mit Ulrich Irnich & Markus Kuckertz

Shownotes

Episode 60 explores the potential and challenges of human-centred AI with Alan Trefler, founder and CEO of Pegasystems, a global leader in CRM and business process automation (BPA). Under Alan’s leadership, Pegasystems has become a powerhouse serving some of the world’s largest organisations by streamlining operations and improving customer experiences through AI-driven platforms.

Alan joins Uli and Markus to discuss what it really means to put people at the centre of AI, and how it is reshaping the relationship between technology and people. As organisations deepen their AI integration, the conversation explores how a human-centric approach can improve customer interactions, internal processes and employee satisfaction.

The discussion goes beyond the buzzwords to address the real-world implications and responsibilities of implementing AI. Alan shares insights into how his company is using AI to create meaningful connections in business, focusing on supporting and empowering people rather than replacing them. Practical examples will show how AI projects are reshaping industries and customer engagement, with ethical considerations in mind.

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Contributors – Hosts: Ulrich Irnich & Markus Kuckertz // Editorial: Marcus Pawlik © Digital Pacemaker Podcast 2024

Zusammenfassung

In this episode of the Digital Placemaker Podcast, we explore the transformative potential of artificial intelligence and business process automation with Alan Treffler, the founder and CEO of Pegasystems. With over four decades of industry experience, Alan is at the forefront of leveraging AI to enhance customer relationship management and streamline digital processes. We dive into the conversation regarding the critical evolution of workforce dynamics in the face of AI adoption. Alan emphasizes that the genuine challenge lies not within AI itself, but rather in the businesses that fail to adapt and harness AI’s advantages effectively.

Alan articulates his vision that AI and automation should enhance human capabilities rather than replace them. He argues that successful integration of AI is about empowering staff to innovate and make better decisions. The fear surrounding AI often stems from sensational media narratives focused on job losses, which only cloud the reality that businesses leveraging AI strategically will thrive. We examine the essential role of human creativity as a catalyst for decision-making in an increasingly automated business landscape, contrasting it with the common misconceptions propagated through various media outlets.

The discussion transitions into the intricacies of business process automation, revealing Alan’s insights into how organizations can better design AI systems to complement human roles. He shares anecdotes from his vast experience, including how statistical AI can inform decision-making and improve customer service by analyzing behavioral patterns. Additionally, he introduces the concept of “Blueprint,” a revolutionary tool enabling businesses to redefine their operations using AI, allowing even non-experts to conceptualize processes across diverse industries. This innovative application illustrates the profound capability of AI not merely to automate but to fundamentally reshape how businesses function.

Throughout the episode, Alan provides tangible examples from Pegasystems’ clientele, showcasing how integrating machine learning and statistical analysis can lead to smarter, more responsive business practices. He reflects on the fundamental need for organizations to embrace change—understanding that AI’s true potential lies in enhancing operational efficiency and customer experience while simultaneously challenging existing business paradigms.

As the conversation progresses, the emphasis shifts towards the cultural underpinnings necessary for successful digital transformations. Alan highlights that a flexible and innovative culture is pivotal for the effective adoption of new technologies. He candidly shares that to foster such an environment, organizations must eradicate detrimental business incentives that promote short-term thinking, urging leaders instead to cultivate a culture that prioritizes sustainable practices and long-term vision.

In conclusion, Alan leaves listeners with a powerful reminder of the significance of perspective in our interactions with technology. He encourages aspiring professionals to maintain an outward focus and embrace the serendipitous nature of innovation, advocating for a blend of strategic foresight and adaptability in navigating the rapidly evolving landscape of technology and business. This dialogue not only illuminates the challenges and opportunities posed by AI but also serves as a blueprint for organizations seeking to thrive in an increasingly digital world.

Transkript

Speaker0:[0:00] Is not AI, or the people in the business is not AI. The real threat to the workers in a business is that a competing business that knows how to use AI will have its people become the winners compared to businesses that don’t adopt AI and try to continue to do things the old-fashioned way. It’s about the people working with the AI to change the business and learn from it. We’ll be right back.

Music:[0:24] Music

Speaker0:[0:40] Welcome to the Digital Placemaker Podcast. It’s Ulrich Ehrnich and me, Markus Kokkatz. Uli, how are you today? I’m very fine. It’s getting a bit colder. The days are getting, let’s say, smaller. The light gets lower, right? So, autumn and winter is coming. Winter is coming. Today, we are excited to dive into the future of artificial intelligence and business process automation with none other than Alan Treffler, the founder and CEO of Pegasystems. Alan, it’s a pleasure to have you on the show. Thank you. It’s a pleasure to be here and look forward to hitting some good topics. Alan Treffler is a true pioneer in the field of business process automation and customer engagement technology. He’s the founder and CEO of Pegasystems, a global leader in software solutions for customer Relationship Management and Digital Process Automation, which he established over 40 years ago. Under his leadership, Piger Systems has grown into a powerhouse that serves many of the world’s largest organizations, helping them streamline operations and improve customer experiences through innovative AI-driven platforms. Beyond his professional achievements, Alan is a former world champion in chess, which speaks to his strategic mindset and passion for solving complex problems. Alan, we have had the opportunity to prepare and learn about you. Today, we will discuss There’s a few key ideas you’ve brought to the table. You say AI and automation should be designed to augment human capabilities rather than replace them, ensuring that businesses harness technology to empower employees and improve decision-making, not reduce room and walls.

Speaker0:[2:08] You say business process automation is not just a technological enhancement, but essential for any company that wants to remain competitive in the digital age. And lastly, you say successful digital transformation is more dependent on creating a flexible, innovative culture than on simply adopting new technologies. Alan, let me come to the first point. In a world rapidly adopting AI, how do you envision the future role of human creativity and intuition in decision-making and innovation? Well, I think there’s always a tremendous role for humans in being able to conceive of new concept and drive the business. And what I found is we’ve worked with the different forms of AI we work with, and as all of us are increasingly exposed to it, I find that it can actually be a tool for creativity. It can help you brainstorm and get new ideas. And in fact, our whole approach to business process automation is really very much geared around being able to bring the power of all the information on the internet with the structure that we’ve developed during the years we’ve worked on organizing and managing business processes with the creativity of people to collaborate and think about where they want their businesses to go. And there’s a huge opportunity, I think, for humans and the systems to operate at a higher level. I fully resonate with your argumentation, Alan.

Speaker0:[3:36] Nowadays, when you’re looking into the newspaper, you find different stories, right? Everybody reduce headcount. And the majority of people talking about AI are talking about how they can reduce headcount with AI. That generates some fear, right? So therefore, I like your hypothesis to say we need to make that more clear to the audience that this is not the case. It’s more how we augmented it and how we generate or increase the capabilities of humans with AI, right? And not AI reduce the humans. Is it in the U.S. the same in the newspapers or is that more a German thing, the German angst thing?

Speaker0:[4:19] I think in this day and age, there’s plenty of angst all over the world. And let’s face it, newspapers and a lot of the bloggers and others, they’re trying to excite people. And so it’s very easy to attach only to the negative aspects because, you know, the negative aspects get people to click and they get people to worry. And sadly, that feeds itself. The reality is there are some things that will be negative that come out of this, inevitably that there are. But the net effect is, I believe, ultimately going to be very positive because we’re going to be able to think differently about how we run our lives and our businesses. And I think that ultimately is the promise of technology in general. And AI just takes this to another very significant level. So the key question is, then how can businesses design AI systems that complement and support human roles rather than supplies? And what’s the way from your perspective?

Speaker0:[5:24] Well, I think that the real thing that people need to do with their businesses is to decide what is the real purpose of their business. What are they actually trying to achieve? And that isn’t something that the AI itself can do. That’s something that does require business judgment, understanding about customers.

Speaker0:[5:46] It’s not something that can just be done by coming up with the perfect prompt here as well. But we do need to recognize that there are numerous things that people do where the AI may be able to do them better and allow people to operate differently. And if we use this with creativity and think about how can we involve our customers differently, How can we involve our staff differently? We’ll find things that both apply the AI, but also increase the outcomes of a business. And I think the greatest risk is not the AI itself. The things that will hurt a business is not AI, or the people in the business is not AI. The real threat to the workers in a business is that a competing business that knows how to use AI will have its people become the winners compared to businesses that don’t adopt AI and try to continue to do things the old-fashioned way. So I think it’s people plus AI. You mentioned chess, which was interesting. And by the way, I was not actually the world champion. I won the World Open, which is a very, very large and important chess tournament. And actually, it was interesting because I was in university at the time. And what happened is I was recruited by the computer science department.

Speaker0:[7:09] To teach computers to play chess, which this was a long time ago. So that was before that problem had been solved, as it has been. Now computers play chess magnificently, better than any human being. Back then, we were trying to teach the computers how to learn to play chess. And it was really using some early forms of AI to be able to do that. And it was really quite interesting. But one of the things that for many years was true is there was a guy named Gary Kosparov, who is a brilliant player and was the world champion himself for 20 years, he once wrote about how the most effective chess player is not a computer and not the world champion, but a strong player, maybe not even the best, who has several computers that they’re working with. And that a human and a couple of computers, he believed, would always be able to vanquish any person or any solitary computer. And I think that is true because people can bring different perspectives to it. And not only is that positive, I think, for playing chess, I think that way of thinking is really important to how businesses frame their use of AI. It’s about the people working with the AI to change the business and learn from it. So do you have any examples or maybe companies you admire in the way they deal with AI.

Speaker0:[8:36] Well, I’m pleased to say that with our customers, we have a couple of really, I think, good examples of how this happens. If you’d like, I could tick off two or three real-life examples, which I think would maybe illustrate this. You know, if I think about it, one of the original things we’ve done with AI for 12 or 14 years now is to use AI in a statistical way. Now, I’m going to talk about generative AI in a moment. But people need to remember there are different types and different use cases for AI. And only by recognizing that will organizations, I think, apply it correctly. So let’s talk about statistical AI. Statistical AI, sometimes people refer to it as machine learning, is using, in effect, math to figure out patterns and from those patterns to be able to predict behavior. And that can be terrific for things such as what we call next best action. In Next Best Action, you’re figuring out, hey, what do you want to do with this customer? So I’m thinking about one of my great clients who’s a healthcare organization.

Speaker0:[9:42] They use it to decide if they should recommend that somebody go to a doctor. They use it to recommend that somebody actually maybe buy something, depending on what’s going on. And they do that based on an understanding of the math of the people and an understanding of the propensity. What is the likelihood that what I offer this customer will be something that they’re interested in and want to hear? Very, very different than the random sort of things we see when we often go on the internet. Sometimes we see on the internet things that appear random. Sometimes we see things that appear like they’re chasing us or hunting us. And both of those are just dreadful. Being able to do statistical AI right to be able to predict next best actions really can get the people who are involved, both the people who work in the company and the customers, much, much better service and much better outcomes.

Speaker0:[10:35] The second form of AI is what I would describe as features. And with generative AI in the last couple of years, you’ve seen a tremendous amount of this. So I’ll give you an example of a feature. One of our other clients is having the AI listen to the voice of the person who is talking to a customer. And by listening to the voice, they can determine, is that service representative saying the things they’re supposed to? There are certain legal requirements that, based on what somebody is trying to do, you need to tell them certain things. Well, the AI can confirm and can also coach the individual to say, hey, you didn’t give this warning to this customer. You need to give this warning. And make sure that people follow the right rules. In addition, the AI can summarize the call, really make things much faster and better. I talk about those as AI features. And the third type, which is what we’re most excited about, actually, is to use AI to change the business itself. We created and released in June a capability called Blueprint. It’s available on pegger.com to anybody’s free. And what Blueprint lets you do is use AI to be able to define and redefine how you want parts of your business to work.

Speaker0:[11:56] So I was actually with a senior banker, and we went through an example of how you would use this in loan processing. And it really did a terrific job of helping rethink how you wanted this to work for a particular customer segment, for a particular type of lending, for particular regulations in certain countries. But then the banker turned to me and said, you know, I’m sick of this banking.

Speaker0:[12:21] I want to start a dog grooming business. with my wife. I want to leave this all behind. Can, can you tell me how to create a dog grooming business? And I said, you know, Pega has not done any work in dog grooming. So everything we do here will be completely dependent on the AI. So, uh, But we put it in, we typed in a couple of paragraphs about the types of dogs and the types of customers and the state he wanted to do it in. And what the AI came back with was truly remarkable. It basically said, well, you’re going to need these types of permits. You’re going to have to hire groomers. You’re going to want a marketing campaign to be able to talk to these types of customers who have these right type of dogs. And I don’t think he was serious about the dog grooming business. But if he is, boy, he’s got exactly the recipe to be able to do that. So I think AI can operate across that entire continuum to help people do everything from making the next best action to making their jobs easier and more responsive to actually redesigning the work that they do. Really cool example. So, Uli, what’s your view? I’m still thinking about pets grooming and how you make business out of that, right? But I know that the pets market, I see that with my wife, right? We are spending a lot of money on our pets, right? So this is a big business.

Speaker0:[13:48] And for me, especially what you said, the machine learning and the statistical AI is sometimes the core, right? You need to understand your data. And what I see so far in the different businesses, that the data structure is still not prepared for having that kind of analysis. And that’s number one you need to do as a company, right, to prepare that. And then if you look to business processes and how you model processes and how easy it is to model processes, that’s also something where low-code together with AI can play a big role, right? Especially in business transformation, what Alan mentioned, right? And this is really for me the exciting part, right? I know everybody gets excited about generative AI, which is nice, but this is just, I would say, the cherry on the cake. It’s not what AI also can do.

Speaker0:[14:45] Well, I think it’s such an important and exciting part. I mean, I will tell you, when two years ago we first began working with some of the open AI capability, The thought that AI could do such a beautiful job with language was, even as a computer scientist, I and I think many others were a little surprised. Now, I had always believed that language was sort of a magical capability that was part of the human soul. I did believe that. And then what I learned in reality is maybe all it required was 1.7 trillion connections and the silicon could do it, you know, because it does so well. But I do think…

Speaker0:[15:34] People are right to be excited about generative AI, but as you were saying, they’re wrong, really wrong, to overlook that different AI has different attributes and capabilities. And if you don’t apply the right stuff, I think you can get people who will be very disappointed by the outcomes and the downsides. So let’s get to the point of business process automation. So how do you see the role of AI and machine learning in advancing business process automation beyond routine tasks? What’s your view for that? Well, I think there are several elements. First, using statistical analysis can give you an understanding of what your actual processes are and what you’re currently doing. And what’s important is to not be satisfied with that. But it’s good to understand what you’re doing. It’s good to understand what’s working and what’s not working. And it’s especially important to make sure you’re being smart about what you’re offering your customers and what your customers might really be looking for. And that very much is based on, to our mind, statistical and machine learning.

Speaker0:[16:45] What the generative can do on the other side is it can say, let’s rethink our business. Yeah, we see we’re doing this, but what should we be doing? What are alternatives that we can be doing? What are the best practices that might be available both on the internet, but also in our own record keeping, where we have done certain things? How do we make sure we are compliant with whatever laws are appropriate? And that’s where you can bring together the statistical AI and the generative AI to be able to create these blueprints for how you want your future to be. And ultimately what we do at Blueprint is after the business people use AI.

Speaker0:[17:31] To be challenged, to rethink their businesses, they then have the ability to import this blueprint into our low-code system, which will then make it operational. So it actually allows you to go in record time, go in things that would have taken months or sometimes years to build, literally can now be done in hours and days. And the thing I’m excited about is they’re better. It’s not just that you do it faster. It’s that you’re challenged to rethink certain parts of the business process. And then the statistical AI can help you validate that the things that are happening are really the things you want to be happening. I know, Alan, in my former times, I had a lot of process mining, and I was always surprised. Here’s your standard process, And then you have a lot of variations, right? And which are not in the systems, which are sometimes done by administration, and you know it, right?

Speaker0:[18:34] And do you see a change when you look at Blueprint, right, that this kind of variation gets smaller? And do you see that? Or what is your view on that? Well, some of the variation will get smaller and some will get bigger. Because one of the things that the AI lets you do is find process variations that are not adding value.

Speaker0:[18:57] And those process variations that are not adding value, you want to get rid of them. But one of the reasons you have them sometimes is that we’re not giving the people doing the work the right advice.

Speaker0:[19:09] So people don’t know that there’s a better way to do things. So they do things with the way that they were taught or the way that they just stumbled into. With the combination of statistical AI and generative AI, you can coach people in real time so that they do the right things, which is terrific for them and the customer and everything that’s going on here as well. However, when I said that they may get bigger also, the variation, one of the exciting things that we’ve been adding to our product is the ability to do what’s called agentic work, the ability to create a digital twin of yourself, to create a sort of automated Uli in which you can tell Uli, hey, to be able to handle this particular problem, I want to be able to do three, four, five things. Use the AI to work with you to come up with that plan that.

Speaker0:[20:05] That you want, and then have an automation in the background, go and do that research, go and do those updates, go do the things that you and the AI agreed should be part of the plan. Now, that means that you’ve actually created a dynamic business process. But in PEGA, what we try to do is record what those processes are so that it’s not just kind of go and tell the AI to just do stuff, but you actually can see what the AI was going to do, and you can collaborate with the AI. So the AI really becomes kind of a more effective version of you.

Speaker0:[20:41] So when you talk to customers nowadays, what are the common challenges people tell you that they have when implementing automation? And how did that change over the past two decades compared to now? Well, you know, it’s interesting. When I talk to customers, which I do all the time, I love it. One of the biggest challenges they’re having is getting clarity as to what all this AI speak means, because everybody is talking AI, AI, AI, generative AI, and the magic and all that. And customers tell me there’s so much BS that it’s really a struggle to understand what’s real and what’s reliable.

Speaker0:[21:26] Because even things that are real may not be reliable in the way that a customer needs. and what is nonsense. So, you know, I think one of the first things customers are trying to do is just understand what this means. That’s why I like, you know, that I always talk to clients about the three types, about the statistical, about the features, which are important, but I don’t think they’re generally game-changing, and about the use of the AI to change the very model of the business itself. I think I have found that when I talk to clients and I’ve gone through those as distinct types of AI, that I’ve been told by clients that they found that interesting and a better structure than the AI miracle workers who just come in and talk about AI. Um, you know, the, the reality is customers hear so much nonsense. And so I think all of us who are in the business need to do whatever we can to help them understand, you know, what is going to be real. And, you know, some of the fantasies that are out there, I can share a couple of fantasies with you, if you like.

Speaker0:[22:35] Sure, why not? Well, I think one of the fantasies is that the customers are going to be able to take their code, their COBOL and Assembler and C++ and Java, and just feed it into an AI, and suddenly their legacy systems, which can’t move to the cloud, which are costing them enormous debt, you know, technical debt and risk and exposure, oh, they’ll be miraculously enhanced. And what I will tell you is that I think that that’s, let’s just say that’s more than a little optimistic. The idea that you’ll be able to discern from code, you know, from 30-year-old code that has itself all sorts of pieces and parts that are no longer used and don’t work the way they do, that you’re going to be able to discern how you should build a modern cloud-native system from that. I think that that’s a little fantastic in the sense that it’s never going to work in a sensible way. Having said that, you do want to use process mining, and you do want to perhaps be able to ingest the training documents that explain to the user not how somebody who wrote the code thought it should work 25 years ago, but explain to the user, hey, this is how we train our people, being able to eat that sort of input and then challenge it.

Speaker0:[24:05] To do a best practice analysis and to then come back and have the AI collaborate with people about what the right way to rebuild the system is. I think that is the magic of legacy transformation right now. And that’s actively being worked on by us. And we’re doing some terrific things with clients to retire some of their legacy systems by being able to use the statistic of statistical ai and process analysis from like process mining to be able to use that with the information about what are the users actually doing and then what should they do you got to make sure you’re asking that question also and uh i i i think that the people who are going to eat all the code and make a miracle happen um i haven’t seen it happen yet and i don’t think we’re too it’s more pac-man who’s to eat in the cold it is that’s right you can imagine how how much indigestion this you can imagine the stomach ache that the pac-man will have based on some of the garbage it will be eating i would say yeah absolutely absolutely looking at process automation right which is more a horizontal orientation on companies right where most of the companies are still in verticals, right, and working in their different verticals.

Speaker0:[25:23] How do we see that change of adoption between, let’s say, this kind of mindset, which is automation, and the reality on organizations?

Speaker0:[25:35] So one of the things that we were fortunate about at Pega is that we worked from the beginning with very large, our first two customers were Citibank and Bank of America. So they were large, they were multinational, they were multi-product, and they also, in some cases, did lots of mergers and acquisitions. So there were many, many types of businesses that would come into the overall enterprise.

Speaker0:[26:05] Had different, you know, childhoods in those businesses and were in some cases are pretty dysfunctional adults. And they were trying to bring them together and turn them into a community. And so what we realized early on is we needed to think differently about process, than, frankly, the competitors and the other people who are out there. Most people think about process, you know, think of a Visio diagram, you know, or a BPMN notation, which, candidly, is just dreadful, just a horrible way to think about process. Because what it is, is it very much ties you in to silos. People usually put screens in those diagrams, and interfaces are in those diagrams. And it really isn’t very healthy. What we decided is instead, we would think in the layers. So in a Pegasystem, for instance, we have something at the heart of the system we call the layer cake. And, you know, I, I think the layer cake, unfortunately it doesn’t translate to all languages.

Speaker0:[27:12] But it is something I think once people see and experience, they, they get, it’s like a mule foie, you know, it’s like lots of different layers. And you think about the business as being constructed by some things that want to go across the company as a whole. And then there are other things that might vary based on, say, the segment of the business. Is it commercial versus retail? Other things that might vary based on the customer category. Are these large commercial or small commercial customers? Some things that may vary by another layer, which is the geography or the country or their regulations that are different for the EU versus, you know, the UK. Being able to organize the process content into layers, where the layers can themselves be combined and can work together, is how we break down the horizontal versus the silos that you were just asking about, Uli. And the idea of saying, hey, we have an architecture that supports layers, but we recognize there are things that might be different in one silo versus Another, just put those in their own layer, but make them so they’re all visible. They all hold together and work as a common unit, even if the IP that drives them.

Speaker0:[28:32] Is specific to any of those layers, customer segment, geography, risk segment, all of those. That’s, I think, the way we’ve worked to reconcile the differences between the need for horizontal, but also the need for some verticals, because there is a legitimate need for that sometimes. So let’s get to the topic of culture versus technology. Alan, why do you believe that a flexible and innovative culture is more important for successful the digital transformations and simply adopting new technology?

Speaker0:[29:04] Well, because as we’ve seen over the years, people can do a horrible job adopting technology if they adopt it in the wrong ways. So if people, for instance, adopt this technology without thinking of what some of the consequences are, they will do things that are bad for their customers in ways that will haunt them, in ways that will be bad from a privacy point of view, bad from whether customers think you’re trustworthy or not, or creepy. It’s very important that people have the standards in mind that are going to make the application of the technology healthy and allow them to prevent mistakes. And if there is a mistake, allow them to correct it. With the wrong culture, you can use the technology to squeeze more money out of the client, but if you do that inappropriately that will be very very bad so i think the culture is tremendously important said if you would be a leader coming to a company where it is the other way around so what what would you do how would you change that game um i would run.

Speaker0:[30:10] That’s simple because because a bad culture is very you know the reason you have to work on culture is turning around a bad culture, um, is, is, is.

Speaker0:[30:24] Very, very difficult. So what you need to do is be working on the culture all the time to be able to do that. I mean, candidly, the only way to turn around a bad culture is to make sure that you bring some people in who have the right value system and that those are the people who set the examples and are rewarded. If you reward, for example, parts of a company just for their earnings, as opposed to looking at the other aspect of what they do, you will have a disaster, I think, in most cases. And we’ve seen examples of that, particularly if you’re measuring short-term earnings in a business like the financial services industry, where some of the decisions, you don’t know if they’re going to pan out or be successful for years sometimes. That’s how we had our mortgage disasters in the United States. So if I were a leader who somehow had been recruited into that situation, what I would do is very quickly work to get rid of the bad incentives.

Speaker0:[31:27] Getting rid of bad incentives is even more important than putting in good incentives there. And sometimes it’s easy to see the bad incentives. I think sometimes it’s easier to see things that are bad than to figure out how to make something good to be great. So I would work on that second, but first get rid of the things that are pushing people to make short-term decisions or decisions that aren’t in the interest of their staff or their customers.

Speaker0:[31:55] So what’s the way you’re doing it at Pega? What are your experiences in the last four decades? How did it change?

Speaker0:[32:02] Well, you know, Pega has changed a lot. I can remember when there were just three of us and then six and eight. And, you know, those first days were very, very exciting. And being able to, you know, frankly, have customers trust us with important parts of their business when we were just a fledgling company. I have enormous empathy for the people who made some of those hard decisions at our clients. And I was always glad that, you know, that we were able to prove them correct. That was something that was emotionally, I think, very important to all of us. Pega now is 5,300 people. I try to make sure we’re really customer engaged. I think that having the engineers be able to understand their customers, bringing these people together, I think is tremendously important. When companies start having internal customers, I’m sure you must have heard of some companies talk about, I have an internal customer. I think that that’s a bad way of thinking. The only customers that really matter.

Speaker0:[33:10] Are the external ones. Not that we shouldn’t be nice to our internal co-workers. We should be very good with them. But we should all make sure we have an eye. You know, nothing makes me more upset, and I think we’ve been able to address it, Becca, than somebody saying, well, you know, yeah, that feature was never used, or this customer did the wrong thing, but I did my part right. You just can’t have that sort of thinking. So I think having, the way I describe it, and the way we talk about it internally at Pega, is we talk about having an external lens, a view of the external world. That view should be of your customers and what they’re really doing. We talk about things being what we call done-done. So it’s not just that we’ve built something or created something. Is it really being used? And is it being used in a way that we are proud of? So I think this external orientation, and also, of course, in this world, which is so incredibly competitive. You also need an external orientation to look at what’s happening in the technology so you can see where it’s going and to look at your competition so you can see the world not just in the way you want to see it internally, but in the way your customers see it. And that can be very cruel. I mean, sometimes it can be very uncomfortable to look at the other things that are going on and realize that it will threaten certain businesses.

Speaker0:[34:40] That things may be more difficult. And, you know, I take a look at how AI will change certain parts of the business. And in some cases, it’s wonderful, but not very friendly. You know, the blueprint I talked about, it now works automatically in 11 languages, right?

Speaker0:[35:02] And I can’t necessarily speak, though. I was in Amsterdam a couple of weeks ago, and the Blueprint designed these processes for the customer in Dutch. Now, I had no idea what it said, but they said the Dutch was really very, very good.

Speaker0:[35:17] You know, the downside is that for people who are translators, their lives have been completely changed, and candidly, not always in ways that are good. On the other hand, our ability to communicate better and in a more effective manner with customers and my customers’ customers in ways that we were never, ever as good or as effective as we are now, that’s an incredible positive as well. So some of these changes, when you look objectively at what’s happening, will be good for some parts of the population and excellent for some parts of the population and difficult and troubling for others who need to think about how they’re going to respond. So thank you both, Alan Uli, for the great conversation. And like always at this point, Uli, what are your key takeaways on today’s episode? Oh my goodness, Markus. I took so many things away, but try to keep sharp on what I noticed now. The first thing is I like your three topics on AI, right? To have the differentiation and mix, let’s say, this kind of educational part in companies and bring it to the center why you want to adopt, right? Because that’s the main question.

Speaker0:[36:38] I wrote down the layer cake because that’s a great answer to the automation and the challenges companies have, right? And the other thing is done-done because sometimes, especially for an IT guy, you deliver something and then it doesn’t show the value you expected. And that means you need to talk to the customer and maybe add something and then it delivers something.

Speaker0:[37:03] The value everybody expecting from and that’s that’s a great um takeaway for me yeah one of my my head of product actually came up with that um maybe 15 years ago because he said he got tired of being told by his team that they were done and then he said well show me the happy customers and they said oh they’re not doing it right and you know anytime you’re blaming your customer you know you’re doing something wrong correct yeah so alan one final question and maybe imagine you would meet your 20 year old um alan what would you recommend to him and to his career well you know it’s it’s um it’s it’s interesting my my career has been i would say very lucky very serendipitous you know the work i did with chess got me into computers the work i did with computers had me exposed to, you know, layer cakes and neural networks back before that was fashionable. You know, that was something I had the exposure into very early, even before it worked very well, to tell you the truth. And, you know, I think the thing that I would recommend to myself again, and that I would recommend to anybody here, is to be externally focused, Really keep your eyes open as to what’s going on. Spend a lot of time trying to learn. And then to be flexible.

Speaker0:[38:32] To not, you know, the people who I think overplan their careers end up operating with old knowledge at a time when new knowledge is needed. So that would be my advice to anybody who is 20. So thanks a lot, Alan, for the time and exciting insights. Thank you, Mark. It’s been a real pleasure. That was the Digital Pacemaker podcast with Alan Treffler, founder and CEO of Pegasystems. For more information on today’s episode, check out the show notes. And if you’d like to receive updates from us every two weeks, visit digitalpacemaker.de and subscribe to our newsletter. Follow us now on your favorite podcast platform so you don’t miss any future episodes. Thanks for listening and see you soon. Uli and Markus. Rock and roll.

 

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