>> Live from Miami Beach, Florida, it's theCUBE. Covering UiPathForward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody. You're watching theCUBE, the leader in live tech coverage. We go out to events, we extract the signal from the noise. A lot of noise here but the signal's all around automation and robotic process automation. I'm Dave Vellante, he's Stu Miniman, my co-host. Guy Kirkwood's here he's the UiPath chief evangelist otherwise known as the chief injector of Kool-Aid. Welcome. (guests chuckling) And Craig LeClair, the Vice President at Forrester. Covers this market, wrote the seminal document on this space. Knows it inside out. Craig, great to see you again. >> Yeah, nice to see you again. It's great to be back at theCUBE. >> So let's start with the analyst perspective. Take us back to when you first discovered RPA, why you got excited about it, and what Forrester Research is all about in that space. >> Yeah, it's been a very a interesting ride. Most of these companies, at least that are the higher value ones in the category they've been around for a long time. They've been around for over a decade, and no one ever heard of them three years ago. So I had covered at Forrester, business process management and some of the business rules engines, and I've always been in process. I just got this sense that there was a way that companies could make progress and digital transformation and overcome the technical debt that they had. A lot of the progress has been tepid in digital transformation because it takes tremendous amount of time and tons of consultants to modernize that core system that really runs the company. So along comes this RPA technology that allows you to build human equivalence that patch up the inefficiencies without touching. I came in on American Airlines and the system that cut my ticket was designed in 1960. It's the same Sabre reservation system. That's the big obstacle that a lot of companies have been struggling to really take advantage of AI in general. A lot of the more moonshot and more sophisticated promises haven't been realized. RPA is a very practical form of automation that companies can get a handle on right now, and move the dial for digital transformation. >> So Guy we heard a vision set forth by Daniel this morning. Basically a chicken in every pot, I call it, a robot for every person. Now what Craig was just saying about essentially cutting the line on technical debt, do you have clear evidence of that in your customer base? Maybe you could give some examples. >> What we're really seeing is that as organizations have to deal with the stresses, what Leslie Wilcox professor at LSE describes as the stresses within organizations and particularly in environments where the demographics are changing. What we're seeing is that organizations have to automate. So the best example of that is in Japan where the Japanese population peaked in 2010. It's now falling as a whole, plus all the baby boomers, people of Craig's and my age are now retiring. So we're now in a position where they measure levels of dangerous overwork as being more that 106 hours a week. That isn't 106 hour a week in total, that's 106 hours a week in addition to the 60 hours a week the Japanese people normally work. And there is a word in Japanese, which is (speaking in foreign language), which means to work oneself to death. So there really is no choice. So what we're seeing happening in Japan will be replicated in Western Europe and certainly in the US over the next few years. So what's driving that is the rise of the ecosystems of technologies of which RPA and AI are part, and that's really what we're seeing within the market. >> Craig, sometimes these big waves particularly in infrastructure, you kind of saw it with virtualization and some other wonky techs, like data reduction. They could be a one-time step function, and not an ongoing business value creator. Where does RPA fit in there? How can organizations make sure that this is a continuous business value generator as opposed to a one time hit? >> Good question. >> Well, I like the concept of RPA as a platform that can lead to more intelligence and more integration with AI components. It allows companies to build an automation center or a center of excellence focused on automation. But the next thing they're going to do after building some simple robots that are doing repetitive tasks, is they're going to say "Oh well wouldn't it be better "if my employee could have a textual chat with a chatbot "that then was interacting with the digital worker "that I built with the bot." Or they're going to say "You know what? I really want to use that machine learning algorithm "for my underwriting process, but I can use these bots "to go out and collect all the data from the core systems "and elsewhere and from the web and feed the algorithms "so that I could make a better decision." So again it goes back to that backing off the moonshot approach that we've been talking about that AI has been taking because of the tremendous amount of money spent by the major players to lay out the promise of AI has really been a little dysfunctional in getting organizations' eye off the ball in terms of what could be done with slightly more intelligent automation. So RPA will be a flash in the pan unless it starts to embed these more learning-capable AI modules. But I think it has a very good chance of doing that particularly now with so much investment coming into the category right. >> Craig, it's really interesting. When I heard you describe that it reminds me of the home automation. The Cortanas and Alexas and consumer side where you're seeing this. You've got the consumer side where you can build skills yourself, you know teenagers people can do that. One of the challenges always on the business side is how do you get the momentum when you don't have the consumer side. How do those interact? >> It's the technical debt issue and it's just like the mobile peak in 2011. Consumers in their hands had much better mobility right away than businesses. It took businesses five, they're still not there in building a great mobile environment. So these Alexa in our kitchen snooping on our conversation and to some extent Netflix that observes our behavior. That's a light form of AI. There is a learning from that behavior that's updating an algorithm autonomously in Netflix to understand what you want to watch. There's no one with a spreadsheet back there right. So this has given us in a sense a false sense of progress with all of AI. The reality is business is just getting started. Business is nowhere with AI. RPA is an initial foray on that path. We're in Miami so I'll call it a gateway drug. >> In fact there's also an element that the Siris, the Cortanas, the Alexas, are very poor at understanding specific ontologies that are required for industry, and that's where the limitation is right now. We're working with an organization called Humly, they're focused on those ontologies for specific industries. So if the robot doesn't understand something, then you could say to the robot Okay sit that in the Wells account, if you're in a bank, and it understands that Wells in that case means Wells Fargo it doesn't mean a hole in the ground with water at the bottom or a town in Somerset in the UK, 'cause they're all wells. So it's getting that understanding correct. >> I wonder if you guys could comment on this. Stu and I were at Splunk earlier this week and they were talking up NLP and we were saying one of the problems is that NLP is sometimes not that great. And they made a comment that I thought was very interesting. They said frankly a lot of the stuff that we're ingesting is text and it's actually pretty good. I would imagine the same is true for RPA. Is that what you see? >> You were talking about that on stage. With regards to the text analytics. >> Yes. So RPA doesn't handle unstructured content the way that NLP does. So NLP can handle voice, it can handle text. For the bots to work in RPA today you have to have a layer of analytics that understands those documents, understands those emails and creates a nice clean file that the bots can then work with. But what's happening is the text analytics layer is slowly merging with the RPA bots platforms so it's going to be viewed as one solution. But it's more about categories of use cases that deal with forms and documents and emails rather than natural language, which is where it's at. >> So known business processes really is the starting point. >> Known business-- >> One example we've got live is an insurance company in South Africa called Hollard, and they've used a combination of Microsoft Cognitive Toolkit, plus IBM Watson and it's orchestrated doing NLP and orchestrated by UiPath. So that's dealing with utterly unstructured data. That's the 1.5 million emails that that organization gets in a year. They've managed to automate 98% of that, so it never sees a human. And their reduction in cost is 91% cost in reduction per transaction. And that's done by one of our implementation partners, a company called LarcAI down there. It's superb. >> Yeah, so text analytics is hard. Last several years we have that sentiment out of it, but if I understand it correctly Craig, you're saying if you apply it to a known process it actually could have outcomes that can save money. >> Yes, absolutely yes. >> As Guy was just saying. >> I think it's moving from that rules-based activity to more experience-based activity as more of these technologies become merged. >> Will the technology in your view advance to the point, because the known processes. okay, there's probably a lot of work to be done there, but today there's so many unknown processes. It's like this messy, unpredictable thing. Will machine intelligence combined with robotic process automation get to the point, and if so when, that we can actually be more flexible and adapt to some of these unknown processes or is that just decades off? >> No, no, I think we talk at Forrester about the concept of convergence. Meaning the convergence of the physical world and the digital world. So essentially digital's getting embedded in everything physical that we have right. Think of IoT applications and so forth. But essentially that data coming from those physical devices is unstructured data that the machine learning algorithms are going to make sense of, and make decisions about. So we're very close to seeing that in factory environments. We're seeing that in self-driving cars. The fleet managers that are now understanding where things are based on the signals coming from them. So there's a lot of opportunity that's right here on the horizon. >> Craig, a lot of the technologies you mentioned, we may have had a lot of the technical issues sorted out, but it's the people interactions some things like autonomous vehicles, there's government policies going to be one of the biggest inhibitors out there. When you look at the RPA space, what should workers how do they prepare for this? How do companies, make sure that they can embrace this and be better for it? >> That's a really tough and thoughtful question. The RPA category really attacks what we call the cubicle population. And there are we're estimating four million cubicles will be emptied out in five years by RPA technology specifically. That's how we built the market forecast 'cause each one of the digital workers replacing a cubicle worker will cost $11,000 or what. That's how we built up the market forecast. They're going to be automation deficits. It's not all going to be relocating people. We think that there's going to be a lot of disruption in the outsource community first. So companies are going to look at contractors. They're going to look at the BPO contract. Then they're going to look at their internal staff. Our numbers are pretty clear. We think they're going to be four million automation deficits in five years due to RPA technology specifically. Now there will be better jobs for those that are remaining. But I think it's a big change management issue. When you first talk about robots to employees you can tell them that their jobs are going to get better, they're going to be more human. They're going to have a much more exhilarating experience. And their response to you is, What they're thinking is, "Damn robot's going to take my job." That's what they're thinking. So you have to walk them up the mountain and really understand what their career path is and move them into this motion of adaptive and continual learning and what we call constructive ambition. Which is another whole subject. But there are employees that have a higher level of curiosity and are more willing to adapt to get on the other side of the digital divide. Yep. >> You mentioned the market. You guys did a market forecast. I've seen, read stats, a little over a billion today. I don't know if that's consistent with your numbers? >> Yeah that's about right. >> Is this a 10X market? When does it get to 10 billion? Is it five, seven, 10 years? >> So we go out five years and have it be close to three billion. I think the numbers I presented on stage were 3.2 billion in five years. Now that's just software licenses and it's not the services community that surround that. >> You'd probably triple it if you add in services. >> I think two to three times service license ratio. There's always an issue at this point in emerging markets. Some of the valuations that are there, that market three billion has to be a bit bigger than that in eight or nine years to justify those valuations. That's always the fascinating capital structure questions we create with these sorts of things. >> So you describe this sort of one for one replacement. I'm presuming there's other potential use cases, or maybe not, that you forecast. Is that right? >> Oh no for the cubicles? >> Yes, it's not just cubicle replacement in that three billion right? It's other uplifts. >> No there are use cases that help in factory automation, in supply chain, in guys carrying around clipboards in warehouses. There are a tremendous number of use cases, but the primary focus are back office workers that tend to be in cubicles and contact center employees who are always in cubicles. >> And then we'll see if the non-obvious ones emerge. >> I think ultimately what's going to happen is the number of people doing back office corporate functions, so that's both finance and accounting procurement, HR type roles and indeed the industry specific roles. So claims processing insurance will diminish over time. But I think what we're going to see is an increase in the number of people doing customer experience, because it's the customer intimacy that is really going to differentiate organizations going forward. >> The market's moving very fast. Reading your report, it's like you were saying yesterday's features are now table steaks. Everybody's watching everybody else. You heard Daniel today saying, "Hey our competitors are watching. "We're open they're going to steal from us so be it." The rising tide lifts all boats. What do you advise clients in terms of where they should start, how they should get started? Obviously pick some quick wins. But what do you tell people? >> I always same pretty much the same advice you give almost on any emerging technology. Start with a good solution provider that you trust. Focus on a proof of concept, POC and a pilot. Start small and grow incrementally, and walk people up the mountain as you do that. That's the solution. I also have this report I call The Rule of Fives, that there are certain tasks that are perfect for RPA and they should meet these three rules of five. A relatively small number of decisions, relatively small number of applications involved, and a relatively small number of clicks in the click stream. 500 clicks, five apps, five decisions. Look for those in high volume that have high transaction volume and you'll hit RPA goal. You'll be able to offset 2 1/2 to four FTE's for one bot. And if you follow those rules, follow the proof of concept, good solution partner everyone's winning. >> You have practical advice to get started and actually get to an outcome. Anything you'd add to that? >> In most organizations what they're now doing, is picking one, two, or three different technologies to actually play with to start. And that's a really good way. So we recommend that organizations pick three, four, five processes and do a hackathon and very quickly they work out which organizations they want to work with. It's not necessarily just the technology and in a lot of cases UiPath isn't the right answer. But that is a very good way for them to realize what they want to do and the speed with which they'll want to do it. >> Great, well guys thanks for coming on theCUBE, sharing your knowledge. >> Thank you. >> Pleasure. >> Appreciate your time. >> Thanks very much indeed. >> Alright keep it right there everybody. Stu and I will be back from UiPathForward Americas. 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