 Live from Boston, Massachusetts, it's theCUBE, covering IFS World Conference 2019, brought to you by IFS. We're back in Boston, Massachusetts, IFS World Day One. You're watching theCUBE, Dave Vellante with Paul Gillin. Boss DeVos is here, he's the director of IFS Labs and Bob DeCoe, who's the vice president of AI and RPA at IFS. Gents, welcome, good to see you again. Good morning. You were on last year, 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, IFS Labs functions as the new technology incubator for IFS, right? So we're continuously looking at opportunities to bring innovation into product and help our customers take advantage of all the new things out there 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 that's what we have been doing over the past year, really be close to our customers. So Bob, you got the cool title, AI, RPA, all the hot, cool topics. Yeah, we'll be good, nice. 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, paint a picture for us. 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'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'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 all together. So this stuff starts in the labs and then your job is to commercialize it, right? 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 the starting point 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. Well, very good question, right? So I think we started, well, first of all, I think IFS has been using machine learning at various points in our products for many, many years. For example, in our dynamic scheduling engine, we have been using neural networks to optimize field service scheduling for quite some many years. But I think if we go back like two years, what we saw is that there's a real potential in our products that if you would take machine learning algorithms inside of the product to actually help automate certain decisions in there, that it could potentially help our business quite a bit. And the role of IFS labs back in the day is that we just started experimenting, right? So we went out to different customers. 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 that vision back in the day was based on three important aspects, human machine interaction, optimization, and automation. And that kind of really landed well with our customer use case. We talked quite a bit about that at the previous world conference. So at that point, we basically decided, okay, you know what, we need to make serious work of this. Experimenting is good, but at a certain point, you have to conclude that the experiments are successful, which we did. And at that point, we decided to look at, okay, how can we make this into a product? And how that normally goes is that we start engaging with them more intensively and starting to hand over. In this case, we decided there was also a good moment to bring somebody on board that actually has even more experience and knowledge in AI than what we already had as IFS 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 IFS labs, but yeah, taking that next step in the road. And then Bob came on board. Christian Patterson made the point during the keynote this morning that you have to avoid the appeal of technology for technology's sake. You have to have, it has to start with the business use case. You're both very technology, very deep into the technology. How do you keep disciplined to avoid letting the technology lead your activities? Well, may I Bob? Yeah, absolutely. So I think a good example is what we see at this world conference as well. It is staying close to the customer and accepting and realizing that there is no, there's no use in just creating technology for the sake of technology as you say yourself. So what we did here, for example, is that we showcase collaboration projects with customers. So for example, we showcase one with SharePak, which is a manufacturing of spouted pouches down here in Massachusetts, actually. And they wanted to invest in robotics together with us. So what we basically did is actually went into their factory, literally on the factory floor, and start innovating there. So instead of just thinking about, okay, how do robotics and IFS applications or one of our other products work together? We said, let's experiment on the shop floor of a customer instead of inside of the ivory tower, as sometimes our competitors do. Does that answer your question? I think it does. I can pick up a little further. Yeah, I'd love to see some other examples too. Well, so 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. 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 maintenance, we would have an asset, we would have sensors on that asset that would be providing real-time data 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 going to 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 place. It's that end-to-end process that's where the value is. We have to bring that together in a way that we can offer it to our customers. There's certainly a lot of talk in the press about machines replacing humans. Machines have always replaced humans, but for the first time in history, it's with cognitive functions now. So people get freaked out a little bit about that. I'm hearing a theme of augmentation 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 adopting them? Is there sort of concern up front? I mean, we've talked to a number of RPA customers that 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, 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 that? Yeah, I'll know if you can kick off. Yeah, absolutely, that's fine. So I think in terms of 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 24-7, they can do it error-free. 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. The augmentation, I think, is where it gets very interesting because 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, that 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, just say there's a 70% chance of this happening, what I've found 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. Either being able to state clearly why that prediction's 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 that I'm right, but I'm going to make you more comfortable, and then hopefully you're going to understand 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 augmenting. Is that essentially what it is? It's sort of giving a human the parameters, the probabilities, 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? Yeah, I think a lot of it is about setting, the thresholds and the parameters within which you want to operate. Often, if a model is very confident, either yes or a no, you'd probably be quite happy to let it automate, take that through. It's the borderline decision where it gets interesting. You probably 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 so that's what you would do, you present it to them. Yeah, and to add to that, I think we also should not forget, is that a lot of our customers, a lot of companies are actually struggling finding quality stuff, right? I mean, aging of the workforce, right? We're all retiring eventually, right? So aging of the workforce is potentially finding lack of quality stuff. So if I go back to the cheer pack example I was just talking about, and some of the benefits they get out of that robotics project is, of course, they're saving money, right? They're saving about $1.5 million a year on money on that project, but their most important benefit for them is actually the fact that they have been able to move the people from the workflow doing that into higher skilled positions effectively countering their labor shortage. They were limited in their operations, but in fact they had too few quality stuff. And by putting the robots in, they were able to reposition those people, and that's for them the most important benefit. So I think there's always a little bit of a balance, but I also think we eventually need robots, we need automation to also keep up with the work that needs to be done. Maybe you can speak to Bobby, you can speak to software robots. When people think of robots, they tend to think of machines, but in fact software robots are where 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 three to five years? I think the software robots, as they are now within the RPA space, they fulfill a sort of part of the overall 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 within systems themselves, the bots can only really scratch 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, for example, within the IFS product, 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 a software bot wouldn't be able to see. So I think it's a combination. The software bots are kind of on the outside looking in, but they're very good at bringing things together. And then inside, 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 deep into the next generation. I want to talk right now about quantum and how we could see workable quantum computers within the next two to three years. What do you think the outlook is there? How is that going to shake things up? Yeah, let me answer this. We're actually having an active project in IFS Labs currently, looking at quantum computing, right? There's a lot of promise in it. There's also a lot of unfilled, unfulfilled problems in there, right? But if you look at the potential, I think where it really starts playing into benefits is if the larger the optimization problems, the larger the algorithms are that we have to run, the more benefits it actually starts bringing us. So if you ask me for an outlook, I say there is potential, definitely, especially in optimization problems, right? But I also think that the realistic outlook is quite far out. Yes, we're all experimenting it and I think it's our responsibility as IFS or as IFS Labs to also look on what it could potentially mean for applications as we have as IFS. But my personal opinion is the outlook is, yeah. So what comes first? At least five to 10 years out, right? What comes first? Quantum computing are 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. I think they're much closer to each other. Okay, so that's quite a ways off. Yeah, yeah, yeah, I think so. Question back on RPA, what are you guys exactly doing on RPA? Developing your own robotic process automation software? Are you integrating doing both? So within the product, if we think of RPA as this means of interacting with the graphical user interface in a way that a human would, within the product, we're thinking more in terms of automating processes, using the machine learning, as I mentioned, to learn from experience, et cetera, in a way that will take advantage of things like our open APIs that were discussed on the main stage today. RPA is very much our way of interacting with other systems, so allowing other systems to interact with IFS, allowing us to send messages out. So we need to make it as easy as possible for those bots to call us. 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 studios where you can drag and drop different components in to do OCR, provide cognitive services, and elements that you could drag and drop in would be to, say, take data from a file and load it into IFS and put it in a purchase order, and you could just drag that in. And then it doesn't really matter how it connects to IFS. It can do that via the API, and I think it probably will. So it's creating the ability to talk to IFS that's the most important thing for us. So you're making your products RPA-ready, friendly. Exactly. It sounds like you're using it for your own purposes, but you're not an RPA vendor per se. You're not saying, okay, here's how you do an automation. You're going to integrate that with other RPAs' leadership products. I think we would really take a more of a partner approach there, right? So if a customer, I mean, there's different ways of integrating systems together. RPA is a good one there. There's other ways as well, right? That if a customer actually 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 part of the ecosystem to make sure that we have sufficient and the right partners in there that a customer has a choice in what we recommend. But basically, we say we want to be agnostic to what kind of RPA vendor sits in there. Notwithstanding, there was obviously a lot of geopolitical stuff going on with tariffs and the like. So notwithstanding that, do you feel as though things like automation, RPA, AI will swing the pendulum back to onshore manufacturing, whether it's Europe or US, or is the cost still so dramatically advantageous to, you know, manufacturing China? Will that pendulum swing, in your opinion, as a result of automation? Good question. I'm not sure it will completely swing, but it will definitely be influenced, right? One of the examples I've seen in the RPA spaces, right? Where a company before would actually have an outsourcing project in India where people would just type over the purchase orders, right? Now an RPA bot scans it in, so they don't need the Indian offshore anymore. But it's always a balance between, you know, what's the benefit, what's the cost of developing technology? And it's almost like a macroeconomical sort of discussion. One of the discussions I had with my colleagues in Sri Lanka, and maybe completely off topic example, we were talking about car wash, right? So us 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 hand. But the difference is, because labor is cheaper there, that it's actually cheaper to have people washing your car while with us in the U.S., for example, that's more expensive than actually having a machine doing it, right? So it is a macroeconomical sort of question. That's quite interesting to see how that develops over the next couple of years. All right, Chess, well, thanks very much for coming on theCUBE. Great discussion, really appreciate it. Thank you very much. All right, you're welcome. All right, keep it right there, everybody. Dave Vellante, Paul Gellin. We'll be back, IFS World, from Boston. You're watching theCUBE.