 Well, hello everybody. John Wall is here on theCUBE. Great to have you with us as we continue our series here at the AWS Executive Summit sponsored by Accenture. And today we're talking about public service and not just a little slice of public service but probably the largest public sector offering in the UK. And for with us or with us now to talk about that is Leanne Anerton who was in with the Intelligent Automation Garage delivery lead at the UK Department of Work and Pension. Leanne, good to see you today. Thanks for joining us here on theCUBE. Hi, thanks for having me. And also with us is Amar Narayan who is a manager director at Accenture. He's the AWS business group for the lead in health and public sector, also UK and Ireland. And Amar, I think you and Leanne are in the same location, Newcastle, I believe, in the UK, is that right? Yeah, absolutely. Yeah, we're here in the North East of the UK. Well, thank you for being with us. I appreciate the time. Leanne, let's talk about what you do. The Department of Work and Pension, the famous DWP in England, you have influence or certainly touchpoints with a huge amount of the British population. In what respects, what are you doing for the working class in England and what does technology have to do with all that? Sure, so for the Department of Work and Pensions, I think the pension bit is fairly self-explanatory. So anybody who is over state pension age within the UK, for the work part of that, we also deal with people of working age. So these are people who are either in employment and need additional help through various benefits we offer in the UK. Those people who are out of work and we also deal with health-related benefits as well. And we are currently serving over 20 million claimants every year at this moment in time. So we're a huge part of the UK government. All right, so say that number again. How many? 20 million claimants every year. With an M, right, so and that's individuals. And so how many transactions, if you will, how many do you think you process in a month? How much traffic basically are you seeing? An extraordinary amount, I'm not even, I don't think I even know that number. Mind blowing, right? So it's a huge, huge amount. Mind blowing. Yeah, so basically we kind of keep the country going. So if the Department for Work and Pensions kind of didn't exist anymore, then actually it would cause an infinite number of problems in society. We kind of help and support the people who need that. And yeah, so we play a really vital role in kind of social care and kind of public service. So what was your journey to extension that? What eventually led you to them? What problem were you having? And how have you collaborated to solve that? So in terms of how we work with Accenture, so we had, in around 2017, DWP was looking at a projected number of transactions growing by about 210 million, which was an extraordinary amount. And I think as we've kind of covered everything that we do is on a massive scale. So we, as DWP as an organization, we had absolutely no idea how we were going to be able to handle such a massive increase in the transactions. And actually, after various paths and ideas of how we were gonna do that, automation was actually the answer. So, but the problem that we have with that is that we have like many governments around the world. We have really older legacy systems. So each of these benefits that we deal with are on legacy systems. So whatever we were going to develop had to connect to all of these. It had to ingest and then process all of these pieces of data, some of which, given the fact that a lot of these systems have a lot of manual input. You have data issues there that you have to solve. Whatever we did, as we've talked about in terms of volumes has to scale instantly as well. So it has to be able to scale up and down to meet demand. And that downscaling is also equally as important. So yeah, you've got to be able to scale up to meet the volumes, but also you've got to be able to downscale when it's not needed. But we had nothing that was like that that kind of helped us to meet that demand. So we built our own automation platform, the Intelligent Automation Garage. And we did that with Accenture. So Omar, I'd like you to chime in here then. So you're looking at this client who has this massive footprint and obviously vital services, right? So that's paramount that you have to keep that in mind. And the legacy systems that Leanne was just talking about. So now you're trying to get them in the next gen, but also respecting that they have a serious investment already in a lot of technology. How do you approach that kind of problem solving, those dynamics and how in this case, did you get them to automation as the solution? Yeah, sure. So I think one of the interesting things, as Leanne has described it, right? It's effectively like, the department has to be running all of the time, right? They can't effectively stop and then do a bunch of IT translation. It's effectively like changing the wheels of a jumbo jet while it's taking off, right? And you've got to do all of that all in one go. But what I think we really, really liked about the situation that we're in and the client relationship we had was that we knew we had to, it wasn't just a technology play. We couldn't just go all right, let's just put some new technology in. What we also needed to do was really sort of create a culture, an innovation culture and go, well, how do we think about the problems that we currently have and how do we think about solving them differently? And in collaboration, right? So not just the, that's just the outsource of a bunch of technology for to, you know, to Accenture Noble, a bunch of stuff. So we very carefully thought about, well, actually, the unique situation that they're in, the demands that the citizens have on the services that the department provide. And as Leanne mentioned, that technology didn't exist. So we fundamentally looked at this in a different way. So we worked really closely with the department. We said, look, actually, what we ultimately need is the equivalent of a virtual workforce. Something where if you already, you know, all of a sudden had 100,000 pension claims that needs to be processed in a week that you could click your fingers and, you know, in a physical world, you'd have another building, all of your kits, a whole bunch of trained staff that would be able to process that work. And if in the following week, you didn't need that, you no longer needed that building, that staff or the machinery. And we wanted to replicate that in the virtual world. So we started designing a platform. We utilized and focused on using AWS because it had the scalability. And we thought about how are we gonna connect something as new as AWS to all of these legacy systems? How are we gonna make that work in the modern world? How are we gonna integrate it? How are we gonna make sure it's secure? And frankly, we're really honest with the client. We said, look, this hasn't been done before. Like nowhere in Accenture has done it. No one's done it in the industry. We've got some smart people. I think we can do it. And we prototyped and we built and we were able to prove that we can do that. And that in itself just created an environment of solving tricky problems and being innovative. But most importantly, not doing sort of proof of concepts that didn't go anywhere, but building something that actually scaled. And I think that was really the start of what has been the garage. So, and Leigh, and you mentioned this and you just referred to Amar about the garage, right? The Intelligent Automation Garage. What exactly is it? I mean, we talk about it, what the needs are all this and that, but Leigh, I'll let you jump in first that Amar certainly compliment her remarks, but what is the IAG? What's that? So, I think exactly what kind of Amar has said from a kind of a development point of view, I think it started off really, really small. And the idea is that this is DWP's Intelligent Automation Center of Excellence. So, its aims are that it makes sure that it scopes out kind of the problems that DWP are facing properly. So we really understand what the crux of the problem is. In large organizations, it's very easy, I think, to think you understand what the problem is when actually, you know, it is really about kind of delving into what that is. And actually, we have a dedicated design team that really kind of get under the bonnet of what these issues really are. It then kind of architects what the solutions need to look like. Using, as Amar said, all the exciting new technology that we kind of have available to us, that kind of sensible solution as to what that should look like. We then build that sensible solution. And we then, you know, as part of that, we make sure that it scales to demand. So something that might start out with, I don't know, a few hundred claimants or kind of cases going through it can quite often, you know, once that's been successful scale really, really quickly. Because as we have 20 million claimants that come through us every year. So these types of things can grow and expand. But also a really key function of what we do is that we have a fully supported in-house service as well. So all of those automations that we build are then maintained and, you know, so any changes that kind of need to be made to them, we have all that and we have that control and we have our kind of arms wrapped around all of those. But also what that allows us to do is it allows us to be very kind of self-sufficient in making sure that we are as efficient as possible. And what I mean by that is looking at, you know, as new technologies come around and they can allow us to do things more effectively. So it allows us to kind of almost do that, that kind of continuous improvement ourselves. So that's a huge part of what we do as well. And, you know, I think from a size point of view, as I said, they started off really small and the idea was this was a kind of center of excellence. But actually, as automation, as Amar alluded to, has kind of really started to embed in DWP's culture, what we started to kind of see is a massive expansion in the types of work that people want us to do and the volume of work that we are doing. So I think we're currently running at around 100 people at the moment. And I think, you know, we started off with a couple of scrum teams and Amar. So yeah, it's really grown. But, you know, I think this is here to stay within DWP. Yeah, when we talk about automation, you know, virtual and robotics and all this, I like to kind of keep the human element of mind here too. And Amar, maybe you can touch on that in terms of the human factors in this equation, because people think about, you know, robots, it means different things to different people. In your mind, how does automation intersect with the human element here and in terms of the kinds of things land wants to do down the road, you know, is a room for people, basically. Yeah, absolutely. I think fundamentally what the department does is support people. And therefore, the solutions that we designed and built had to factor that in mind, right? We're trying to best support and provide the best service we possibly can. And not only do we need to support the citizens that it supports, the department itself is a big organization, right? We're talking between sort of 17, 80,000 employees. So how do we embed automation but also make the lives of the DWP agents better as well? And that's what we thought about. We said, well, look, we think we can design solutions that be both. So a lot of our automations go through a design process and we work closely with our operations team and we go, well, actually, you know, in processing your benefit, there are some aspects of that processing that benefit that are copy and paste, right? It doesn't require much thought around it, but it just requires capturing data. And there's elements of that solution or that process that requires actual thought and understanding and really empathy around going, well, how do I best support this citizen? And what we tended to do is we took all of the things that were sort of laborious and took a lot of time and would slow down the overall process and we automated those. And then we really focused on making sure that the elements that required the human input was made as user-friendly and centric as we possibly could. So if there's a really complex case that needs to be processed, we're able to present the information in a really digestible and understandable way for the agents so that they can make a informed and sensible decision based around a citizen. And what that enabled us to do is essentially meet the demands of the volumes and the peaks that came in, but also maintain the quality and if not improve, you know, the accuracy of the claims processing that we had. So how do you know, and maybe Leigh and you can address this, how do you know that it's successful on both sides of that equation then? Because Amar raised a very good point. You have 70 to 80,000 employees that you're trying to make their work life much more efficient, much simpler and hopefully make them better at their jobs at the end of the day. But you're also taking care of 20 million clients on the other side too. So what's your measurement for success and what kind of like raw feedback do you get that says, okay, this has worked for both of our client bases, both our citizens and our employees? Yeah, so we can look at this both from a quantitative and a qualitative point of view as well. So I think from a, let's take the kind of figures first. So we are really hot on making sure that whatever automations we put in place, we are there to measure how that automation is working, what it's kind of doing and the impact that it's having from an operational point of view. So I think the proof of the fact that the Intelligent Automation Garage is working is that in its lifetime, we've processed over 20 million items and cases so far. We have 65 scaled and transitioned automations and we've saved over two million operational hours. I was gonna say that again, that's two million operational hours and what that allows us to do as an organization, those two million hours have allowed us to, rather than people as Amal said, cutting and pasting and doing work that is essentially very time consuming and repetitive, that two million hours we've been able to use on actual decision making. So the stuff that you need a sentient human being to make judgment calls on and kind of make those decisions, that's what it's allowed us as an organization to do. And then I think from a qualitative point of view, I think the feedback that we have from our operational teams is equally as great. So we have that kind of feedback from, all the way up to the director level about it's kind of, I said that freeing up that time but actually making the operational, they don't have an easy job and it's making that an awful lot easier on a day-to-day basis. It has a real day-to-day impact, but also there are other things that kind of the knock-on effects in terms of kind of accuracy. So for example, a robot will do as exactly as it's told, it doesn't make any mistakes, it doesn't have sick days, it does what it says on the tin and actually that kind of impact. So it's not necessarily counting your numbers, it's the fact that that then doesn't generate a call from a customer that kind of says, well, I think you've got this wrong. So it's all that kind of these kind of ripple effects that go out, I think is how we measure the fact that A, the garage is working and B, it's delivering the value that we need it to deliver. Robots probably ask better questions too. So we'll do that. Wow. Real quick, just real quick before we head out. So the big challenge next, Eureka, this works, right? Amar, you put together this fantastic system, it's in great practice at the DPW, now what do we do? So it's just in 30 seconds, Amar, maybe if you can look at, be the headlights down the road here for DPW and say, this is where I think we can jump to next. Yes, I think what we've been able to prove, as I say, is that it's scaled innovation and having the return and the value that it creates is here to stay, right? So I think the next things for us are, I can still expand the stuff that we're doing, keeping hold of that culture, like that culture of constantly solving difficult problems and being able to innovate and scale them. So we are now doing a lot more automations across the department, across different benefits, across the digital agenda. I think we're also now becoming almost a bit of the fabric of enabling some of the digital transformation that big organizations look at, right? So moving to a world where you can have a venture of an architectures and being able to sort of scale that. I also think the natural sort of expansion of the team and the type of work that we're gonna do is probably also going to expand into sort of the analytics side of it and understanding and seeing how we can take the data from the cases that we're processing to overall have a smoother journey across our citizens. But it's looking, you know, the future is looking bright. I think we've got a number of different backlogs of items to work on. Well, you've got a great story to tell and thank you for sharing it with us here on theCUBE. Talking about DWP, the Department of Work and Pensions in the UK and the great work that Accenture is doing to make 20 million lives plus a lot simpler for our friends in England. You've been watching theCUBE, the AWS Executive Summit sponsored by Accenture.