Only 12% of Companies Generate Value From AI. Here's What They're Doing | Sanjeev Vohra, Genpact

[00:00:00] [SPEAKER_01] Do legacy companies have to worry about being overtaken by agentic native startups in their industries? It used to be that the IT department owned all this stuff, and now that's kind of broken that silo.

[00:00:15] [SPEAKER_00] If you have 100 agents, one of the agents go rogue, do you have a process to find out which agent has gone rogue, which is not behaving properly? The companies who will lean faster are not looking for the perfect answer, would have more chance towards success.

[00:00:27] [SPEAKER_01] Don't wait until you understand everything, because by the time you understand everything today, the technology would have moved on to tomorrow. You just have to jump in and swim.

[00:00:40] [SPEAKER_00] You know, I joined Genpact around 18 months back, and I am truly enjoying what's going on in the industry and what I'm doing in Genpact. It's quite being in the engine room, if you may. And let me explain.

[00:01:05] [SPEAKER_00] I mean, I have always been at the intersection of business and technology for the last many, many years, more than 35 years, and did a lot of work in the business side in the first half of my career, and the second half was mostly consulting and strategy and technology. And with only one intent of leveraging technology to the best business outcomes and people involved in the enterprise,

[00:01:32] [SPEAKER_00] and how we can make our lives easier and more productive and efficient by using technology. Now, various waves of technology have gone from the mainframes to client-server technologies like an ERP is like SAP and Oracle's to internet technologies that started in late 90s to digital technologies later on a decade back plus.

[00:01:58] [SPEAKER_00] Right. And then between that was a cloud, the whole wave of cloud and data analytics and AI finally. Right. So what really attracted me in Genpact was the DNA of the company. And that's process intelligence. And the way the company has that process intelligence, we call it the last mile, which means understanding how the process actually operates.

[00:02:27] [SPEAKER_00] So it's not about advising the client, but it actually operating the process itself. And that's why I said being in the engine room itself. And that gave me a lot of inspiration and idea of how to actually deploy artificial intelligence to make sense of the business outcomes, if you may. That's how I landed here.

[00:02:48] [SPEAKER_01] Yeah. And I remember when we spoke before, just for people who may not know, Genpact is short for generating impact. And it was spun out of GE Capital, as I recall. Is that right?

[00:03:07] [SPEAKER_00] Yeah. It was born in 1997. So it was inside General Electric, started in 1997 as General Electric Capital International Services. That's how the name was.

[00:03:21] That's true.

[00:03:23] [SPEAKER_00] And then later on in 2007, 2005, it became an independent company. And 2007, it got listed in New York Stock Exchange. So that's the story. But you're right. Genpact means generating impact. Yeah. That's how the name came.

[00:03:38] [SPEAKER_01] Yeah. And just to sort of define things, process intelligence, can you define that for us?

[00:03:48] [SPEAKER_00] Well, absolutely. We live it every day. Look, we keep calling there is no AI without process intelligence. There's no artificial intelligence without process intelligence. And the reason is because the value of AI is when you embed that into the workflow, the way you actually work and operate. And that knowledge of how the work gets done. Yes. Irrespective of how many IT systems or technologies you're using in a day.

[00:04:16] [SPEAKER_00] But when you get up in the morning, if I'm a buyer, let's say, if I'm a buyer who purchases stuff. And if I'm going through looking at, you know, 10 screens in a day, making the flow in terms of how I operate, how I make the first contact with my supplier. How do I send an email to them, get a confirmation about the quotation that I receive? How do I look at comparing those quotation from multiple suppliers?

[00:04:43] [SPEAKER_00] How do I make a judgment call in terms of where do I need to buy from? How do I release a purchase order? This all is a transaction done by a buyer, right? So, and this flow itself can be totally be powered by AI. So that's what we call the intelligence of knowing this process, which sits on multiple systems in every organization enterprise. It's quite a complex thing. And having that understanding is intelligence of the process.

[00:05:12] [SPEAKER_01] And you've done this study of speaking to 500 senior executives from large corporations. Can you talk about what the focus of that study was?

[00:05:26] [SPEAKER_00] Yeah, I think, look, the AI has been there for ages, as everybody will tell you. And we would have read and discussed in multiple forums earlier. But last few years have been quite defining, especially after the chat GPT movement, if you may, November of 2022. A lot of people started seeing AI in their hands, which was not the case earlier. So consumers started seeing and experiencing AI in a very different way than a small set of people earlier, right?

[00:05:56] [SPEAKER_00] So we have billions of people right now, 20% of the world population is actually using some sort of a chat system, whether it's a chat GPT or something else or Google, but they're exposed to this new way of interfacing with AI today. Last year was a time when a lot of companies had already been investing in AI, but they increased their investments, but they were not seeing the value coming through, which means AI generating the business outcomes finally.

[00:06:24] [SPEAKER_00] And we heard this signal in the market, very loud and clear from our clients, our client discussion from the partners. And that led us to kind of initiate and commence a study with CXOs and trying to understand that from the CEOs and CXOs to understand what's holding them back, like why they are not able to see the value. That was a genesis of the study.

[00:06:48] [SPEAKER_00] But obviously the study, by the time we did the study and when we came out with inferences, a lot of things also evolved in technology at that point of time. And we could totally understand the hurdles that they're facing. And also we could clearly see the companies who are leading in the pack, what exactly they are doing differently than the others and how others can actually learn from them while they're still evolving.

[00:07:15] [SPEAKER_00] Because I think the target state is yet to be achieved, if you may, because it's going to take some time to get there.

[00:07:22] [SPEAKER_01] Yeah. And one of the things certainly is breaking silos, particularly in data within business units so that it can be used across the enterprise. But what are the key points that you learned from talking to these executives about what should be done?

[00:07:47] [SPEAKER_00] I think what should be done, I think the first of all, let me give a perspective of what's holding them back, because that could be very useful for our discussion. Maybe it goes back to the point that you were raising. I think, and we're talking about enterprises, we're talking businesses, large businesses, you know, who are, and small businesses, but they are into serving their customers with their goods and services, frankly, right?

[00:08:11] [SPEAKER_00] Most of them, whether it's a consumer good business, or it's a bank, or it's a telecommunication or it's a, you know, or it's an energy company. And what came out very clearly from the study, I mean, a few things, but one of the biggest constraints that everybody felt was around talent and culture.

[00:08:37] [SPEAKER_00] There's acceptance in the company about AI being used, but the amount of effort being put to embrace AI and to learn how to use it is still not very clear and still not yet there, right? So six out of 10 executives, roughly, agreed that a lot of effort need to be done to rotate the talent in the company and make that talent ready for AI adoption.

[00:09:04] [SPEAKER_00] And only 43% of the companies actually are investing in some sort of a training program or education. So there's still a lot of gap in terms of the intention and education and training and experimentation with technology, especially the new ones which are coming up now.

[00:09:23] [SPEAKER_01] Yeah, that's interesting. We were, I'm at a conference right now. We were talking yesterday with some people about identifying who in the organization has an aptitude for working with AI because not everyone does. And it tends to be younger employees.

[00:09:46] [SPEAKER_01] I mean, in your conversations with these business leaders, and what interests me is oftentimes it's not the most senior people in the organization.

[00:10:01] [SPEAKER_01] It could be some fresh young hire who is just AI native in their thinking and may, in that regard, be more valuable to the organization as it goes through this transition. Did you run into that?

[00:10:19] [SPEAKER_00] Yep, absolutely. Look, I think the difference which has happened in the last just last 12 months is the top management of the company and even the board of most of the companies are, they've become very eager to talk about AI. S&P 500 companies, and I've been following them for last more than 15 years now, very regularly in terms of their posture on and their announcements on AI, you know, generally.

[00:10:50] [SPEAKER_00] And we can talk the numbers, but the most important thing to talk about is in just last quarter, 60% of S&P 500 companies talk about AI in their earnings call, if that makes sense. So that means they're talking about earning calls, which means they're talking to their boards also. The board is also interested. They all want to invest in AI. They want to know, let's go AI, right? So one is about the talk. The second is about experiencing AI.

[00:11:15] [SPEAKER_00] And I think many of the senior leadership is in the last few quarters have started experimenting as well. So they are touching and feeling the AI because they really want to do it because they want to start somewhere in life. So then, okay, because it's going to happen. So let me start experimenting with that and practicing with that or learning about that. And learning can happen multiverse ways. I mean, you can go to Silicon Valley tour and you can learn from there. You can learn from media.

[00:11:43] [SPEAKER_00] You can experiment with your own team and you can talk to people that you have recently hired in your AI COE and you can talk to them and learn from them. And the people at the lower level obviously are coming ready anyway. So from the education system and the people are now, they are young, they are experimenting, they're using AI in daily life and they are getting more exposed to AI. What we are seeing very clearly is that the issue is lying in the middle level.

[00:12:11] [SPEAKER_00] The people who are extremely busy operationally in the company, they have tight days, they have busy days, they have staff reporting into them. And they are becoming very pivotal for rotation and that's where I think the unlock need to happen, if you may. And it's an everyday discussion.

[00:12:38] [SPEAKER_00] Last night I was having a dinner with one of the clients and they also talked about frozen middle. That's how they kept on saying that, you know, how can we address the middle part of our organization? How can we get them to understand that the speed is important and they don't have so much of time to be leading in a conventional way of learning, if you may.

[00:13:27] [SPEAKER_01] Yeah. What about being overtaken by agentic native startups in their industries?

[00:13:41] [SPEAKER_00] I think in every industry there will be natives which will challenge the status quo. And it has also happened a decade back with digital technologies. So now the technology has gone to intelligent technologies, right? Right. And so let's say AI is intelligent technology. So I think, yes, absolutely. In every industry, the companies which are smaller in size have an advantage, I would say, because they have less number of people.

[00:14:09] [SPEAKER_00] They can pivot faster with technology. They can grow faster on a gentic workforce. You know, if that makes sense. Absolutely. I think this could be very advantageous for companies at a particular scale. But you know what? I mean, I think at the end of the day, the scaling of company also depends on the ambition of and the leadership of the company itself. So it's not only the technology that plays a role. Human judgment and everything else is going to be super critical.

[00:14:37] [SPEAKER_00] And I don't think the mission, the vision, the strategy of the company or the leadership is, you know, is replaceable by, you know, agentic or AI. I mean, that stays there for running a large enterprise or a small to large enterprise. But I do think that there's advantage for the companies which are smaller in scale and more native in the way they operate. But the good question that you were, let me just bring on one more thing.

[00:15:02] [SPEAKER_00] I mean, as our own company, Genpact as a company, we started our own journey saying that for running our own operations, we want to be using a new agentic workforce, not for our services business, for running our own enterprise. Let's say, for example, our own finance department or function, our own legal function, our own HR function. We are a people-based organization. We are a professional services organization.

[00:15:28] [SPEAKER_00] So our people organization is relatively bigger in size, as you can imagine, because we have 140,000 people to manage. Right? And our IT function, our CIO function. So we started with them a couple of years back. And we said, look, what we're going to do is we're going to – we already had a cost target for that, saying that we want to increase our efficiency. And we went through that.

[00:15:54] [SPEAKER_00] But beyond efficiency, we also wanted to increase the experience of our employees. Yeah? Experience of our suppliers. Experience of our clients. So we went in that direction. We had a very strong strategy around that. We are halfway through that, Craig. We are halfway through that. But we have right now around close to 150 agents operating in the company. Now, can this be 1,000 tomorrow? Maybe.

[00:16:22] [SPEAKER_00] But the point is not about whether it's 150, 1,000. They're operating and they're acting just like humans would do. For example, one of the most inspiring examples that I have in our company is that our chief listening officer – we have a chief listening officer. She is agentic. She is agent. Her name is Amber. Yeah, her name is Amber.

[00:16:45] [SPEAKER_00] So, you know, and the beauty of this agent is that before that, as any company would do, you will do a survey once in a year to understand how people are feeling, what's their mood, do they feel that they understand the strategy of the firm, do they feel that they're connected to the company. You know, all that matters. All that matters because for any company, people is the biggest asset. So, you need to know how your people are feeling in the company. The conventional methods, you will do that survey once in a year. At the most, you can do it twice a year.

[00:17:13] [SPEAKER_00] It is a lot of – it's an expensive venture to kind of program to do a survey and then assess that and then discuss in the leadership meeting and say, okay, how are we doing? What kind of action we can take? The good thing about Amber is Amber doesn't sleep. And it is hyper-personalized. So, she actually talks to all of us, understands our behavior, and is able to give a recommendation as well in terms of what action I should be taking to ensure that I stay more engaged with the company, right?

[00:17:44] [SPEAKER_00] And last year, we were able to do half a million interactions through Amber. So, I'm just giving you one example, but again, a very positive example of not just saving costs or productivity, but actually improving the employee engagement.

[00:18:02] [SPEAKER_01] Yeah, and understanding the organization in a more holistic, real-time manner, I would imagine. You have in this study – I should ask, why did you conduct the research, first of all? But in the report, you talk about a maturity index and stages of maturity and where companies sit.

[00:18:32] [SPEAKER_01] So, why the research? Can you explain the maturity index and where does Genpact sit in that if you're sort of client zero? You know, that would be interesting, I imagine.

[00:18:50] [SPEAKER_00] Look, the thing is that when we did the research, we kept – so, we did both the things. In the research, definitely GenPAC is one of the clients, for sure. It sits in the top level. The top 12% of the people are what we're calling as leaders. It also – and there's a definition of the leaders, the companies which are able to move forward in terms of their journey on artificial intelligence and adoption of artificial intelligence.

[00:19:18] [SPEAKER_00] More importantly, they know how to adopt at scale. So, it's not about using for one function, one use case – no use case. It's not about experimentation of AI. It's actually using in production environment, generating tangible business value and having the process to – most importantly, having the process to assess that value.

[00:19:48] [SPEAKER_00] And that's what we are calling the companies which have – are on that journey of looking at how to assess the business value and business outcome for the firm by putting the investment in the space. And that's what is a 12% of the companies that comes out of the report. The most important element of the report, as I said earlier, is the three roadblocks. And the three roadblocks was – one was talent readiness or organizational readiness for adopting AI.

[00:20:16] [SPEAKER_00] The second one was technology complexity and data complexity has increased tremendously. And it cannot be underestimated in terms of what all need to be done. Many companies have tech and data debt. So, they need to figure out how do they want to get rid of the debt before they become a company of the future. Some companies have technologies which are dated now and they won't help them to become an AI-powered company because that will drain them down.

[00:20:45] [SPEAKER_00] They have to get rid of those debts.

[00:21:15] [SPEAKER_00] In our P&L by 50% by leveraging artificial intelligence. So, if you have those kind of clear outcome-driven purpose, then the programs will start flowing from top CEO downwards. And it will have the skin in the game and it will have legs to stand up and generate that level of adoption in the firm.

[00:21:38] [SPEAKER_00] If you're just doing a small experimentation sitting in a lab environment or hiring an AI guru but the guru is sitting on the side, I think it will be very hard for the companies to generate the real outcomes which can come from embedding AI into their process flows.

[00:21:53] [SPEAKER_01] Yeah. Give me the four levels of maturity and the percentages. You mentioned 12% are the leaders but the other breakdowns.

[00:22:05] [SPEAKER_00] So, we have four levels. The first one is leaders. We call 12% of the people are in the leaders quadrant. The second one is what we call advanced which are 15% of the people and we believe there's 15% over the next two to three years. And I'm saying two to three years because the change is very fast. So, God knows how fast it would be. But let's say optimistically two years. These companies could become the leader quadrant. So, that can move into that segment very quickly.

[00:22:35] [SPEAKER_00] You have assisted. The third one is assisted which is the largest part of the population, 47%. And then you have emerging. We didn't call them legards because it looks like light. I mean, just many other reports you will see they call it legards. But we just said emerging which means they may be still thinking but in fairness, they haven't figured out yet what to do. That's 76% of like one quarter of the company roughly.

[00:23:04] [SPEAKER_01] And assisted means that they haven't put AI into production workflows. That they're using it as like a chat GPT on the side.

[00:23:24] [SPEAKER_00] You will see like many companies who are doing co-pilots. You must have heard about that. I don't think that's the future. You know, that's important. Like many things we can do through co-pilot. Like I can have, you know, if you are using any of the Microsoft products, I assume that you can do the co-pilots. You can, the same thing applied to other collaboration tools as well. I mean, they call it something else. But essentially, they are all AI assistants to you which can summarize your emails, which can do something else. It doesn't release your effort that much.

[00:23:54] [SPEAKER_00] It just makes your life a little easier. So, I can increase my, let's say, efficacy. Or, but it doesn't make my system more efficient because I still, I'm still, or maybe I'll go for one extra cup of coffee today, right? Because, you know, that's what's happening. But, but if you look from running a business perspective, unless you get AI into the action mode. So, AI from execution to AI into action, it's hard to generate that level of value

[00:24:23] [SPEAKER_00] where you feel that now I need less amount of time for doing this effort across my function, let's say.

[00:24:34] [SPEAKER_01] Yeah. Well, yeah, that, I mean, you mentioned the GPT moment and this, the assisted segment are using, like, co-pilots or, you know, foundation models for answering questions. But the big change in the last year has been the agentic rise.

[00:25:03] [SPEAKER_01] And for me, as an individual, as a consumer, it was really OpenClaw. I mean, to me, that was the GPT moment where I could suddenly build an agent. And I've done that. I mean, it's amazing. And I can see that not that everyone should be using OpenClaw,

[00:25:28] [SPEAKER_01] but there are these platforms now where you don't need to be a coder. You don't need to be deep in the AI tech. That's also creating an interesting shift. It used to be that the IT department owned all this stuff. And now it's, that's kind of broken that silo. Yeah. Can you talk about that?

[00:25:53] [SPEAKER_00] Yeah, absolutely. Very good. Just before meeting you, I was, I was with one of our clients and a team. And we were discussing exactly the same thing as you're just mentioning. So this is becoming real. Last two months have been phenomenal. Phenomenal, right? Just like you were saying, three weeks back, I just worked with our team to just create tons of application in one week. Right. Wow.

[00:26:24] [SPEAKER_00] Just to kind of feel the power of how much can we do in a week's timeframe. Wow. It was amazing. It was amazing. You know, as to how we would be able to deliver things. Just to give a view of what tune off, tune up impact we're talking about. We wanted to make an application that in a, in a traditional way would have taken six months and 10 people. Right.

[00:26:54] [SPEAKER_00] We could finish that work in 24 hours. Right. That's a power. Now that power, it's a, it's a brand new application. Obviously, when it's not a brand new, it takes, it is more difficult because that's, you are dealing with debt and legacy and everything else. But, but that's a power of new, new software, new AI, you know, which is in the hands of everybody right now. So, so absolutely.

[00:27:19] [SPEAKER_00] And discussion that is happening right now with most of the enterprises, which are large enterprises, which have a lot of people who are, who are in this segment of building, building AI or building systems, if you may. And also the people who are into the business who are, we call them AI practitioners. But here's a way that I would qualify.

[00:27:41] [SPEAKER_00] I think the, I think the, one of the things we are seeing right now is that if you're an engineer who's into the building mode, very soon we will have 10X engineers. So you will be 10 times more, 10 times more productive. Right. If you do it the right way, if you do the right way using the AI tools. So the future, future builders have to be the ones who, who just don't start with coding everything.

[00:28:10] [SPEAKER_00] They use English for coding rather than, I mean, that's, that's a change which is happening. Right. So, so language itself is changing in terms of how you code work and how you build systems actually. So that's 10X engineers. And then we are seeing the people who are into the business side of the world who are, who are either accountant, let's say. They're accountant, they are lawyers, you know, they're on the business side. They can be 3X more practitioners. So that's the formula, 10X, 3X, right? That makes sense.

[00:28:40] [SPEAKER_00] And we can see that very clearly. And that's a discussion we were having just now saying that, how do we get you there? Like we were discussing, how do we get there? And we did come with a, come with a method and madness around that because you just can't get there. It's a, for one person can do a lot of things, but when you talk about a company, which, which has 10,000 people, it's harder to do it because then you need some sort of a consistent process or a, or a mechanism to get them to the same level.

[00:29:09] [SPEAKER_00] Otherwise, what will happen is some people, two or three of them will do great and other people are not able to catch up with them. So how do you bring them up to speed? And that's the whole change a company has to go through. One of the change vehicles we have that we did last year, we call it AI Gigafactory.

[00:29:25] [SPEAKER_00] So that's one thing, that's one, this mechanism, this one mechanism saying, hey, here's a model which can help you truly scale the AI building of AI systems in the future and which can draw a huge amount of efficiency in the system that you would like to have. Right. Again, it's a systematic process. It doesn't happen overnight.

[00:29:48] [SPEAKER_00] I can tell you, Craig, it's a lot of learning, unlearning, but, but it's a, it's a topic of discussion right now, frankly.

[00:29:57] [SPEAKER_01] Yeah. You know, people used to talk about shadow IT, you know, you, if you, if, if, and then, you know, since chat GBT, that, that there are going to be people in the business that are technologically savvy, who, who will sort of run ahead of everybody else. That's a little bit what you're referring to. It's how does a company manage that?

[00:30:26] [SPEAKER_01] Because you don't want, I mean, you were saying 150 odd agents at GenPact. You don't want every employee to be building an agent and plugging into the company system. I mean, that'll be chaos. So it's a, how do you manage that? Yeah.

[00:30:44] [SPEAKER_00] Yeah. You know, there's a, there's a whole, right now, a lot of people are worried about that concept, actually, that if we let the tech go in everybody's hand, we will have a lot of agents floating in the company. And, and what happens if we have, you know, an agent swarm, like, you know, just like the data swarm issue, right? Yeah. A lot of agents moving around. I think, yeah, I think absolutely fundamental to how you want to build a governance and strategy around agent tech workforce.

[00:31:11] [SPEAKER_00] So the governance model has to be in place for any enterprise. You definitely need to give time for, time and time for people to kind of experiment with technology, definitely, because you want to, you want to initiate that process. And you want people to be proactive because ideas will come from the field of people who are actually doing work, who are working on the process.

[00:31:33] [SPEAKER_00] However, you also want to make sure that there is a more standardization and consistent process of leveraging something which is built very well across the entire company, right? So, so orchestration of agents is one thing which is in play right now. The governance of agents, which means if you have X amount of agents, do you have inventory of those agents? Do you know what technology you are using for those agents? Are those technologies safer for building those agents?

[00:32:00] [SPEAKER_00] Because you can even, you know, use something which is a little unsafe or not, you know, not so well, not so responsible or have issues with data privacy or other issues that you want to make sure that all of a sudden taken care when you are dealing with data. And that's a corporate data enterprise assets. So, yes, absolutely. That's part of the discussion.

[00:32:21] [SPEAKER_00] The way it's been handled is the way we are thinking about handling is providing a platform for hosting these agents in a very unifying way. Right?

[00:32:40] [SPEAKER_00] And this platform helps ensure that all the governing policies are taken care of before they go into production, if you may. So, it takes care of the company's policies of AI, responsible AI policies, ethics policy, ethical policies.

[00:33:02] [SPEAKER_00] And it also makes sure that while you have multiple agents being built either on different technologies or provided by different partners and vendors, actually, because you must be seeing a lot of vertical solution AI companies now flourishing. So, you may be using one of those guys. It doesn't matter. You can use anything for what you like to use.

[00:33:26] [SPEAKER_00] But at the end of the day, when you have a lot of, let's say in your company, you have 100 agents floating around on different technologies used by many people. You need to make sure that you are able to govern them. You are able to see how they're performing in terms of the response time, in terms of even compute and cost as well. That's super critical because AI is expensive. You want to have a daily log of how much money you're spending actually on agents. Right? Otherwise, you can be surprised one day.

[00:33:56] [SPEAKER_01] That's right. Yeah. Again, I was talking to somebody yesterday and they were saying that sales agents are now, sales personnel are in many companies spending $70,000, $80,000, $90,000 worth of tokens a year. And, you know, someone's got to look at how is that balancing with the actual sales.

[00:34:27] [SPEAKER_01] So, not only are the systems secure, but are they adding to the bottom line of the enterprise? Or are they just another spend or cost center? What are the barriers that you mentioned some of the barriers, but what are some of the other barriers that you see companies facing?

[00:34:51] [SPEAKER_00] I think the biggest barrier I see is the companies. There are many companies who come and say, I want to start on this journey, but I want to see the journey map completely. I don't think that exists. Right? Now, my advice to everybody is, you know, because AI is not an SAP implementation.

[00:35:17] [SPEAKER_00] It's not a homogeneous technology implementation that we can write a playbook and you can implement. It requires a massive amount of change. It can be deployed across multiple systems. It can be deployed across the entire stack in your company from your mobile application to your real enterprise applications. It can be applied, you know, at database level. It can be applied for governing your systems.

[00:35:46] [SPEAKER_00] It can be applied at your infrastructure level as well. So it can apply in every stack of your company, the entire thing. And because of its heterogeneity or the way it can be applied, you know, it becomes much more complex as a topic. And more than that, I think the technology is evolving so fast that there'll be nobody who will have a perfect answer. So if you're looking for perfection, you may not be able to start.

[00:36:13] [SPEAKER_00] So the advice is to progress over perfection. You know, start fast, lean fast, experiment, move ahead. Don't waste time. And I think that's a paradox right now. Some people want to, you know, be a fast followers. But I think we just have to lean in right now.

[00:36:33] [SPEAKER_01] Yeah. Yeah. What did you learn from the research? I mean, you have tremendous insights. And are there case studies or client studies you can talk about?

[00:36:47] [SPEAKER_00] Look, there are two things which are very important learning from the study. I mean, let's say many things in the study, you know, you're aware of. You're aware because you're also reading like a lot of things, you know, which you're aware of. It also reinforces some of your beliefs and pieces, if you may. Right. Studies also reinforces thesis.

[00:37:06] [SPEAKER_00] But when you look at two things which stood out for me personally was data has always been a massive issue for the companies. And there's a lot of people, a lot of us have been working on data because everybody thinks that if the data is not there, then you can't make AI work. Data is a fuel for AI. Right. Yeah.

[00:37:32] [SPEAKER_00] What we learned in the process is beyond data, in fact, more than data, integration across different technologies will become more challenging as AI comes as a system of innovation, a system of work for the companies. So we clearly see that integration of technology is becoming a massive area for companies to think about, which means CIOs and CTOs of the companies, mostly CTOs of the companies.

[00:38:00] [SPEAKER_00] They will have to think about the new target architectures. What to buy, what to build, how to keep the system architecture much simpler and not invest in 15,000 things and flashy things that they will see during the journey because everybody will keep pumping in new technologies to them. They have to be very selective and conscious of experimenting.

[00:38:23] [SPEAKER_00] But when they take something of when they make choices, they have to be more prudent in making those choices of what they want to invest in. If that makes sense. So that's number one, which came out very clearly. The technology complexity is increasing. The integration will become more difficult. Right. And the choices they have to make in technology. The second thing was around governance. AI governance. It's a topic. Everybody talks about it. But investments are not going at the rate which it should be going in the company. You also ask the same question.

[00:38:53] [SPEAKER_00] If you have 100 agents, one of the agents go rogue. Do you have a process to find out which agent has gone rogue, which is not behaving properly? Like, can you see a command center in front of you? Do you have a cockpit? Can you just see how they're operating? I think that level of inventorization of agents so that not everybody can build agents on their own and you don't have a sense of what's going on in the departments and functions, in the line functions, if you may.

[00:39:20] [SPEAKER_00] That AI governance and an investment in that will be super critical. And what we found in research is that 99% of the companies do not have a very comprehensive, I would say. They have something. Maybe they have a policy. But they don't have a comprehensive end-to-end AI governance program.

[00:39:41] [SPEAKER_01] Yeah. And GenPact, how do you work with clients? Do you do an audit of their business and then decide, you know, point to areas where they should start implementing AI? Is it primarily agentic AI that you're advising on?

[00:40:06] [SPEAKER_01] And do you build or do you have partners that you bring in who have products? Or do you advise that clients build themselves?

[00:40:19] [SPEAKER_00] Look, our heritage is we are very good in operations, which means finance and accounting. Which means that's what thesis or sorry, that was a history of, you know, what I was calling GenPact history, right? I mean, it's a process company. Like we can run processes. We can operate the process very efficiently. We do billions of transactions every year. Like it's billions of transactions.

[00:40:45] [SPEAKER_00] So we have a command on doing that for 800 clients, right? That's what we do. Finance and accounting. Closing of the books. Claims processing. Underwriting. Procurement. You know, all these processes, right? So supply chain. I mean, all these forecasting. All these processes we can do.

[00:41:11] [SPEAKER_00] Now, what we're doing is we are implementing AI into that process. And we are making it much more efficient. Much more efficient. You can just think about it on a good day. Like if I take an example, you're asking me client example. We are working for a distribution, B2B distribution company called Wesco. And we have, it's a $22 billion company, 20,000 employees. Very well known in North America.

[00:41:39] [SPEAKER_00] And we are processing millions of invoices for them, right? In that space. What we are deploying right now is based on our process intelligence knowledge. We have built something called agentic solutions on those processes. So the process is accounts payable. Accounts payable. Everybody knows accounts payable, right? But when it becomes contextual to the environment of your own company, it has becomes very complex. That's why there's so many people involved to just match the invoices.

[00:42:10] [SPEAKER_00] The process is nothing else but you release a purchase order. The goods gets received in your company. And the vendor sends you an invoice. You make the payment only if all these things actually match. That's what the process is called accounts payable. It's as simple as that, right? It seems simple. It can have many issues and many escalation as you can imagine when things don't match. The quality doesn't match. The date doesn't match. The product description doesn't match and so on and so forth, right? When it's sent.

[00:42:39] [SPEAKER_00] And these are all different things from different companies. And we talk about millions of that happening every day for a customer. So we have four solutions there which we implemented. It's called an accounts payable suite. We capture the entire documentation through a natural language understanding. So AI module. We do the matching of this through a very advanced module. We are able to find anomalies which should not happen again.

[00:43:06] [SPEAKER_00] For example, I should tell my suppliers that, look, you have not been compliant over the last three invoices. So you better change your systems or whatever. So that's anomaly detection. We call trace, AP trace. And then there's a last model called as AP assist which means while you talk to your customer suppliers, you may require an agent to talk to the supplier rather than a human actually to just save time and energy if you may.

[00:43:35] [SPEAKER_00] And all these package come together. So that's what we're implementing on this client. But that's how we are doing it. Based on a process knowledge, we have built a genetic solution. We're deploying back to the operations, taking 50% of the effort out or more than that depending on the journey we are going through. But our interests are, we can easily foresee a significant portion of the work through AI in the coming months and year as we go and mature that as the AI gets more trained, if you may, Craig.

[00:44:05] [SPEAKER_00] It just starts, start giving us more efficiency. And what we are doing is from human led process, we are moving to human validation process.

[00:44:15] [SPEAKER_01] Yeah. Yeah. And just so I understand, are you running these processes for the clients? Yeah. And you're building or bringing in tech partners to build that automation?

[00:44:34] [SPEAKER_00] No, we have a tech services. So we are both. So we are running the process and we are building this AI ourselves. We have AI. So we have two practices, AI practice and the operation practice, two practices. We work together. So we build AI. We run their AI as well, right? And so both actually.

[00:45:01] [SPEAKER_00] And we use technologies, which obviously are coming from big tech companies, as you know. So we're using the same technology, but we are building and designing the AI systems and deploying them, managing them for the same client and also managing their operations.

[00:45:15] [SPEAKER_01] Yeah. Where do you see all of this going? I mean, what do you envision as the enterprise of the future? I mean, certainly things are going to get faster. They're going to get more efficient. Everyone hopes that the enterprises will grow rather than shrink headcount.

[00:45:46] [SPEAKER_01] I mean, that has yet to be seen. But on a macro scale for the economy, do you think about that? I mean, this is going to completely transform enterprises, I would imagine. Yeah. And by extension, economies.

[00:46:07] [SPEAKER_00] Yeah. No, it does. You know, I think the time will tell, frankly, how the new roles will get established because we all are very optimistic of new roles being designed because of the changes that we are going through. We never had a role called as AI trainers. We have now those roles, right? I'm just saying, I mean, there could be new roles coming in the companies which are different than what we used to have earlier.

[00:46:34] [SPEAKER_00] And that's an evolution every company or enterprise will go through. And the other thing is that, you know, AI also is able to help the companies who will adopt AI first. Let me say this with the companies who are the early adopters of AI and move fast will also grow their top line and take more market share and become more competitive in the industry, if you may.

[00:46:57] [SPEAKER_00] And that should help the employees and everybody else to really be more efficient, effective, but also see growth in the same companies, you know. But yes, absolutely. One thing is clear, your overall company's performance on headcount will be slightly different metric, as you can appreciate over time.

[00:47:17] [SPEAKER_01] Yeah. Yeah. This report, you have these levels of maturity. Presumably, you're going to be doing this report annually now. Yes. How do you expect that to shift? Oh, that's what I mean. How do you expect the 12% to grow?

[00:47:43] [SPEAKER_01] How do you expect the emerging to move into, are they going to leapfrog, assist it and go right into, yeah.

[00:47:55] [SPEAKER_00] Look, that's a great question. Look, we wrote the report. If you see the report title, it's called Autonomy by Design. And it says Scaling AI for Enterprise Value. I think if I fast forward a year later, which is, by the way, we are already starting the next report. So we started working on that for this year. It's going to come in October this year, in 2026.

[00:48:21] [SPEAKER_00] And you can imagine, I mean, we are now from design, designing a scalable solution in the company, because that was the problem statement of last year saying, I'm not seeing the enterprise value. Can I see the value? I think that threshold we have crossed in terms of understanding our clients and working with our clients. Some of the clients are really going in that direction faster. And this last two, three months have been phenomenal, frankly.

[00:48:45] [SPEAKER_00] And if you ask me, since January this year, there has been more pull from the clients, because they are now feeling that they can be left behind. The FOMO is coming. The FOMO is coming. That I will be left behind if I don't move fast, which is all great news. So I can easily foresee this year is going to be more about autonomy in practice, actually.

[00:49:12] [SPEAKER_00] So it could be more about putting this into real works. And we can probably share more stories at this time about the clients, which are putting a scaled operation systems in place and generating very tangible value. Till last year, it was more about a few companies and it was less value, more of designing, if you may. I think this year could be companies which can come forward and tell that I have done this much already. Right.

[00:49:40] [SPEAKER_00] So that's what is going to be the most exciting part, as I can see. Yeah. Right.

[00:49:46] [SPEAKER_01] You know, you've been talking to, I mean, you surveyed these 500 executives, but you also talked to clients. I remember three or four years ago, there was a cottage industry of people like me who were going around and talking to C-suites and just explaining, this is what a neural network is.

[00:50:15] [SPEAKER_01] This is what supervised learning is. And then this is what generally, you know, these executives are busy people and they didn't study AI in college. So they've got to absorb an enormous amount of information in order to make right decisions. Is that part of GenPAC's role? Do organizations need an AI whisperer in the C-suite?

[00:50:44] [SPEAKER_00] Yeah, they do. Every company needs it. There has to be some nudge. I mean, you mentioned about your story of using AI. Can I ask you a question? What did you build, by the way?

[00:50:55] [SPEAKER_01] It was amazing. Well, the first thing I built was an agent to read my emails and categorize them and draft. I get pitches a lot from PR companies to draft responses. And then I just go through the drafts and approve or send, send, send, send, or change. Then I built a chat bot for my podcast where you can go, you know, I use it myself.

[00:51:22] [SPEAKER_01] You know, who talked about post-transformer architectures? And I'll be building other things. I mean, I've built an app, a travel app. And so, yeah, it's fun. I mean, it's exciting.

[00:51:38] [SPEAKER_00] That's good. That's good. So, the reason I'm asking this question is because two or three weeks back, you know, it starts from the top, right? So, any company, the change starts from top. The change has to start from top. So, because then people see their role models. Everybody wants to be the CEO of the company. There are many people who want to be CEO of the company. When they see their CEO doing something, they say, oh, gosh, I'm not doing that. Like, I need to catch up, right? We live in a competitive world.

[00:52:08] [SPEAKER_00] Like, there are a lot of competitive people, actually, who love to, especially in the business world, right? So, three weeks back, our CEO, BK Kalra, he was writing a code. And he was getting to GitHub. And he was trying to get access to GitHub. So, he was figuring out how to get an access to GitHub. I was watching him in his room. I said, I was quietly watching and seeing what he was doing, actually.

[00:52:33] [SPEAKER_00] And next day, I did mention to the entire leadership council, committee, or global, we call it GLC, Global Leadership Council. I mentioned to them saying, with a statement saying, our CEO in the court. And the idea was here that while he was doing it, everybody started doing it after that. They all started to explain. And that's a change. That's a change. So, absolutely.

[00:52:58] [SPEAKER_00] I think if the C-suite cannot create their own personal use cases, just like you were doing for your app. He was, by the way, building a restaurant app for himself in New York, right? Which restaurants to go or how to find the right ones.

[00:53:16] Yeah.

[00:53:17] [SPEAKER_00] But I think if you don't use it for, if you don't use technology yourself and experiment, you don't experience as well. You don't experience the speed of technology and how fast it's moving now. Like, you don't actually embrace it completely. You don't get ideas of how it can actually, what could be the impact to your organization and your company. Yeah.

[00:53:44] [SPEAKER_01] And you see, I mean, you said that people see the CEO doing it and think, wow, I better focus on this. I would imagine it's as much the other way around that the CEO sees his managers understand the stuff much better than he does. And he's got to, like, figure it out.

[00:54:06] [SPEAKER_00] Yeah. Yeah. And the learning of the learning is also both ways, right? Learning is a both-way process. So, and especially if you have a strong AI talent in your company and if the management or executives actually spend a little bit more time with them, they will learn a lot in terms of, you know, how they see this use of technology and can probably help them. Help them from the business side because, at the end of the day, technology is of no use if you don't deploy in the process as I was selling.

[00:54:35] [SPEAKER_00] So, unless you have a problem identified properly, you can't use technology and you can't have the best use and best outcome coming out of it.

[00:54:41] [SPEAKER_01] Yeah. Do you, in this survey, do you see that there are still CEOs or business leaders who really don't understand this stuff at all yet? And because it's, you know, if you're running a manufacturing company in a legacy industry, it's a burden to have to learn this stuff.

[00:55:10] [SPEAKER_01] If you're busy running this company, understanding your market, this is, you know, it's an entirely new domain that you have to become at least competent in.

[00:55:23] [SPEAKER_00] I think people are reading a lot. And there's overwhelming information in the media. I think people have to experiment it, practice it. That's a change. That's a change. That's right. Without touching, like you touched the code, OpenClaw, you could see that you would have changed next day morning saying, gosh, like what I'm doing, right? That feeling, that, the feeling, you have to feel the technology. That's right.

[00:55:50] [SPEAKER_00] And if you feel the technology, you will not be able to understand the implication of that today and tomorrow.

[00:55:56] [SPEAKER_01] Is there anything I haven't asked that you want listeners to know, either about GenPact or the study or AI adoption in general?

[00:56:07] [SPEAKER_00] I think two things which will maybe, one. This is a defining year in my view, personally. I think last year was more a foundational year where the technology was getting in hands of enterprise architects and business leaders and even technologists in the company.

[00:56:30] [SPEAKER_00] This year, I think we should be able to see a lot of advancement in using it in the way that can generate truth or tangible economic value for the companies.

[00:56:44] [SPEAKER_00] And I think the companies who will lean faster and are not looking for the perfect answer would have more chance towards success and performing in the right way by leveraging this power full tool that's in the hand of us right now.

[00:57:07] [SPEAKER_01] Yeah. And I think that's a good point to end on. And don't wait until you understand everything or because by the time you understand everything today, the technology would have moved on to tomorrow. You just have to jump in and swim. Yeah.

[00:57:31] [SPEAKER_00] And, you know, be close to right people, right partners. I mean, embrace right people, challenge them, ask them to execute fast. Speed will be super important. Speed will decide the fate of the companies who will be leaders versus who will stay emerging or like us, right?

[00:57:50] [SPEAKER_01] That's right. Yeah. Well, it was a fascinating report and it's going to be fascinating to see the next one, how things have changed because of the speed of change at this point. No, perfect. It was great seeing you and meeting you today, Greg. Yeah. Yeah. I hope we meet again. I enjoyed the two aborted conversations and I enjoyed this one. So, yeah.

[00:58:16] [SPEAKER_00] No, next time we wanted you to see our platform that we talked about. I think we're called the Amistro. That's the name we have for the platform. But we would like to show you how you can see hundreds of agents being orchestrated. I would love that. Where are you guys? New York. Well, Fifth Avenue or so.

[00:58:37] [SPEAKER_01] Yeah. Because I'm in West... Next to Grand Central, actually. Oh. Okay. That's easy. I live... I'm in California now, but I live in Chappaqua up in Westchester County. So, yeah. I would love to come in and meet you. That'd be wonderful.

[00:59:03] [SPEAKER_02] I love you. I love you. I love you. I love you. I love you. I love you.