(bright music) >> Announcer: 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 as he leads the team, the Business Automation offering management team driving the automation platform altering multicloud 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 peel back all the announcements and automation which is data process, transformation, innovation scale, all kind of points to eigth 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, interesting observations as we look back over the year. I called this the AD for BC before COVID and AD, the Anno, not the Anno Domini, but Anno Damum 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 this demand on the supply side we're seeing competition. More competition in the automation space. And I believe any company that's got more than two guys in a go in the back in a 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 this competition from you know, two guys in the garage or you know, 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 right? So, you know, there's an underlying hybrid cloud kind of model that's been standardized. Now you have all these things now on top thousand flowers, blooming or apps, if you will more apps and more apps, more apps, less of the kind of like CRM, like the... you're going to have sub systems 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. >> What'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 mighty we've got this perfect opportunity for rejiggering, 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 then you want to prove. Second big thing is you want to make sure that you start with the data. Just like any, 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 them 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 that 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. >> Yeah, sure. A good example. There's a, there's a client of ours in Morocco and it's not a big country but it's a very interesting story. They, the company is called CDG Prevoyance. CDG Prevoyance, this is a, it's a French company, obviously. That was my French accent. But there they are a company that does pension benefits. So think of this as you're putting money away, you're in 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. It was a very paper-based business. Extremely paper-based. Like the forms that you had to fill out. The way that you engage with, with CDG was was a very form-based thing. Like document based thing. They, 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 on 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, Old companies change. The companies that you engage with digitally are the ones that give you that kind of experience where it doesn't, you know 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 the, 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, you know, where the pilgrims landed, and this, this, this whole voyage is going to be done without human interaction. It's all going to 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 the car down a highway. >> So this is an autonomous ship, then. >> This is an autonomous ship. Exactly. So think of this as there is there's nobody piloting this thing. It's all piloted by software. The software is, 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, you know, steer in the lanes. Different from steering a car you steer a car between the two white lines. You know, you might have a dashed line here and a white line here. You steer the car to come in 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, you know, potential this, potential that. Like this, this thing is really exciting. I find this whole data, AI decisioning, fascinating. >> Dave, Dave Alonzo is going to love this next question I'm going to ask you. He's my co-host of theCUBE. You always talk about data lakes. How about data ocean? Now we have a data ocean out here which I've always used the metaphor ocean so much more dynamic, but here literally the data is the ocean. You got to factor in conditions that are going to be completely dynamic, wave height, countermeasures on, 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... >> No, it's so it's all driven so it starts with the sensors, the sensor, 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 you know a boat comes or a whale comes. 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, 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, you know the person on the left has the, has the priority. If you're overtaking, you have to steer clear. All those kind 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 say, stay out of my Harbor. Very interesting. >> It actually is. It actually encapsulates a lot of business challenges too. You have a lot of data mashing up going on. I mean, you've got navigation, what's under the water. What's on top of the water. You got weather data over the top. It's good to own the weather company for IBM. That helps probably a lot. Then you've got policies, you know? And policy based decision-making. It sounds like a data center and multicloud opportunity. >> It is exactly. That's why I love this opportunity because it's, it's it's almost the, the complete stall from being a business problem to being an experiment problem. Because the way that these, these guys, these engineers built this thing, they're, they're looking for research. They're looking for the ability to really press that edge of where AI and uh you know, machine learning and decisioning come together with ocean research, because what they're doing is social research. They're looking for water temperature and whales and that kind of stuff. >> Unmanned vehicles, unmanned drones is another another big thing we're seeing that with, with, from 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. I want to get back to the the announcement that you guys made a while back but I want to stay on this point real quick. If you can just comment. Business leaders that are curious around automation, really the ones that have to invent this. Think about the autonomous ship. On top of 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, so I think the, the advice is that you look for areas of opportunity, like be, be discreet and be like just choose the thing that you want to go after. In the, in the Mayflower case what they were doing was they were looking for a way to navigate in the Harbor. Opens, you've got this big wide 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. Then pick adjacent things, go fix that. And eventually it gets to the point where you have straight through processing, which is where everybody wants to get. >> I can imagine great opportunities for you guys and your team. Congratulations on all that work. 'Cause there's certainly more to do. I can see so much happening as you guys are building out the stack and acquiring companies. You know, last month you guys had announced to acquire process mining company, myInvenio. 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, take us through the, the what this acquisition means for IBM. >> Sure. So myInvenio is a, is a business. First, just get the facts. myInvenio is a business and it's a 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. Companies spend noodles of money on procure to pay as an example. But inevitably there are humans in that, in that process humans means that there are ways to become more efficient. You could change a person's job. You can change a person's profile. All of that is what this tool is about. This, this tool gives us an excellent addition to our portfolio, our 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 myInvenio means to us. It puts us in a position of having a complete set of technologies that match up with Gartner's hyper automation market texture. That gives us a very powerful advantage in the marketplace. So I'm very, very happy about this acquisition. >> Yeah. Ed, thanks for coming on theCUBE. Really appreciate it. Final word. I'd love to get you spend the last minute just talking about IBM's commitment to open and also integration um, integrating with other companies. Take a minute to explain that. >> Yeah, sure. So the, the, the open part is something that we've understood for very, 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. You have, 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 inter-operate with other people, other, you know 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 are really looking for. They want to have the flexibility to move. They want to have the flexibility to put the best, you know 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, Ed, thanks for coming on theCUBE. I really appreciate it. >> Okay, John. Thank you. >> Okay. Cube coverage of IBM Think 2021, virtual launch. I'm John Furrier, your host of theCUBE. Thanks for watching. (bright music)

>> Announcer: 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 as he leads the team, the Business Automation offering management team driving the automation platform altering multicloud 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 peel back all the announcements and automation which is data process, transformation, innovation scale, all kind 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, interesting observations as we look back over the year. I called this the AD for BC before COVID and AD, the Anno, not 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 this demand on the supply side we're seeing competition. More competition in the automation space. And I believe any company that's got more than two guys in a go in the back in a 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 this competition from you know, two guys in the garage or you know, 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 right? So, you know, there's an underlying hybrid cloud kind of model that's been standardized. Now you have all these things now on top thousand flowers, blooming or apps, if you will more apps and more apps, more apps, less of the kind of like CRM, like the... you're going to have sub systems 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. >> What'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 almighty we've got this perfect opportunity for rejiggering, 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 then you want to prove. Second big thing is you want to make sure that you start with the data. Just like any, 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 them 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 that 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. >> Yeah, sure. A good example. There's a, there's a client of ours in Morocco and it's not a big country but it's a very interesting story. They, the company is called CDG Prevoyance. CDG Prevoyance, this is a, it's a French company, obviously. That was my French accent. But there they are a company that does pension benefits. So think of this as you're putting money away, you're in 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. It was a very paper-based business. Extremely paper-based. Like the forms that you had to fill out. The way that you engage with, with CDG was was a very form-based thing. Like document based thing. They, 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 on 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, Old companies change. The companies that you engage with digitally are the ones that give you that kind of experience where it doesn't, you know 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 the, 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, you know, where the pilgrims landed, and this, this, this whole voyage is going to be done without human interaction. It's all going to 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 the car down a highway. >> So this is an autonomous ship, then. >> This is an autonomous ship. Exactly. So think of this as there is there's nobody piloting this thing. It's all piloted by software. The software is, 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, you know, steer in the lanes. Different from steering a car you steer a car between the two white lines. You know, you might have a dashed line here and a white line here. You steer the car to come in 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, you know, potential this, potential that. Like this, this thing is really exciting. I find this whole data, AI decisioning, fascinating. >> Dave, Dave Alonzo is going to love this next question I'm going to ask you. He's my co-host of theCUBE. You always talk about data lakes. How about data ocean? Now we have a data ocean out here which I've always used the metaphor ocean so much more dynamic, but here literally the data is the ocean. You got to factor in conditions that are going to be completely dynamic, wave height, countermeasures on, 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... >> No, it's so it's all driven so it starts with the sensors, the sensor, 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 you know a boat comes or a whale comes. 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, 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, you know the person on the left has the, has the priority. If you're overtaking, you have to steer clear. All those kind 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 say, stay out of my Harbor. Very interesting. >> It actually is. It actually encapsulates a lot of business challenges too. You have a lot of data mashing up going on. I mean, you've got navigation, what's under the water. What's on top of the water. You got weather data over the top. It's good to own the weather company for IBM. That helps probably a lot. Then you've got policies, you know? And policy based decision-making. It sounds like a data center and multicloud opportunity. >> It is exactly. That's why I love this opportunity because it's, it's it's almost the, the complete stall from being a business problem to being an experiment problem. Because the way that these, these guys, these engineers built this thing, they're, they're looking for research. They're looking for the ability to really press that edge of where AI and uh you know, machine learning and decisioning come together with ocean research, because what they're doing is social research. They're looking for water temperature and whales and that kind of stuff. >> Unmanned vehicles, unmanned drones is another another big thing we're seeing that with, with, from 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. I want to get back to the the announcement that you guys made a while back but I want to stay on this point real quick. If you can just comment. Business leaders that are curious around automation, really the ones that have to invent this. Think about the autonomous ship. On top of 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, so I think the, the advice is that you look for areas of opportunity, like be, be discreet and be like just choose the thing that you want to go after. In the, in the Mayflower case what they were doing was they were looking for a way to navigate in the Harbor. Opens, you've got this big wide 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. Then pick adjacent things, go fix that. And eventually it gets to the point where you have straight through processing, which is where everybody wants to get. >> I can imagine great opportunities for you guys and your team. Congratulations on all that work. 'Cause there's certainly more to do. I can see so much happening as you guys are building out the stack and acquiring companies. You know, last month you guys had announced to acquire process mining company, myInvenio. 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, take us through the, the what this acquisition means for IBM. >> Sure. So myInvenio is a, is a business. First, just get the facts. myInvenio is a business and it's a 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. Companies spend noodles of money on procure to pay as an example. But inevitably there are humans in that, in that process humans means that there are ways to become more efficient. You could change a person's job. You can change a person's profile. All of that is what this tool is about. This, this tool gives us an excellent addition to our portfolio, our 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 myInvenio means to us. It puts us in a position of having a complete set of technologies that match up with Gartner's hyper automation market texture. That gives us a very powerful advantage in the marketplace. So I'm very, very happy about this acquisition. >> Yeah. Ed, thanks for coming on theCUBE. Really appreciate it. Final word. I'd love to get you spend the last minute just talking about IBM's commitment to open and also integration um, integrating with other companies. Take a minute to explain that. >> Yeah, sure. So the, the, the open part is something that we've understood for very, 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. You have, 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 inter-operate with other people, other, you know 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 are really looking for. They want to have the flexibility to move. They want to have the flexibility to put the best, you know 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, Ed, thanks for coming on theCUBE. I really appreciate it. >> Okay, John. Thank you. >> Okay. Cube coverage of IBM Think 2021, virtual launch. I'm John Furrier, your host of theCUBE. Thanks for watching. (bright music)