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Artificial intelligence is changing the way real work gets done. But big ideas don’t drive change. People do.
The ones who roll up their sleeves, modernize data, and bring AI to life where it matters most. In the workflow.
This is for them. For you. The visionaries. The innovators. The leaders turning potential into performance and pushing their organizations forward.
Everyone’s talking about the promise of AI and what it can do. On this show, we’re talking about making it real.
Learn from the experts who are driving it forward and walk away with everything you need to bring AI to life in your organization.
Rohit Kapoor (00:00):
What we know is speed is critical. So any use case that you pick up or any implementation you have to be able to make the return on that investment be visible in a very short period of time. And these days, that short period of time is as little as 30 days. And so you really need to move with high velocity.
Shirley Macbeth (00:23):
You're listening to Make It Real, brought to you by EXL. I'm your host, Shirley Macbeth, and on this show we're exploring how artificial intelligence is reshaping workflows, industries, and the way real work gets done. And yes, we're going to make it real. Everyone's talking about AI and wants to use it in their organization, but the reality is that many leaders are struggling with how to use AI effectively in their organization and get the real benefits from it. So joining me today is Rohit Kapoor, the CEO and Chairman of EXL, a global leader in data and AI. And Rohe really brings a unique perspective to this conversation through the work that EXL is doing to empower companies that are really in complex industries to transform their businesses with AI. So we're going to dive right in and talk about why the conversation around AI is really important and it's critical right now. And then we'll also tackle how to overcome those barriers that often hold organizations back from realizing that great potential that we see from AI. So welcome Rohit, and thanks for joining us today.
Rohit Kapoor (01:37):
Thanks, Shirley. It's exciting to be here on the first podcast by EXL on this topic.
Shirley Macbeth (01:43):
Excellent. Well thank you so much. So Rohit, you talk to executives all the time about AI adoption. You're out there talking to many leaders and there's that promise of AI, everybody's very excited. And then we see there's studies out there that talk about there's a definite gap between that hope and the potential of AI and what is being realized by many organizations today. Can you talk about what's causing that gap?
Rohit Kapoor (02:10):
Sure. So actually it's very simple. The basic promise on using and leveraging AI is that the return on investment is going to be phenomenal. And I think what organizations are finding is it's actually very hard to deploy AI and getting that return on investment is actually quite elusive if you don't implement it the correct way around. So many use cases that clients have tried to implement AI for just does not provide the return. And I think you can characterize this and you can place them into three different buckets as to why does this happen. Reason number one is many of the processes where AI is implemented, it's being implemented as a point solution trying to get productivity benefit for a particular task. And therefore what happens is while each task provides the productivity benefit and the promise is being realized at the task level, you really don't get the benefit at the process level and there is no real cost savings that drops down to the bottom line because the same person is doing part of their job in a much more efficient way, but then they're also doing the rest of their regular job in the same way as they did before.
(03:38):
A second thing is in many situations it's not the application of AI across the enterprise. It's only applied to a small subset of processes and operations where the AI is being applied, and that means that you're not really getting scale benefits and you're not really getting the real power of AI coming through in terms of that return. And then finally, many times the choice of the use case is such that the benefit that can be provided by the use of AI is very, very limited. So it typically ends up being that the return on investment is actually quite low, and that's why clients don't feel like embracing AI as aggressively as they ought to be. The trick is pick the right use case. Make sure that when you implement AI, you're not only implementing AI, but you're transforming the process. So that means you're going to handle the process and you're going to work on that process differently and by reconfiguring the process, you will be able to shrink down the workforce and be able to get a realizable benefit associated with that. And then lastly, work on those types of use cases that can be leveraged across the enterprise, whether that's a horizontal application of AI or whether that's a vertical application of AI, but it needs to extend itself either horizontally or vertically and provide the necessary benefit to the client.
Shirley Macbeth (05:14):
You said so many great things there, so I want to unpack that a little bit. So I heard you say when you're thinking at the task level, you're not getting that full benefit, that you really need to think bigger and across horizontally and deeper for some of the broader use cases. And I also heard you say pick the right use cases is critical to this. So let's unpack that a little bit. How do you advise executives and CEOs to think about what's that right use case? And it seems daunting, right? So something that's horizontal and huge, how would you approach that?
Rohit Kapoor (05:44):
The choice of the use case is absolutely critical and I think it'll get driven by a couple of fundamental factors. We should pick a use case, which is a large problem and an area where there can be a significant amount of efficiency or effectiveness or an ability to have a significant amount of growth associated with it. So it's got to be something which is meaningful. Second, the use case has to be such, which means it is ready for the application of AI and that involves having data being available to apply the AI to it. Many times we find our clients tell us, I'd love to be able to deploy AI in a particular use case, but that data is sitting across multiple silos, it's not accessible and you simply cannot apply AI in those situations. You also have to pick and choose those use cases where it would be easy to demonstrate the value of the application of AI very, very easily and quickly. We have a diagnostic tool that we use to help our clients figure out where should we start and deliver the benefit of the use of AI to them. And then once that credibility has been established, then we can actually take that forward in the organization and apply that across multiple use cases.
Shirley Macbeth (07:07):
That makes a lot of sense. I want to probe deeper on data. You talked about the importance of data and we know that data fuels AI, but often it's very challenging to get that data house in order, if you will. Can you talk a little bit more about data and why that's important and what are some of the areas in particular with things like unstructured data that are critical to get an order as you move towards AI?
Rohit Kapoor (07:32):
Yeah, so if you think about the application of AI, are there two things which are critical. There's data and then there's the algorithm or the LLM and 90% of the problem. And the effort relies in getting your data estate in order. Most organizations have grown through acquisitions, they've got legacy infrastructure and technology platform sitting there, and therefore the data sits in multiple silos and it's not interconnected and it doesn't talk to each other further. The data is not clean and it's not standardized. So you might have fields and records on one platform being used in one particular way and on another platform in another way. You also have the unstructured data issue that you spoke about. Almost 85% of all data that exists in an organization is sitting there as unstructured data, whether that be in the form of handwritten notes or that be in the form of call logs or that be in the form of voice interactions that you've had with your customers.
(08:41):
It's all sitting there. So what we find is only 30% of organizations say that they have data available in a usable format. And so the very first task is to get that data estate in order. And what we have found is that one of the best use cases of applying AI for any organization is to use AI to get that data estate in order so that data is ready for AI. And so it's literally about applying AI to get your data ready for AI, and we've developed a suite of agentic solutions, which we call as EXLdata.ai. These are about 65 different agents that we've put together and that allows us for the right amount of data discovery, the ability to have data lineage be established, have data governance, have data cleansing, data labeling. So it's a very comprehensive capability that we've created that brings down this task of having your data estate be AI ready down from a few months and bring it down and bring it down to a few weeks.
Shirley Macbeth (09:59):
That's incredible. Well, when you think about using AI to solve your AI challenge, that's very exciting and the time shrinkage of how you're able to do that, that's phenomenal. Let's talk a little bit about some of these workflows. You've been talking about workflows and sort of these big horizontal areas that can be transformed when you think of something like a big healthcare organization or an insurance or these very complex a bank. Can you give a few examples of the types of use cases that can be transformed using AI?
Rohit Kapoor (10:31):
Sure. So I'll give you two examples. The first one is what we did for an insurance carrier. They were receiving a number of emails with attachments associated with these emails, and their underwriters had to literally open thousands and thousands of emails, open the attachments, reach out for the relevant information that they needed to make an underwriting decision, and then price a policy and then go back to their customers and offer a price associated with that insurance policy. Well, we took that entire journey and now we are using agentic AI to quickly read through all of these emails, open the attachments, pick up the relevant fields and the necessary input variables and be able to reduce the cycle time from a week to a few hours. What that does is it allows the insurance carrier to go back to the broker with a quote in a much faster response time.
(11:37):
You know this that in today's day and age, if you ask for a solution or answer to a question, if you get that response time quickly within a couple of hours, your likelihood of making a decision is so much higher. So we've seen is that the quote to conversion ratio has gone up by 7%, which is huge for an insurance carrier, and it's enabled them to process a volume which is three times the normal volume that they would handle in any other normal operating environment. So this kind of a deployment has been really enormous for this insurance carrier. Another example would be we work with a utility company in the UK, which is one of the largest utility companies in Great Britain, and what they have done is worked with EXL to embed agentic AI across their workflow across all of their customer journeys. So today the onboarding of a new customer that's done leveraging AI, the entire process, which is from meter reading to the cash payment that's done using AI. When a customer moves location and they want to close and shut down their service and want to move to a new location, all of that is handled by AI. So today we've got almost about 35% of all of their back office and mid office processes being worked upon with AI, and it's really elevated the customer experience, it's reduced the cost in a very meaningful way, and the client is working with us in other areas to help them embed AI into the workflow.
Shirley Macbeth (13:28):
Those results are phenomenal. I'm glad you mentioned customer experience because listening to what you're saying, I mean the organizations probably had a lot of productivity and other benefits in mind, but I think one of the real wins here is how fast you're able to serve that customer and get answers faster, be more responsive. Can you talk a bit about that?
Rohit Kapoor (13:48):
So in this new age of AI and innovation, I think speed is one of the most critical things and the most critical attributes of value. I think it is somewhat underappreciated, but the value of that is tremendous. And what speed does is it eliminates friction points and break points and therefore allows you to be able to have a much smoother end-to-end process journey. And at the same time, the customer experience is so much better because the responsiveness is so much faster. So I think whether that be in insurance or whether that be in utilities or that be in banking, I think the speed and the pace of being able to implement this change and be able to provide responses becomes a very, very important attribute for being able to deliver the ROI on the AI implementation.
Shirley Macbeth (14:40):
That's incredible and we all want that as consumers of all of these types of businesses to accelerate what you're trying to get to quickly. So it's win-win all around. I wanted to talk a bit about when you bring in AI, and it's not just about an AI magic wand, right, that you apply AI and all is good, there's so much more. When you think about the backdrop of things like change management and cultural shifts and new ways of working that are really part of these implementations, and I should mention humans. Humans in the loop, do we need humans anymore? And of course we do. Talk a bit about the broader impact that you're seeing with these deployments of AI in the workflow.
Rohit Kapoor (15:22):
So there are three things that I'd like to talk to you about which are really effective in the adoption and implementation of AI into the workflow. The first thing is, even if you have all the data and you have the LLM and the AI, you need to have the contextual knowledge and understanding when you're implementing AI into the workflow. And that contextual knowledge and understanding is absolutely critical for the successful implementation of AI. The second piece of this is AI. When you first apply it, the results that you get are actually not very good. The accuracy levels may be only around 60%. The response time may be quite slow, so it's very clunky. It's not actually giving you the desired results. So what you need to do with AI is to fine-tune the AI algorithm alongside with the contextual knowledge and understanding of that business and that process and fine tune it and then get that accuracy level up, get the speed up, and get the reliability up significantly.
(16:34):
So that's the second part of it, which is this iterative style of being able to implement AI into the workflow. And then finally, it's the part that you touched upon, which is the change management. We as humans don't really like change. We like the status quo and we just like our life not to be changed too much, but when you implement AI, you're necessarily going to change the process and the humans need to change in terms of how they work with AI, how they use AI, and how they need to serve their end customers. So that change management involves a very strong level of communication and over investment in terms of what might happen in a scenario planning building and then actually helping your clients make the shift and make the pivot to the new operating model with which this entire customer journey needs to be operated. That's a very, very important attribute, which I think many times organizations fail to invest in or don't really have the adequate amount of focus on it, and I think investing in that early and continuously is really critical for the success of that AI implementation.
Shirley Macbeth (17:51):
That's amazing. For CEOs that are looking at implementation and the speed and all of those things that we've been talking about, if you could think back a year or two years when you first started on this AI journey, what would you want them to think about? What do you know now that maybe we didn't know two years back?
Rohit Kapoor (18:09):
Well, what we know is speed is critical. So any use case that you pick up or any implementation you have to be able to make the return on that investment, be visible in a very short period of time. And these days, that short period of time is as little as 30 days, and so you really need to move with high velocity. The second is you've got to bring your people along, and it's not about just one part of the organization that's going to be playing in AI. You really need to reskill everybody in your organization and get them to be comfortable using AI or adopting AI or working with AI. And I think that education process of reskilling the entire organization becomes a very, very important attribute as you make this transition and you make this journey through.
Shirley Macbeth (19:03):
Thank you Rohit, you've talked about so many amazing things, and I'm just trying to distill it down for our audience. If they were to take away just three key takeaways, this is what stood out to me from our conversation, and I'd say number one is really, if you're thinking about AI, organizations should pick the right use case. It's really critical to pick the right use case that has transformative effect across the organization and also is something that can show ROI really quickly to show that momentum and do that. So picking the right use case is critical of what I heard you say. What also stood out to me was the importance of getting your data estate in order. And then number three, I think this people piece of an organization thinking about reskilling and bringing your people along for the journey of these AI in the workflow. Your people are a big piece of that, how to help them understand AI, rethink their business processes, et cetera. Sometimes that is what I heard you say is not always invested into the appropriate level. So I think number three is about really reskilling and bringing your people along. So thank you, Rohit, thank you so much for the time today. I really appreciate it.
Rohit Kapoor (20:11):
Thank you, Shirley. And I think you've got the three. Absolutely right.
Shirley Macbeth (20:16):
Excellent. I have one final question for you as we wrap up and at EXL. I know we talk a lot about the notion of innovation at speed, and I just would love to close on describing if we could hear in your words what that means to you and how other leaders can take this concept and maybe apply that to their AI journey.
Rohit Kapoor (20:36):
Yeah, so Shirley, as you know, we've got over 60,000 employees at EXL, and my hope would be that we have all 60,000 innovating constantly, and for us to be able to pull this thing together very, very quickly, in order for that to happen, you really need to have a mechanism which allows for distributed innovation to take place and the ideation to take place in a distributed environment, but at the same time, the ability to build and create on a particular idea in a centralized controlled environment and do that very, very rapidly. So we have this concept of innovation in 30 days at EXL, which is the timeline we give ourselves for any new idea from ideation to actually deployment. So that's something which I'd love to be able to push at EXL and that velocity and speed feels just about right.
Shirley Macbeth (21:39):
Excellent. Thank you. Rohit Kapoor, thank you for joining us today.
Rohit Kapoor (21:42):
Thank you.
Shirley Macbeth (21:46):
Thanks for listening to Make It Real. We hope today's conversation gave you ideas, insights, and inspiration to help bring AI to life in your organization. Remember, big ideas don't drive change. People do keep learning, keep experimenting, and keep embedding AI where it matters most. Follow along so you never miss an episode.