 Live from Las Vegas, it's theCUBE, covering UiPath Forward Americas 2019. Brought to you by UiPath. Welcome back to the Bellagio everybody. You're watching theCUBE, the leader in live tech coverage. It's day two of UiPath's forward three conference. And Courtney Bradbury is here, sorry. A R&D specialist at American Fidelity. She's joined by Michael Setikasi, who's the senior director of business development Boston based data robot, but Michael's from Seattle. Guys, welcome to theCUBE. Thank you. Courtney, let's start with you. I know you guys are kind of do benefits solutions, but maybe talk a little bit about the company and some of the big trends that are driving what you guys are doing. Absolutely, so I work with American Fidelity. It's an insurance company based out of Oklahoma, but our main focus is providing solutions to our customer's pain points. So we're a niche based organization that focuses mainly on education, so the public sector, so education and municipalities and providing solutions and benefits to our employers and our employees that we work with. Cool, and Michael, you guys obviously data science is your thing, but describe a little bit more about what you guys do. Yeah, we're an AI enterprise company. What we're really trying to do is democratize the use of AI machine learning within organizations. And we really appeal to both data scientists and business users that understand their business and data and want to do more. So Courtney, your title is really interesting, R&D Special Projects, so you got like this little sandbox you get to play with. RPA was on the hype cycle now, it's in the trough of disillusionment, but it's kind of an early play around with things. How did you get into RPA, where are you guys at? What's this R&D thing going on? Right, so with research and development, it gives us a lot of space to work with emerging technologies and AI and RPA and how those two things come together and anything new that we see and exciting, we're able to apply that technology. It's one thing to think, oh AI, let's cool, let's do that, but if it doesn't benefit your customer at the end of the day, if it's not driving decisions in your organization, then we don't want to do AI just because it's cool. We really want to do AI because it's what benefits our customer. So we got into RPA because when we saw a demo and it was like, whoa, if that's real, if that's what we think it's going to be, that's a game changer. So you have RPA and you have AI kind of coming up at the same time and whenever it was first coming out a few years ago, they're siloed, they're separate. What we started to do recently is to bring the two industries together and really bring together the RPA component and the AI component to really become an IPA or intelligent process automation so that way we can really start to transform businesses. So this is interesting to me, Michael, because as Courtney was saying, most people think of these things as separate and more aspirational down the road. You guys are AI experts, what are you seeing in terms of these two domains coming together? You hear about intelligent automation everywhere, right? And we are pushing it hard and we're seeing a lot of customers and potential prospects look at it, but I have to give credit to American Fidelity. They are ahead of the curve. They are combining this ability to use an RPA process and a machine learning model to really automate things and provide better customer service and get to the end point faster and more efficiently. So I think they're ahead of the curve, but you're going to see more and more of this in the marketplace. So Courtney, a lot of the customers that we talked to, this is kind of my observation, is they're automating obviously mundane processes, but frankly really crappy processes. They're really screwed up in a lot of ways and they're throwing RPA at the problem. It sounds like you have a little different philosophy around how to apply automation. Can you explain that? Right, so you don't want to automate something that's bad because then it's going to break a lot and it's just not a good idea. So what we've tried to do, you know, requests in the door, there's always a stopping, if somebody has to make a decision in the past, it's been okay, well we can automate the first part and the last part, but it's going to have to stop in the middle for you to make a decision. And what DataRobot has allowed us to do in the past, it was really hard to actually apply machine learning because you had to have these data scientists and they'd have to spend months trying to figure out what model fit the data and is it, you know, retraining the model is really difficult. DataRobot makes a data scientist's job so much easier and actually applicable to the workplace where you can scale, like it enables scaling because without DataRobot or without a service like that it's impossible to scale. So it allows us to implement AI with our RPA to then not just automate the mundane processes, but the small decisions that we make every day just because we do our jobs every day and we know how to do our jobs, AI enables us to automate those processes as well. And you're doing that in an unattended way or is it an attended automation? Both, so there's some processes that we have to have a human select things and make certain decisions along the way or there's some processes that are completely unattended. With any automation, you know, your goal is always to automate 100% but in reality you're usually going to get around 80% of a process automated. So what we try to do is we go for the 100% rarely get that but then you can kick out the 20% for human review and so maybe the 20% that's not fully automated maybe we can make stop points for human interaction there but there have been some processes that we have been able to fully automate. So Michael, data scientists complain that 80% of their time has spent on wrangling data and getting the data ready to actually build a model. I presume that's what you guys do, you solve that problem, right? So we definitely solve some of that, right? If you get the data all in one place, DataRobot takes care of a lot of the data preparation that's involved in data science. We've also have ways to kind of manage the best places you store your data so that if other people use the platform they can see where to get it to. But overall I would just say like when you look at UI path and the way it's growing it's such an exciting growing company like we heard Daniel yesterday mention like their growth from customer from year to year how they're the fastest enterprise software growing company out there. So you combine that RPA market with this growing machine learning market and there's a ton of excitement. And I mean that's what you're seeing at the conference today. So you guys have data scientists on staff, is that right? Correct, yes. And so what does this mean for them? Does it mean you just need less of them or they spend more of their time doing productive work? It means they spend more of their time doing more productive work instead of trying to figure out what model to fit. Because if you're a data scientist you know or an actuary or any data analyst any of those things you might know five models that you try to fit everything to. What DataRobot enables us to do is not be stuck to those five models that we know. It enables us to combine models and choose models based on that data. So it really helps us with the modeling. Are you, I should have asked this before, are you still in R&D? Are you in production or where are you at in terms of the maturity? Oh no, we're in production, we're in production. We have two IPA processes in production today and we're working on increasing that as we go. We have over 150 RPA processes in production as well as many, many just machine learning. So we're working on combining those now. So we have many machine learning, we have many RPA and we're working on increasing our IPA. What have you seen as the business impact? Do you have enough data yet to sort of secure? Absolutely, you know, we don't try to focus on ROI. What we try to focus on is how is this impacting our customer and how is this impacting our employees' lives? You know, there's obviously a lot of fear around automation but what at American Deli what we try to do is show how this is going to improve our employees' lives and we're by no means trying to cut jobs. We're actually going to have a net increase of jobs over the next five years. We're re-skilling our workforce and we're really focusing on how it improves our employees rather than focusing on ROI. So you're not on the ROI treadmill? So how did you get your CFO to sort of agree with all this? We do track ROI, it's not something we share publicly but humans and our employees then ROI. Is that because, I mean you're not, you know, virtually every customer I've talked to says, well, we're not firing people. We're just getting, you know, more productive, we're shifting them to more interesting tasks, et cetera, et cetera. And if you do the ROI calculation, you should say, oh, I don't need as many humans to do this anymore and so you'd say, okay, FTE costs and then you apply that and it's kind of a BS number because it's not like you're cutting people. So it's not a hard ROI. Is that why you don't focus on ROI or you just think it's a worthless metric or? No, actually I'm sorry, you said you do have it, you just don't share it publicly. Right, we just don't share our ROI publicly and I don't think it's, I don't think it's made up or it's fake. You know, I've never met an organization that says they have more people than they have worked for people. Like there's always work. I really enjoyed the first video opening of UiPath. It's since the beginning of time, humans have worked, you know, and everyone thinks that automation is going to like take, get rid of jobs. There's a lot of controversy over that but realistically, if you think about the first Industrial Revolution, that was after the Industrial Revolution hit, that was the biggest economic upturn but had seen since that time. We're in that same space now. It's just hard to see it with where we're at. So it's only going to increase, work is only going to increase. It's definitely going to change. I think it's naive to think that jobs won't change and there will be jobs that will be eliminated, jobs functions, but I don't think there's elimination of humans needed, if that makes sense. Well, yeah, it does. I mean, you guys sound like you're pretty visionary about how to apply technology to your business and Michael, I mean, Courtney's right. Machines have always replaced humans. This is nothing new. First time ever that it's in cognitive function so that scares people a little bit but what else are you seeing in the marketplace that you can share with us? We're just seeing increased use of automation. So like you might think when you talk to a robot, you're using us for the top 1% things that a company might do, right? If you're a bank, you might use us to help out, figure out how you can more efficiently lend customers money and make sure that you're making good investments but what we're finding is automation and machine learning models are being used everywhere. They're being used in marketing now, right? An example could be the show. We'll get leads from the show. Let's run some machine learning to understand what leads to follow up on first because we'll get the best result. We're seeing machine learning in HR, right? Making sure their employees are happy, tracking employee churn through machine learning. So I think what we're seeing is it's being adopted more broadly, which means you need more people. We're not replacing people. So why UiPath? Well, you know, whenever we started the vendor process and started looking at several vendors, the UiPath product just was unmatched, frankly. Their ability to, and there was the UiPath that we've chosen to go with. We have a COE, but we've also chosen to go with a democratized model where everyone in the organization will be able to build robots. We're training people to build robots. We have, each department has people that are dedicated. A certain portion of their time is built, building robots, and UiPath really made that available with their products for anybody to be able to work with. So you have a COE? Yes. It was interesting, Craig Leclerc this morning, I don't know if he saw his keynote, but he kind of made the statement, it was sort of an off-handed statement. It's that COE, maybe that's too asking too much. You know, he almost, you didn't use the term tiger team, but I inferred. Rather just kind of get a tiger team of some experts, but talk a little bit more about your COE. So we kind of go with a hybrid model. If you think about, you know, typical, it's weird because RPA is only a few years old, and we're thinking typical RPA, but people usually either go with a COE or completely democratized. We've really gone with a hybrid model. So we have a COE with governance where we've set a loose framework of what to follow, and we have code standards, and we say, you know, follow these things. We have a knowledge library that we share, a handful of full-time RPA developers teach and help grow that knowledge throughout the organization. So that way we have people in every area that can also develop. So our developers are not our only key developers. Our developers are focused on the IPA, on the AI. Other people throughout the organization are focused more on RPA, so we can really make a big difference more quickly. Do you have a software robot sort of that automates auditing and checks for compliance? Yeah, so we have one of our robots, what the function that it does is audit one of our inputs. So we do have robots in almost every area that, yeah, we do have audit robots. Has it cut the auditing bill? Is that part of the ROI? You don't have to answer that. Michael, last question for you is, where do you see this all going? This is very interesting to me because I've inferred from a lot of the conversations that the PepsiCo guy was up yesterday talking about an AI fabric throughout the organization, not just tactical projects. And that kind of interested me, but I expected it's much further off. I'm hearing from Courtney that it's actually real today. What's your sort of prediction or forecast for the adoption of this more advanced intelligent process automation? Is it kind of just starting now and it's going to explode, am I just missing the mark here? No, I think you're 100% on. I mean, first off, I think, like I mentioned earlier, RPA and machine learning separately are in these incredible growth stages, right? And we think, you know, our message to customers now is if you're not thinking about how you're doing AI and machine learning, you're already behind because your competition is. And so you better get thinking about it. I think we're going to get to that level with intelligent automation, with RPA plus machine learning very soon. I do think right now we're in that infancy stage where people are looking for use cases and they want to hear great stories. And so I do think American Fidelity is ahead of the curve, but they're not going to be ahead of the curve for long. It's catching up. If you're not doing it, we're going to eventually get to that point where you'll have someone like Elon Musk or Masayoshi Sun say, if you're not thinking of intelligent automation, you're already going to be left behind. All right, well, congratulations on the work that you've done. It's a really awesome story. Thank you. Thanks so much for coming on theCUBE. Yeah, thanks for having us. Great, thanks for having us. All right, keep it right there, everybody. We'll be back from UI path forward day number two. You're watching theCUBE right back.