PegaWorld iNspire 2024: Streamlining Business Operations with AI & Automation

Welcome to PegaWorld. Who's excited to be here? Woohoo! Absolutely. And it's great you picked the best breakout session to attend first. Um, but if I could do a ask a favor of the audio visual team in the back to switch over to my PowerPoint slides, I only have two. So thank you so much for joining us today. Um, I think the keynotes were amazing. Great energy, great passion.

You see a lot of the investments that we're making in terms of technology coming to market. But the truth is the technology is the technology. The bits are the bits. What's fascinating for us, and the reason why you come to PegaWorld is not just to hear about the bits and the technology, but to to figure out how your colleagues, how partners in the industry are putting that technology to use to drive outcomes for their businesses. Now it's really interesting to hear. So I joined Pega about four years ago, and the first thing I did when I joined Pega is I read every case study that we had, and I discovered that over 80% of what Pega automates in the back office, over 80% of what you use Pega for today is automating back office operations processes. So when we thought about PegaWorld, we thought about, hey, what's the story we want to tell about how people are using our technology? It was all about driving operational efficiency. So today I'm I'm joined by a pretty powerhouse panel today.

And so I want to give a warm round of applause for Fernando Pozuelo, for Rexi Josef and for Swagata Mukherjee. So thank you so much for joining us, my esteemed panel. I appreciate it. And so what we're going to do is we're going to do a bit of a panel discussion. I'm going to encourage my speakers to be opinionated. One thing is I haven't heard their answers, which is sort of fun to hear. I've shown them the questions, but I haven't heard their answers, so I'm excited to hear it too. I am excited for you to also pose questions here, so we'll make sure we have some time at the end to be able to have those conversations. So the first thing I want to do is have each of my speakers sort of introduce themselves and talk about a little bit of their focus in their company.

So I'm going to start out with Swagata here. Can you introduce yourself and talk about a little bit of what you do? Swagata. Hi, I work at Primerica and I lead the enterprise Automations and transformations in there. Um, it's been such an exciting journey ever since I joined there because, uh, this is an organization who is, uh, you know, in the crawl, walk and run phase of trying to transform through all the processes that we've, uh, that we have, which are really, um, manual, a lot of manual processes, a lot of manual work, and we are trying to bring in all the best possible technology to help us get over the manual and help focus the human efforts onto more important areas than just clerical parts. So it's really been a very exciting journey for me, and it's I'm sure it's going to be a long journey to go through because, you know, once you reach a goal, it's there's something else also always to do. So thank you so much for that. And and you know, can you help us understand a bit more about what you do and sort of the vision for transformation in your organization? Um, hello.

Yes. Uh, I had the digital process automation team within, within, uh, Corbridge financials. Uh, most of you have not heard about Corbridge Financial because, uh, we were a company that ipo'd Appeared about a year and a half ago. We were originally the life and retirement division for AIG. Um, I joined the company originally when AIG for about six years ago, and then we ipoed ourselves. Our company is going through this huge transformation in itself. Um, it's it's different to be a company that is, you know, you have acquired and you're trying to merge it within an organization. But when you're divesting an organization out of it, it also means a significant amount of work. Um, mostly around systems, technologies.

There's people there are processes that needs to be improved. So my team, we have been in the forefront of changing how our operations work. Uh, we have some really great success stories of leveraging the product. Pega we do have other technologies also in our process, uh, space, uh, in our digital communication space that all all work together. And my goal is as a vision for my team is to have this all integrated in one cohesive solution that can be provided to our customers, our agents, our producers, and our distributors. Very cool, very cool. Thank you for that. And then finally, Fernando, can you can you share a bit a little bit about what you do and your focus on the vision? Yeah, sure.

Um, my name is Fernando Pozzolo. I am coming from Spain. I've worked for Siemens for the last 23 years, and since October last year I am acting as Pega service owner. Um, you may know that Siemens is transitioning from being a very well known industrial traditional company to be a technology company, and at Siemens, we believe that digital technologies and digital transformation can offer new and innovative ways for businesses to achieve their goals. On one hand, as a technology company, we sell products, software and services can can help our customers to achieve their digital transformation goals. Faster, easier at scale, but on the on the on the other hand, we have to make sure that our own business remains also up to date in terms of digitalization. Internally, we say that it is the digital backbone of Siemens of the business. And to support the digital transformation of Siemens, we have defined some strategic priorities and key enablers. There are many technologies out there and key enablers that can support the digital transformation of a company such as Siemens, but an example of those are application development and process automation.

And because of that, we have enabled at Siemens a platform ecosystem to support application development and process automation. And we use strategic platforms like Pega to digitize simple workflows, build applications, and automate complex end to end enterprise level business processes. And it's not only that. What we also do is that on top of the platform. We offer our customers with a flexible business model supported by providers, so that they can choose what to get from us from our complete service offering, from simple platform provisioning to application development and or application maintenance maintenance, so that they can realize their ideas of digitalization with full flexibility. And with this and with these examples, we are supporting Siemens vision of digital transformation, at least partially. You know. Absolutely. And whenever you talk about strategic platforms and Pega being one of those strategic platforms, how do you think Pega plays a unique component of that vision for Siemens?

Yeah. Um, Pega has some strengths compared to other solutions. And the way we are positioning Pega Siemens is when we need certain key features or functionalities that with other technologies it might be more difficult to achieve. I'm going to give you a few examples. Um, first of all, we use Pega when we want when we want to focus on the complete end to end process. That is when we want to automate a process from start to finish and not only a part of a part of it. And these are usually big, complex, end to end enterprise level business processes that are that usually go also across regions or organizations. I'm referring to processes such as purchase to pay or opportunity to cost. We also use Pega when we need high scalability and capacity for variation.

You know that you can start small, but at the same time, you know that you can scale to the entire enterprise. And also you can easily vary your applications across regions and organizations because Pega gives you many possibilities for reuse without duplicating the underlying code of an application. And maybe the third key feature or functionality we are using Pega for. It is about when we need high transparency and traceability, because with Pega you can handle digital workflows in highly regulated areas such as finance, compliance or human resources. Um, now, I really would like to give you a real example of how we are using Pega at Siemens with an existing applications focused on a big, complex, end to end enterprise level business process with high needs of scalability and capacity for variation. We have a business unit which is called Global Business Service or GBS, and they provide different sorts of business services internally but also externally. In the past, they used to handle the order management process manually, relying on a shared email mailbox on several disjointed applications. And as you can imagine, this approach led to lots of issues low productivity, lack of transparency, and an overwhelming volume of requests. Using Pega, our colleagues in JBS managed to build their own digital order management application, improving the efficiency and the overall level of automation and digitalization of the entire process.

And not only that, the application has also really streamlined the order management process, providing more visibility and transparency for order managers, sales teams and finance departments. And it has been such a success that they have already implemented it in 34 countries. Users have grown by ten from 500 to 5000, and they have already integrated it with 34 ERP systems, reducing tons of manual effort. And back to your original question why do I see? I think Pega is so unique, and I think that, in my humble opinion, the answer it is because of its architecture, because with Pega you can put decisions and processes at the center. And also you can, um, you can create layers to create variation of your applications and facilitate reuse. Wow. That's pretty impressive. You did that about as well as Alan Trefler.

We're going to go play chess with him. And then I'm going to recommend he put you on stage next to you Fernando. So first of all amazing transformation at Siemens. Thank you for that sharing that story. But I want to come back to Rexy and get a little bit and come back to I think Fernando brought up this big theme of, hey, here's where we're thinking about Pega, where they fit in our stack, the types of problems whenever we think about that sort of strategic alignment in your organization, how do you think about aligning technology? Pega being one of those to that Strategic imperative for your organizations. Let's start with you. Okay. So, um, I think, uh swagata.

And I, we come from an industry that's highly regulated, which means that when we look at large scale transformations or even, let's say, use of AI within our business, I think the key part that we always want to focus is on creating a group that would actually focus on setting up the right set of guidelines on how to implement it, where it needs to be implemented. So that's one of the key strategies that we would like to start off with. Like our first strategy was to actually go ahead and create those guidelines and put that into our gating process. Like any each and every set of, uh, almost every tool now comes with an AI or a AI capability, and they want us to be somehow leveraging it. Um, now we are a shop that actually wants to deliver it, but we are also very prudent about how we want to use it. And that's where our guidelines and processes, which includes compliance, risk, legal, our architecture, our leadership, design teams. We all get involved in defining those guidelines. That's included into our entire gating process, which even tells us that if I'm going to bring a new feature, it goes through that checklist of items that we want to look through, and if it passes the master, then we go forward. And I think there are some of those initiatives that we're doing.

The second part that we would like to look at is definitely, um, get our data house in order. We are a legacy company. We we have policies that goes through generations, which means our systems have been there. That has been pretty, pretty legacy. So get those data into a place in a way that we can serve it up to. Any sort of AI engine or any sort of systems and platforms is the second part of it that is very important for us. And that's that's where we are focused as well. Um, for us, AI does not translate exactly to artificial intelligence, but more towards assisted intelligence. When I say that it comes with the fact that we don't want AI to do, you know, we don't want the AI to run amuck in our organization.

We want to do it is we want to have human deciding or looking into the decisions that the AI have shared, and then taking the final call on what needs to be done. So this is this is our approach and defining that that that strategy and standards within our organization. So we have a team that actually focuses a lot more on it. And as in the coming years, we actually will start focusing and implementing a lot of those solutions around it. No. It's fascinating. I love the way you sort of went through the layers of being able to tell the story based on the business priority data and systems down, and I think, I think that's that's a, you know, a great way for us to think about where you put the priorities for these initiatives. So I got how do you look at sort of strategic alignment in your organization. So when we look at technology.

Pega has such a such a fast journey over the last decade. It's been mind blowing and there is so much that we could implement that there is so much that we could embrace as an organization, as any organization. However, when we look at our organizations, and I'm absolutely going to second axiom on this front is we have to look at our existing processes. We have to look at regulations. Very importantly, we have to look at everything else, which we also need to comply with when we want to roll out a solution overall. So defining the process of that strategy and understanding the timelines, understanding the bandwidth and everything else that comes with it, it's very important to establish how we're going to roll out something or any concept, any technology, anything new that's coming up that Pega actually has to offer. So we have heard a lot of the a lot of new things on the keynote today as well. And we walked out of that meeting thinking, oh, I want to implement all of that. But when we stop and think, we have to make sure we have an existing, a proper process to actually embrace that so that we are not compromising the data integrity so that we're not compromising the data security that we have for our customers.

And yes, we have a lot of legacy systems. It's an insurance company. So all the data that we have has been there for years, like 40, 50 years that we have in there. So there has to be some cleansing processes. There has to be some understanding and some acceptance of the fact that we have to process those data in a proper way, process our entire strategy in such a way so that we actually get the best benefit of it instead of, um, you know, affecting ourselves in any detrimental way. So our strategy, um, regularly involves conversations around how we're going to shape up those new technology, the new pieces of work that we come across, the new projects that we are trying to put on the unified platform, which is Pega, and how are we going to make sure that the data really aligns? How are we going to make sure that we are not compromising on any of the security or the compliance regulations? So all of that, we have a team to actually follow that process. And it's very much very similar to what Rex has been talking about.

Okay. Fantastic. So a lot of decisions that you sort of need to think about in terms of how and where you put the technology in place, really governed by how you're driving your transformation. So no, I love that. I appreciate that now, you know AI is everywhere. It's the big theme. And so, Fernando, I want to come back to you and talk about, you know, when we think about AI and machine learning technologies, you know, we spend a lot of time talking about it this morning. You know, when we think about trying to be able to learn from our data, what are some of the use cases that you're thinking about at Siemens right now? Leveraging AI and machine learning.

All right. Um, if we talk about AI, um, everybody is talking right now about generative AI, which, as you know, is it is a specialized area within AI that generate content such as text based on patterns learned from training data. And at Siemens, we are fully aware about the hype around generative AI, and we do already have a number of use cases where we where we've implemented it and others that are under evaluation. I will mention a few, a few of them. Um, the first use case is to use AI to create support chatbots. I guess that everybody knows that all chatbots follow predefined scripts to provide very limited responses, while new chatbots based on AI can understand contexts and generate more natural conversations, offering a human like interaction. And we do already have chatbots implemented in Pega using AI that can interact with users in real time and provide responses to frequently asked questions and assistance along execution. We have other applications where AI is a priority topic for categorization and classification of emails, because with this technology we can create different case types, selecting the right type depending on the nature of the email. Then, if we combine email classification and categorization with the use of AI to accurately Extract entities or key information, such as customer names or their numbers or email.

Issue issue description from emails. Then this data can be can be then fed into Pega. And then we can fully automate the creation of a case filling the information gaps. Then we have another use case which is for automatic uh, automatic email responses. Um. Imagine a system that can provide instant reply to customer uh, to customer emails with accurate responses. That is, a customer sends an inquiry. The AI analyzes the email content and generates a response. And this not only provide immediate assistance to your customer, but also frees up the support teams to handle more complex issues.

Improving efficiency and overall customer satisfaction. Then another use case. It is about case Summaries, because with AI you can process the case information and extract the most critical points to generate concise and meaningful summaries. And with this, for example, support agents or managers can quickly get the status of any case and facilitate faster and well-informed decision making. Another one we are looking at is to use Process AI for case resolution. That is, imagine that we can guide our agents to the best resolution path of a case. This is by analyzing historical case data and the specific one of each new case. We can suggest the most, um, the most effective steps to be taken. And this will not only speed up the resolution time, but also will increase the likelihood of achieving positive outcomes.

Uh, then we also have another one, of course, which is for automatic email translation translations, because we have a team in Czech Republic that it is interacting with a German team. And the magic thing here is that both teams are interacting with seamlessly, seamlessly with each other in their own language. Then we have another one that we call GPT, and this is about when we have attended, and you get a 200 pages document that no one wants to read. Then what we want to do here is to support the lead manager, uh, whenever we have a lead, then we take the document, we put it in Pega, and then we facilitate the lead manager, the lead manager asking the GPT agent with the most common questions about the document, and then maybe a final one. It is also about entity extraction, but not from emails, from documents, contract documents, because we want to make sure that versions are similar before and after the signature so that the two documents are exactly the same. Wow, that was quite a few use cases. Yeah, that was pretty impressive. I anticipate a lot of, like, little AI robots running around Siemens doing all sorts of great work. It's fantastic.

That is the next thing. So, you know, and I think that plays into your comment about Siemens being very much a technology company and that that investment in those use cases. You know, one of the things I hear a lot from organizations is that there's there's anxiety about embracing AI, you know, as it relates to the risk. And I think probably all of you probably risk is something you're all getting very, very comfortable with whenever you think about applying these technologies. But whenever I come back to my colleagues in the financial services space, you mentioned risk left and right when you talked about strategic alignment. So let me start with you. How do you think about managing the risks in terms of AI in your organization? So, um, I stated earlier, right. Um, managing risk starts at the top at the very start of what our what our priorities and goals are looking like.

So we have what we call as, uh, a team that we created called the Cobridge Digital Responsibility Forum. Now, it doesn't mean that we are digitally responsible. So let's be clear with that. Uh, the intent here is we feel we know that with AI, great power comes great responsibility, right? So we also know with that it actually means that we have to be responsible ourselves, get ourselves, make sure that we understand what and how best to utilize it. So that's where we start off our first journey. Incorporate every single program coming from the business, be it coming from the IT, or be it coming from technology sources or the operations, wherever we come in, it goes through this forum, which actually is a team of our folks from compliance, legal risk and team that actually evaluate them based on our current conditions. So we are setting up this gating process, and that's that's the first step towards managing the risks. And then we evaluate.

And we do go through a lot of other checks and balances within each and every stage of the program. And once we have gone live as well, there are certain instances as well where we we are evaluating the outputs of, of, of uh, the, um, the feedback that we're getting from our GenAI systems, uh, to evaluate, to see its efficacy and efficiency. And as we would do a lot more of our checks and balances, we are trying to improve that. It all comes from the fact that our industry is regulated life and Insurance. We cannot afford to have bias creep in through any of our sources of data that we feed in, including developers own, unknowingly adding some biases to the entire process. We would want that to be controlled, and that's that's the start of our process. So I'm very proud about what we are doing. So it's the team we are I'm part of that. I'm my team is part of that forum.

So we are also looking into it. So as we bring in new technologies, it will go through that rigor, as I'll call it, not the wringer, the rigor. No. Absolutely no. Keep keep the risk at the forefront and then be able to manage that with the outcomes of the of the project in mind. Makes total sense. So how do you think about managing risk as it relates to AI in your organization? Everything is always going to have some risk. So we what we try to do in our and our CTO is extremely committed to this is we have to be able to address and manage the risk.

Because if we say that, no, we're not going to transform because there is a risk. You know what our competition is going to. So we don't want that. None of us want that. So what we try to do in our risk management process is we have a very stringent legal and compliance team, team set up who help us through understanding and help us through managing if we need to create new policies, new standards around how we would be managing a new risk, which has come up in the industry because automation and AI. So automation is one part of it. AI is sort of the next step of it. Automation has lesser risks than definitely AI, because AI is actually, if you're trying to translate out using an AI, it might be spitting out words which might mean something completely different in French, for example, or any other language. So the compliance system to actually make sure that it is addressed and it does not put us in a legal, legal issue.

That's something that we have to keep a way of addressing that. So those kind of risks are things that we continuously as we come up with new projects, as we come up with new ideas of implementing something, there is always a forum where we go and check those pieces that are we, are we following the standards or do we need to create new standards because of this? And how can we make sure that we are protecting the customers data, which is really of utmost importance? We have to protect our customers data because it's Insurance, right? There's so much of data in there. So we need to make sure that we're protecting that data, managing that risk. And we are absolutely there. We cannot have biases, as, uh, Rexy said. So you actually stole my word as well.

So but that's a very right word that we want to use for our development team as well, because a lot of this is offshored outsourced. So when they are putting in some sort of a code or some sort of a standard, we just need to make sure that there isn't any inadvertent introduction of, um, any, uh, anything which might lead to a leakage or anything which might lead to any sort of data integrity issues. So those are very important pieces. Yeah. Now, all the conversations I hear about AI right now are the same conversations we were having about security in the cloud 15 years ago, not the exact same conversations, but the same concerns and the same level of risk that we have to address similarly. And so hearing your perspectives on how you manage that programmatically is great to hear. And I think something we all learn from. So, Fernando, I want to come back to you for a second and just sort of say, as you're on this journey of digital transformation at Siemens, you talk about all these use cases. What are you learning as you go through this?

Okay. There are a number of lessons to learn that I can share with you. And the first one is just that you have to stay in front of the way. Why? Because in this rapidly evolving landscape, I think it is crucial to keep a close eye to technological technological advancements in AI and automation. You also have to be brave on adopting new tools and methodologies, and don't wait for others to lead the way. You have to be in front of it, so that is very important. Another lesson learned is that you have to remove obstacles as soon as possible. OK.

When implementing AI and automation, it is normal to find unblocking points. Some of them are technical or there might be related to the technology adoption itself. It doesn't matter. The lesson learned here is that is that you have to remove remove those obstacles promptly and address these blocking points as soon as possible. For example, I'll give you an example. Um, are there any dependencies? Are there dependencies? I mean, are there dependencies with your current architecture depending on what you want to do? Uh, do you need Cloud three or Cloud two?

You don't know, but that is a clear answer that you need to get. Also are clear. The terms are conditions, so all those new technologies are free for you for use. Are they included in your contract? If not. If not, what do you have to do to get them in? How much do they cost? How long will it take? Uh, all that you need to start working on that as soon as possible.

And of course, um, something that is a must. Are there any implications in regards to cybersecurity or data privacy? You cannot forget about that. So the third lessons learned would be to determine opportunity opportunities with your customers faster and define use cases, that is, that you have to engage with your customers to determine opportunities, understand what are their pain points and and needs. Then you have to define use cases where AI and automation can create an impact. And finally, you need to be agile in exploring those opportunities. And maybe the last lesson learned that I would like to share with you is fail fast, but learn faster. Uh, because, um, you do not. You don't want to waste too much time on something and then realize that the original idea doesn't work.

So MVP should create the expected value. So again, it's not a good idea to invest a lot of time and then realize that what you wanted to achieve it doesn't work at all. That's why to develop an MVP minimum viable product as soon as possible, it is a must to test the that that you can get the expected impact. Okay, so fail fast, but make sure that you learn even faster. Yeah, absolutely. Some great lessons there. Thank you. And I'm going to jump over to Swagata. What are some of the things that you've learned?

Well, a few things about um, so I'm just going to take one example of a very large digital transformation that we are trying to do right now with sales automation and CDH, uh, in Pega and we have our business sponsors, uh, on that. We are we are constantly challenged on how what can we serve up to our customers, what can we serve up to our reps and everybody else who are in that process? And I always, uh, think that it's only when you're challenged is when you can make history. You not at your comfort zone. So we always keep thinking of new ways and new, um, processes of how we can address a concern, address a ask that comes through from business or any technology or compliance, and particularly when there are conflicts trying to resolve them. Um, it takes a lot of discussion. It takes a lot of, um, bandwidth. It takes a lot of time to actually resolve something in a way which is going to benefit all the parties and get to that, that point. So, um, that that is one of the key lessons that we, uh, we have been learning as we go along, that resolving the conflicts, resolving the different needs of different areas, regulatory needs, business needs, and so on and so forth, those make a lot of difference on how we're going to do automation.

No, that's really cool. I love that quote. If you're never challenged, you can't make history. I think that's really cool. I've never heard that before, so thank you for that. That's great. What are some of the best practices that you've learned, Rexy? Best practices. Uh.

I'll say honestly, there is no magic bullet or bullets. I'll say I'll be. I'll be honest on that. Uh, invest into the people. The usual suspects of people. Process technology and data. Uh, people who know the technology that you're going to implement. If you're going to use a product like Pega, you need to have a team that knows really well Pega in you, with you. They need to govern that entire technology.

And I'm not talking just about Pega if I if it's almost every SaaS products that we have, uh, we would like to build a competency ourselves, at least a skill set, because each product that you use comes with its small box. You have to do it within that box, whatever that product offers. And that's very critical. We need to understand that that's where the power of the product comes out. That's where you exploit the power of the product as well. So the people needs to know that the consumers of who we develop that also needs to understand this is where you use this product for. And that's a very critical component. These are the use cases where you use product where I heard Fernando speak about Siemens building it out based on specific set of use cases and identifying the right set of use cases. That's very important to us as well.

You need to know where products like Pega plays best, or products like Salesforce plays, or products like, uh, you know, our digital communication platforms. It's best if you use it for the wrong thing. You are failing itself. So we are the people who decide on that. So that's that's what critical best practice as well. Um, a very critical success factor for entire delivery that we have seen is the planning that we put together to every single initiative that we start, if we know the planning and if we are very, very strong about how we plan, how we design our solutions. Even before you write the first set of code, I sound very waterfall ish. But no, that's you can still do it in an agile fashion where you put together your plan in such a way that you align each and every single step of identifying what the outcome of that step should look like, and how far are you off from that outcome? We have been very successful on that.

I think, uh, I will I'll proudly say the last six years of our journey within Corbridge, uh, leveraging products like Pega, uh, we have successfully developed and delivered a significant set of applications, including in Pega. In fact, our session, there's a session tomorrow. I would strongly welcome all of you to come in tomorrow. The power of planning that we put in to deliver 12 applications in a year, right? So we started in January. We ended in December every once or every month we deliver an application. It was part of our transformation journey. It was a significant use case. We are having a session tomorrow in the second half.

Please join that session to listen to those planning sessions as well. So it's that's how we do the best practice is put that plan plan in place and deliver. No absolutely. And please all the plugs you want for future sessions I think that's great. So so first of all, thank you so much for that discussion. I think that was great. We're going to go off script and ask some other questions now. So if any of you do have questions, go ahead and walk up to the microphone and you can go ahead and ask the panel. But let me start out with an initial question for you.

And you know, this is just something that's been in my brain for a little while, which is, you know, with the advent of generative AI. Right. Um, it's forcing us to think differently about investments. It's forcing us to think differently. Not so much, because maybe we don't think differently about that, but because our stakeholders in the business think differently about that. So I want to pause and ask a question. I'll start with you, Fernando. How how is the advent of generative AI changing the decisions that you have to make at Siemens whenever you think about operational efficiency? Uh, this is a journey in my opinion, and technology comes always the first.

And then people different follow a different pace. So as it is a journey, uh, I think that there's still a time that has to happen so that people get used to the technology and understand it and can really incorporate it to the decision making process for, for example, uh, but as from my experience, the technology always comes firm and that is up to us to make the best user adoption possible so that we can make the most of the technology as soon as possible in our organizations. And I don't think we are there yet. So we see the possibilities. Uh, but when it comes to real implementations, there are a lot of a lot of to do. Uh, because techie guys like ourselves, we we have it in our DNA. So you don't need to tell us, uh, please use it. But when you go to the business and you try to tell someone, hey, you have been doing this, like, ages, uh, why don't you try in this other way, then, is when people adoption the technology, it is important. And and that's why that's what I see in Siemens.

Yeah, absolutely. A little tempering of expectations. No, I appreciate that. Uh, Rexy, how do you think about that? Uh, so at the start of the program, I said that we are in a journey of transforming. We just separated ourselves from a fairly large organization, and we had our own multiple set of, uh, operational challenges and transformation requirements. So we are in that journey right now as we speak. Uh, but we want to leapfrog, right? We don't want to be taking the normal path of going one step a.

Step b. Step c. Step D. We already know that we are out there. There are the technologies that are out there to help out. So what is stopping us from leapfrogging. So we are taking that deep dive and jumping into opportunities. A lot of those opportunities are mostly coming from our operational back office infra. In fact, our service centers as well, where we're looking at opportunities to see if we can handle our reduce handle times.

We have been going against multiple systems and trying to unify that into a single platform to see if we can reduce our handle times. We have automated our email solutions, so all our email ingestions are now automated through Pega, right. So the email triaging and the automation is happening through that. We have we have also built in a lot of other use cases where we are looking at opportunities to, to, to bring in, uh, knowledge assistance, uh, agent training. We are looking at capabilities around reducing the onboarding of agents in our contact center or our service centers or back office operations, because we have so much we have so many products, generational products that we have been selling over over multiple years that has its own set of nuances. And unless we train them very well, they are not they are not able to provide able support to our customers. Um, so that's one of the reasons why we are looking at tools and technologies like a GenAI that can actually summarize those kind of interactions for our agents to serve, help them serve better. That's how we reduce our GenAI. So that's how we reduce our average handle time.

So those are the areas of focus. Uh, there's so much of focus that is coming in our organization. So we are excited for the next couple of years is going to keep up for us. Yeah. No that's great. So tempering and then it was kind of cool. Rex he was definitely talking. You were definitely talking about sort of the aggressive hate. This is places we can use it, which is pretty exciting.

So I know you were nodding your head whenever I was kind of posing the question about, hey, you have business people who are super excited about it. You have technical people who are sort of tempering it. How do you how is how is Generative AI forced you to think differently? So, um, the reason I was nodding my head was mostly because, um, when we are looking at a new technology, anything which comes into the market, and it was same when Cloud came in, it was same when, uh, robotics came in, etc.. So when we look at those, there is always some sort of, uh, there is an excitement. Plus there is a resistance depending on the section of where, where we are actually trying to implement this. So the way we, uh, we try to take the journey at Primerica is we are trying to get our internal systems and our internal processes, and we have a lot of legacy that we need to overcome. And we need to, you know, attend to so that we have all our all our internal processes lined up. So we're trying to use a lot of our robotics processes and automation and, you know, gradually morph into the AI journey as we go along.

Um, because from from our customer point of view, the people whom we serve, uh, the represent the agents and reps and all, there is some, uh, sometimes we find resistance of like, do I want the AI to actually take the decision or do I want to take it myself? So that's still, uh, there is some sort of a morphing exercise which needs to happen for them and everybody else to embrace the fact that, okay, it is good to trust it. It is, it is. We can't trust it. It's capable enough. So, uh, that journey, we are still in that journey. So it's probably going to be a little bit of time before we get to the point of, okay, yes, we can do all of it, or maybe we can do 80% of it or whatever. But the automations that definitely help automations, as in the robotics and everything else that we have. That definitely helps us showcase the fact that, yes, you can trust this system so you can do more with it.

And yes, the system can suggest whether your new client, the customer that you are seeing on the screen as an agent, whether we should suggest that, okay, he has um, he's almost trying to, um, he's in a spot where he's probably going to buy a house. So why don't you suggest a mortgage? So things like that is kind of coming up. Fantastic. Fantastic. Listen, thank you all for your insights. We really appreciate hearing how each of you are addressing things like operational efficiency in your organizations. And can I ask the audience to please, please give our panelists a round of applause?