#359 AI, Economics, and Value: Dr. Michael Proksch’s Unique Perspective

Mehmet: [00:00:00] Hello and welcome back to a new episode of the city or show with my man today. I'm very pleased joining me. Dr Michael Proksch joining me from the east coast in the u. s. Dr. Michael the way I love to do it is I keep it to my guests to introduce themselves. Tell us a little bit more about You know what they do and what you are currently up to.

Mehmet: So the floor is yours.

Michael: Yeah. Thank you very much for the invitation. I really appreciate it. Um, I usually don't like to talk about myself really. I'll do an introduction for myself. Um, my, um, I'm today chief scientist, uh, at, um, accelerate, um, working for the education industry in the U. S. Working with lots of, uh, um, organization over my lifetime and, uh, there of data and AI.

Michael: I started my journey as, um, as, uh, academic in, [00:01:00] um, economics, um, spend time at universities and, um, Germany, Australia, sometime in Singapore, and then came to industry a couple of years ago, had my way through market research, worked for a consulting company in Europe and in Asia, and um, had the chance to learn, the ropes and data and I understand all the challenges and overcome the challenges of value creation.

Michael: As my background is in economics, we, um, I was always focused on value creation was the technology that was an interest that the analytics that was really close to me, but it was eventually value creation that, uh, That I wanted to achieve. And uh, what we realized a couple of years ago when we were in, when I was in consulting, was that there's different factors that actually contribute to value creation.

Michael: And those are, [00:02:00] um, technology of course, um, as value creation in the, in the field of ai. This is a technology algorithmics or ai, um. And then we have the field of data. Everybody talks about data. Then you have the business part. And then you have a psychology component and many organizations agree with me that, you know, those 45 components are critical in order to create value, but the, but the problem.

Michael: I think is that, um, this relationship and for many people, that's hard to understand. Um, and that's why so many organizations fail in the area of value creation is that those five factors are not additive. So it doesn't help if you, if you have them. Um, For example, you have a department that does technology, you know, the CTO, and then you have the analytics department, and then you have the, you know, the business [00:03:00] departments, and then you have the, you know, your CDO, somebody takes care of data, and you

Mehmet: have a

Michael: transformation officer, takes care of transformation.

Michael: And if they never talk to each other, then no value gets created. But that's an additive relationship. You have them all. So, but considering that it's, When they start talking to each other, that's when it starts to become interesting. And then when they start to create value. So the relationship between those factors is actually not additive, it's multiplicative.

Michael: And that's something I realized, um, a couple years ago. I started my journey to focus on all those five factors at the same time. And, um, On that journey, I realized, um, when we were focusing on those five factors, we started to create and see value and especially in the field of AI today. And, you know, but there were everybody talks about hype and reality.

Michael: And, you know, it's really important to to go back to the roots [00:04:00] and understand where we actually, you know, take a step back and look at it holistically and see where value gets created. And, um Then, after my journey in consulting, I came to the U. S., worked for, um, Aetna CVS, a big, um, health insurance company.

Michael: And then I got my chance to switch to a, um, you would call it an AI unicorn. Um, A, uh,

Michael: good AI company, um, working worldwide for them, especially in the field of insurance and field of transformation. Um, then, uh, a couple years ago I started to write right on the book, so, um, the, where I get invited today to one or two podcasts to talk about the book. Um, and then the book was, uh, it's not, it's not just my, my book, I, I, I wrote it with the, um.

Michael: the community, actually. Um, so I got the chance to because what we realized was [00:05:00] that this value creation problem, many, many, many, um, um, practitioners have that today still. How do you create value with AI? And, uh, where I started talking about that topic more and more and more because I, I, been most of my conversations that came up.

Michael: Um, there were less technical. There are very value driven and people are under the pressure to create value with the technology that exists today. So I was very lucky to get Two of my mentors and friends as coauthors. One is Nisha Palival, managing vice president of Capital One, and the other one is Dr.

Michael: Willem Vlietert, CIO of Premier Tech in Canada. And then we wrote on that, uh, holistic framework, and we all have the little piece to that big puzzle, um, from our background, our skills, knowledge. Um, the more we talked about it, the more we realized that in the, in the community, the AI achievers are actually [00:06:00] following it.

Michael: So they're already doing it. They're seeing that multiplicative relationship and, um, and have a specific approach to it. So we're, uh, we are really lucky to get lots of industry contributors to, to the book. Um, um, lots of AI achievers. We have 14 AI achievers from, um, the, the CDIO of, uh, Hewlett Packard of, uh, CBS, uh, and the CDA of, uh, Starbucks.

Michael: Now, the, the city and so on. Um, we we're, we are very lucky to, um, to. you know, got the chance to learn from them because they contributed with their own personal stories and learnings and so on. So we, um, just were, you know, earlier this year, we were lucky to get the Wiley, that's our publisher to publish the book with us.

Michael: Um, and then, uh, yeah, today work for Accelerate and as a chief scientist, and we do a lots of, you know, lots of projects around data and AI and, um, especially working for customers like University of Maryland [00:07:00] Global Campus, which is a. global organization, 100, 000 students, 10, 000 employees, 20 countries, 180 locations.

Michael: So we're serving them as a, um, in the field of data management and, um, and AI. So I, uh, did get around a little bit in a long story short. I spent my lifetime and now in data and ai, it wasn't expected. It, uh, it started in aca in academia and, uh, economics. And whenever people ask me, you know, where are you gonna be in five years?

Michael: I said, I, I don't know anymore. Um, if I tell you I lie, 'cause uh, you know, if you would've asked me five years ago, I wouldn't have told you where I'm today. And exactly. I, I want to be, um, you know, the journey has so many weird turns. Um. I think I'm going to be still in the field of AI and value creation.

Michael: That seems to be the, the, the constant, um, through my last 10 [00:08:00] years, last decade to create value and, um, yeah, I think it's a journey and, uh, the journey is a long one and, you know, we just talked about it, you know, your journey and, I saw that. Lots of overlaps and so everybody's going on their journey and I try to learn from as many, many other journeys as I can.

Michael: I'm a very curious person. Um, I like to share. People are actually interested in learning from me. Um, I have been, you know, teacher and lecturer at universities and, you know, um, just, uh, Just had the chance to work with Thunderbird University's Thunderbird School of Global Management and work on their global analytics program.

Michael: And, um, so I'm Yeah, that's a long story short about me

Mehmet: great to get a great to have you here with us Mikey today. So one thing you mentioned which It's very valid [00:09:00] Especially we are living in the era of of course like when I say the era of AI The accelerated AI I call it. I like to call it this way When I started the show, you know, like chat GPT was just released, you know, and we still, people didn't know what, what's going to happen.

Mehmet: So of course, when I used to ask the question about, okay, tell me what, what do you think going to happen in, and usually the time I used to say five years, because usually, you know, in tech, when, when even People who are, uh, in, in executive position, they know you plan basically for five years and then maybe, of course, you have a longer vision for 10 years.

Mehmet: But now with what started to happen, I'm not able to ask. And I say, Hey, I will not ask you to tell me what are your predictions for five years, because what we predict today. Probably like maybe after tomorrow, some startup will come and they would do it. So to your point, that very, very, very, very valid.

Mehmet: Now, one thing, you know, when you were talking [00:10:00] about the reason behind writing the books and of course, your experience at what you see a common theme, it looks like for me, and of course I want to hear your opinion about it. So you, you were mentioning value creation. And you mentioned like, it seems like we have some silos and these silos, they don't talk to each other.

Mehmet: Like, and I think this is a big problem because I had another, and it was not about value creation in AI, of course, of AI, but I mean, value creation for any tech, right? So value creation for whatever it is, even for example, with digital transformation like or value creation for adopting certain trend, how much is the communication and who do you think, um, you know, should should be assigned to make sure that everyone within the organization, uh, they are talking to each others.

Mehmet: So because this is something there are different opinions about it, but because as someone who [00:11:00] immersed himself in this, so love to hear your feedback about communication specifically.

Michael: I think, um, It is a hard topic and it doesn't, doesn't just, um, impact, um, the big organizations. It impacts even, even startups, right?

Michael: So if you think about a startup and, um, you, you can apply that, that framework to different, you know, scenarios and situations. So if you look at a startup that builds a product and then the product is, you know, focused on technology and analytics, then usually they. You know, and I made this mistake myself.

Michael: It's thinking, okay, you built a product and now you leave it to this to the customer and to figure out, you know, how to apply it best to their business. And, um, they need to bring their own data. It needs to be good data. And, uh, you know, they need to really want it. Um, if they don't want it, then they shouldn't use it, you [00:12:00] know, with that, with that, um, With that point of view, many organizations fail because they don't take care of the other, you know, they create those silos by intention, right?

Michael: It's like, uh, I leave that to somebody else. And I think that's the biggest, that's the biggest problem. If we, if we think about, we are not responsible for, for those, um, individual factors of value creation. Uh, if we start seeing our responsibility in it, and that could be. as a startup, as a startup CEO, or it could be as a, you know, in an organization.

Michael: Even, and it doesn't matter which, which role it is there that, you know, in those In that organization, there's different types of, um, scenarios where we have a centralized department, everything's centralized under CTO, everything's centralized under CIO, everything is decentralized, and [00:13:00] you have that in different, uh, business departments.

Michael: They have their own AI initiatives, and some organizations, they are, uh, They're more buying than building. Some organizations are building more than they're buying some, that's a good mix between all of them. And it's really hard to say, okay, that's, that's the way to do it. I think the important piece is that they start talking to each other or that there's an authority that can make decisions, um, across them.

Michael: So, and I think, um, that's, uh, That's the, from an organizational perspective, I think that's the hardest part for an initiative to be successful. How do you, how do you do that within an organization that you are Getting that, uh, multiplicative relationship and get them all aligned. I mean, the easiest way is you have somebody that does it all.

Michael: So, and to find that, but to find that [00:14:00] golden egg is really, really, really hard, somebody that, you know, is not, you know, doesn't just understand all the bits and pieces, but also focuses on all of them. So many times we have, we're coming from a specific background and, um, you know, we have a computer science degree, you come with a computer science degree.

Michael: And of course, by, you know, your interests are in computers and technology and the science part, the analytical part, you know, you want to focus on that, which is, uh, there, right? But then, you know, leaving the other bits and pieces out creates a bias towards, you know, those two factors and the others are becoming, you know, less important.

Michael: Um, and that's, that's the trap. So you kind of need to have interest in all of them. So that's really hard to do. Like I, when I came, I didn't have much interest in technology because my background was in technology. So my background was business. So I, uh, [00:15:00] I always focused on the business part, the analytics part, because I love the econometrics and it was my background too.

Michael: It's like, uh, statistics. Love it. Big fan of it. Um, psychology, because I, you know, was very interested in that. Um, and I have, you know, my PhD partially about it. So it was like, um, the, uh, for me, the, the technology and the data part were the boring part. Like, okay, I don't, you know, leave that to somebody else.

Michael: But the problem is if you start doing that, then it falls apart again. You know, and you can't build a stable and viable product. So I had to get entrenched in technology, you know, back in the days that was not much that wasn't distributed computing. So we started, you know, plug our mail service together.

Michael: That was distributed computing back in the day, right? Um, or. The, uh, company, uh, the, the, uh, consulting company, we, we started building, um, our own dashboard technology because there was nothing cool and interesting and that, that, you know, tell [00:16:00] stories. So, we built our own dashboard technology in Ruby, right?

Michael: Um, Or, you know, there was no ready to go analytics packages like, you know, I have today. So we, we built lots of stuff from scratch, you know, broke many times and, you know, but we had to do it because they just didn't exist. And it's not because I was highly interested in being an engineer. It was what I had to do and, uh, my team and, you know, we built those solutions and, um, I, uh.

Michael: I learned to love it over time, and now I'm, uh, um, I'm pretty good at it, I would say in technology and engineering and, but it's not because I, I, I came from that area and then today, you know, but I have to focus on all of them because like, uh, if you, if you think about that relationship to, to get those to focus on, let's say you have $5 million and pour it in technology and, uh, you know, by, [00:17:00] you know, cloud, cloud computing, you know, if you don't have any of the other pieces.

Michael: It's going to always waste it, right? Or you, you, you pour it into analytics by wonderful analytics solutions for 3 million. It doesn't help you if you only have three, cause then you don't get anything else done. So you need to, you need to lift them up all step by step because if one factor of them is zero based on the multiplicative relationship, the value created is always zero.

Michael: So, and that's like, if you. And you can play that game through, right? If you, if you don't have technology, the thing breaks, whatever you build could be the best solution, you know, then, uh, the value is zero. If you don't have, uh, you know, if you don't, if the people don't want it, you know, and you build solutions that people never use, you know, um, then.

Michael: The value is always zero. And I was in those conversations when I asked, okay, what's the return investment, right? Um, oh, we have many, many, many, many, many, many million dollars of return investment. I said, okay, cool. How many people are [00:18:00] using it today? Oh, no, no, we don't really have a user. So how do you, how do you have a return investment if you don't have a user?

Michael: Um, and then I was like, uh, yeah, theoretical, we have that return investment, but that's not what I asked, I asked the real return investment, you know, you can always, you know, um, cheat your way through it, right? It was like, uh, yeah, theoretically, if everybody would use it, but that's not the point, you don't have anybody that's using it.

Michael: So it's like, um, you, you, you know, wherever you play that game, you don't have good data, you know, um, Then nothing comes out of that. So back in the days, we started focusing all of them. We started to standardize data. We bring in external data. We, we, we focus on that part to, to make that even, we, we've built business solutions by working with business and we found, um, You know, we found ways to focus on value creation for everybody.

Michael: You know, that's like when people always, organizations always focus on their own value creation as an [00:19:00] organization serving their shareholders, right. Which is good because they are paying for it. And then you have the second cog group. You always focus on the customers, right. Try to, you know, benefit them or at least not, you know, put any strain on them.

Michael: Um, And you always forget the people in the middle, the people that are actually using it, your employees. So you go and say, you must use that now. Especially

Mehmet: if it doesn't work.

Michael: Um, or you, as you said, right, for me as a, as an, uh, uh, you know, CDAO, you go somewhere and say, okay, uh, you know, I, I built that wonderful tool for you.

Michael: And people look at you, oh, that is great. And, uh, they never going to use it, um, because they were involved. They don't see their own value. And, you know, with the field of AI, everybody thinks you make them unemployed. Right. You take away the job, which is 90 percent of the cases, not true, right. It's, uh, um, even more, I would say.

Michael: Um, and today in 99 percent of the cases, it's not [00:20:00] true. Um, But, uh, you know, that's the, that's the, the narrative that gets told everywhere, right? We're going to, going to lose our jobs. And, you know, 10 years ago, people said, okay, and, and, you know, in five years, nobody's going to have a job anymore. Right.

Michael: Five years ago, three years ago, and Chechu IT came, right? Oh, everybody's going to be unemployed in two years. Haven't seen that big wave yet. You know, it's like, uh, you know, there's always lots of hype. And then, you know, the reality kicks in and you realize that's actually not the case.

Mehmet: Right. I, I, I, you mentioned something, which actually it was the next question I prepared for, uh, which I will relate to the AI later.

Mehmet: And it's, it's the question is from the book Titan itself. And for the surprise, it's not AI. It's the word value, right? Um, like Especially in business. And [00:21:00] I'm happy because I'm talking to you today and you have this nice fusion between the technology and business. So this gives you really a different perspective because from my humble experience, the word value itself, it's very different.

Mehmet: You know, if I'm talking to a CEO, then I'm talking to the guys who can actually implement the technology itself, right? So, and let's take AI because, of course, we're talking about AI today and data analytics. So, What should we do to have a common ground for everyone, whether we are a startup, whether we are a big organization and define, because, you know, for me, at least on the personal level, uh, you know, there are some words, which I feel they are vague.

Mehmet: If you don't put them into, into the, you can understand me like, and yeah, this is the value. What I [00:22:00] mean by value is this, tell me a little bit. It's more about this. So, because we need to define the value, Michael, first. And

Michael: I, I totally agree with you. Um, and that's, that's like how we, how we start the book.

Michael: It's actually about, um, when I started how people create value and you ask an engineer and they tell you with great technology and you started, you're asking, data scientists and they're going to tell you with accurate models. You go to a business leader and they tell you with their, you know, with when you know your, your KPIs, then you can create value when you go and, um, when you go and, uh, ask, uh, you know, the, the, whoever is in charge of data with, with high quality data, they're all right.

Michael: And, um, but they all have a different definition of value. Um, And I think that's, uh, that's exactly what you said. It's, it's, it's, it's a challenge here. Um, because there's different ways of looking at value and we're, we're always, um, [00:23:00] lacking a common ground there. Right. But there's also even if we, if we have one, definition of value that that might not be correct, right?

Michael: Usually we, uh, we focus on business value, right? Um, which means it's related to the organization, which is good because they're paying for it. Um, but then if we miss the other types of value, then, uh, The other definitions of value we're going to run in a problem. And that, that depends on the, on the, on the, on the stakeholder, right?

Michael: So you have the organization you want to create business value, you have the customer you want to create, you know, product value, and then you go to an employee and if you don't create value for them, it's going to fail too. So the, the definition of value and, um. is here really critical, because like, what is, what is value for the employee?

Michael: And so when people ask me, okay, where do you, where do you [00:24:00] focus on when you start, um, with organizations, or where should organizations focus on, um, the field of AI in order to create value? That's at the, Focus on focus on the areas where, you know, you don't hurt anybody on one and number two, where you create the most value for everybody.

Michael: Um, and low hanging fruits are usually in process optimization or when people ask me where to start in the mailing department because it's really hard to, uh, With your with projects to hurt a lot of people and have a have an adoption problem and you started in a in a in a department that already have clear processes and you don't have lots of people working in it.

Michael: Um, so that that that could be one. But, um, in general, the definition of value is context specific and very, let's say, you [00:25:00] stakeholder specific, but everybody needs to see value or it projects gonna fail from my experience. Um, humble experience. As you said, I like that. Um, It's like where, where we have seen stuff becoming successful is when you, when you create value for everybody.

Michael: Right. And, uh, and the definition of value here is critical, as you said, like if you go and for example, I remember projects when we were going and, um, building a solution where we optimized return on investment. Right. And then, uh, um, one of the people that actually supposed to use it, uh, said, uh, yeah, that that's really nice, but I don't get measured there by return on investment.

Michael: I get measured by KPI X. And, um, when you focus on return investment and optimize return investment, my KPI X goes down. And, uh, that was learning for me that, you know, We're actually cutting salaries by [00:26:00] putting the importance on a new KPI that wasn't used. So value creation for them is like value goes down if they use, you know, there was different interests, right?

Michael: So the organizational interests, their return investment. Yes, we focused on that. We focused on the customer, but we didn't focus on the employee. Um, so we had to learn that, you know, that. You know, if we don't, we have to change the incentives for the employees in order to, you know, to align the value, you know, the values for, for everybody.

Michael: And that's, um, and that's a critical piece or, you know, yeah.

Mehmet: I

Michael: love the way you describe it. One thing, sorry, because like, and it's not always obvious. Right. So for example, um, we talk about it in the book, um, when we talk about the, the adoption of dashboard technology. Um, we have [00:27:00] dashboards, you know, they exist in the two thousands or something.

Michael: The first dashboards came out and, you know, we, we have now 20 years, we have dashboards. So, uh, the, the usual adoption based on numbers we've seen is like roughly 30%, something like that, right? And below. So 70% of the dashboards we're building are wasted. So why is that the case? Right? Usually people talk about and, uh, and misattribute the, the cost to technical problems, right?

Michael: It doesn't look nice. It's not the right technology. It's uh, you know, the data we can trust. But, um, I think the, the, the, the reasons are way more emotional. Do, can I, if I work with that, is how is that dashboard gonna be used? Is it really you get used to import to improve my performance? Or do I just get measured?

Michael: On my performance is my next, you know, and a [00:28:00] half a year. Do I have a gonna? Am I going to have a bigger paycheck? Or am I going to have a manager that's going to tell me off comparing me to my peers and tell me that I don't have enough X, Y and Z. So and that's where, um, you know, where it becomes critical if, if they don't see value, if it's not clear the value for them, the adoption is not going to be, it's not going to happen.

Michael: Right. And that's, that's why I love the question about, you know, the definition of value because it differs between different people.

Mehmet: Absolutely. And, uh, but you know, the way you've put it is, of course, everyone, and I like this approach, of course, everyone would have their own, um, how you call it, like definition of value.

Mehmet: Yeah. So as a product person, Uh, is value of different than a salesperson, different than a marketing person, different than an engineer. But at the end of the day, and [00:29:00] this is what I liked in the way you, you put it, everyone like, let's call them, uh, independent contributors, right? So individual contributors.

Mehmet: So each one is actually contributing for the business. So at the end of the day, like I'd say, Kind of when you do the, and I know like you would love this because you come from the, uh, but that background. So, you know, when you have the formula, you put the sum of everything together and this, everything together is actually, it's, it's a by itself have maybe some of the different areas that they look after.

Mehmet: So I like this one now we're talking about AI and, but the thing also you mentioned. A couple of times, things are changing very quickly. So how this affects on the value creation, specifically when we talked about, you know, and the book itself, it's called The Secrets of AI Value Creation. Now, And I, I believe, and I can [00:30:00] imagine the answer, but I want to hear from you.

Mehmet: Like, I believe it's not something like, okay, we do it once and then we're done. Right. So, so it's, it's kind of should be a process. So, Like walk, walk us through this process and you know, if, if you have touched base on that in the book, also feel free to, to mention like, you know, how, how you, you approach that as well.

Michael: We touch base in the book, um, because like, uh, in the topic of AI and that, that thank you for bringing that up, is actually that everybody seems to throw overboard what we learned in the past. It's like, uh, because of ai everything is different now, right? But, um, the. the rules haven't changed. Um, how we approach them might be slightly different, but, um, the, the general rules have not really changed, right?

Michael: So if we think back, uh, 20 years ago, and as I started actually thinking about the book and, you know, um, One of my co authors, Wilhelm Bieler, actually asked me, okay, I, um, what has [00:31:00] changed to process automation, you know, going back in the 90s? So what's different now? And we're going back and forth and back and forth and back and forth.

Michael: And he always came back with, but we did that, you know, that, We did that in the nineties, and that was important in the nineties, too. And we realized, wait, you know, so many things that haven't actually changed. It's like the nuances change, and that's a critical piece. It's like the nuances on the on the technology and where we are today in our society, and, um, they they they change.

Michael: And but the general approach hasn't changed, right? Um, change management. Um, going back to Carter and, you know, knowing what We, we know about change management that hasn't really changed. We know, um, you know, we don't know how to run projects in general, right. Um, talking about, talking about, uh, you know, waterfall or agile and, you know, we, you know, that since the nineties and.

Michael: That hasn't really changed. We just need to apply it to a [00:32:00] new technology and find the nuances in order to make it work or strategy. We use a well known strategy approach to create an AI strategy, right? Um, in the book, but this approach is 20 years old, right? Um, it really serves the purpose of building an AI strategy, right?

Michael: Or when we talk about, you know, the challenges of value creation, when you go and then read about it in the book, they apply to other technologies too. Um, and AI has its nuances and, you know, it's, It's the, it's slightly different and it's a different expectations towards the technology and that changes how we approach it.

Michael: But the, the general rules haven't really changed, right? When we think about how AI creates business value, right? Um, and we started writing about that in the book, um, chapter two, I guess. Um, and people always think about like the industry use case [00:33:00] catalog, right? Where everybody's coming and say, okay, in that industry use case catalog, I should do this.

Michael: Um, but then, uh, My question is always, okay, what do you expect, you know, to, to, to happen if you build that? Um, oh yeah, we're going to provide tremendous value. But how do you define value? Like in the 80s, we, we know from, from Michael Porter, right? That, uh, you create value through, you know, and, and creating a competitive edge, right?

Michael: And how do you create a competitive edge? By building something that is unique to your organization and is against your industry. It's not an industry use case by definition because it's a competitive edge. And that's where, you know, we, it's the same approach, right? How do you, how do you build something that's unique to your organization can support that?

Michael: Um, if you're a low price, you know, organization that, you know, sells low price products. It doesn't help you [00:34:00] to, you know, go after something that doesn't support that, um, positioning, right? If you're a high premium product, you might want to focus on quality. So you can focus on, you know, using AI to boost your quality, but, uh, you will have specific processes and specific requirements and specific things why you're successful.

Michael: And, and, and those are, that's where AI comes in. The approach is still the same, right? It's still Michael Porter, the 80s. Um, how to, how to do it and how to get there. And then we use AI to do it, but it's, um, you know, that hasn't changed. It's, it's, uh, it's, it's, it's always interesting to me when, when, when people seem to, you know, come with a totally blank sheet and say, I know nothing.

Michael: It's not true, you know, a lot, you know, you just, uh, you just, you just don't know how to use AI for what you already know. And that's totally different, totally different ballgame, right? Because, um, then things start to [00:35:00] change and people see value, um, when they apply, when they apply it to what they already know.

Michael: That's usually the case for AI, where I see where value gets created. It's not by doing something totally new, it's by, you know, improving what, you know, organizations are already doing, right? But, you know, doing it just a little bit better, faster, more accurate, higher, whatever it is. But it's going, it's serving the organizations already, you know.

Mehmet: Right. What you mentioned, you know, like, uh, Michael is, you know, it's a very, very, very important point. And I will tell you why my point of view. And of course, but but with AI, I will, I would want to hear your opinion on this. So whenever there's a technology, whatever this technology is there, we try to adopt this technology, right?

Mehmet: And we think, [00:36:00] and when I say we, you know, all of us, probably, maybe sometimes, is okay, I will give you an example. So also for the audience to, they will resonate a few things. So when the cloud computing became the mainstream and people start to feel like, okay, if I don't jump on this cloud thing, I'm going to miss a lot.

Mehmet: And, you know, even I know personally people who were told by even their C level management, Hey, why we don't have cloud yet? And they start to rush. Okay, let's modernize, you know, because it's about modernization. It's about this. And then what they ended up doing is just pushing physical servers or virtual servers in the data center, just putting in the cloud and using old, old architecture, right?

Mehmet: Now with AI, I hope that we will not see the same thing. Although I saw some very bad examples where people say, Hey, [00:37:00] um, let's put whatever material, whatever data we have and train it in the AI. And of course, a lot of people that came to the show, they said, AI is not like your magic pill. Like it's not like, because it's garbage in garbage out, right?

Mehmet: So whatever you train your day, if your data is garbage, you're going to have garbage output. And I think Michael, the point that people miss is whenever we have a competitive edge technology, I like to call it disruptive technology. We need to think in a disruptive way as well. So if we, and you just mentioned, like we use the same, we just changed the jargons, but actually we're doing the same thing.

Mehmet: About, you know, the nuances that you mentioned, don't you think, and now with AI becoming something no one can Skip it or ignore it or saying no, it's just a hype. It's gonna pass and then everything will go back to normal Don't you think and don't you agree with me that organizations need to change? To really we've been [00:38:00] saying this and it's not me It's like people leaders in the industry who are saying like every company today and even startups applies to them as well You need to think you are a technology company yourself and the technology company's main Purpose is to keep disrupting itself and disrupting the industry behind it.

Mehmet: So how are we going? Do you agree with me first and you know, do you think that we have we have enough? I would say leadership prepared for this If, if it's the case.

Michael: That's a hard question. I think it's a good question, but, uh, I think that, um, the disruption piece is, uh, is contextual. Um, AI hasn't.

Michael: Disrupted every organization, every industry, uh, yet. So it, uh, first of all, the topic of AI, people always, you know, associated right now today with, you know, chat [00:39:00] GPT but you know, AI exists for many, many, many, many, many, many, many, many years. You know, you have financial organizations and insurances, and they use, um, analytics and however you want to call AI for, you know, decades.

Michael: Um, and then, yes, it's more accessible today, and you have the compute today, you have lots of data today, yet you are able, you know, use for the purpose of AI. But, uh, but the disruption is, uh. It's more technological than it is, um, um, uh, economical. Um, if you look at, if you look at, for example, the S& P 500, right, and you see which companies are striving and which are, which are not, you see, you know, most companies today that are, you know, in AI, that those are the ones that are making big, um, you know, big waves, but it's [00:40:00] not, uh, but there are technology companies.

Michael: Not like there are, you know, the, the, uh, you know, and it's not like other industries where you see huge, you know, huge jumps. Um, but AI definitely can influence those industries, but it's not because of the disruptive technology. It's about the application of it. And I think that's, uh, that's where it separates that, uh, disruptive piece.

Michael: Where you think about, um, You know, it's like, uh, Netflix, for example, and, uh, you know, Netflix started many years ago and they, they shipped, uh, you know, hard drives to, you know, CD ROMs to, um, to, to people. And then when streaming technology and internet made it possible, they. They used it for their industry and that's where they became [00:41:00] disruptive.

Michael: So, but it wasn't, you know, it was the, the, uh, the, uh, the availability of it and how they applied it to it. And I think, um, that's the, that's the, that's the critical point. Then, uh, it actually, you know, they're reading about their journey. It's, it's very fascinating because there were, you know, lots of, you know, And when you read those journeys about those disruptive companies, there was lots of random factors involved and luck and, um, but, um, and, uh, lots of grit and commitment to, to creating value, but nobody wanted to be, I think nobody, nobody started with, I want to be disruptive, right?

Michael: It's like, uh, um, it starts from the value side, right? And I think, um, eventually you, you are disruptive through something that came. You know, that came up, you know, [00:42:00] um, yeah, I think that's the, that's, that's the, that's the component. I think, uh, yeah, I think, I think innovative, yes. Thinking disruptive is really hard.

Michael: It's like you need to, you know, in hindsight, you, you know, But beforehand, you just don't, you know, it's like, it's like the stock market, you know, you, you know, in hindsight, everybody's gonna say, yeah, you know, that was clear that NVIDIA with AI, you know, I didn't invest in NVIDIA when it was still a, you know, a company that sold, you know, um, um, hardware for, you know, gamers.

Michael: In the, in the 2000s, right? You know, if you would have known that, you know, it all comes up and, you know, today GPU is the important piece and so on. Why didn't you invest for a dollar? And now you're going to be billionaire. Um, you didn't do it. So now it's clear, right? Um, you know, in 2010, you just didn't.

Michael: Um, and that's, I think [00:43:00] the, um, it's, it's the same thing with the disruption, right? You just don't know. And, you know, something becomes disruptive and, you know, and there are so many startups out there that, you know, could be disruptive, but you just don't know which one it is, right? There's like so many other factors.

Michael: Do they have the funding? Do people know about it? Is it really the value it creates? Is that the right time and the right moment? You know, does it, You know, but yes, I think, uh, the disruptive question is a, is a good one, but I can't clearly answer it to be real.

Mehmet: Yeah, no, of course. It's, it's just, you know, like, I, I just throw my, my, my, um, my opinion on this matter.

Mehmet: Yeah, maybe, maybe, yeah, I should have, uh, lowered the bar a little bit. And instead of saying disruptive, saying innovative, that's true. But, you know, I always try to this people, what they do to me. So people, they, it's good. It's a good question. You know, like people, they try, no, no, I'm saying like, when I say, [00:44:00] um, I'm not talking about the question itself, how, how I look to the companies the same way.

Mehmet: For example, they say, Hey, we wait from you, you know, the best. And so I'm waiting from everyone. The best of the best is to be disruptive. Of course, if they are innovative. We are all happy, right? So this is, this is what, uh, you know what I meant by this. But even the, even the word

Michael: disruptive, is it, is it, is it always a good thing?

Michael: Like, um, depends, you know, um, sometimes we need to rely on stability in, in, in, in some areas, right? If you, if you think about a disruption, yes, but, uh, um, disruption doesn't always mean it's, it, it benefits every, every stakeholder there, right? If you think about, um, yeah, um, Netflix was disruptive. It definitely changed the market, but it also, you know, made lots of companies and their employees unemployed.

Michael: Those that like for the customer it was this it was yeah, like it brought huge advantage for the customer and netflix [00:45:00] Actually lost in that game right it's like was it good or bad I don't know it's um, you know for me it's good I can watch it at home. Um, but is it is it

Mehmet: always happens, right? So this is always happens whenever we have Like, you know, when the cars came out, you know, we, we, we had the same thing.

Mehmet: And, you know, like every, when, when the email became like mainstream, I'm, you know, like we, people who used to deliver the postman, like, I don't know if there's still any postman today, but yeah. Right. Yeah. But I

Michael: mean, also on the, on the other hand, even, even if you, you know, people ask you to be disruptive and the big innovator and, you know, I know you're working in that field as well.

Michael: You go to a company and say, look, that's how you can disrupt it. Right. So, um, that means you need to be bold and you need to, you know, you need to take risk, right? Which a lot of startups do by, you know, by, you know, by how it's made because they're, they're very risky how they start. Um, but big [00:46:00] organizations usually don't take that risk.

Michael: So you come there and say, you want to be the disruptor and say, Hey, tomorrow you're gonna, you're gonna sell your product for half the price. So that's gonna, that's gonna disrupt the market. Right, and now you're gonna see how many how many executives we're gonna gonna help you to be that disrupt You know, I don't think that's gonna you know, there's like even if you come there with it with a good disruptive idea Is it gonna you know?

Michael: You know, all those organizations take it. If you read about, you know, Amazon and Jeff Bezos and, you know, he went to a lot of organizations and was disruptive right in his business model and wanted to collaborate and wanted to sell the same Netflix eight, they went to block busters and wanted to sell and, you know, and if there weren't.

Michael: You know, expensive to be really honest, like, you know, thinking back and knowing what they're worth today. Um, and, and those organizations had the chance to buy them and, and be disruptive. And they decided not to, and then, you know, [00:47:00] went in the oblivion and disappeared. But they had the chance. They, they got, you know, they, they, You know, the option to be innovative and disruptive, like they decided not to, right?

Michael: And that's like, um, it's the other thing, right? People are usually the, you know, don't like to take risks. And especially as those big organizations, it's not, it's not common, number one. And number two, um, the business models usually don't allow to take lots of risks because what we're trying to do is we try to you know, stabilize performance.

Michael: Yes, stabilize, reduce risks. So those organizations by intention tried to reduce risk, especially when they become big and big and bigger. They're, you know, they're more and more risk averse. Um, and that that makes it also hard to breed innovation and big organizations, right? That's where we have those incubators and things like that.

Michael: They're, you know, innovation and, you know, they, they buy startups and, [00:48:00] you know, but, um, I like that. I like that disruptive question. But it goes, you know, it it, it's another discussion. It's another podcast we have to . Yeah, of course, of course. And, and, and, and disruptive industries and, and how to disrupt them.

Michael: And I, I'm not an, not, not an expert in it either, so I wouldn't, you know, that's just my talk about my experience there. I haven't disrupted. Uh industry yet And I don't think there's a lot of people that can claim it. Um to have disrupted lots of industries or be an expert in it Um,

Mehmet: but I absolutely Absolutely.

Mehmet: No. No, I can I can fully understand now just like final thing I want to ask you today michael and this is related to And we were talking about it before we started actually a future now You AI, you know, we have to accept it start to raise the questions and you just mentioned like [00:49:00] people might lose jobs Of course when disruption comes, um So but i'm more interested from the perspective you're seeing it Like i'm not i'm i will not ask you like what are the new technologies that will be out there?

Mehmet: Of course, like we need a no one has it today a crystal ball to to predict, you know things and what can come But what i'm interested in, you know, how far the ai will will will And especially, you know, people talks about our jobs. I would not say be eliminated. I would say we have to change. I mean, we're going to be doing something different than we do today.

Mehmet: So interested to hear, uh, you know, from from you about this topic.

Michael: It's a hard topic. Um, because we just don't know. Right. So it's like, um, If you think about, for example, writing, that's something that is really close to us [00:50:00] right now, because we see HHPT and so on, right? Um, and I Or, or, you know, generative AI and, and art, right? Well, we read this, you know, that some, some artists submitted a picture created by generative AI and won the prize for it.

Michael: Um, the question is, what do we want to do with it? They will put on it. So I just, um, had a quote somewhere and I, and I forgot where it was, but it was, um, I thought it was really, really great that somebody said, uh, I don't want AI to write my essays and to draw my art. I want AI to clean my laundry and do my dishes so I can focus on my essays and my art.

Mehmet: Yeah, I saw that.

Michael: All right. And I thought that, that was really cool. And I, I apologize that I don't know who I just quoted, but I, it wasn't from me, but I [00:51:00] thought it was really brilliant. And I think, and I think, uh, as soon as we have lots of AI, we're gonna start going back and see, because we humans, we like, Usually we're like something that is unique, something that is special.

Michael: Um, and when AI and writing becomes a commodity, we're going to start appreciating, um, more and more again, somebody who's writing something unique. So and I think, uh, when I read emails today, they all sound the same. Because I'm using AI and chat GPT to write email, but somebody is like, you know, when I, when I read emails that are not perfectly written and, uh, you know, they sign best regards where regards is capitalized.

Michael: They have a typo in it. And I know, oh, cool. Those people actually wrote that email. And I, and I really appreciate that, [00:52:00] um, that email. Um, because it's, uh, you know, it's a human. And then I think we're gonna, we're gonna, we're gonna see that, um, you know, uh, he, you know, we put more value than on, you know, human interaction and what humans are doing.

Michael: And I think that it can be a good thing. Like when I, when I look at videos on LinkedIn, that was a video a couple of years ago where. They have this new RoboDoc and I, and I thought this is cool. This is really great. Um, and the applications are huge, right? What you can do with it. And then I saw that that person that is, you know, demoing it and kicking the dog.

Michael: And it's like, you wouldn't kick a dog. a real dog and you wouldn't, so for that person that, you know, that, that robot dog was totally normal and, you know, became a commodity because they saw it every day and [00:53:00] they didn't push it, they kicked it. And that was for me, is that how we're going to treat AI in the future?

Michael: Because it becomes a commodity and it's going to be around us. So we're going to, you know, We're gonna kick AI and, um, value the humans more, or, you know, that's a, it's a philosophical question, but it's a, you know, it's, um, then it also defines our jobs, right? I can go something, somewhere where AI does the job, or I go somewhere where the human does the job, and I think we're gonna appreciate the humans more, um, eventually.

Michael: and, and what they're doing, because like, uh, I don't think I can create something disruptive. It's like, that's the definition of, and I really like that, um, I write about in the book, um, there's, um, um, a professor called Eckhoff from Wharton [00:54:00] School. And, uh, he wrote about in the 80s, which is, uh, he wrote from from data to wisdom and he separates, um, you know, he writes about that hierarchy and that that journey from how data becomes information.

Michael: Information becomes knowledge. Knowledge when understood, um, can become wisdom and, uh, According to him, data doesn't have any value, which I agree with, because like the data point itself is not valuable. If you put it in context, it becomes information and then it starts to become valuable because you can compare it to something.

Michael: And then you put it in, um, to knowledge and it, connects it to, you know, how to apply it, um, then it becomes, you know, interesting and then invaluable. And then when you start understanding and putting value to it, then you can create something that helps you grow. Um, [00:55:00] but a machine can, and, uh, according to Akov, and I mostly agree with that until today, um, can grow.

Michael: So machines can help us to do things more efficient, which is great. By applying knowledge that we already know in a way that already exists. Um, or puts A and B together in a way that, you know, both exist, but, you know, it's really hard to put something together that has never been done before and AI wouldn't do it because it hasn't been trained on while humans, when they start, you know, combine something new.

Michael: And something creative, they're gonna grow and knowing what to put together in order to grow is wisdom. And I really like how he's writing about it. I can't even, I can't even repeat it because it's brilliant paper. It's freely available. I read it at least 20 times and still find things that fascinate me.

Michael: [00:56:00] Um, one of the best books. I would say they're the best paper I've ever written, uh, uh, ever read. Um, and then, but that's actually is, is the, is the problem we're, we're having. Um, if AI is only able to, to, to gather and create knowledge, but it doesn't understand its value, then it doesn't We can't make wise decisions, right?

Michael: And that's like where the differentiation between humanity and AI or human intelligence and, and artificial intelligence goes. So how many You know, we usually think that intelligence is related to wisdom because wise people are very intelligent. But that's actually not true. It's the it's the other way around is like, you know, they are very intelligent, but it's, you know, you have very intelligent people that are not wise.

Michael: Um, you should see it that way. And that's exactly unofficial intelligence. You have somebody [00:57:00] Artificial intelligence that can do and repeat things that they learned, but they can do something that is very wise, because therefore you need to understand the value of the knowledge and what they're doing.

Michael: Um, you know, what are we trying to achieve going back to the value topic, right? Right. So, and that's, that's where AI is struggling, right? You optimize with AI one specific outcome, but, you know, AI won't tell you, wait, wait, wait, you shouldn't go down to 50 percent of the price because that, you know, it's gonna, you know, disrupt the market.

Michael: You're gonna, you know, you have problems in India and China and, you know, the people are gonna quit. I was never trained on that scenario. You would know that there are other consequences that might happen. Um, and, you know, think beyond what you already know or have an idea what, you know, could happen. Um, so you wouldn't do it, you know, AI might not.

Michael: That's why, um, You know, because it doesn't put value [00:58:00] beyond what it was trained on. So, and then there's no, you know, no ethical considerations, right. What happens if I fire a hundred thousand people? Um, yeah, I optimize price, you know, I optimize my costs. Cool. Good. That was the, that was what I tried to do, but maybe my customers might not react positively to that message.

Michael: Right. And I like, uh, you know, or that could be, you know, I could have, Other things that, you know, might become problematic, legal constraints, compliance, whatever other stuff that, you know, um, you would know that are impacted. Um, long story short, but, uh, a long answer to it, to the question, I guess. It's, it's, it's a complicated one, right?

Michael: It's, uh,

Mehmet: it is, it is, it is indeed. It is indeed. Um, Michael, like, you know, like, uh, very, I would say, um, deep insights. You gave us today. [00:59:00] So what I'm going to ask you is where people can first, you know, find more about you and how they can get the book.

Michael: You can, you can buy the book at Amazon and Barnes and Noble and wherever you go, the book is available online.

Michael: And, um, And then, um, yeah, uh, we would love to have your feedback. So we don't claim that we know it all. Um, we have, uh, we wrote the book for, and with the community. So we would love to get your feedback, um, your experiences where you agree or disagree. Um, so please, you know, reach out on, on LinkedIn or the website to the book.

Michael: You can come there and can, you know, discuss the topic with us and, you know, feel free to reach out. There's no, um, no reason to hesitate. So we are. Very eager to learn from you as well.

Mehmet: Great, thank you again, uh for for your valuable time first and for your valuable knowledge that you shared with us today Of course, you know, I [01:00:00] will I will put the links for the audience so they can find it easily Um, and of course for your linkedin profile Um, and this is how usually I end my episode.

Mehmet: So this is for the audience. So if you just discovered this podcast by luck, thank you for passing by. I hope you enjoyed it. If you did, so please don't forget to subscribe, give a thumbs up and tell your friends and colleagues about this podcast. And if you are one of the loyal Followers and listeners who keep coming.

Mehmet: Thank you for doing so keep sending me your feedback and your questions your suggestions I read them all I don't skip any and Hopefully you enjoyed today's episode and we will be very soon again in a new one Thank you very much for tuning in. We'll see you again very soon. Thank you. Bye. Bye