 From Times Square, in the heart of New York City, it's theCUBE, covering Imagine 2018. Brought to you by Automation Anywhere. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in Manhattan at the Automation Everywhere, Imagine 2018. About 1,100 people talking about RPA, Robotics Process Automation bots, really bringing automation to the crappy processes that none of us like to do in our day-to-day job, and we're excited to have a practitioner who's out in the field, he's talking to customers all the time. It's Weston Jones, and he's the Global Intelligent Automation Leader for ENY. Weston, great to see you. Yeah, thank you. Good to be here. Absolutely, so it's funny. You said you've been with these guys for a number of years, so when did you get started? How did you see the vision when nobody else saw it? And here we are, five years later, I think, since you first met him. Oh, I know, it was just funny. I mean, years ago, I saw Automation Anywhere at the conferences. They were one of the small booths, just like everybody else was, talking about automation. Now, I've watched them for several years, and then I decided one year when we were looking at some of our offerings to bring in RPA and talk to our leadership about it, and kind of the light bulbs went off. And so from five, six years ago, till today, we've been working with them, and it's really amazing to see kind of how things have changed and how the adoption has taken place. Right, right. It's such a big moment in a startup, especially software company, when you get a big global integrator like you guys to jump in, advisory service. It's really hard to do. I've been in that position myself, and you guys don't make the move unless you really see a big opportunity. So what did you see in terms of the big opportunity that made you basically bet your career on this vertical? Well, so when I went to our leadership, I had, in the meeting, I had our global shared services leader. And so we have like 7,000 plus people in our shared services, and he was very skeptical. And we had to do 20 plus proof of concepts with him, and HR, IT, finance, et cetera, to get him excited about it. Now, he's our biggest fan, and they actually promoted him to run our global internal automation team, where now we think we're one of the largest users of automation, we're one of the biggest users within tax, we use automation anywhere within tax. We have over 750 bots working, and we have a goal to have 10,000 plus by 2022. So we're really pushing the bar and scaling. From 750 to 10,000, what are we, 2018 and four years? In four years. That's our goal. So where did you find the early successes? What kind of bots specifically, what type of processes are kind of right for people that are interested, see the potential, but aren't really sure kind of how to get started or to get that early success. Yeah, I mean, it's just almost like anything else, the quick wins, you know, start with things that are very rules-based, that have a lot of people, FDs associated with them. You know, our thing wasn't that we were actually, you know, eliminating FDs, we were just developing capacity, because we're a company that's growing, so we need more, instead of hiring more and more people, we took all that mundane work out of people's jobs, a lot of them focused on things that were more value-added. So the block and tackle stuff, like what, like give me a couple of examples, simple, stupid examples. HR onboarding, you know, we hire, we onboard 60,000 people a year. HR onboarding is something that's a very repetitive activity, logging in and out of multiple systems. And it was something where, you know, we were hiring HR professionals that knew how to do talent management, that knew how to do, you know, all these things we really wanted them to do, but we had them focused on doing a lot of very transactional type activities. So we said, why don't we use the technology for that, let's free these people up, so they can then focus on developing talent, career ladders, other things that we really wanted them to focus on. Other things like, you know, payments, matching and payment application, things like that, password resets, you know, a lot of stuff that you, I mean, you can just think of in your head, I mean, a lot of stuff in finance, a lot of stuff in HR and IT, even our supply chain too, we're doing like T and E's, we're doing a lot of automation in our T and E area. But that to say, I mean, I've mentioned all back office things, we're also doing a lot of front office. So for example, in our tax department, we use almost exclusively automation anywhere to do tax returns for clients. And we have, I think over a million plus hours that we've eliminated using automation anywhere. But how do you automation anywhere attach return? Well, tax return is, you know, very complex set of rules. And you basically, once you kind of load the rules in for certain activities, I mean, it's just like pulling data from one system into another. You know, doing multiple tax jurisdictions. Is it just like particular steps within that, within that you just kind of pick off one little process at a time, one little process at a time? True, and then you can also put in, you can do a nice interface in the front end, you can have people giving you the data and then you let the automation then get the data to the right parts within the tax return. So I'm curious in terms of the people that create the bots, who are they? Kind of what skill sets do they have? And do you see that changing over time as you try to go from 750 of whatever it is, a 20X multiple over four years? Do you see kind of the population of people that are able to create and implement the bots growing? How do you kind of managing the supply side on that? So we have a philosophy that 70% of its process, 30% of its technology. So we're fortunate that in our advisory area, across all the major functional, all the functional areas, supply chain, HR, finance, et cetera, we have process experts. So we use those process experts to get the process down. And then what we do is we have core development teams around the world. We have a big team in India, a big team in Costa Rica, we have a team in China and elsewhere. And those are the developers. And so our process people map out the process and then hand that off to the developers. So the developers, we basically, with Automation Demo's help, we've trained them to do the work and they've made it more and more as time goes on, they made it easier and easier for them to develop bots. And so we've been able to take people almost right out of college. We've hired some high school students. We take people that two thirds of the American population doesn't have a college degree. So we hire non-college degrees and teach them how to do this. Not that it's easy and to be really good, you have to have time and experience. But we can teach them to do these types of activities for us. That's amazing. So I wonder if you could share, what are some of the biggest surprises, kind of implementation surprises or ROI types of surprises that you found in implementing these 750? Yeah, so one thing I tell people about is, if you talk about the gardener hype curve, you go up and you fall into the valley of disillusionment. There's going to be four or five of those valleys that are going to happen and you just need to power through them because the technology is so compelling and the benefits are so compelling. I mean, there's over a dozen benefits, whether it's cost savings, improved security, better accuracy, whatever. So some of the surprises were scaling. So when I talk about the DIPSS, DIPSS, the first one is going to be data. People are going to realize that their data isn't quite there in order to do the more intelligent activities. The integration. So integrating the RPA with the more intelligent pieces of the IQ bot and other things. How do you do those integrations? How do you take other tools outside of that and integrate them? The third is penetration. I mean, penetration is very small right now and what happens is people tend to look at a whole process that needs to be automated when what you need to do is you need to think about breaking those processes apart. Like FP and A for example, may have a couple dozen steps to it but there are pockets of steps that are very automatable. So for example, pulling data, structuring it, normalizing it, getting it into some kind of report. That can all be done by automation and handed off to someone to do more cognitive activities. So the penetration is very small right now. We'll continue to grow. The savings, you know, have realistic expectations on savings. You know, when this first came out of the door, a lot of people were talking very, very high numbers. I mean, you can get it every once in a while but the saving numbers just be realistic about that. And the last part is scaling. We found scaling to be something that, you know, at the time when we were doing it, very few people had done it. So to figure out how do you scale and how do you develop a bot control room? How do you manage the bots? How do you manage the bots interfacing with people? How do you manage the bots interfacing with other technologies? It's a lot more to it than just putting the bot up and letting it work because they need care and feeding ongoing because, you know, it's not related to the automation network technology. It's more of the other things it touches. Like website changes, like upgrades to different systems that the bot has to execute with. Those are going to constantly change and you just need to kind of make sure you're adjusting the bot to actually work in those environments. So those are kind of the four or five things that we've seen. And when we go from, you know, 750 to 1000 to 10,000, I mean, we think we're going to see much more orchestration type things. You know, how do you orchestrate in a more automated way across the bots, the people, and then the other technologies? Right, it's funny on the scale issue because they were talking about, you know, how do you go from 10 bots, you got 750 to 10,000, and there's been a concept thrown around that they are a digital workforce implying that you have to manage them like a workforce. You got to hire them, you got to train them, you got to put them in place, you got to kind of keep an eye on them, you got to review them every now and then. And really, you know, it's an active management process. It's not just, you know, set and forget. Yeah, and we're hoping that we'll have, we have some of this already, but we'll have bots managing bots, we'll have bots auditing bots, we'll have bots orchestrating bots. So, I mean, that's all going to eventually happen. I think we can do some of it today, but it's going to be more and more common. The orchestration piece is really the thing that is going to be new, that it's going to drive a lot of people to start to scale. Right, the other two consistent themes that you just touched on that we talked a little bit before you turned the cameras on, right, is the Mars law, my favorite. Oh yeah. You know, we overestimate in the short term which Gartner might call the hype cycle, but we underestimate in the long term. And really, the other one is kind of this DevOps. Again, there's DevOps as a way to write code, but I think more importantly is DevOps as a culture, which is just look for little wins, little wins, little wins, little wins, little wins. Before you know it, you've automated a lot and you're going to start seeing massive returns on that effort versus the throw it in, we're going to get this tremendous cost savings on day zero, day one, or day 10, or whatever it is. That's really not the strategy. Well, I think a lot of people maybe don't like to hear this, but it's a journey. I mean, you start out using the technology where you can. It's not a technology play. It's solving your biggest, most complicated problems. That's the key. And whatever technology you need to do that, use that. So you'll do the RPA, then you get more benefit when you add the IQ bots and the intelligent stuff and you get more benefit when you start adding, you know, technologies are even ancillary like blockchain, IoT and things like that. You'll get more and more kind of benefits from this technology. All right, Wesson, well thank you for sharing your story. It's good to get it from the front lines and good luck on making 20,000 bots in 48 years. All right, he's Wesson, I'm Jeff. You're watching theCUBE from Automation Anywhere. Imagine 2018, thanks for watching.