In the latest episode of the Futurum Tech Podcast, The Interview Series, Daniel Newman welcomes three experts to talk about robotic process automation (RPA) and intelligent automation (IA). Francis Carden is one of the founders of OpenSpan and is currently VP of Digital Automation and Robotics at Pegasystems. Lee Coulter is CEO of transformAI, and Jon Gilman is CEO of Clear Software. Together, they gave Dan some great insight on the future of RPA and IA.
To start off, they defined RPA as a method of automating existing work, such as rote work people have been doing on their own for years. Think of all the tasks Amazon’s Alexa does now for people, chores that we once had to do ourselves, manually navigating across systems without her help. That’s an example of RPA now.
And while you can use AI to help RPA, you really don’t need to for most tasks, as actual RPA is rules-based task automation. For example, reconciling bank statements in a finance department is a perfect job for RPA, as it’s a rules-based process that can be automated without human involvement. This is where RPA excels.
On the other hand, IA takes different tools in the RPA toolbelt and creates an exoskeleton to help humans get things done faster. IA is necessary when there are lots of paths to go down, requiring some input from humans. For instance, at a call center, there are lots of triggers that could change how a customer service rep helps people. In this situation, a chatbot integration that can take a customer’s phone number and look up information would help get the job done faster.
The three guests went on to explain how they want to get people to stop using RPA as a catch-all term. After all, RPA is essentially a tool inside the toolbelt of intelligent automation. It’s a tactical way to automate tasks, while IA is orchestration of work. And that’s important, since it doesn’t matter if a human or robot does the work, as long as it’s orchestrated from end to end.
During the interview, Francis, Lee, and Jon mentioned that some of the issues with RPA vendors is that they’re saying their products are low code, but they’re not actually replacing the underlying systems. So the best thing to come out of RPA is bringing to life the recognition of how poor some of these systems are, which means maybe we can finally upgrade them and get on the IA journey!
After all, RPA can only get you so far, which many organizations are now realizing. They used it for simple tasks like resetting passwords. Now they want to do bigger things, like using it for a call center. They need orchestration, which should be a single digital experience to be true intelligent automation.
So, what’s next for IA? Well, according to Jon, getting customers into IA is going to be a big effort. It will take a while to have all processes automated, as a huge amount of business process reengineering needs to happen. So, we can’t really claim that in 10 years we’ll have automated 90% of processes. But in the next 20 to 25 years, a huge amount of front office jobs will have disappeared due to automation, leaving people to forget about the mundane tasks that can be automated and focus more on the tasks that require human involvement.
If you want to learn more about what Jon, Francis, and Lee discussed with Dan concerning RPA and IA, be sure to listen to the podcast today!
Daniel Newman: Welcome to the Futurum Tech Podcast, FTP. I’m Daniel Newman, your host, and today’s special edition, we’re going to look at the evolution of RPA, talking to some experts with a ton of background going way back in the time machine. Just kidding, they’re not that old talking about RPA, where this business is heading. We’re going to get all kinds of different background and we really hope you’re going to have some fun here. So strap on in.
Before I do introduce our guests, I do have to remind everybody that this show is for information and entertainment purposes only. So while we might be talking about companies in this industry that are publicly traded, we are not soliciting or providing any sort of investment advice. We’ve got three outstanding guests with a ton of background on the topic of RPA, all of its associated technologies, which we will get into a little bit as automation, AI, and other evolutions, and we may say even revolutions are taking place around the technology. But before we go into our questions, let’s just do a quick set of introductions here. First, I’m going to have a Francis Carden introduced himself. Francis, welcome to the show.
Francis Carden: Hey, thank you. Thanks for having me. So I’m Francis Carden. I was one of the founders of a company called OpenSpan, which I guess was one of the early RPA vendors, but our product was not called RPA at the time. It was called Desktop Automation, but ultimately it’s around, I mean, this is where it all started around automating the user interfaces on the desktop. And so really I’ve been doing that, I guess. Yeah, talk about old probably for about 35 years, where we’re trying to optimize the very hands and eyes for the keyboard, and the mouse, and the screen of the humans so they can be more efficient and sometimes automate everything they do so you actually don’t need the human in the loop at all. And other times where we’re just optimizing the work, they do get work done faster, smarter, but through the existing user interfaces,
Daniel Newman: Well, it’s really good to have you on the show, Francis. And for everyone out there, he may also have been at one time called the godfather of RPA, but we’ll let you decide if that’s actually the case. Next up, Lee Coulter. Lee, welcome to the show.
Lee Coulter: Thank you. So, Lee Coulter, currently CEO of TransformAI, also the Chair of the IEEE Working Group on Standards and Intelligent Process Automation. I’ve been in the world of business process transformation, shared services, BPO, and automation for about the last 20 years. Currently trying to get our third standard released in working group. Francis is a member of that working group and really happy to be here.
Daniel Newman: Yeah, that’s a serious project, getting standards pushed through no matter what industry it’s in and IEEE is a very important one. So thanks, Lee, for joining the show. Jon Gilman. Jon, welcome to the Futurum Tech Podcast.
Jon Gilman: Thanks. I’m Jon Gilman, CEO of Clear Software. We created an intelligent process automation platform that’s really focused on helping large organizations streamline big, heavy, end-to-end processes like order to cash, and call center operations. And it was really born of the work we used to do in our prior lives as consultants at Accenture and Deloitte. So we would implement massive ERP systems like SAP and Oracle eBusiness Suite, and PeopleSoft. And ultimately what we found is our customers weren’t getting the return on investment that they were promised with these systems. So we built the platform on top of initially SAP just to make people faster at their jobs, very similar to what Francis’s background is, and over time that’s evolved into process automation for just about any technology platform out there.
Daniel Newman: Yeah, it’s a very interesting one. We’ve been tracking it, what you guys are doing over there. As an analyst and founded a research company, the RPA space has been really noisy over the last say two, three years. I mean, before that it was certainly humming and that’s why your backgrounds go back decades, and not years. It’s kind of like AI. People are like, “Oh, AI is new.” AI is not new. This conversation’s been evolving for a long time. Work is being done in this space for a long time.
But I do think we’ve sort of hit an inflection point, and I’d like to start off kind of talking about a little bit of the foundation, but I want to quickly kind of move to this inflection point of where RPA is going to go as things like automation and AI become much, much more everyday topics for almost any business, in any industry across the planet. So Francis, since you are “the godfather” I’m only going to say that twice in this show, so I don’t get to do it again, of RPA, you’ve been doing this since the 80s and by the way, I was born in the 80s.
Jon Gilman: So was I.
Francis Carden: Old, yeah.
Daniel Newman: So how would you, so how would you-
Francis Carden: I used to be the young guy.
Daniel Newman: Yeah, well I’m 81, so I’m old eighties if that makes you feel better. So how would you describe RPA to the average person? We take acronyms for granted in our industry, but how would you describe it to someone?
Francis Carden: Well, put me in an elevator and tell me to do it in a minute, we’ll be going up to about the 150th floor because this takes a while, but let me try. So think of it like, what was life like before Alexa? You had to run around and make sure you turned all the lights off. Although, in my family sometimes they don’t know we have an off switch.
But run around, turn the lights on and off. You have to open the garage doors, set the heating, set the alarm, unset the alarm, do all this stuff. And Alexa, you just ask Alexa, and she goes do it, right. And if you think about now, you can even group those automations together, you say, “Hey, I’m leaving the house.” And then Alexa will just do everything in one go. Think about the tens of millions of people sitting at their desk asking to work with all these different applications.
It might be an ERP, it might be SAP, it might be Office, it might be Outlook, all that stuff. We are manually navigating across, our users are manually navigating those different systems, and actually we’re paying them to be the pre-Alexa, the bashing away at the keyboard and the mouse. And so what RPA has tried to do is to say what if we could automate some or all of that bashing away at the keyboard, and the mouse, and the screen.
It’s a lot of rote work. But if you could do that, those people not only make less errors, but you also going to make them faster, and in some cases you don’t need them at all. So RPA is about automating the existing stuff. Now it’s got a bit carried away with itself because now, people are saying RPA can do anything. Well that’s predominantly what it was. You can use AI to help RPA, but RPA really doesn’t need AI pretty much automating rote work that a human does, that’s entirely managed through a well-documented process. Well, hopefully a well-documented process. Often it’s not. Is that a good summary? That makes sense.
Daniel Newman: I think it’s a good start. I think it’s a good start. No, I think that’s terrific.
Francis Carden: Very simplistic.
Daniel Newman: Yeah. Anytime you associate it to Alexa or someone’s iPhone, it instantly makes sense. If you could do a Facebook analogy that might even help people more, but we’ll-
Francis Carden: I’m not on Facebook. I’m too old for that.
Daniel Newman: Oh no, don’t say that. Don’t say that. Actually, I only see old people on Facebook.
Francis Carden: There you go.
Daniel Newman: That’s the trend. So that’s where we’re heading. So Lee, I’m going to, I’m going to throw a question your way. I think you are the chairman. It says, I want to make sure I got that right, but you are the chairman of the committee of the Institute of Electronics Engineer’s Standards. IEEE would have been easier, but that’s okay. And we’re involved in the defining of what’s now intelligent automation. Talk about intelligent automation and how it’s differing from RPA. I think this is that inflection point that I was really trying to get at.
Lee Coulter: Yeah, it’s a great question. And I was on a personal crusade about five years ago for people to stop using RPA as the catchall term. It was invented by Blue Prism when they were trying to figure out how to describe what their product was, and it’s really, RPA is technically server-based, unattended task automation, rules based RDA, robotic desktop automation is human in the loop client server. So it’s desktop and server based interactive assisted automation, and intelligent automation incorporates predictive or prescriptive analytics, or narrow augmented intelligence services for intelligent orchestration of tasks, or the performance of engaging with unstructured data for the purpose of making dynamic or contextual decisions. There’s a lot of a $10 words in there, but hopefully that was relatively straight forward.
Daniel Newman: No, I think it is. I think maybe putting this into perspective is the key right now, which is Jon, I’d love to throw it kind of your way, because with Clear Software and what you’re doing is implementing it. And I remember the first time I met you, I’m like, “Oh, you’re doing RPA.” And you were kind of like, “Well, not really.” But really, and I remember we kind-
Jon Gilman: I think I cringed when he said that, but.
Daniel Newman: Yeah, I think you did. But I think the problem is, is once something becomes sort of the known, you have to be careful as a company, because you start trying to redefine terminologies before the market accepts it. Then what ended up happening is nobody knows what to buy from you. So you guys have kind of been walking that line because what you’re doing really isn’t, “RPA”, it’s more this intelligent automation. But at the same time, when you want to be found in Google search, or you want someone to read and quickly understand what you do, you’re kind of shadowboxing with RPA versus IA. So talk a little bit about the real world and real life and the implementation of intelligent platforms looks like. Maybe some real life examples.
Jon Gilman: Yeah. I think to kind of compare what we see in the marketplace for RPA versus it would help. So a real life example in the RPA world would be a lot of things in back office finance. So you think about reconciling bank statements in your finance department, or trying to apply cash against receivables. Those are largely rules-based processes that are driven by the underlying systems that they live in, and they can be automated. And there’s not a whole lot of different paths that you can go down when you’re trying to apply a payment against the receivable, or you’re trying to determine where a transaction on a bank statement goes to a certain general ledger account.
All of that can primarily be automated, and doesn’t really need a whole lot of human involvement. And that’s really where RPA has excelled, is back-office, autonomous tasks that don’t require human input. But where IA comes into the picture, and really kind of what we’re doing, is taking these different tools inside the intelligent automation tool belt, and using them together to sort of create an exoskeleton for humans to get their processes done faster. So probably the best example that folks on the street would understand is within the call center. So there are all kinds of different inputs and triggers into the automation process that can help a customer service rep that’s handling a phone call with a customer much more quickly. So when you think about chat bot integration, like a Microsoft chat bot allows you to essentially take a customer’s phone number, and go look up their customer information in SAP, or in Salesforce, or in Oracle.
You may have some NLP technology that’s going to help you navigate through some frequently asked questions. You may have things like buttons that may invoke a very complex process in the back-end ERP system. But at the end of the day, what you’re trying to do is allow that customer service rep to get their jobs done much faster. So, one, you don’t have angry customers that are sitting on the phone with a Comcast, or Peoples Gas for 45 minutes. So you have happier customers and you have folks that don’t need a computer science degree to figure out how to handle a customer inquiry. So that’s kind of the differentiation between IA and RPA, is that RPA is really kind of a tool inside the tool belt that I like to call intelligent automation.
Francis Carden: Oh, I agree. I tend to side with Daniel, I tend to side with Jon a lot on this as well. So RPA is very clearly what it is, and when you talk about intelligent automation, there are multiple terms out there. iBPMS, digital process automation, digital IPA, hyper automation now Gartner talking about. All of those technologies I think underneath them is probably 15, 20 different technologies, including RPA, that make up how you can optimize your business across the business. RPA tended to be very tactical, back office, just try to automate the as-is. In fact, Gartner gives the, it’s a bit of a tax on legacy because you’re not replacing anything. And what Jon was talking about is much more about the orchestration of work and not just one tool is going to solve the problem if you want to live in a digital world.
Daniel Newman: Yeah, I think that makes a lot of sense. I think there’s a lot of challenges to using RPA in sort of a modern architecture of the enterprise and enterprise IT, right. So when companies were running software applications on a mainframe that had very little turnover and change building some “bots” that were able to automate processes, it could be fairly stable, but that’s changed a lot. And so Francis, I’m going to actually have you build on what you were just saying because PEGAs got to be working on this too. I mean you’ve got these new evolutions, you have cloud, right. Cloud native applications. You have what people are calling artificial intelligence, or the utilization of machine learning, and deep learning to improve algorithms and improve automated tasks and processes. So I’m sure PEGA’s working on some of this stuff. Talk a little bit about how PEGA’s kind of evolving products that you brought into the company when you joined, or that were there when you joined, to today.
Francis Carden: Yeah. And I think that I’ll start off by just being a little bit counter to the IEEE on the standards, because I think the RPA vendors are seeing a resurgence. We started with RBA, as Lee pointed out. Now the other RPA vendors are saying that’s human in the loop, but it’s not. What human in the loop is actually somebody on a desktop instigating a bot on the server. Whereas our RBA, or what we’ve now being known as attended RPA where if a robot is sitting on the desktop, it’s almost like robot in the loop. If humans working away and our robot kicks off, but right there on the desktop there’s no additional server or hardware to have to put in place. But we’ve got customers where they’ve got 35,000 users each with their own robot, and it takes over at the right time just to optimize those processes.
And so it’s one of the reasons that Alan Trefler actually acquired PEGA because of our unique deep robotics. And I know that I don’t mean to be selling to your audience, but I think there’s a clear demarcation between human in the loop and RPA attended robot in the loop. It’s massively different and it scales. But the good news is everybody’s jumping into that part of it. So what we’ve done at PEGA is taken those two technologies and intertwined them, if you like, with the point Jon was making, which I think is summed up by orchestration. I don’t care whether a robot does the work, or a human does the work, or AI takes over some the work, or whatever it is. That needs to be orchestrated from end to end, ultimately solving a customer problem to get to some kind of outcome. Give them a mortgage, or tell them they can’t have a mortgage, give them a new file, whatever it might be, right.
And in the digital world, the consumers I know don’t use Facebook, but the consumers, the millennials expect almost instant gratification. They want to get an insurance quote, or if they want to get their car fixed, because they’ve been in a smash, they expect that near instantaneous, because they don’t want to sit on the phone for an hour and a half, which we will use to have to do when we were subservient to some of these telcos and et cetera. And so what PEGA’s been doing is taking all the best of breed of all these technologies from low-code, which allows you to build applications really quickly, ultimately to maybe never even need RPA. In fact, one of our customers says for every robot they build, they have an end of life strategy for it, because they actually ultimately want to get it digital, right. And now if you start adding in things like a pass to that low-code is the ability to expose that application to any channel or any user interface.
But the citizen developer doesn’t have to think about where that process might be exposed to, because it’s Facebook today but it might be Marmite and cheese tomorrow, right. And so you literally have got this whole orchestration going on, which is the backbone of PEGA, or in their case management system. And it can dip in and out of the right UI, or the right AI, or the right RPA, or even the right, as you said, cloud. [inaudible] In certain industries they still want to be on premise. And to me that’s intelligent automation. And as I said, I think Forrester did the best initially, at the start of this when they called it digital process automation. They actually called out the end of the digital transformation is dead. It needs to be redefined because it’s an overused word. I think there are many overused terms, but there were about 20 technologies that make up true intelligent automation. Orchestrate that, get it done, and you’re in a different league.
Daniel Newman: Yeah. And I think getting to that league is kind of the race right now, in the industry as a whole. I think a lot of companies are rolling out different things. I actually think the market is starting to agree. My assessment would be that the market is starting to agree that RPA is an old term. But it’s funny because it only really happened in the last 12 months.
Francis Carden: You’re right. It’s a good point. I think that maybe it’s less, but it’s an old term, because it used to be called screen scraping. But what frustrates me is that they’re trying to call it intelligent automation. It’s not. It’s part of intelligent automation, but it’s not the intelligent automation.
Daniel Newman: Well, and then part of that depends is did they actually change anything? Go ahead Jon.
Lee Coulter: It’s the battle between the marketers and the technologists, and the fact that the simple reality is that the term RPA has become a catchall. To Francis’s point, when you’re able to initiate a process with automation, engage with unstructured data, make dynamic or contextual decisions and complete a process end to end, there’s a lot of different pieces involved in getting that to happen. And I think what we’re seeing is a convergence where the objective becomes to get the, we call it stretching the use case, to the point where you’re really removing a piece of work, or a role from the enterprise, or demonstrably improving the efficiency or effectiveness of a whole roll in a business.
And there are things that are just going to be, in 10 years time, they just won’t exist as a component of enterprise labor, and Francis’s point on low-code and no-code, and being able to deploy those with enterprise resilience that’s happening today. And those that started with the most simplistic tools are moving toward the more sophisticated capabilities, and those that began with the highly sophisticated capabilities are looking to make those as simple to deploy and easy to consume at the enterprise level. So it’s an exciting time for, we’ll just use the term automation.
Francis Carden: I think Lee, Lee, you raised another good point, right? And what do you think of this? Is that the RPA vendors now saying their products are low-code, and yet not a single RPA vendor actually lives in the low-code quadrant. And the reason is, is because they actually, and it’s marketing, to your point about marketing, they believe low-code is what I would have classed as a visual IDE, right, like Visual Basic or even, or Macro. And I get so frustrated with the RPA guys saying, Oh, it’s now intelligent RPA, or it’s now low-code RPA, and it’s like at the end of the day that’s screen scraping.
It’s not replacing the underlying application or the systems you’re getting optimization. But if you keep wrapping, and wrapping, and wrapping the old stuff, eventually it’s going to go pop. And I think the best thing to come out of RPA is it’s bringing to life the recognition about how poor some of these processes are, and leading organizations to intelligent automation say, “Okay, can we at loss get rid of them once and for all?” Not overnight, necessarily.
Sometimes it is, but we need to be on the intelligent automation journey and borrow robots to plug gaps.
Lee Coulter: You and I have a long standing chat about the value of entering an application through the user interface. When you have somewhere your initiative is somewhere on the IT priority list, and you have access to APIs, and we’ll call it more sophisticated support that allows you to engage with applications electronically. With a lot of cases, the back office is somewhere 88th on the list of priorities, and making a self-serve option available to the business to help them automate their own stuff, I think will continue to have a place of value in the enterprise. Call it screen scraping, I think it’s a bit more sophisticated than that in a lot of cases, but clearly, the more elegant permanent solution is with APIs, but it’s always an option.
Jon Gilman: Yeah, and I think the key point there is that RPA can only get you so far, and I think a lot of organizations have started to realize that said, “Hey, yeah, great. We used it in back office finance, we used it in IT to reset passwords. But now we want to do bigger stuff. We want to do call center, we want to do order to cash. What’s next?” I need that orchestration and orchestration should be, at least in my opinion, I’m very biased, but it should be a single digital experience. So you need BPM capabilities in order to enable that. So to another pet peeve like Francis mentioned, when I hear RBM vendors talking about how they have attended automation and it’s really, it’s seven applications running on the desktop. You still have Salesforce open, you still have SAP open, but you have a bot runner running in the corner. That’s really a Rube Goldberg experiment. That’s not true, intelligent automation. So if you want true intelligent automation, I personally believe it’s got to be a single digital experience.
Daniel Newman: Yeah, I think Jon, you definitely are looking at what needs to happen. I think you guys are kind of saying the same thing, but the business has to lead, not the technology. And so people are building technology that can do something. But as I always say, just because you can, doesn’t always mean you should. There’s a lot of that kind of technology right now. So as you said, some bot running in the corner, but it’s not really making people better at work. In the end, it’s really about making people better at work. In my latest book, it was called Human Machine, and we kind of came up with these three concepts of a big brother, big mother, and big butler basically being the three premises of these human-machine partnerships. It’s to surveil, it’s to guide, and it is to enable.
And so intelligent automation really needs to be big butler. It needs to be enabling people to do their jobs better. It needs to be enabling organizations to work more efficiently. You guys being out there in this every day and I’ve got a couple of questions, I got to tie this together so that people can, if they’re on their treadmill their run is about to end. If they’re on their Peloton, their class is almost over, and you shouldn’t be listening to this and your instructor at the same time. Either way, whatever it is you’re doing. I do want to get a couple more things out of you guys. Lee, as this IA space is continuing to evolve, and like I said, we’re kind of debating. Is it IRPA, IA? Is it IPA, Jon, I know you like IPA. What are your customers asking for now? What are they really starting to ask for that they weren’t asking for in the last few years?
Lee Coulter: Yeah, that’s a great question, and it gets to this notion of stretching the use case. So if you look at service initiation, or process initiation, it’s a phone call, it’s an email, it’s a document. And if you look at the use cases of three to five years ago, every time you put a process up there and you say, “I’m going to automate this.” You end up getting a chainsaw out. And everywhere that you have a contextual decision to make for orchestration, or you engage with some sort of unstructured data, you trim that off with a chainsaw, you just automated the bits of it that were their tasks. So what’s really happening today, and I think we’ve heard it in different ways, is people are seeking for end-to-end processes that can make high confidence decisions for orchestration, and when they encounter unstructured data can go deal with that.
Whether it’s voice to text, sentiment, OCR, OIR to be able to do these things on an end to end basis. And I think that’s really where the IA space is going. It’s intelligent because it’s contextual, it actually understands it has some knowledge of where it is, and what additional information might be needed to get the process to complete successfully without bringing a human into the loop. And it’s capable of doing all of those things that are still very, very mundane but require what has traditionally been the need for human cognition. And we use OCR just as a simple example, because everybody can wrap their brain around that one. But that’s really, I think, at the end it’s end-to-end, to end to include unstructured and contextual decision making.
Daniel Newman: Yeah, I mean that’s really the crutch of where we’re at today and really where it’s going, because that’s the funny thing about this tech Lee, and to your point is that what people are starting to ask for? I think Andy Jassy says as well at AWS all the time, he says we meet the customer where they are. And this industry though is kind of interesting because it’s right in the middle. The customers don’t always know where they want to go, and they don’t really understand what’s possible. And that’s really where the folks like all of you need to really be providing that value is what can be done. What can I really do for a business to help usher in the next era of whatever transformation, and whatever term you want to use?
So Jon, I want to kind of end this with you. And of course after Jon answers, Lee and Francis, feel free to kind of chime in on this, but let’s just do the lightning round question here. Where do you see intelligent automation going in the future, Jon?
Jon Gilman: Well, I’m a contrarian, and, well, so are Lee and Francis, so I hope we agree on something in this question. But you know there are a lot of starry eyed futurists out there that will tell you that everything’s going to be automated within five to 10 years, and every one of those starry-eyed futurists has been wrong, 100% of the time. I mean we were told that we were going to have fully autonomous cars by 2020, that everybody was not going to be driving anymore, and it’s 2020 and I’m still driving a car. So I tend to take a little bit more of a jaded approach because I’m sitting there with my customers every day understanding their business processes. Getting them to intelligent automation is a massive effort. So I think there’s a lot of folks out there who haven’t gone down this road yet who are thinking, Oh, I’m just going to bring in RPA.
I’m going to bring in a little bit of ML and AI, and six months later I’m going to have all of my processes automated, or at least semi-automated. And it’s just not true. It’s just not a realistic expectation. There’s a huge, huge, huge amount of business process re-engineering that needs to happen before you can even think about using anything within the IA space. We come into a lot of our customers, and they don’t execute the same process the same way across different offices within the US, or across different geographies or even two people sitting at desks right next to each other are doing a process a different way.
So you have to be able to execute your processes consistently the same way across your organization before you can even think about putting RPA, or anything on top of that process. So there’s a huge effort to get that done. So I don’t want to say that within 10 years we’re going to have automated 90% of the front office processes, because I know that’s just not true. Organizations have to figure out how to comb their hair and tie their shoes consistently the same way before we can do that. So, I think definitely within the next 20 to 25 years we’re going to see a huge percentage of front office jobs disappear. That’s going to take some time. That’s not going to be within the next 10 years.
Daniel Newman: All right, so contrarians, let’s give the floor back to Francis and Lee. Do you agree with Jon and kind of any adds to this whole where is it going to go in the next let’s say one year, five years tops?
Lee Coulter: We’ll let Francis wrap us up. I’ll just add a couple of dimensions that Jon touched on. The business has to lead. What is fundamentally different in automation is that it’s digital labor, and IT enables, it doesn’t drive. This is not a typical IT project. It’s changing the way the enterprise operates. It bears a lot of similarity to the continuous improvement era in the eighties and nineties, and if we had more time, I’d dive deeper into Polanyi’s paradox, which is the very reason that this is not going to transform the world the next five years. The reality of Gartner’s adoption curve with the early majority, late majority and laggers is very, very, very true. I read recently that 85% of the automation market remains untapped. I would offer it’s more than that, but the message that I want to leave here is business has to drive, and it’s about process transformation, and those are not easy things. Those are difficult for an enterprise.
Daniel Newman: I love that you said that because I feel like I said that, Lee, so the business has to drive. So Francis take us home.
Francis Carden: All right. Listen, we’re in agreement and we say things differently. But I think generally I think it’s a lot faster than people think. I think that one of the biggest benefits of intelligent automation is actually bringing that business and IT alignment that is so critical for the future generation of applications, automation, and development. I’ve been writing about IT and business misalignment for the last 30 years, because it’s hard to bring those two very different groups together. It’s just been impossible, and everybody knows that. But if you could imagine sitting down with business where they can actually describe in a low-code model, or a no-code model, what they want, and from that generate an application that’s completely governed underneath the covers by IT, you are building applications to the point that Jon was making. You can build these applications in days and weeks.
Now, it takes longer for bigger applications, but we’ve had customers that have taken 53 versions of the same application, bear in mind that comes with all the costs and maintenance that’s involved around that, down to one. And so the general idea over the last 35 years is that we’ve been building on a platform of object disorientated programming. Nothing was reusable, everything was copied, and customized, and copied again, and customized again. And we’ve got this plethora of absolute monstrous applications out there. Some of them are great applications that work, but if you look underneath the covers, it’s kind of like there’s been a lot of rainwater and some of them are getting ready to fall into sink holes. You can’t keep wrapping them with RPA. So low code, to me, is going to be the on, I call it a digital revolution because I think people are underestimating these technologies that have come up, and absolutely surpassing all this tactical stuff. And if you look around at all the players in the low-code space, which is not the RPA vendors, it is becoming that digital revolution for business, and I’m really excited what the next five years holds for us.
Daniel Newman: Yeah, it’s going to be an exciting ride. There’s no question about it. I really do see the convergence of forces. You’re seeing some of the biggest companies on the planet starting to talk a lot more about automation, and building it into what they’re doing. It’s really kind of the marriage of platforms, infrastructure of software. And so it’s been really a wonderful opportunity for myself as someone that say it, keeping a track in the industry to talk to all three of you.
I want to thank you guys. So Lee, Francis, and even Jon. No, especially Jon. I want to thank all three of you for joining us here on the future of tech podcast. It was a really, really fun show. It was a great for everyone out there. I hope everybody got a lot of value out of this. Go ahead, check out the show notes. We’re going to go ahead and put the links to all three of their companies and organizations so you can track, follow, and learn more about them. But for this episode of the Futurum Tech Podcast, I’m Daniel Newman and I am signing off. We’ll see you all very soon.
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