 Hello, everyone, and good afternoon. We are back with more of theCUBE's live coverage of Forward Six, UI Path Forward Six. I'm your host, Rebecca Knight, along with my co-host and analyst, Dave Vellante. We are talking about the future of work here, really. This is an exciting- These are- It's a real house, Rebecca. It's my jam, it's my jam. And I'm really excited about this next session because it's about the life sciences. We're talking about the future of hyper-automation at Merck. I want to welcome to our show, Sonny Kosak. He is the associate director, software engineering manager of hyper-automation at Merck. Welcome, Sonny. Thank you, thank you very much. And Grant Harris, he is the director of consulting services at CGI. Thank you very much. So, let's talk about, I want to ask you, Sonny, first of all, because you are relatively new to Merck. You've had a long career in financial services, have recently moved over to Merck. How are you bringing what you know from financial services about automation and the journey that organizations must go through to automate their processes to Merck? Well, yeah, that's a great question because, you know, the exciting part is I get to bring all that industry knowledge I have within automation. So all the technology, all the different tools, all the best practices, they translate right over. And then it's exciting for me because I get to learn a new industry, I get to meet new team members and we have some new challenges. But really what we're trying to focus on the process. Once I know the process, I know the business language, now we can talk about what tools can we use to automate that. And so thus, I get to learn a new process but processes are the same. They use same type of servers, the same type of hardware, same type of tools. So it's a very easy transition at this point. Yes. Yeah, I think, you know, the fact that they're both regulated environments where it kind of goes hand in hand that you have to be very extra cautious about people's information, all of the data that goes back and forth. So I think that they really lend themselves well together as industries. But the business drivers are different, right? Yes. But you're saying a lot of that is sort of opaque, I guess, from the compliance pieces, right? Very much so. And my job here isn't to drive the business, it isn't to worry about compliance, it's to meet with the business, understand the process. If you can explain to me your process, if you can tell me your steps, tell me your pain points, tell me your goals, then I can take a figure out and I can go create a solution and design it using tools. Maybe we use RPA, maybe we use a homegrown microservice, maybe we use AI, maybe we can use OCR. Those tools apply to any industry, any process. So again, I just need someone who can speak business language. If you can talk business, I can understand, we can automate. What role does data play in that whole process? Well, data is critical, right? Cause it's the lifeblood of all the automations, right? You have proper data, you procure it, it helps you do proper testing, it helps you move your production from point A to point B, it gets you your results. And everything we're talking about is data points. This becomes even more important when you get to generative AI because you're not so much worried about algorithm and models, you're worried about procuring that data, fine tuning that data, feeding it into that AI model. And then those results you could take and we can automate and extend on. And that's why we really love UiPath because it helps us automate both ends of that AI which is taking that data to new heights, right? To be purpose. Is a lot of time spent up front figuring out whether it's data quality or where data is or what the single version of the truth is or what's been done? I think that a lot of that goes into it as well as determining what the value is going to be both to the business as well as what the cost is going to be. Is it going to be XYZ life cycles? So are you going to be able to maintain this for a number of years or is it something that where it's just going to be a one use robot? Where there's still value in that but your ROI is not exactly the same. So how do you balance that? The balance, the benefits to the business against employee impacts, their job security which of course is a big question on a lot of people's minds is people worry about AI coming for their jobs. How do you calculate that and think about that both in terms of real hard numbers in ROI but also in terms of the messaging to employees? That's a fantastic question. So to David's point, you spend a lot of time up front understanding your data, understanding your model but you understand the process. How complex is it? How many pain points are there? How many decision points? How many labor hour, how much labor hour goes in there? Is there compliance involved which now you have risk mitigation which you need to worry about? Compliance and audit issues. So you want to try to keep those costs down. You want to keep the labor costs down but you also want to bring value to the business. So the way you do that is that analysis up front very much as David pointed out and then you can calculate and put those numbers into hard numbers and you take that calculation, take it to your finance department, have them validate. My numbers close, are they right? Are they wrong? And then once you get into production you look at the actual savings and compare that to see how close your calculations are. But yeah, it just takes time and some due diligence. No shortcut. And Merch for building that out for a number of years and they have a quite robust process where they're both calculating the value up front and then bringing in to see does it actually equal why? So were we able to save the proper amount of money with this? Were we able to save the proper amount of time? And I think that that's definitely a key as far as how the business is going to view what the processes are going to be able to do for them. And it's a pretty straightforward to get to the value or is it a little art and science? Because I'm sure there's debates, a little double counting going on sometimes. There's a few pieces that I am maybe a third of it that's a science and the two thirds it's an art and it's just a matter of asking the right questions at the right people and really understanding kind of the numbers underneath the numbers. But again, if you give that due diligence and you look for it, you can find every process, every business group operates a little different, they have different details. You just get better of asking those questions and you tell them what you're looking for and they can often lead you down that path for you. You don't have to solve the problem for you, you tell them what you're trying to solve, they know their numbers, they know their business, they can help you figure out that calculation and they want to. And part of that, I mean, certainly subjectively they can say, take that away, I'm in trouble, I need that, so okay. But there's so many dependencies. And a mapping from infrastructure, applications, the business processes, the external ecosystem and it's essentially your job to build that mental map. Well, we have a large team to help out and what you're talking about is process engineering. And so process engineers come in and they help us fully understand from end to end, not just the process, the steps, but all the systems, the access to the systems, the cost of each system, the cost to support. So you have a large team of people to help you figure those answers out. It's your job to make sure, are they asking the right questions, they got the full thing, and can you present it so that the financial people will respect your decisions. Right, so you got to translate it. So you essentially set a baseline and then try to figure out, okay, when we make a change, what's the business impact going to be in terms of however you want to measure it, productivity, revenue generation. Right on the head, yeah, you got it, yeah. And then, like you said, the most important part is you have to defend it, right? Well, and I think that the experience goes to that where as the models are built up over time, you get more and more experience as to how do we evaluate things on the beginning so that your numbers at the beginning more closely align to the numbers that you're getting at the end of the project. Are you able to come in with, hey, this is best practice, this is what's achievable, kind of a little mini benchmark, or maybe you've got data that you can share. Here's a range of likely expectations. Is that sort of part of the art? Yeah, that's absolutely part of the art. And when you're working with these automations over a period of time in each of the individual towers within Mark or within any organization, they're going to be different. So not every manufacturer is going to be able to save the same amount of money on a certain process, but you do have that overall background and the more data that you're able to build up on what you've done in the past and how that's really achieved the ROI, the better you're going to be in the future as far as calculating that information. How do you handle it when there's a, let's say it's an influential business line manager, maybe here she's a P and L manager, has a big voice in the company, drives a lot of revenue, maybe a lot of people working for them, but there's, and there's like a pet process or application that's not getting a lot of usage. So you know, the value might not be there or if we did this, it would drive more value and more people could touch it, but that influential individual is like sort of holding on to his or her territory. You're smiling, you've seen this before, right? There's beefdoms in every organization. To be honest, early in my career, I did very poorly at this because I was a blunt object and I was too soon, so I made some enemies up front and I found out, you know, you do a little finesse, you use a little sugar, you explain and you go through. So if you can explain it and you could show them the numbers, sometimes they just back away. If you explain it and they show them the numbers and they still justify it, it's their job. I'm going to make the best application I can, the best solution they can be offered, but ultimately it's their decision. So it's my job to offer all the data so they can make the best decision possible and then let them justify it. That's the only way to handle. Change is hard and a lot of people are resistant to it, of course, but my question for you is, where are we in all of this transformation? I mean, you're here and you think, oh, the future of work, it's here, everything's different, everything's automated, we're using AI, but there really is, and there truly is a sense that things are changing and we are on the precipice of all of these revolutionary technologies. So where are we in the sense of how much is automation and AI actually changing the way people do their jobs, the customer experience, and the way the enterprise operates? Well, probably the first biggest change that I would list is the way that the business interacts now with IT. We have a very close relationship with these automation projects because we're having to come together to understand the process and the solution and we take them along the journey with us. So first of all, you have a much tighter collaboration, there's a respect for both. Now, as far as impacts on the business, once you start getting wins for them, they start coming up with ideas and all this is making your company more efficient, it makes it more scalable, it makes it more productive, which all that leads to not just better numbers but a better customer experience, products get to the market faster, so you can't speak enough about the productivity gains that you come across. Whether it's hard numbers, whether it's better products, whether it's faster products, and it's just continuing to grow, the tools are getting better, the people are understanding it more, the business, we're getting more of them creating citizen development, right? With them creating automations themselves. So it's an unbelievable change the business has embraced and it's better for everyone, everyone. And I think when you take a look at it, America as well as a lot of other companies, they started out with the RPA, hitting the low hanging through the swivel chair jobs where it was just something that's really easy to do, but then when you get further down, once you knock all of those off, you have to figure out, going more into the hyper automation route, where you're looking at not just structured processes, you're starting to look at unstructured processes and developing solutions that would really be able to benefit within those processes as well. It's so true, Rebecca's question, like how far along are we in that sort of hyper automation? I just, you know, Isaacson spoke yesterday, I just finished listening to and reading, I go back and forth in his book, and it was something in there, it was Musk's kid who asked him. He had a lot of them. One of them, I know it was X or Y or whatever. Asked him, how come the future doesn't look like the future? That was a pretty interesting question, it's true, right? Read about, you see all these sci-fi movies, anyway, but hyper automation is the future, where you're completely transforming the organization. I mean, we're starting to see glimpses of it. IT and business have a much tighter relationship than they did 10 years ago. So where are we in hyper automation? It was in your title. As I would say myself, I would say we're somewhere between toddler and adolescents. Right, I really would because I think we got a long way to go. A ton of growth, we have come a long way. And if you look back five years, you look back a few years, it's a great jump, it's tremendous. That change has been impactful, but we're just beginning. I think what we will see in 10 years, 20 years, we can't imagine, we really can't. And it's going to impact so much more, the businesses are going to leverage automation everywhere. Maybe we won't have jobs, so. Well, as the mother of adolescents, I can't wait until my AI starts rolling its eyes at me and stop acting as though I'm disgusting at every moment. So my question for you about life sciences, because this is the thing about AI and the future of AI. It's so, the use case for life science is so exciting and so impactful because it could really speed up drug development and the accuracy of our research. Where do you see it really affecting how drugs are developed and how actually medicine and care is provided to the patient? Yeah, when we talk about faster to market, coming from the financial industry, everything I was doing was to make the internal processes better, to make the banks more money. Here we're seeing products that actually get to the market. We're seeing better vaccines, better pharmaceuticals. We're seeing an impact and coming out of COVID, this is something that really kind of drove me towards in this way and I'm so happy to get into the life sciences industry. It's an impact that just makes you feel like you're giving a better impact into the world and to human life and you can't translate that to dollars, right? Right, right. Has there been anything here in particular that has caught your attention? Any surprises, anything that you'd like to see UI path do that could really make a difference in your business? The thing I'm most excited about coming out of here will be is the autopilot and the potential that it could bring not just for coding but for testing and I really love to see testing. The more you can test your automation, getting to the point with data, the more you can test that, the more you get, the more stable your production is, the easier people can sleep at night, the more relaxed your team is and then thus you have better products that you can develop, right? So yeah, autopilot society. I think that the closer relationship with SAP is absolutely brilliant and as we work a lot with SAP and a number of our clients, it just makes a lot of sense because when you're going out and touching applications outside of SAP, there's not really any great tools and some of the automations that you can build and that we've built in the past work brilliantly within SAP, pulling in some of the disparate data and making sure that you have a good end to end process. It's interesting because there was such a long period of time when you talk to SAP's customers, they really wanted this, you know, they were looking forward to it and for whatever reason it didn't happen and then boom, all of a sudden. Yeah. Somebody must have knocked the UI path and SAP heads together and said, guys, fix this problem and there's a lot of potential there. Really is, absolutely. You've talked about the rise of citizen developers and that is something that we're hearing a lot about at this conference and other conferences like it. Will that affect the skills needed in life sciences and other kinds of roles where we are seeing this rise of citizen development or are they truly non-technical people learning technical skills? 10 years ago, I would say it's not really feasible, right? It's really such. The more RPA has gotten mature, these platforms has gotten mature, UI path has got co-pilot. I think we're there. I think we have citizen developers who are creating attended bots, right? They're not creating the really complex processes. They're using their laptops or desktops and they're automating repetitive tasks that they understand and they know and they don't have to be super technical. They don't have to go through compliance things. They don't have to do security testing. They don't have to do all this other development tasks that you would normally to deploy into production. They could do it on their desktop. They could test it on their desktop. They can run it. And so absolutely and with autopilot, it becomes that much simpler with recording. So absolutely, I think we're there. I think they could do it. And so in a couple more years, I think we'll almost have as much citizen development as professional development now where you want to coin it. Have you guys seen use cases where the AI heard around the world sort of nipped at the heels of the traditional RPA business, but at the same time, created new opportunities for platforms like UI path to further add value to organizations. You know that Yin and Yang of, is it a tailwind or a headwind? Yeah, so that's a thing. And then it's almost like there was a great talk this morning on where AI has gotten to the point where it's passed human productivity, right? In many use cases. So, and that speaks to that point. And then it's going to get to a point where AI surpasses human intelligence and that it's a better alternative, a better approach. And that's that singularity moment where we approach. Yeah, right, Ro, indeed. So let's be careful what we ask for and how far we get down there. It's a very interesting proposition. I think that with a lot of the robots that are developed now, there's still a trust issue. You still need to have humans in the loop to make sure that the processes that are coming out of the black box actually make sense. So, you know, when I look at it from both sides of the business, the LLMs as well as the RPAs and any of the AI's that are out there, it's making sure that humans are still in the loop because there are still a lot of issues that are not necessarily solved with any of these, where you need to keep people informed with their analytics, you need to keep them informed with their reports and you need to make sure that things are just running on an ongoing basis and that a program upgrade or anything like that is not, you have to have a break fix out there, so you have to have those self-healing robots. How far away is the day where that human in the loop, maybe doesn't go away, but becomes less critical for the reason you talked about? Yeah, when I'm looking at human in the loop, I personally, I think that it's still a couple of years off because there are certain processes where you can definitely remove people from the loop almost immediately, but when you're looking at fully removing humans from the loop from a lot of different decisions, I think that there's going to be a certain level of trust and regulation in a lot of different industries where somebody like the FDA is going to say, maybe you shouldn't really be doing that. One of the examples we often use years ago, four or five years ago, maybe even more, Gary Kasparov, the chess master was on theCUBE, and he was telling us that the IBM supercomputer beat him, and he's so competitive, he started a competition. You know this story? Started a competition to have humans and computers compete against the supercomputer, and the human plus the computer always won. And he would have competitions every year, and so you'd have the best human plus computer teams. And I personally hope that stays around for a long, long time. I like to drive. Well, I'm not sure about that, but that may actually go human out of the loop, but to your point, I really do think human in the loop will always be there. I agree with Grant completely. Even if it's to the point, whenever I automate a business process, I tell them, we can reduce headcount, but I don't want to replace it. I want someone to know what's going in and at least someone to come out. If they're not in the middle, they need to do analysis, right? They need to do high value work. Somebody could take that and make that even more valuable. Now, there are some prescriptive steps where we can get rid of humans altogether where they just, you feed it the data and it comes out a hundred percent. But you know, I would like to keep driving too, but I just, there are cases where I see both, but you're always going to have humans in the loop, right? We got to have something to do. Yes, but here is my huge question right now, because we know that we are in the era of remote work and we know that Gen Z, the generation that is really starting their careers, not working in an office, at least as regularly as we all did, frankly, when we were starting our careers. So they're not getting the mentorship, the education, shall I say, of being in the workplace and knowing how these processes work and here's what we need to know. So the question being, how knowledgeable is that human in the loop going to be? Even a few years, does that at all? Do you think about those questions at all or? I worry about mentorship the most, but when you think about the educational processes and look as into it, Gen Z is a digital natives. So they've been growing up learning, their education has been online from a very young age and they've been able to adapt to those different ways of learning. But I do think that there's definitely a gap where people of our generation used to have mentors that you talk to on a daily, weekly, monthly basis so that you could have the guidance as to why are we doing this process, what is the necessary information? And that's definitely a gap where we need to continue to try and fill it in. Some of the return to work policies that are really aimed at making sure that people are just meeting each other, talking to each other, having the interactions face to face. You just can't replace that. Stuff's important, exactly. Well, Sonny and Grant, thank you so much for coming on theCUBE, it's been a great conversation. Thank you so much. Thank you guys, really appreciate it, thanks. I'm Rebecca Knight, stay tuned for more of theCUBE's live coverage of Forward Six. You're watching theCUBE, the leader in live tech coverage.