 From around the globe, it's theCUBE with digital coverage of IBM Think 2021, brought to you by IBM. Welcome back to theCUBE coverage of IBM Think 2021. I'm John Furrier, host of theCUBE. We're here with Ed Lynch, Vice President of IBM Business Automation. Topic here is AI powered business automation. Ed leads the team, the business automation offering management team driving the automation platform through multi-cloud and built-in AI and low-code tools. Ed, thanks for joining me on theCUBE today. Thank you, John, thanks for having me. So automation is really the focus of this event. If you appeal back all the announcements, automation, which is data, process, transformation, innovation, scale, all kinds of points to automation. How has the past year changed the automation market? It's been a fascinating ride. Fascinating ride more than just the COVID part, but some interesting observations as we look back for the year. I called us the AD for BC before COVID and AD, the Anno Domini, but Anno Domuo, meaning year of the house, living in the house. The thing that we really learned is that clients are engaging differently with their, let's say the companies that they work with. They're engaging digitally, not a big surprise. You look at all of the big digital brands. You look at the way that we engage, we buy things from home, we don't go to the store anymore, we get delivery at home, work from home, completely different. If you think about what happened to the business on the business side, work from home changed everything. And the real bottom line is companies that invested ahead of time in automation technology, they've flourished. The companies that didn't, they're not so flourishing. So we're seeing right now, we're seeing skyrocketing demand. That's bonus for us, skyrocketing demand. And also, that's the demand side on the supply side, we're seeing competition, more competition in the automation space, and to believe any company that's got more than two guys in a goat in the back in the basement are entering the automation space. So it's a fun time. It's a really fun time to be in this space. Great validation on the market, great call out there on the whole competition thing. Cause you really look at that, this competition from two guys in the garage or early stage startup, but the valuations are an indicator, it's a hot market. Most of those startups have massive valuations, even the pre IPO ones are just like enormous valuations. This is a tell sign that process automation and digital supply chains, value chains, business is being rewritten with software. So there's an underlying hybrid cloud kind of model that's been standardized. Now you have all these things now on top, you know, a thousand flowers blooming or apps, if you will, more apps and more apps, more apps, less of the kind of like CRM like that. You're going to have subsystems, large subsystems, but you're going to have apps everywhere, everything's an app now. So this means things have to be re-automated. Yeah, that's your advice for companies trying to figure this out. So my advice is start small, like one of the big temptations is that you can jump in and say, God, you might have, we've got this perfect opportunity for re-jiggering, rebuilding the entire company from scratch. That's a definition of insanity. Like you don't want to do that. What you want to do is you want to start small and you want to prove. Second big thing is you want to make sure that we start with the data, just like any good management system, you have to start with the facts. You have to discover what's going on, you have to decide which piece you're going to focus on and then you have to act. And then act leads to optimization. Optimization allows you to say, I'm looking at a dashboard, I'm making progress or I'm heading in the wrong direction, stop. Those kinds of things. So start small, start with the data and make sure that you line up your allies. You have to have, this is a culture change then, you have to have your CEO lined up from the top and you have to have buy-in from the bottom. If any of those pieces are missing, you're asking for trouble. Can you share an example of a customer of yours that's using intelligent automation? Take me through that process and what's the drivers behind it? Yeah, sure. A good example, there's a client of ours in Morocco and it's not a big country, but it's a very interesting story. The company is called CDG Prevoyance. CDG Prevoyance, it's a French company obviously, notice my French accent, but they are a company that does pension benefits. So think of this as you're putting money away, you're in the US, you have 401ks in Canada, we have RSPs, you're putting money away for the future. And the company that you're putting money into has to manage your account along with millions of other accounts. And this is where CDG started, was a very paper-based business, extremely paper-based, like the forms that you had to fill out, the way that you engage with CDG was a very form-based thing, like document-based thing. The onboarding time to actually enter a new account for a new employee looking to get their pension plan done was weeks. With automation, they changed from being a paper-based thing to being an electronic-based thing, they changed the workflow associated with gathering information, getting them onboarded. They onboard now in minutes, as opposed to weeks. This is an example of the kind of thing. Now, if you go back to the first question that you asked, how have companies changed? The companies that you engage with digitally are the ones that give you that kind of experience where you don't have to crawl through broken glass in order to engage with them. That's what CDG did. And they managed to really ring out some of the human labor out of that onboarding process. Great, great stuff. You know, this Mayflower is an exciting story I've been checking out and they're using this decisioning together with you guys with automation. Can you tell me about that? Mayflower is really exciting. This is one of those things that just jazzes me. It jazzes me because I think to myself, how the heck did they do that? So the Mayflower is a boat. It's like a sailing vessel like any other sailing vessel. It's 15 meters long. It's powered entirely by solar. It's making a voyage from England to Plymouth, the landing place where the pilgrims landed. And this whole voyage is gonna be done without human interaction. It's all gonna be powered by the machine. So you think about autonomous vehicles. You think about this whole story of autonomous vehicles. Piloting across the ocean is way different than piloting a car down a highway. So this is an autonomous ship then. This is an autonomous ship, exactly. So think of this as there's nobody piloting this thing. It's all piloted by software. The software is my business software, interestingly. It has all these sensors that allow you to say, oh, there's a boat over there, steer clear of the boat. But more importantly, when you come to the harbor, you have to negotiate the marks. You have to steer in the lanes. Different from steering a car, you steer a car between the two white lines. You might have a dashed line here and a white line here. You steer the car to the middle, very easy. Steering a boat, that's really hard. Steering a boat in the middle of the ocean, when you've got monstrous waves and you've got potential this, potential athlete. This thing is really exciting. I find this whole data, AI, decisioning fascinating. Dave, Dave Vellante is going to love this next question. I'm going to ask you, but he's my co-host of theCUBE. You always talk about data lakes. How about data ocean? Now we have a data ocean here, which I've always used the metaphor ocean, so much more dynamic. But here, literally, the data is the ocean. You've got to factor in conditions that are going to be completely dynamic. Wave height, countermeasures on navigation. All this is being done. Is that, how does it all work? I mean, has it all been driven by data scenarios? I mean, what? No, so it's all driven. So it starts with the sensors. The sensors, you have a vision sensor that tells you what it sees. So it sees boats and it sees marks. It sees big waves coming. It's all powered by weather data. So there is a weather feed. But more importantly, like the sailing across the ocean part, you don't have to worry other than when a boat comes or a whale comes that you steer clear of it. Fine, that part's relatively easy. When you come close to the shore, then you have to make decisions about where to go. And the decisions are all informed by data. So you gather all this data. You run machine learning algorithms against the data. You run a decision priorities mechanism. And then you have to confer with the rules. Like what are the rules of navigation? I don't know if you're a sailor, but the rules of navigation on the open sea are actually really simple to understand because it's the person on the left has the priority. If you're overtaking, you have to steer clear, all those kinds of things. In a harbor, it's way different. And so you have to be able to demonstrate to the government that you have open decisions, an open decision making mechanism to steer around the marks. The government wants to know that you can do that. Otherwise, they stay out of my harbor. Very interesting. It actually encapsulates a lot of business challenges too. You have a lot of data mashing up going on. I mean, you got navigation, what's under the water, what's on top of the water. You got weather data over the top. Good to own the weather company for IBM, that helps probably a lot. Then you got policies, policy-based decision making. It sounds like a data center and multi-cloud opportunity. It is exactly, that's why I love this opportunity because it's almost the complete distort from being a business problem to being an experiment problem. Because the way that these guys, these engineers built this thing, they're looking for research. They're looking for the ability to really press that edge of where AI and machine learning and decisioning come together with ocean research. Because what they're doing is ocean research. They're looking for water temperature and whales and that kind of thing. Yeah, unmanned vehicles, unmanned drones is another big thing. We're seeing that from managing this. This brings up the point I see about leaders in the industry and I know we don't have a lot of time. You want to get back to the announcement that you guys made a while back. But I want to stay on this point real quick but you can just comment. Business leaders that are curious around automation, we're really the ones that have to invent this because if you think about the autonomous ship, how about the autonomous business? I mean, here at theCUBE we have a studio. What about autonomous studio work? So the notion of automation, if you're not thinking about it, you can't do it. What's your advice to people? So I think the advice is that you look for areas of opportunity. Like be discreet and be like, just choose the thing that you want to go after. In the Mayflower case, what they were doing was that we're looking for a way to navigate in the harbor. You've got this big white ocean, you can go wherever you want to. Navigating in the harbor is much trickier. And so what they did was they applied technology, very specific pieces of technology to that specific problem. That's the advice that I would give to a business. Don't look to turn everything upside down. That's craziness. Like you're in business for a reason. What you want to do is you want to pick a specific thing to go after and go and fix that and pick adjacent things, go and fix that. And eventually you'll get to the point where you have straight through processing, which is where everybody wants to get. I can imagine a great opportunity for you guys and your team. Congratulations on all that work. There's certainly more to do. I can see so much happening as you guys are building out the stack and acquiring companies. Last month, you guys had announced to acquire Process Mining Company, My Invia. What does that announcement mean for IBM and the AI powered automation because you guys also have business deals with others in the industry. Take us through what this acquisition means for IBM. Sure, so My Invia is a business. First, just get the facts. My Invia was a business and it's a company that's based in Italy. They do what's called Process Mining. Process Mining is a tool that does what I was just talking about. It allows you to identify places where you have weakness in your workflows. Workflows like big macro workflows, like procure to pay, the ability to go all the way from buying something to paying for it. Companies spend oodles of money on procure to pay as an example. But inevitably there are humans in that process. Humans means that there are ways to become more efficient. You could change a person's job, you could change a person's profile. All of that is what this tool is about. This tool gives us an excellent addition to our portfolio or automation portfolio, which allows clients to understand where the weaknesses are. And then we can apply specific automations to fix those weaknesses. That's what My Invia means to us. It puts us in a position of having a complete set of technologies that match up with Gartner's hyper automation architecture. That gives us a very powerful advantage in the marketplace. So I'm very, very happy about this acquisition. Great, Ed, thanks for coming on the Cube. Really appreciate it. Final word, I'd love to get you to spend the last minute just talking about IBM's commitment to open and also integration, integrating with other companies. Take a minute to explain that. Yeah, sure. So the open part is something that we've stood for very long time. One of the jobs that I had a long time ago was open source and bringing open source into IBM. I'm a very strong proponent of open source. Open means no barriers to entry, no barriers to substitution. And what it means is you have a fair fight. We all have proprietary technology. We all have intellectual property. Sure, but if you have an open base, then what that gives you is the ability to interoperate with other people, other competitors, frankly. That to me is goodness for the client because at the end of the day the client doesn't get locked in. That's the thing that they're really looking for. They want to have the flexibility to move. They want to have the flexibility to put the best technology in place. So we are strong proponents of open. All right, Ed Lynch, Vice President of IBM Business Automation, AI-powered business automation is coming. Autonomous vehicles, autonomous ships, autonomous business, everything's going automation soon. We're going to have the autonomous cube. And so, thanks for coming on the cube. I really appreciate it. Thank you. Thank you. Cube coverage of IBM Think 2021 Virtual. I'm John Furrier, your host of theCUBE. Thanks for watching.