelligence, right? So for any business process to Insurance, we have so many documents coming through. So what we have built is this plug and play approach where we have built in a document intelligence framework, which can connect behind the scenes to any of the OCR tools which we have internally. Right. So it is exposed via APIs. So whatever the framework we have built, it's a API based framework. So it's exposed. So Pega can call it or any capability can call it. Share the document behind the scenes. It connects to a corresponding OCI tool based on your configuration. Extracts the data for the document, sends it to the UI and Pega. Where you have the extracted information sitting in front of the UI along with the document. So that's what we have built. So this is not like specifically made for Pega.

It's like a reusable document intelligence framework we have built. It can be plugged into a web application. It can be plugged into any of the platforms that can make a call API call to this document. And you might have to think about just an integration that what we built to the single system. It's not about that the document can be structured unstructured and then semi-structured, and the integration should be able to handle like any kind of document that goes from the source system Into use this framework, able to send it to the OCR tool and able to extract. Actually, the models are going to be built on the OCR tool in order to extract any kind of structure and structure and semi-structured document. And then the integration should be able to handle any kind of data that any kind of document that goes in. And then it should able to identify the model and then model extracts and send it back.

And it can be maybe one field, it may be ten fields. And if it is unstructured, it's a bulk of the bulk of text that's going to come from extraction, right. And then this layer is should able to handle any kind actually. And also it able to tell actually from source system from where the document is coming and then response back and it has a batch processing and it has a bulk document upload, like a source system can send a bulk documents to the OCR tool to extract. And this like integration reusable module should able to handle any kind of those situations. Yeah. We have introduced in all the business processes, we can dig deeper into that. So in terms of hallucination, there was a question about hallucination. So we have certain guided deterministic prompts that we have written to extract the, you know, the set of fields that we want to extract out of that document.

For example, if we get an endorsements file, we ensure that out of that endorsement, we figure out what type of product is it? Is it maternity, is it vision. And then based on that specific use case, we extract 30 or 40 of those fields. So our the prompts that we have written make sure the hallucination is less and the accuracy is maintained. The models that we build give the same amount of accuracy every single time we run that process. And for such documents, it's going to be we know from where it needs to be extracted from the document. But if an unstructured. As what Sadiq mentioned, we have. The prompt and then prompt will be sent along with the document and then the back end. Whatever the OCR tool that the built in with an AI should be able to extract and bring it back.

And at the same time, we use the same integration patterns for summarization. Because it's an insurance industry where a particular claim comes in, it has to be summarized from the policy documents that the user is subscribed to. Right. And with the prompt and response like you able to get back the summarization also along the same integration, like common integration layer that what we built. Yeah, I had, I love what you guys put together the last slide and at the end it had, uh, value and kind of value realization. I'm curious if you had to estimate the financial impact of the work that you've done, what is that number like? How do you think about the quantification of the value? Yeah. So I'll start and then probably we can. So there's a business process re-engineering team. What they do is the whole job of is to understand the overall landscape and then understand what kind of business value it's going to create.

So by investing X amount of dollars, we are going to realize this kind of benefit over a period of three years or five years or two years. That's what they're going to build. What Pega has transformed this overall journey for us is what our implementation cost. We used to have earlier with the legacy platforms, that has gone down by 40% because of the reusability aspect and the redesign, like you can design it much more faster and it's much more faster to scale as well, because the reusability aspect. Was that the main business case, Maybe I'm looking at your rep a little bit, but was that the main business case for the investment in Pega is you can reduce reusability by 40%. Therefore you get the the sign off, right? Okay, so you're asking about how we got Pega in, correct? Yeah, yeah. So okay, so if we if we can go to that point of how we got Pega in.

So again, it's going to be the business process reasoning team. They were looking for enterprise scale workflow product. So she touched upon it like looking at multiple products in the market. That's how we started scouting for it. We had like 10 or 20 products. We were looking at it and then we finalized with three products, and then we did POCs with three products with Mexico underwriting, which Saadiq touched upon. Right. And then once we finalize two final contenders, one Pega was one, I'm not going to name the other one. But again, we had two folks. We did that. Two. So there are multiple facets to basically figure out the business outcomes of it. So one is the platform and the tool itself. And the second is the value. The process that we're re-engineering is generating in terms of handoffs, like less manual handoffs, less errors, reducing policy issuance times, faster claims, faster claims management, more visibility, not just in terms of claims management, but also reporting auditability.

So all of us are in a highly regulated industry, especially when you go to Latam. They want us to keep a data for and for auditability purposes for over ten years. And if legal asks for a report, there are multiple reports that you can generate right out of the dashboards with Pega. That was also another reason. So in terms of manual reduction hours savings on Platform and what the outputs that we get in terms of re-engineering the business older process with a new outlook. So all these things made us. So also we have a calculator that we use basically. So if you are onboarding a new use case for reimagine or re-engineering, we look at these items that I just spoke about, reduction of manual hours, Platform costs, and then the value that will generate for the business. And then we look at it in terms of ROI as well.

And that's when a use case gets prioritized. And a huge role is, I mean, we are it, but we work hand in hand very closely with the BPR team, business process engineering team to build that calculator so that we get the necessity funding or the money in alliance to modernize our solutions. And also, just to add to that, is the Pega team support also to bring in Pega, the extensive support that what we got from. Thank you. Thank you. I think we are at time. Thank you guys.