>>Bly from Boston, Massachusetts. It's the cube covering ifs world conference 2019 brought to you by ifs. >>Okay. We're back in Boston, Massachusetts ifs world day one. You walked into cube Dave Vellante with Paul Gillen boss Devoss is here. He's the director of ISF I F S labs and Bob Dico who's the vice president of AI and RPA at ifs jets. Welcome. Good to see you again. Good morning bossy. We're on last year. I'm talking about innovation ifs labs. First of all, tell us about ifs labs and what you've been up to in the last 12 months. Well, I have has Lapsis a functioning as the new technology incubator. Fire Fest writes over continuously looking at opportunities to bring innovation into, into product and help our customers take advantage of all the new things out there to yeah. To, to create better businesses. And one of the things I talked about last year is how we want to be close to our customers. And I think, uh, that's what we have been doing over the pasta pasta year. Really be close to our customers. So Bob, you got, you got the cool title, AI, RPA, all the hot cool topics. So help us understand what role you guys play as ifs. As a software developer, are you building AI? Are you building RPA? Are you integrating it? Yes, yes. Get your paint. >>I mean, our value to our customers comes from wrapping up the technology, the AI, the RPA, the IOT into product in a way that it's going to help their business. So it's going to be easy to use. They're not going to need to be a technical specialist to take advantage of it. It's going to be embedded in the product in a way they can take advantage of very easily that that's the key for us as a software developer. We don't want to offer them a platform that they can just go and do their own thing. We want to sort of control it, make it easier for them. >>So I presume it's not a coincidence that you guys are on together. So this stuff starts in the labs and then your job is to commercialize it. Right? So, so take machine intelligence for example. I mean it can be so many things to so many different people. Take us back to sort of, you know, the starting point, you know, within reason of your work on machine intelligence, what you were thinking at the time, maybe some of the experiments that you did and how it ends up in the product. Oh, very good question. Right? So I think we start at a, Oh, well first of all, I think ifs has been using a machine learning at, at various points in our products for many, many years of Trumbull in our dynamic scheduling engine. We have been using neural networks to optimize fuel serve scheduling for quite some many years. >>But I think, um, if we go back like two years, what we sold is that, uh, there, there's a real potential, um, in our products that if you will take machine learning algorithms inside of the product to actually, um, help ultimately certain decisions in there, um, that could potentially help our business quite a bit. And the role of ifs lapse back in the day as that we just started experimenting, right? So we went out to different customers. Uh, we started engaging with them to see, okay, what kind of data do we have, what kind of use cases are there? And basically based on that, we sort of developed a vision around AI and a division back in the day was based on on three important aspects, human machine interaction optimization and automation. And that kind of really lended well with our customer use case. We talked quite a bit about that or the previous world conference. >>So at that point we basically decided, okay, you know what, we need to make serious work of this, uh, experimenting as boots. But at a certain point you have to conclude that the experiments were successful, which we did. And at that point we decided to look at, okay, how can we make this into a product and how to normally go system. We started engaging with them more intensively and starting to hand over in this guys, we decided the most also a good moment to bring somebody on board that actually has even more experience and knowledge in AI and what we already had as hive as labs. But that could basically take over the Baton. And say, okay, now I am going to run with it and actually start commercializing and productizing that still in collaboration with IVIS laps. But yeah, taking that next step in the road and then then Bob came onboard. >>Christian Pedersen made the point during the keynote this morning that you have to avoid the, the appeal of technology for technology's sake. You have to have it. I start with the business use case. You are both very technology, very deep into the technology. How do you keep disciplined to avoid letting the technology lead your, your activities? >>Well, both. Yeah. So, so I think a good example is what we see this world's going fronts as well. It is staying closer to customer and, and, and accepting and realizing that there is no, um, there's no use in just creating technology for sake of technology as you say yourself. So what we did here for example, is that we showcase collaboration projects with, with customers. So, for example, we show showcase a woman chair pack, which um, as a, as a manufacturing of spouting pouches down here in Massachusetts actually, uh, and they wanted to invest in robotics to get our widows. So what we basically did is actually wind into their factory literally on the factory floor and start innovating there. So instead of just thinking about, okay, how do robotics and AI for subrogations or one of our older products work together, we set, let's experiment on the shop floor off a customer instead of inside of the ivory towers. Sometimes our competitors to them, they'll start to answer your question. >>Sure. I can pick up a little, a little feasible. Yeah. Well, so in, I think the really important thing, and again, Christian touched on it this morning is not the individual technologies themselves. It's how they work together. Um, we see a lot of the underlying technologies becoming more commoditized. That's not where companies are really starting to differentiate algorithms after a while become algorithms. There's a good way of doing things. They might evolve slightly over time, but effectively you can open source a lot of these things. You can take advantage, the value comes from that next layer up. How you take those technologies together, how you can create end to end processes. So if we take something like predictive, we would have an asset. We would have sensors on that asset that would be providing real time data, uh, to an IOT system. We can combine that with historical maintenance data stored within a classic ERP system. >>We can pull that together, use machine learning on it to make a prediction for when that machine is gonna break down. And based on that prediction, we can raise a work order and if we do that over enough assets, we can then optimize our technicians. So instead of having to wait for it to break down, we can know in advance, we can plan for people to be in the right the right place. It's that end to end process where the value is. We have to bring that together in a way that we can offer it to our customers. There's certainly, you know, a lot of talk in the press about machines replacing humans. Machine of all machines have always replaced humans. But for the first time in history, it's with cognitive functions. Now it's, people get freaked out. A little bit about that. I'm hearing a theme of, of augmentation, you know, at this event. >>But I wonder if you could share your thoughts with regard to things like AI automation, robotic process automation. How are customers, you know, adopting them? Is there sort of concern up front? I mean we've talked to a number of RPA customers that, you know, initially maybe are hesitant but then say, wow, I'm automating all those tasks that I hate and sort of lean in. But at the same time, you know, it's clear that this could have an effect on people's jobs and lives. What are your thoughts? Sure. Do you want to kick off on them? Yeah, I'll know. Yeah, absolutely. That's fine. So I think in terms of the, the automation, the low level tasks, as you say, that can free up people to focus on higher value activities. Something like RPA, those bots, they can work 24, seven, they can do it error free. >>Um, it's often doing work that people don't enjoy anyway. So that tends to actually raise morale, raise productivity, and allow you to do tasks faster. And the augmentation, I think is where it gets very interesting because you need to, you often don't want to automate all your decisions. You want people to have the final say, but you want to provide them more information, better, more pertinent ways of making that decision. And so it's very important. If you can do that, then you've got to build the trust with them. If you're going to give them an AI decision that's just out of a black box and just say, there's a 70% chance of this happening. And what I founded in my career is that people don't tend to believe that or they start questioning it and that's where you have difficulty. So this is where explainable AI comes in. >>I do to be able to state clearly why that prediction is being made, what are the key drivers going into it? Or if that's not possible, at least giving them the confidence to see, well, you're not sure about this prediction. You can play around with it. You can see I'm right, but I'm going to make you more comfortable and then hopefully you're going to understand and, and sort of move with it. And then it starts sort of finding its way more naturally into the workplace. So that's, I think the key to building up successful open sexually. What it is is it's sort of giving a human the, the, the parameters the and saying, okay, now you can make the call as to whether or not you want to place that bet or make a different decision or hold off and get more data. Is that right? >>Uh, yeah. I think a lot of it is about setting the threshold and the parameters with within which you want to operate. Often if a model is very confident, either you know, a yes or a no, you probably be quite happy to let it automate. Take that three, it's the borderline decision where it gets interesting. You probably would still want someone to look over it, but you want them to do it consistently. You want them to do it using all the information to hand and say that's what you do. You're presented to them. And to add to that, um, I think we also should not forget they said a lot of our customers, a lot of companies are, are actually struggling finding quality stuff, right? I mean aging of the workforce riots, we're, we're old. I'm retiring eventually. Right? So aging of the workforce is a potential issue. >>Funding, lack of quality. Stop. So if I go back to the chair pack example I was just talking about, um, and, and, and some of the benefits they get out of that robotics projects, um, um, is of course they're saving money right there. They're saving about one point $5 million a year on money on that project, but their most important benefits for them, it's actually the fact that I have been able to move the people from the work floor doing that into higher scope positions, effectively countering the labor shortage today. They were limited in their operations, but in fact, I had two few quality stuff. And by putting the robots in, they were able to reposition those people and that's for them the most important benefits. So I think there's always a little bit of a balance. Um, but I also think we eventually need robots. >>We need ultimation to also keep up with the work that needs to be done. Maybe you can speak to Bobby, you can speak to software robots. We've, Pete with people think of robots, they tend to think of machines, but in fact software robots are, where are the a, the real growth is right now, the greatest growth is right now. How pervasive will software robots be in the workplace do you think in the three to five years? >> I think the software robots as they are now within the RPA space, um, they fulfill a sort of part of the Avril automation picture, but they're never going to be the whole thing. I see them very much as bringing different systems together, moving data between systems, allowing them to interact more effectively. But, um, within systems themselves, uh, you know, the bots can only really scratched the surface. >>They're interacting with software in the same way a human would on the whole by clicking buttons going through, et cetera, beneath the surface. Uh, you know, for example, within the ifs products we have got data understanding how people interact with our products. We can use machine learning on that data to learn, to make recommendations to do things that our software but wouldn't be able to see. So I think it's a combination. There's software bots, they're kind of on the outside looking in, but they're very good at bringing things together. And then insight you've got that sort of deeper automation to take real advantage of the individual pieces of software. >> This may be a little out there, but you guys >>are, you guys are deep into, into the next generation lot to talk right now about quantum and how we could see workable quantum computers within the next two to two to three years. How, what do you think the, the outlook is there? How is that going to shake things up? So >>let me answer this. We were actually a having an active project and I for slabs currently could looking at quantum computing, right? Um, there's a lot of promise in it. Uh, there's also a lot of unfilled, unfulfilled problems in that, right? But if you look at the, the potential, I think where it really starts playing, um, into, uh, into benefits is if the larger the, the, the optimization problems, the larger the algorithms are that we have to run, the more benefits it actually starts bringing us. So if you're asking me for an for an outlook, I say there is potential definitely, especially in optimization problems. Right. Um, but I also think that the realistic outlook is quite far out. Uh, yes, we're all experimenting it and I think it's our responsibility as ifs or ciphers laps to also look on what it could potentially mean for applications as we FSI Fs. >>But my personal opinion is the odd Lucas. Yeah. So what comes five to 10 years out? What comes first? Quantum computing or fully autonomous driverless vehicles? Oh, that's a tricky question. I mean, I would say in terms of the practical commercial application, it's going to be the latter in that much so that's quite a ways off. Yeah, I think so. Of course. Question back on on RPA, what are you guys exactly doing on RPA? Are you developing your own robotic process automation software or are you integrating, doing both say within the products? We, you know, if we think of RPA as, as this means of interacting with the graphical user interface in a way that a human would within the product. Um, we, we're thinking more in terms of automating processes using the machine learning as I mentioned, to learn from experience, et cetera. Uh, in a way that will take advantage of things like our API eighth, an API APIs that are discussed on main stage today. >>RPA is very much our way of interacting with other systems, allowing other systems when trapped with ifs, allowing us to, to send messages out. So we need to make it as easy as possible for those bots to call us. Uh, you know, that can be by making our screens nice and accessible and easy to use. But I think the way that RPA is going, a lot of the major vendors are becoming orchestrators really. They're creating these, these studios where you can drag and drop different components into to do ACR, provide cognitive services and you know, elements that you could drag and drop in would be to say, ah, take data from a file and load it into ifs and put it in a purchase order. And you can just drag that in and then it doesn't really matter how it connects to YFS. It can do that via the API. And I think it probably will say it's creating the ability to talk to ifs. That's the most important thing for us. So you're making your products a RPA ready, friendly >>you, it sounds like you're using it for your own purposes, but you're not an RPA vendor per se. You know what I'm saying? Okay. Here's how you do an automation. You're gonna integrate that with other RPA leadership product. I think we would really take a more firm partner approach to it. Right? So if a customer, I mean, there's different ways of integrating systems to get our RPA as a Google on there. There's other ways as well, right? That if a customer actually, um, wants to integrate the systems together using RPA, very good choice, we make sure that our products are as ready as much for that as possible. Of course we will look at the partner ecosystem to make sure that we have sufficient and the right partners in there that a customer has as a choice in what we recommends. But basically we say where we want to be agnostic to what kind of RPA feminists sits in there that was standing there was obviously a lot of geopolitical stuff going on with tariffs and the like. >>So not withstanding that, do you feel as though things like automation, RPA, AI will swing the pendulum back to onshore manufacturing, whether it's Europe or, or U S or is the costs still so dramatically advantageous to, you know, manufacture in China? Well, that pendulum swing in your opinion as a result of automation? Um, I have a good, good question. Um, I'm not sure it's will completely swing, but it will definitely be influenced. Right. One of the examples I've seen in the RPA space ride wire a company before we would actually have an outsourcing project in India where people would just type over D uh, DDD, the purchase orders right now. Now in RPA bolts scans. I didn't, so they don't need the Indian North shore anymore. But it's always a balance between, you know, what's the benefit of what's the cost of developing technology and that's, and it's, and, and it's almost like a macro economical sort of discussion. >>One of the discussions I had with my colleagues in Sri Lanka, um, and, and maybe completely off topic example, we were talking about carwash, right? So us in the, in the Western world we have car wash where you drive your car through, right? They don't have them in Sri Lankan. All the car washes are by hands. But the difference is because labor is cheaper there that it's actually cheaper to have people washing your car while we'd also in the us for example, that's more expensive than actually having a machine doing it. Right. So it is a, it's a macro economical sort of question that is quite interesting to see how that develops over the next couple of years. All right, Jess. Well thanks very much for coming on the cube. Great discussion. Really appreciate it. Thank you very much. You're welcome. All right. I'll keep it right there, but he gave a latte. Paul Gillen moved back. Ifs world from Boston. You watch in the queue.