 From the SiliconANGLE Media Office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Stu Miniman. Hi, I'm Stu Miniman, and this is a CUBE conversation from our Boston area studio. Welcome back to the program. Bobby Patrick is the Chief Marketing Officer of UiPath. Bobby, good to see you. Great to be here, Stu. All right, Bobby, we're going to tackle head on an interesting discussion that's been going on in the industry. Of course, artificial intelligence is this wave that is impacting a lot. When you look at earnings reports, everyone's talking about it. Most companies are understanding how they're doing it. It is not a new term. I go back reading my history of technology, Ada Lovelace 150 years ago, when she was helping to define what a computer was. She made, it was the Lovelace objective, I believe they said, which was later quoted by Turing and the like, is that if we can strive it in code, it's probably not artificial intelligence because they're not building new things and being able to change on there. So there is hype around AI itself, but UiPath is one of the leaders in robotic process automation and how that fits in with AI and machine learning and all of these other terms. It can get a bit of acronym soup and we all can't agree on what the terms are. So let's start with some of the basics, Bobby. Please give us RPA and AI and we'll get into it from there. Well, robotic process automation is, according to the analysts like Forrester, part of the overall AI broader, kind of massive, massive market. AI itself has many different routes or deep learning and machine learning, natural language processing and so on. So I think AI as a term covers many different grounds. In RPA, AI applies two ways. It applies within RPA in that we have a technology called computer vision. It's how a robot looks at a screen like a human does, which is very, very difficult actually. You look at a Citrix terminal session or VDI session, different than an Excel spreadsheet, different than a SAS app and most processes run across all of those. So a robot has to be able to look at all of that, all of those screen elements and understand them right. So it was AI within computer vision around understanding documents, looking at unstructured data, looking at handwriting, conversational understanding, looking at text and an email and determining context, looking, helping with chatbots. But a number of those components, it doesn't mean we have to, we built that all ourselves. What RPA does is we bring it all together. We make it easy to automate and build and create the data flow of a process. Then you can apply AI to that, right? And so I think, look, two years ago when I first joined UiPath, putting RPA and AI in the same sense that people laugh. Year ago, we said, you know what, RPA is really the path to AI in business operations. Now, we say that we're the most highly valued AI company in the world and no one has ever disagreed. Yeah, so it's good to lay out some of the adopting because one of the things, right, they look at and say, if I'd looked at this product two or three years ago, it's not the product that it is today. We know how fast software is making changes along the way. Second thing, automation itself is something that we've been talking about my entire career. When I look at things that we were doing five, 10, 15 years ago and calling automation, we kind of laugh at it because today automation absolutely is making a lot of changes. RPA is taking that automation in a very strategic direction for many companies there. So yeah, it's the conversation we had last year at your conference was, RPA is the gateway drug, if you will, of that environment because automation is scared a lot of people. Am I just doing scripts? What do I control? What do I set? So maybe just give us that first grounding of kind of where that automation path has come and is going. Yeah, so there's different kinds of automation, right? You said we've had automation for decades, primarily in IT. Automation primarily was around API to API integration and that's really hard, right? It requires developers and engineers. It requires them to keep it current. It's expensive and takes longer time. Along comes the technology, RPA and UI path, right? Where you can automate fairly quickly. There's built-in recorders and you can do it with a drag and drop of like a flowchart and you can automate a process and that automation is immediately beneficial. I mean, that outcome is immediate and the cost of doing that is small in comparison and I think maybe it's the long tail of automation in some ways, right? It's all of these things that we do around a SAP process. The reality is if you have SAP or you have Oracle or you have Workday, the human processes that are around that involve still a spreadsheet. They involve PDF documents. And one of my favorite examples right now that's on YouTube with Microsoft is Chevron. So Chevron actually has hundreds of thousands of PDFs that are generated from every oil rig every day. It has all kinds of data in different formats, tables and different structured and semi-structured data and they would actually extract that data manually to be able to process and analyze that, right? Working with Microsoft AI and UiPath RPA, right? They are able to automate that entire massive process and now they're on stage talking about it than Microsoft and UiPath events, right? And they call that AI. That's applying AI to a massive problem for them. They needed the robot to be completely accurate though, right? You don't want to worry that the data that's being extracted from the PDFs is inaccurate, right? So machine learning goes into that. There's exception management. It's processed part of that as well. But they call it AI. Yeah, some of this is just the people in the industry, the industry watchers is, we get very particular on different terminology. Well, let's not conflate artificial intelligence or augmented intelligence with machine learning because they're different environments. I've heard Forrester talk about, right, it's a spectrum though. There's an umbrella for some of these. So, we like not to get too pedantic on individual terms itself, you know. Well, let me give you some more examples because I think the term robotic and RPA, yes, it's true that the vast majority of the last couple of years with RPA has been very rules-based, right? Because most processes today, like in a call center, there's a rule, do this and this and this and this. And so you're automating that same rules-based structure. But once that data is flowing through, you could actually then look at the history of that data and then turn a rules-based automation into an experience-based automation. And how do you do that? You apply machine learning algorithms. You apply DataRobot, LMAI, IBM Watson to it, right? So, but it's still the RPA platform that's driving that automation. It's just no longer rules-based, it's experience-based. A great example at UI Path Together, Dubai recently, was Dubai Customs. They had a process where when you declared something, let me say you box a chocolate, they had to open up a binder and find a classification code for that box of chocolate. Well, they use our RPA product and they make a call out to IBM Watson as part of the automation. And they just write in pink box of candy filled chocolate. And it takes its deep learning and comes back with a classification code, all as part of the automated process. What happens? Dubai Customs lines go from being two hours to being a few minutes, right? It's a combination of our RPA capability and our automation workflow capability and the ability to bring in IBM Watson. Dubai Customs says they applied AI now and solved the big problem. Yeah, one of the things that I was reading through the recent Gartner Magic Quadrant on RPA and they had two classifications. One was kind of the automation does it all and the other was the people and machines, things like chatbots, some of the examples you've been giving there seem to be that common. Where do those two fit together? Are those distinctions that you make? Yeah, I mean, Gartner's interesting. Gartner's a very IT centric analyst firm, right? And IT often in my view are very conventional thinkers and they're often not the fastest to adopt breakthrough technology. They weren't the fastest to adopt cloud. They weren't the fastest to adopt on-demand CRM. And they weren't the fastest to jump on to RPA because they believe, hey, why can't we use an API for everything? And the Gartner analyst is kind of in the beginning of the process of the Magic Quadrant, you know, spent a lot of time with us and they were trying hard to say that was what you should solve everything with an API. That's just not reality, right? And it's not feasible. And it's not affordable, right? And so, but RPAs, it's not just the automation of a task or process. It's then applying a whole set of other technologies. We have 700 partners today in our ecosystem. Natural language processing partners, right? Machine learning partners, right? Chatbot partners that you mentioned. So we want to make it very easy and a drag and drop way to be able to apply these great technologies to an automation to solve some big problem, right? What's fun to me right now is there's a lot of great startups that come out of, say, insurance. So they come out of financial services and they've got a great algorithm and know the business really well. And they probably have one or two amazing customers and they're stuck. We, for them, this came from a partner of ours, you're becoming, you, UiPath, you're becoming our best route to market because you have the data, you have the workflow. And so our job, I think in some ways is to make it easy to bring these technologies together to apply them to an automation, to do that in a democratized way where a non-engineer can do this. And I think that's what's happening. Yeah, those integrations between environments can be very powerful, something we see because every shop has lots of applications, they have lots of technical debt and they're not just sweeping the floor of everything they have. What are some of the limits of AI and RPA today? Where do you see things going? Yeah, I mean, I think deep learning, we see very little of that, that's probably applied to some kind of science projects and things within companies. I think for the vast majority of our customers, they use machine learning within RPA for computer vision but by default. But they're still not really at a stage of kind of mass adoption of being able to go, which algorithms do I want to apply to a process? So I think we're trying to make it easier for you to be able to like in drag and drop AI, we call it to make it easier to apply. But I think we're in very early days and as you mentioned, there's market confusion on it. I know one thing from our 90 plus customers that are in our advisory boards. I know from them, they say that their company struggles with finding an ROI in AI, right? And I think we're helping there because we're applying it to a real operation. They say the same thing about blockchain. I don't know, Stu, do you know of a single example of a blockchain ROI? A great example? Yeah, it reminds me, big data was one of those that over half of the people failed to get the ROI they want. It's one of those promises of certain technology, the high level, let's poo poo Bobby things that actually have tangible results and got things done, but you weren't following the strict guidelines of the API economy. Well, true, exactly right. But you look at, what's great, what I find amazing is, so I mentioned on another one of our talks, conversations that 23,000 people come to UiPath events this year, to our own events, not trade shows, other things that's different. And they want to get on stage and talk. They're delighted about this and they're talking about, generally speaking, RPA is helping them go digital but they're all saying their ambition is to apply AI to make those processes smarter, to learn from, to go from rules-based to experience-based. I think what's beautiful about UiPath is that we are a platform that you can get there over time, right? And you can apply different, well, you can't predict perhaps the algorithms you're going to want to use in two or three years. We're not going to force you. You could apply any algorithm you want to an automation work going through. And so I think that flexibility is actually, for customers, it's very, they find it very comforting. Yeah, it's one of those things I say, most companies have a cloud strategy, but that needs to be kind of written in, not etched in stone, but you need to revisit it every quarter. Same thing with what's happening in AI and in your space. Things are changing so fast but they need to be agile. They need to be able to make changes. So in October, you're going to have a lot of those customers up on stage talking. Where will this AI discussion fit into UiPath forward in Las Vegas? Yeah, so we talk a lot about our AI fabric framework. And so it's around document understanding, getting smart, helping robots get smarter and smarter, what they see on a screen, what they see on a document, what they see with handwriting, and improving the accuracy of visual understanding, looking at face recognition and other types of images and being able to understand the images, conversational understanding, the tone of an email. Is this person really upset? How upset? Or a conversational chat bot or else? Really evolving from mimicking humans with RPA to augmenting humans. And I think that story, both in the innovations, the customer examples on stage, I think you're going to see the sophistication of automations that are being used through UiPath grow exponentially. Okay, so I want to give you the final word on this. And I don't want to talk to the people that might poo poo or argue RPA and AI and ML and all these things. Bring us inside your customers. How does that conversation start? Are they coming in from AI, ML, RPA or their stage? Or is there a business discussion that usually catalyzes this engagement? Our customers are starting with digital, right? They're trying to go digital. They know that digital transformation has been very, very hard, right? And there's a real outcome that comes quickly from taking a mundane task that is expensive and automating that, right? So the outcomes are quick, often projects that involve our partners like Accenture and others. The payback period of the entire project with RPA can be six months, it's self-funding. What other technologies do in B2B is self-funding in one year? So that's part of the incredible adoption growth. But every single customer doesn't stop there. They say, okay, I also want to know that this automation, I want to know that I can go apply AI to this. And it's in every conversation. And so there's two big booms with UIPath and RPA. The first is when you go digital, there's some great outcome, there's some productivity gain, it's immediate, right? Like I said, the payback period is quick. The second big one is when you go turn it from a rules-based to an experience-based process, or you apply AI to it, there's another set of business benefits down the road. And it will keep getting, as more algorithms come out and things, you can keep applying to it. So this is sort of the gift that keeps on giving. And I think if we didn't have that connection to machine learning or AI, I think the enthusiasm level, though, of the majority of our customers would not be anywhere near what it is today. All right, well, Bobby, really appreciate digging into the customer reality, RPA, AI, all the acronym soup that's going on. And we look forward to UIPath forward at the Bellagio in Las Vegas this afternoon. It'll be fun. All right, I'm Stu Miniman, as always. Thank you so much for watching theCUBE.