 Hello, everyone, and welcome back to theCUBE's live coverage of UiPath Forward 6 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host and analyst, Dave Vellante. We have two guests for this segment. We have Vaibhav Bansal, he is a VP at Everest. Thank you so much for coming on. Thank you. And Harpreet Makan, she is the practice director at Everest, thanks so much. Thank you so much, Fabi. Excellent, so Vaibhav, I'm going to start with you and just really get your thoughts with, this is the end of the day here at Forward 6. What went on? What took place? What are some of the most interesting conversations and questions that you're being asked here? Yeah, so I think one of the most interesting conversations we have today is around how AI is going to have an impact on the entire intelligent automation space. So basically how AI and automation are coming together, how they are converging. And I think often my reply to some of that is taking a step back, right? Go into a bit of history of AI and automation. So if you look at AI, right? So historical, AI is not new, right, by the way. So it has been there for like past 60, 70 years in some form. I think the initial pursuits in AI were more academic. In the recent years, they have become more professional, right, so that's the only difference we see. So that's about AI. Similarly, when we talk about automation, right? So automation, if we go 10 years back, right? So we had more of rule-based or script-based automation and we had this entire journey of robotic process automation or RPA. Over the last five years or so, we have seen AI starting to have an impact on automation. And so there was already an overlap which was there, so we have, you know, productized versions of AI, like intelligent document processing and conversational AI, which are already there, right? So it's not something which is very new. It's just that today, you know, is the right time, right? So it's the summer of AI. At the same time, I think organizations worldwide have now realized the benefits of bringing together AI and automation. So if you look at it, you know, automation is more like the limbs of the body, right? Whereas AI is the brain, right? So both need to coexist with each other, right? So if you just have AI, right, you can solve a small part of the problem, right? So which is a subset of the, maybe the entire process, right? Similarly, if you have automation, right? So you could probably, you know, automate repetitive tasks, but bringing them together really brings the best out of both of them. Is it the summer or the spring? I don't know, whatever you want to call it, but the winter is definitely gone. Because some of my tomato plants are this high, right? The spring, they're this high, you know? Yeah, I mean, of course, there's a lot more to come, right? So while we are talking a lot about AI, I think we're still, we have just scratched the surface, right? So of course, there have been recent advances with Generative AI in all coming in the last one year or so, but I'm sure all of us have heard about, you know, artificial general intelligence, right? Which is like the North Star for AI, right? So I think there's quite some time for all of us to get there. So yeah. Tell us more about the Everest Group. What do you guys do? What's your different roles? You have research methodologies and help the audience understand more, if you would. Yeah, maybe I can start. So Everest Group is basically an unlisted firm. Been around for a while, right? In the early 90s, if I... Yeah, we've been there for more than 30 years. Early 90s. So we, again, cover research in multiple areas which span across business process services, information technology services, even solutions related to pricing and all, right? So there are different service lines. So we do cover research in all these areas. At the same time, we do advise our clients, you know, with the consulting engagements or where there's a specific problem solving which is not covered explicitly as a part of our research. So we, especially I and Herpreet, you know, represent a practice where we cover everything to do with automation and AI. And, you know, that would mean technologies such as RPA, intelligent document processing, conversational AI, process and task mining, even process orchestration, so that's the entire spectrum. And now, of course, with Generity, we're coming into picture. It's going to have a significant impact on all of this. Yeah. And as we have been covering all these different areas, I think in the last one, one and a half year, AI is again something that has been impacting the entire landscape. So the way we define this intelligent automation ecosystem, we think of it in terms of layers. So like rules-based automation or RPA is just one of those layers. There are multiple other technologies like we have mentioned, intelligent document processing, process mining, task mining, conversational AI. All of these technologies, there's a significant impact of AI, there's significant belief in the potential of AI that we are seeing. Yeah, that's what I wanted to add. And these layers, have they historically been point products or platforms? What's the evolution of product to platform? Yeah. So the way we have covered this is more in terms of products, especially more defined or mature layers like RPA and AI-based automation, whereas it comes to some newer layers like process mining and task mining, there also we see productized solution emerging. Now with all of these coming together is where the platform plays becoming prominent. We are seeing the emergence of intelligent automation platforms as a whole. And when we look at a platform as a whole, then AIML or low-code, no-code becomes a key horizontal layer of that platform. What's your fundamental methodology? Is it a combination of sort of buy side research, survey work, demand side research, talking to companies, technology companies, maybe you could describe your methodology and maybe some of the sort of core sort of findings if you will of your recent research. Sure, I think I can talk about the most recent ones. So from a methodology standpoint, we follow a similar methodology for all our research. So our proprietary research, which we call as the peak metrics, therein it is a mix of supply side, buy side research, we speak to enterprise, we speak to providers, we do surveys, we gather information from both the sides and then do our independent analysis to arrive at the final research output. My team recently published task mining and RPA research for this year and RPA is an area that we have been covering from last seven years now and I think one of the first analysts from Shadooso. So yeah, we've seen a massive evolution there. So the number of players that have been covered this year are close to 25 players, with obviously four or five of them being the leaders in that particular metrics, the way we define, we categorize players into leaders, major contenders and aspirants. That is the final output that we come up with. Do you size markets or not so much? You do. We do size markets. Yes. So what's happening in the markets? So again, I think we have been sizing markets by each of these individual areas, right? So typically across most of these areas, we see growth rates which are like 20, 25% plus growth rates, right, over the last at least two to three years post COVID, right? So that's the growth trend which I only see. I mean, I can talk about a few other major trends, right, which we see in the entire automation market today. One is of course, you know, with generative coming into picture this year, everything is getting disrupted, right? So especially there are areas which are more AI based automation such as IDP and conversational AI, right? So which are probably a bit ahead in that race, but others are not very far behind, right? So today from a supply perspective, we have almost every provider in these areas, you know, who is already integrating their solutions, existing solutions with large language models, right? So that's one very noticeable trend. Apart from that, again, I think the inherent need of automation, right? So that continues to be there and it's very robust. The market is still probably just 15 to 20% tapped. There is still a large percentage of the market which is out there which needs more education and awareness about automation, right? To bring out the best outcomes. There are other enablers, so for example today, a lot of enterprises are moving to cloud and adopting software as a service solution. So from that perspective, it makes easier for some of the providers to offer those solutions at a relatively convenient, cheaper prices and making the adoption easier. So do you think generative AI is a headwind or a tailwind? Is it incremental or dilutive to RPA and automation? I guess you could maybe take those separately, right? But the answer however you feel is appropriate. Yeah, no, that's a very good question. I mean, in other words, sometimes, you know, we put that as is it more like a threat or a boon for intelligent automation? So I would say there are arguments on both sides. If you're just asked for a very short answer, I would say overall, it's definitely a boon or it's a tailwind, right? So of course, we could have debates on both sides. For example, it could lead to disruption, it could lead to, you know. It already is. It already is. It could lead to some of the mediocre players getting vanished, right? Or maybe emergence of some new generic native players, right? So those kind of disruptions are definitely going to be there. But I think by and large, it is going to aid automation. It's going to make existing solutions better, more accurate, more efficient. So we are going to see those kinds of gains and at the end of the day, it's going to help everyone. So that would suggest that it expands the market overall, it expands the TAM overall. You would agree, I presume, yeah? Yes, I would agree. And just to add something to your last question, so we recently published a countries discovery and automation playbook. And therein, we did a survey with different enterprise personas. We asked the same question, how do you see AI impacting the different technologies? And we asked it, like, per technology to get new answers. Like, do you see AI as being core to certain technology? Or do you see AI enhancing a certain technology? Or do you see it disrupting? And we got very interesting answers. Like, for example, for RPA, it was clearly CXOC AI enhancing RPA. When it comes to technologies like intelligent document processing, they see it disrupting or transforming, like, at the core of it. And when it comes to some of the other technologies, like process mining, task mining, again, they see it complementing very well. So this is one of the outputs that came from the enterprise survey we conducted. So when I think about some of the great software platforms, obviously Oracle, SAP, IBM, great software platform, but they got a lot of software. But ServiceNow, Salesforce, Workday, Adobe, all of these companies, Snowflake, now, Databricks, all of these companies have an AI strategy. You're going to be out of business if you don't have one. So my question is, how do you see customers consuming AI? And the answer is probably both, but is there any research that you've done or gut feels that you have? Will they consume it more as part of an existing software platform, or are they going to go out and tap third party LLMs, build their own, because they want to have control over their own destiny? Do you have any research on that or a sense, given that you've followed the market for a number of years? Yeah, I think based on at least my point of view, there would be actually both, because there are enterprise who are very heavily invested in the existing landscape. And let's say, the systems that you talked about, the software stacks, they are expected to obviously try to find ways to use AI on top of these or in addition to these. But there are next-gen or new digital age companies that are coming up who are not that heavily invested, and they are expected to explore standalone use cases also. So I see a mix of both. And both of these actually don't need to be mutually exclusive, right? So while we have some of these traditional product companies like Salesforce and Adobe and all those you mentioned, right? So they are coming up with their own AI enhancements or AI modules, right? And they have pricing, which is specific to those modules also. At the same time, there is always a need of players, for example, UI paths, right? So who can help enable these platforms talk to each other, right? So to be that underlying layer, which connects different platforms, right? So otherwise, I think it will be a challenge for these platforms to talk to each other. You mentioned of migration, cloud migration earlier. And we all remember early days of cloud, financial services companies said, oh, I'll never go into the cloud. It's not secure. Of course, CIA changed that early on. And now everybody's comfortable in the cloud. At the same time, with LLMs, first of all, if you're going to bring the AI to the data, a lot of the data is not in the cloud. Much of it is in video form, but other data. As well, there's concern about IP leakage. People don't want to necessarily move the data. You're seeing companies like OpenAI actually doing a lot of the training in their own supercomputer. Tesla does the same thing. So are you discerning any patterns in terms of, we know a lot's going on in the cloud, no doubt. Is there any evidence that that pendulum will swing? I think in a way, like, pendulum has been swinging for some time, right? So, I mean, at least I know some data. I recall some data points, so probably today we are at a stage where more than 50% of automation solutions are already on cloud, right? So there is a significant percentage of on-premise, which is still there. But I think more and more organizations are realizing that need to move their data to the cloud and then appropriately have their intelligent automation solutions over cloud. I think we have seen some inhibition in some of the more regulated industries, like banking, health care. But as the solutions become more compliant to these regulations, even they are taking that route. However, it's not to say that this transition of this shift will happen suddenly. So again, I think we need to probably take a slightly decoupled approach, right? So rather than just trying to have everything on cloud and then thinking about using something like automation and AI, rather have two different approaches for what is on-premise, for what is more legacy, versus what is already on cloud and what is actually ready to adopt AI and automation solutions. So I think that kind of a dual strategy could help. And of course, in the longer term, the plan should be to even shift that on-prem part to the cloud. And you know this, right? AWS is in the fullness of time. Everything will be done in the cloud. Companies like Dell, HPE say, oh no, not everything will be done in the cloud. A lot will be done on-prem. Companies like UiPath don't care. Licensing software, running the cloud, running on-prem, doesn't matter to us. So I think you're right though. It has sort of, the momentum has slowed down a little bit. But the thing I wonder is, will it accelerate now? Will LLMs be a boost? I saw a Wall Street analyst report the other day that they were presuming significant growth in the cloud or return to 30 plus percent cloud growth in 2024. We'll see. Who knows what's going to be in this crazy market. But when you talk to customers, there's concern, but you don't see the innovation outside of the cloud. The cloud has so much innovation, so many tools, so much optionality with LLMs, that feels like that's going to be the place where the action is. Yeah, and I think compared to last maybe five years ago, I think the belief in AI is much more higher today than it was five years ago. And I think that belief is probably pushing everybody to explore things that they were scared of or they were skeptical about. Obviously there are still concerns and all conversations that we have with enterprises, there are concerns about whether the IT is ready, whether they're in price ready. There are a lot of concerns that we hear, but there is still that inherent wish to explore all those solutions and to achieve that potential. So I think that that is something that is expected to take the kind of growth that is being predicted. Yeah, that's why I was asking if it's spring or summer because it's crazy right now. There's so much action, a lot of VC investment. And yet, when you look at what people are actually doing, now maybe it's different for automation, but what people are really doing with AI, it seems to be all the stuff that we're doing with AI. We're summarizing documents, we're ideating, maybe getting some help writing code. All right, and so a lot of experimenting. What are your thoughts on how long that will last before there's like a clear, and specifically in the context of automation because that's your wheelhouse, before, because right now there's actually activity going on, there's spending and it's probably taking away from other areas. I think you probably see that. It's not like IT budgets are growing, they're not. So as AI spending increases, it's coming out of other buckets. How long do you think it will take before we actually either see definitive business value getting dropped to the income statement or CEOs are gonna pull and CFOs are gonna pull the plug and not pull the plug, but dial down? Yeah, no valid question. In fact, the recent wave of AI which is kind of spurred by generative AI, right? So here, at least in this year, right, 2023, we have seen a lot of experimentation and POCs and pilots, all of those happening. A lot of enterprises experimenting. So you're right, right? In a way, most of that budget was not planned because nobody had thought about this at the beginning or at the end of the last year, right? So this humongous impact, right? So yes, while you're saying there is a lot of investment, a lot of it is unplanned. Now, as we move into the next year and we are actually around the time of the year when companies are planning for their budgeting and all. So we do see planned investments getting into JNAI. It could either be taken out of their existing AI budgets or probably there is a new budget which is allocated for JNAI. So in fact, like if you ask me, I would say the investment is actually going to increase further, right? So I agree, we are at like a hype, right? So we are still at the peak of hype with respect to JNAI, but at least for the next one year, I would say there would be a lot more investment which will be happening in this space. We have still not witnessed a lot of impact or result coming out of this. So probably takes another six to 12 months for this to play out in the market and for organizations to really form a point of view around what's the real impact. And then maybe it's that decision time which you're talking about. And then have measurable, sort of obvious impact. Do you think that will come from, it's probably a combination, but where do you think it will mostly come from? Labor savings? Your favorite topic? Or will there be revenue generation? Yeah, again, valid question. I think like all, I would say automation and AI, the major impact is going to be on labor savings or productivity. I wouldn't deny that. Of course the impact, at the same time, there will be revenue enhancing use cases, especially in some of the areas like marketing and sales, where you could use any AI to create a lot of opportunities, the cross-sell and upsell kind of opportunities. So ultimately it could lead to those as well, but I think probably 70, 80% would still be labor savings. Having said that, I think today we are still restricted. Most of us are still thinking about use cases which existed earlier and how a generating way is going to have an impact on those use cases. So that's why we are still thinking of incremental benefits. A lot of thinking still hasn't happened on net new use cases, which would emerge just because of generating AI. So probably those are the places where we could see greater revenue generation opportunities. The saddest use case, this is a terrible joke, it's really not a joke, it's true, but the use case of writing better phishing emails is definitely helping the hackers. The hackers are on it. That's revenue generating. I'm laughing, but I'm crying at the same time. That's how they're side of AI. Bye, Bob and Harpreet. Thank you so much for coming on theCUBE. A really fascinating conversation. Thank you. Thank you, thanks a lot. Thank you for having us. I'm Rebecca Knight for Dave Vellante. That wraps up day one of theCUBE's live coverage of Forward Six. We'll be back here tomorrow and we'll see you next time.