Lari:
0:13
So, hello and. I'm Lari Numminen, the host of Wonderful Work, a podcast where we hear that voices and insights that make business operations just a bit more wonderful. In this episode, we get to hear from Stig-Martin Fiskå. Stig-martin is an entrepreneur at heart, having started his first company at the age of 16. Stig-martin has created several successful companies in the technology space and is now the head of artificial intelligence data interna, Internet of Things, Industry 4.0 at COG in the Nordic. Welcome to the show, Stick Martin.
Stig-Martin:
0:48
Thank you Lari. Good to see you and good to invited.
Lari:
0:51
Yeah, you, you have an absolutely amazing background and, um, love to go into the, the realm of, of your current work, but in this show we do like to start from the very top and get to know our guests a bit better to begin. let's start from, from some interesting questions. tell us something wonderful about yourself or something surprising most people don't know about you.
Stig-Martin:
1:13
Sure, Sure. So let's, let's get a little bit of personal, uh, touch going there. Uh, when I met my wife, uh, uh, and we'd agreed to cook every day, every second. In between us and for the first six months we didn't create the same dish ever. Great complet with different unique dishes. Uh, for six months there we're going. So that tells you something about both, uh, interest and in, in, uh, foodie, uh, department, but also I would say a bit of a competition going on there.
Lari:
1:46
Absolutely. And I, I would be amazed that if I could do the same dish, uh, or, you know, more than a week at a time, but it's, it's great to have you here. Stigma. When you were a child growing up, what did you want to become?
Stig-Martin:
1:59
Yeah, good question. I, I, I thought about that and, uh, I, I can't really remember from like really young age, I have to say. Um, uh, I'm not good at looking backwards. I'm better at looking forward, but, um, Uh, I don't think I thought too much about it until I entered my teams. Uh, and then I just wanted to, you know, I thought it would be a good idea to copy my dad and become a lawyer. Uh, but at the same time, I, I thought, you know, making your own money was a good thing. Um, and I'm not really sure. Where that came from, honestly. Um, but I didn't see a blocker in creating your own company. Uh, so as a, as a relatively young guy, uh, set up my own company with some fellow, people from school and, uh, and got going, making my money. So I guess you could say that I've been an entrepreneur. But we didn't use that word. We didn't think about it at as, at the time as anything. And startup certainly wasn't a word at that time.
Lari:
2:58
absolutely. Um, now, like your father, you did study law in university, you also studied philosophy. Uh, how did you end up then working in technology?
Stig-Martin:
3:08
Yeah, it's a bit of the background, so, uh, act actually when I was 15, 16 or something like that, I. Uh, had a lot of American friends, um, growing up in what used to be there or still is, I guess the old cap capital of Norway in Stavanger. Uh, and, uh, this American friend of mine from the international school in Stavanger, he was, uh, showing me this webpage that he created, uh, as a project, Netscape Gold, three hour or something like that. we're talking way back. And then, and, uh, I thought, well, if he can, then I can. And I didn't really have much, uh, interest in internet and computer at the time. My dad bought the 4 86 and, uh, and uh, I think there was even some kind of early surround sound bought to that, uh, computer and everything. Um, but he was a bit disappointed that I didn't care much to use it. Uh, but. I think the link between someone showing me something with building and me being a Lego fan at the time, uh, got me creative, the creative part going so, uh, so I reversed engineered at that time, the largest online website in Norway and that's how I got going in technology. And I figured. If people thinks this is magic, then why don't I just make some money on that? Uh uh, and then, uh, I also reversed, open source, uh, cms, reversed engineer that. So I learned, you know, databases and that stuff, uh, and made some money. But I thought this, I can't live this, this isn't serious work. I'm just playing. Nobody taught me anything. There's no degree. And at that time there was no, there was. Tutorials online or anything like that. Right. So, uh, so yeah, I thought I had to get serious and then go to law school. I was really bored in law school. Uh, you're not allowed to be creative. You're not allowed to build things, uh, in the same manner that I like to do it. Philosophy. That was great, and I could think out loud and do the things I, I love to do. So, So back in, uh, I think during the law school, uh, years, uh, I found some fellow. Tech people that, uh, struggled getting a job after their, uh, um, computer science degree because this was.com or after the.com and, uh, I joined them, uh, part-time. Really, I didn't think that this would be something I'd do outside the, other than outside the studies and, and now I ended up being part of that industry from there.
Lari:
5:50
And that's remarkable. I I have to say that there's not many times in, in history when there's gonna be computer science majors who are looking for a job. So you, you, it sounded like you were in the right place at the right time. Yeah. Um, at the kind of disruptive early days of, of technology and, and reverse engineering. Uh, there's many other ways that you can learn things, but, but that, that does sound like it's really learning from the source. Um, Stick. Martin, In this episode we are gonna talk about advanced technologies, but before we get into those topics, let's talk about kind of the foundations here. Uh, many of the listeners will be in business operations in the Nordics, and these leaders will have organizations with different levels of data maturity. Right. Um, what is your take on data, digital maturity, uh, what are the foundations, do you think? Is it about the people or the technology or about.
Stig-Martin:
6:42
Well, of course there's a, there's a, there's a balance between all. But I, I mainly these days I think it's, uh, it's people that's the focus. And, and why do I say that? Because I think if we implemented the technology and what it allows us to do today, we would've a ramped up much, much faster than we are doing today. So, so it's about knowledge, it's about, uh, People knowing the art of the possible. Uh, and like you mentioned today, you wouldn't find any, any computer science people or, or, uh, majors, uh, looking for jobs were lacking people who has the knowledge lacking people who knows the realms of the possible. and also I'm afraid that a lot. Where we used to be and still are, is run by it, which isn't really about digital to me. There is a difference there, uh, when we enter into the business and the actual operations of things and not just the support function. So very much about culture, very much about people and knowledge, I would say. And that's, that's where we are. The technology is in many cases already far beyond what most people can imagine.
Lari:
7:55
Absolutely. And I, I like the word that you used the art of the possible. I think some people are realist and they, they see the, the cup half full. Um, they see bad data and legacy. It systems a lot of problems when it comes to digital. Um, now do you have any advice how you can kind of advance digitalization in those kind of realms?
Stig-Martin:
8:15
Yeah, it's a, it's a good question and, and honestly, uh, there are no silver bullets. Uh, and we're still in early days. Organizations are very different. Uh, I I, back to like the, it thought it's about standardization and, and buying off the shelf, which is a lovely expression that people love, uh, to, to use. But in reality, they're, they're all in bits and pieces. There is no standardization. There is no way to do this because it's been thought about as it, uh, and not about the, the actual business and operations and, and, and data. Data didn't used to be sexy. Now it is sexy again. Uh, to, to be quite blunt, I think, uh, people haven't understood what they did about the data before. Uh, There are many ways to look at this. So some, some tendon, A very traditional way to look at it is, is to do what we call data modernization. It's bringing these systems together, uh, and bringing, bringing, uh, the data up to up to par, so to say. Uh, making sure that the CRM talks with the, the other parts and, and, and so on and so forth. That tends to be very heavy. Uh, projects, um, and it needs to be done to some level of extent. But what we used to fail, uh, early on in industry here is to do that for three years or something and spend a lot of money, moving data and cleaning up data. And we didn't really think about the business outcomes at the same time. So what I would recommend. You have to do this in, in several work streams, but I definitely will recommend leading by the use cases and, and where you can see, you can create early on benefits optimization, new revenue streams, whatever, whatever it is, and then build a foundation just good enough to start showing those results. and the way I'd like to set this up, cuz that. Focus from the end result downwards all the time, drive it that way. It's painful sometimes. Uh, and it sure does not always solve everything at the first, uh, try. But you learn so much more and you keep focused on the outcome. The other advice would be get the organization involved. Don't treat it as a classical IT project where you just outsource it. Uh, I know a lot of people are in housing this stuff today. That could be good. Uh, but you also need someone who's done it before, who has the drive and the incentives to just get things done and not get the sucked up by the internal politics and, and meetings. Uh, so having that push from an external, uh, party that's. You know, actually it's a good thing that costs money to use consultants because that creates the incentive to get something done And at the end of the day, what I see a lot of people are also trying to do is transform their organization around data driven. Hmm. And that's the third part. So bring the organization, not just the techies, not just the, the people who are used to driving technology projects, but bring the forefront of business development forefront of, of, uh, of the, whoever runs the process inside the company, into this and get their hands dirty. It's the only way to learn. So let's,
Lari:
11:37
uh, double down on that. You mentioned data driven, uh, approaches. Um, Can a leader develop a data driven culture in people, or does it need to come from somewhere outside?
Stig-Martin:
11:49
It's a, again, no silver bullet. I don't have a, finished answer here, but honestly, depending on your organization, so if you're Spotify, Sure you can do it because you have the, you have the incentives. You started from the right spot. You're digital native, so, so to speak, and all of that. Most cases, I would say no, you either need to hire, uh, you probably both need to hire, uh, some of, uh, some people's been there before and knows what it means. Uh, and then you need some, some help to lift the organization. It's about upskilling, it's about getting your hands dirty. Uh, and, and also what is data driven. Uh, when I speak to sea level or VP or whatever, uh, they define it completely different and. Again, back to what I mentioned about the realm of possibilities. If you have never done data driven, how can you, uh, measure the outcome? How can you measure where you're going? How can you measure the KPIs if you're succeeding or not? So I think you need some help there. And I think the, the, the right mix is, is, uh, getting some people in that onset and has done it before. That would be ideal. You won't find many of those in the market, to be honest, So I think you need some external, uh, help as well. But, uh, Yeah, that balance is important.
Lari:
13:06
and that's a very interesting point, and when you reflect on it, um, a lot of companies might think that they're data driven, but they're actually data informed. So it's like everyone will have a different perspective and view on things, but, but bringing people onto the same page about what your definitions and goals are usually is a good starting point. Um, Steve Martin, you've worked a lot across the Nordic region. Um, do you have any kind of observations, juicy observations you'd like to share about the digital maturity, uh, or confidence uh, across your work?
Stig-Martin:
13:36
Sure, sure. Actually, in, and we've done some studies on this in the Nordics as well, and, and then quantify the maturity. Interestingly enough, what we see is that there's being overspend a lot of money in this area, in the Nordics, but the results aren't coming. Uh, so the, the return on investments isn't really there yet. actually that, uh, one of the things that I get met the most with, with my fancy title of artificial intelligence. And when I meet clients, uh, and, and, uh, look into. New possibilities, uh, together with them. One of the first things they meet me with is saying, We're not gonna have any ai, ai, we don't want any ai. We're not there. Which first of all, tells me that they don't know what AI is, which is fine. Uh, you need to define that as well. But, uh, uh, there was a period. Let's say three, five years ago where AI fair dust could be just, uh, sprinkled over anything and you still can't to some, uh, to some extent to get some funding and VCs in there and stuff like that. Uh, but then also broke the trust around this. so a lot of, uh, top leaders. Uh, kind of burnt themself, I think, on this stuff. I'm seeing that the market, the latest during Covid, I would say the last part of the covid, they're starting to ask the right questions. And a lot of things starts by asking the right questions, and I think, um, they're now asking, How can I be successful? What is the right scope of this stuff? And, and where should I really start? So we're starting to right. Ask the right question. We're still thinking a little bit of this, like a project, an IT project. We'll started in January and we finish in April. And, uh, and then it, it should, uh, uh, you know, get a, give an uplift. And I think that's where we need to get in the Nordic. A lot of, a lot of companies, a lot of leaders haven't understood that yet. Uh, that, uh, essentially whatever you're doing in your company, Is already, uh, a process. It's already run by algorithms, but the algorithms are way too often us. So I do this this way today because I've done it that way, or that actually works. Uh, and, and honestly that is the same thing as ai, cuz that's what AI can do. It can take that process, that recipe, but automate. Maybe do it faster, maybe do it at a 1% smarter, and then by keep reinvesting and, and tuning this and, and by the end of, uh, you know, two, three years, maybe it can do it a hundred percent faster and with a lot better results. Uh, and, and that's the transformation part, uh, where people don't get, they, they can't run this as a project. It's a, it's a change, continuous change. Those, uh, discussions starring barely in the Nordics, but we're, we're lagers for sure in the Nordics. Okay. Let,
Lari:
16:44
let's bring people up to speed. Let's, let's, let's start from the foundations, uh, and, and get them going. Mm-hmm. Um, now let's demystify a, a artificial intelligence a bit. You, we talked about very interesting topics there, but let's, let's go to the core basics of it. Yes. How would you explain artificial intelligence to your friends
Stig-Martin:
17:02
and family? Yeah, it's a good question. And the first thing I, I try to do is to take away this thought about Terminator or, or this, uh, almighty, uh, entity that's, uh, that's the know-it-all and all that stuff. It. There we will invent that, but that's not really what we're talking about in, in this context. Uh, but we're talking about applied ai and we're talking about the, like I mentioned, the processes and the recipes that we follow in our companies today and in our society today. That's what we're talking about. We're talking about taking what is. Common daily tasks. Uh, re reputable tasks, preferably, and, uh, making them automated. That's what we're really talking about. And driving a bit more on insights. So there are two types, main types of applied ai. It's either taking a process and, and making it automated or it's doing something with more insights. So understanding and processing. Um, large amounts of data, and I'm, by large amounts, I don't necessarily mean Google amounts, but you know, if, if you, if you come, um, to a larger Excel sheet, then at least I start losing control. So I think it's murdered that someone helps me, uh, digest that for me. And that's, that's basically the first parts of, of what I'm, I'm, uh, thinking about in ai. Uh, and I try. Explain that to people, but AI can be a lot more of those things. But for the purpose of a business, uh, leader, It's applied AI and it's about automating and making the things you do smarter and faster today. Uh, could also be creating new revenue streams in some extreme cases. Actually, we do see a little bit about that then in, in the nordex already, which is really cool. Yeah,
Lari:
18:53
and I think those, those use cases, sometimes they, they bring out their excitement, but then for, for many companies, they, they kind of create the hype as well. Yes. Um, For many companies, machine learning and applied AI is literally just taking away the boring, repetitive tasks or even parts of tasks in processes that most people will not see. Then end result of it. And it's a lot less exciting than, than the movies and science fiction may, may have your peer. Mm-hmm. Um. You've already hinted at these, and I'm interested to know what are the kind of most, uh, who should be most interested in, in a large or organization about artificial intelligence?
Stig-Martin:
19:31
I think, I honestly think it, it's the whole layer of things and that there, this is gonna be a little bit like when internet arrived and people said, uh, you know, that's a fat And we're still in those early days, uh, of, of an infancy of internet around AI and, and data driven. Organizations. So the C level should definitely be interested. The CFO should be extremely interested as this is about optimizing current operations. It's about taking down the risks. so a CFO should love this, a CEO. So should also be very mindful because it can run off your business and you can refocus your resources on new investments, uh, using people less on the, let's say, mundane tasks, if that's a, if that's a phrase we can use, but it also can bring a lot of insights. So the CEO and the CEO should all care for that because he can bring you insights to, of your own organizations and where to optimize. So, so those are the, those are the top level ones. And then of course, everyone else as well.
Lari:
20:38
And, uh, those make a lot of logical sense. I think what the kind of audiences we often talk to are business operations where finance, procurement, there's a lot of, uh, functions that have a lot of repetitive work. And, and in those cases, the, like you said, the process are, are some things that can be streamlined or automated or in some cases replaced. Um, if you have enough volume and if it's a big enough problem, then this could be something that can impact the whole company. But the, the, the financial leadership is definitely one that, that, that takes a lead. Um, what are some of the common mistakes you see in adopting ai?
Stig-Martin:
21:17
Oh. Um, I think the most, most common one is that they hire, uh, internally, and I'm not against that at all. And having internal resources that you work with is hands on and have understanding, but they hire some, typically the data scientists, one or two, maybe three, maybe four, uh, data scientists, and, and they. Then they just, uh, they just place them somewhere, uh, to, to, uh, figure out some AI So that's, that's a very common mistake. And, and still thinking about this as a, fairy dust that just will, uh, will appear. And, and the answer to that is, it's a whole line. Uh, capabilities and, and, and, uh, knowledge that's needed, uh, not the least about your current operations and current business. Uh, so a data scientist can do a lot of good things, but they need a whole team around them with the also other technical capabilities. You can't just do it with a data scientist alone. Uh, so that's, that's a very common. The other one is the one I spoke about and I alluded to earlier, that people drive them as it projects rather than a change of doing business, a change of running my company. Um, so they, they think about it as something they ramp up and then should ramp down and then it's just there. Um, that's not gonna happen. Uh, there are some edge cases that, that you can say that it can be used, but most of the time making yourself data driven and. AI is about the continuous change, which I'm sure most leaders don't like because, you know, another one of those So those are, those are the main common ones. Um, uh, the third, the third one, uh, which is coming more and more now because we're getting past the two first one. Uh, Or that the, the business, uh, side of the, of the operations isn't involved enough, they're putting this on a team that's either internally but outside of the daily operations and daily business model. Uh, or, uh, or with someone like me who's a consultant and, and they put it, uh, uh, as an outside project or an outside team, you have to integrate it properly. Uh, you have to. Use the momentum about some of these cases to train your organization, uh, in a different way of thinking, in a different way of operating. Uh, so that that's coming more and more clearly that then they, that we need to get past that as well.
Lari:
23:43
Yeah, and it's kind of, it's not magic dust. bad process doesn't become a good process just with the amount of AI you put into it. But it's a good advice that like, you know, when it comes to hiring data scientists and so forth, it's often better to just start with the core foundations. Is this a big enough business problem we, we can solve and, and assembling a team, whether it's external talent or internal talent to, to work on it.
Stig-Martin:
24:08
Yeah. And, and, and I think, you know, sometimes, and this is also the fault of the industry, uh, that I represent. We, we go to large, uh, and we're, we're, we're shooting for huge numbers or huge changes. Uh, find those few nuggets where we can spin, spin and show change, uh, at a pace where, where your organization is comfortable, but at the same time getting pushed a bit. so, so find those. Nuggets where you can, uh, prove ease relatively easily with, I don't know, six to nine months, a max kind of, uh, timeline to have some really outcomes and start telling that story and start building around it, onboarding more and more people and, and use cases around that, that, that's a better advice than saying, you know, I'm, I'm gonna change the way I do things from the, from today to tomorrow. Never gonna work.
25:01
Yeah.
Lari:
25:02
Incremental improvements that can be stacked is always gonna be better than a radical re-engineering of things, uh, with a bit of tech thrown into it. Um, stigma. Martin, let's talk about your new role or your role at cognisant. Many of people know Cognisant, uh, as a leader in the technology consulting space. Your focus is really broad, uh, if I'm honest, and your, your flavor to what you do across the Nordics. Uh, it has different spins, You work on ai, Internet of Things. Industry 4.0 and, and other aspects. you, you touched on the idea of hiring data scientists into your organization, and it's certainly something that, you know, in your role, you are doing a lot of hiring. Um, now do you think every company needs to be hiring machine learning experts or, what kind of advice can you give companies and leaders in organizations about the talent war that's ongoing or at the.
Stig-Martin:
25:55
Yeah, It's a really hard question. I wish I knew the answer to that one. Um, Cause it's the same for us, right? We need, we need to attract to talent and, and, and the war is really there. depends on the size and the scope of your company, but I, I think, uh, You if you're anything but a local shop, uh, at the end of the day, I honestly think you need to, to actually at some point invest around, uh, machine learning or something similar around data. Um, if that means that you need to hire all these people. Probably not, not for everyone. The, the main reason would be that it's, it's going to be hard for you to find that talent. I think one of the things we need to do is to recognize that this is all emerging and I try to bet more and more on, on the young and upcoming actually. And not just because there's a war on talent, but also because they have the mindset of, I'm jumping into this to learn now, which is a very good mindset, Yeah. Uh, and I think that's very important and I, I. I would give them a chance and sit really close to, to the young and hungry ones, uh, and try to, you know, uh, coach them Uh, but they will, they will really quickly understand a lot of things. They are very hungry, um, and they have a lot to prove, and I would use that energy to, to, to help me in this. Uh, that's for sure. Don't ask for the one with 15 years of experience every time, it's, it's this joke, right? where, uh, uh, basic, uh, CV ask is I want a candidate that has worked with the Power BI for 17 years and then Power Bay was just invented in 2015. So, and that happens all the time. Uh, and I, I don't think our associates, uh, are done learning and I don't think anyone will be. And I certainly need to learn every day cuz this is moving really, really fast. So, uh, Get a grasp, get your fingers dirty again. I think I get back to that all the time. Uh, don't do a PowerPoint presentation thinking that you now can hire the right people. Uh, don't go to a, uh, one of these, uh, breakfast shows or something like that and, and think that you can now you know what you're doing because you don't. So, so get some, uh, people to spar with you. I think that's, uh, best advice and then articulate. Outcomes and goals you really and value you want to create. Uh, a lot of the talent is very focused on value. Well, how can they be part of something and how can they, uh, be changed and, and contribute to the society in a, in a meaningful way through their work? Uh, and then they're really, really hungry to. to be challenged and to learn and to use their skills. Um, so those are, those are things you will have to answer if you get to get to the right talent.
Lari:
28:59
Yeah. And, and I, I think that what you touched on there, it is really a global, uh, war and talent where the likes of the Googles or Microsoft or Cognisant, really large players will have the same opportunity to, to address that talent, whether they're living in Norway or living in the Nordics or anywhere in the world. Um, the. The need is real. it's the game of every company to really apply what they're doing to this global challenge. Um, now I have to say that, you know, working in such a large organization, you have a pretty unique view on technology. Uh, like you said, Cognisent has over three and 350,000. Colleagues, and you probably have access to all the tools, all the fancy stuff, everything. Microsoft, Google, anyone provides, all the software imaginable. So, um, not many Nordic businesses will have that same, uh, access, uh, for investments and resources and tech. Um, what kind of, if you're advising Nordic businesses and looking really at the future, looking, looking at what's, what's coming up next, what kind of key trends would you anticipate, uh, for the next year and. Yeah.
Stig-Martin:
30:07
Uh, it is true. We do get access and exposure to a lot. Uh, and, uh, I make a point of, of, uh, stepping into it myself as well. Uh, cause like I said, learning every day. Um, I think, I think the key trends is going to be that we're going to have, uh, a more somber approach to a lot of these things we are going to, and the clients are going to the market and the whole are going to. Um, have to realize and have a sober approach to the fact that these people and these processes don't necessarily exist today. Uh, and then you'll see some people, uh, or companies being totally disrupted, um, and. And be marginalized. Um, so that's one trend you'll see. Another trend is, is from the software vendor side. So you spoke about the Microsoft and the Googles and the Amazons of the world. So you're also going to see that we are getting disrupted from the inside. They can't grow unless, I mean, data engineers, there aren't not enough data engineers. So that's one area where, where, uh, I, I would guess that, uh, our big, uh, global software vendors will go in and try to take away some of that pain. So you don't need so many data engineers because they can't grow if, if the market doesn't have enough data engineers. So you'll see areas. Where we have invested in people and skills that will disappear, be disrupted and probably way faster than than anyone ever seen before. and, and that will actually not just be within, you know, my field and my, my, uh, area. It will also be outside of that field. So doctors will be disrupted because AI will enter their domain. Lawyers will, uh, be disrupted because AI is the. So on and so forth. So knowledge work will be very, very disrupted in the next coming, uh, five to 10 years. So even less, I think. We will get used to have to change our way of thinking and our way of doing work and what life much faster than we're used to today. And, and again, that will spur a lot of debates, a lot of, uh, legislation around these areas because if it threatens jobs, that's when politicians usually, uh, get their act together and start creating, try to create some kind of a balance or, or something. So those are the really big, big trends I think that we'll see. Uh, and then of. Lastly, you will find AI in places where you never thought you, uh, found AI before. And not only in, in, in old factories and, and, uh, autonomous cars driving the roads, which people, some people still don't believe is happening, but is actually happening. Uh, but you'll find it in the dentist office. You'll find it with teachers, uh, and, and you'll find it probably plumbers and, and construction builders will also, cuz I know they're looking into this right now so that, that all of those things will happen. Uh, but at the end of the day, it's already happening right now. If you look at how dependent you become on your smartphone and how driven that is actually by analytics and uh, ai and how much we give it consent to run our life, either consciously or unconsciously. And, and that's what's gonna happen. We are going to get this increased curve or usage everywhere, and no one, including people like me, will have the full over. Uh, and then we'll just look back at some point and say, Whoa, we really changed. We as humans really, really changed and, and, uh, yeah. So it's exciting. Yeah,
Lari:
33:48
and I, I think you can see those changes with what you've mentioned just now with, with the internet. It's, it's a progress that happened first slow and then very fast. Yeah. With smartphones, things happened very slow and all of a sudden you don't even realize that you've been a part of the change. So people have a hard time imagining. Short term changes, but then in the long run, if you put perspective, AI is likely not gonna change anything in your life until you realize that it's already changed it. Exactly. Um, now if you're a business leader and you wanna build a business case for investing in ai, you wanna stay ahead of the curve, uh, what kind of tips would you have?
Stig-Martin:
34:27
Yeah, if you really wanna invest and back to my, uh, I know I'm repeating myself, but, but get your hands dirty. I, I, I was lucky enough, I've been lucky enough to meet a lot of, cool people, uh, in my job. And, uh, I was, uh, also lucky enough to meet, uh, the chief business officer of Google some years ago, and I asked him that question, How do you stay on top of all of this stuff in the most disruptive or one of the most disruptive organization that that exist? Uh, and he, I, I will never forget his answer because it was so somber. Um, and so, uh, realistic, uh, he said anyone that comes up to me will have to draw it on the whiteboard until I understand it. So j just spend some time and actually invest in understanding. You don't have to be the coder, you don't have to understand Python and machine learning to do it, but you have to understand the concepts and, Uh, you can't, again, realize the realm of possibilities if you have no clue what, where, where it is, and it's all darkness. Um, I hope we'll see more. Education or courses for leadership and, and mid management around technology in this area so they can, uh, understand better how to, to roll with it. I'm a little bit surprised how little there is actually other than the seminars and stuff that's more on a hallelujah level, um, and height level. Um, and then, and then I think find some trusted partner. Uh, and I don't necessarily mean being a consultant company. Us actually, I mean, look at your. Uh, neighbor or your, even your peers and competitors, what are they doing? Go together and, and talk about these issues together. Um, go to someone who's doing something completely different than you, but you know, they've come a bit further than you and have a conversation and, and start engaging. And I think partnering in that sense. It's underestimated, uh, not just, of course in AI that's relevant and everywhere, but you have to get the grips with the fact that there are too few resources and the ones who get them will accelerate at some point really, really fast if they succeed at, when they succeed. So partnering up is going to be way more crucial, than people are thinking about today. Um, and then don't do everything. Try to see if there's some, what, what is unique for you, uh, and is, what is your area of, uh, where you can excel and then leave everything else to standard stuff, right? Don't, don't try to do all, all things. But partnering, I think is number one, honestly.
Lari:
37:21
Partnering makes sense. When you think about it, it's, it's like any change. You, you don't want to go at it alone, especially if you're inexperienced. Um, and yeah, I, I can see that there is an increasing amount of, um, educational resources you can follow, but until that's widely available, then yeah. And accessing people who've been there, who've learned some from some of the mistakes just makes sense. You skip some of the bad stuff. Um, stick Martin, uh, we're getting to the end of the interview here. But I, I do wanna ask, um, do you have any recommended resources listeners should follow?
Stig-Martin:
37:53
Yeah, so, I, I actually, um, I use um, medium to com quite a lot. Uh, there are several, uh, areas around there where you can follow. So that depends a little bit on the level you are at. Of course, a lot of that can be very, Uh, detailed. Uh, but there are some high level stuff that you can follow and, and read, uh, and keeps you updated. Uh, and a lot of it is also sharing about what's gone wrong. I think that's important. Uh, I would also recommend those who just want to get a. For its grasp to invest in, in, uh, in the chief, uh, decision officer of Google's YouTube, uh, series. It's free and all of that stuff. And it's, it's, she's made it into something that's quite. Um, tangible and understandable, uh, and it doesn't require you to, spend hours and hours. It's quite easy to get it, and it's, I would say very good, very up to date. And she touches on what's important and tries to take away the, hype basically. So that would be a, a good advice. And then there are good pod podcasts out there, uh, interference from from Finland as as one which I actually follow. Uh, and then leadership posts, podcasts, uh, which you can listen to in between. I think that's a very useful, uh, useful way of looking at it and. I know that's outside. Uh, um, but for perspective, I think it's also useful to, to read the Hararis books. Uh, the human, uh, Davis is the, is the latest one I believe. Uh, and it gives you perspectives on, on, on where we're going. It's very thought provoking and not necessarily correct in every way, but it gives you perspective and it touches on a lot of the things that will change us as a society around this. And, and it also gives you the perspective on, on how thoroughly, um, through and through the change. So I think that's very helpful. Um, so that's from a leadership, uh, angle, but I.
Lari:
40:01
Stig-Martin, that sounds great. I think that was a, a good variety of, uh, resources there. We'll make sure to include in the show notes of the podcast when it's released. Um, so Stigma Martin Stu Tuck, it's been absolute pleasure, absolute pleasure to hear your insights. It's time to wrap up here. So this has been The Wonderful Work Podcast produced by Work Fellow. Um, thanks for your time. Subscribe and stay tuned for more views and insights that make the world of business operations just a bit more wonderful. Goodbye
0:13
So, hello and. I'm Lari Numminen, the host of Wonderful Work, a podcast where we hear that voices and insights that make business operations just a bit more wonderful. In this episode, we get to hear from Stig-Martin Fiskå. Stig-martin is an entrepreneur at heart, having started his first company at the age of 16. Stig-martin has created several successful companies in the technology space and is now the head of artificial intelligence data interna, Internet of Things, Industry 4.0 at COG in the Nordic. Welcome to the show, Stick Martin.
Stig-Martin:
0:48
Thank you Lari. Good to see you and good to invited.
Lari:
0:51
Yeah, you, you have an absolutely amazing background and, um, love to go into the, the realm of, of your current work, but in this show we do like to start from the very top and get to know our guests a bit better to begin. let's start from, from some interesting questions. tell us something wonderful about yourself or something surprising most people don't know about you.
Stig-Martin:
1:13
Sure, Sure. So let's, let's get a little bit of personal, uh, touch going there. Uh, when I met my wife, uh, uh, and we'd agreed to cook every day, every second. In between us and for the first six months we didn't create the same dish ever. Great complet with different unique dishes. Uh, for six months there we're going. So that tells you something about both, uh, interest and in, in, uh, foodie, uh, department, but also I would say a bit of a competition going on there.
Lari:
1:46
Absolutely. And I, I would be amazed that if I could do the same dish, uh, or, you know, more than a week at a time, but it's, it's great to have you here. Stigma. When you were a child growing up, what did you want to become?
Stig-Martin:
1:59
Yeah, good question. I, I, I thought about that and, uh, I, I can't really remember from like really young age, I have to say. Um, uh, I'm not good at looking backwards. I'm better at looking forward, but, um, Uh, I don't think I thought too much about it until I entered my teams. Uh, and then I just wanted to, you know, I thought it would be a good idea to copy my dad and become a lawyer. Uh, but at the same time, I, I thought, you know, making your own money was a good thing. Um, and I'm not really sure. Where that came from, honestly. Um, but I didn't see a blocker in creating your own company. Uh, so as a, as a relatively young guy, uh, set up my own company with some fellow, people from school and, uh, and got going, making my money. So I guess you could say that I've been an entrepreneur. But we didn't use that word. We didn't think about it at as, at the time as anything. And startup certainly wasn't a word at that time.
Lari:
2:58
absolutely. Um, now, like your father, you did study law in university, you also studied philosophy. Uh, how did you end up then working in technology?
Stig-Martin:
3:08
Yeah, it's a bit of the background, so, uh, act actually when I was 15, 16 or something like that, I. Uh, had a lot of American friends, um, growing up in what used to be there or still is, I guess the old cap capital of Norway in Stavanger. Uh, and, uh, this American friend of mine from the international school in Stavanger, he was, uh, showing me this webpage that he created, uh, as a project, Netscape Gold, three hour or something like that. we're talking way back. And then, and, uh, I thought, well, if he can, then I can. And I didn't really have much, uh, interest in internet and computer at the time. My dad bought the 4 86 and, uh, and uh, I think there was even some kind of early surround sound bought to that, uh, computer and everything. Um, but he was a bit disappointed that I didn't care much to use it. Uh, but. I think the link between someone showing me something with building and me being a Lego fan at the time, uh, got me creative, the creative part going so, uh, so I reversed engineered at that time, the largest online website in Norway and that's how I got going in technology. And I figured. If people thinks this is magic, then why don't I just make some money on that? Uh uh, and then, uh, I also reversed, open source, uh, cms, reversed engineer that. So I learned, you know, databases and that stuff, uh, and made some money. But I thought this, I can't live this, this isn't serious work. I'm just playing. Nobody taught me anything. There's no degree. And at that time there was no, there was. Tutorials online or anything like that. Right. So, uh, so yeah, I thought I had to get serious and then go to law school. I was really bored in law school. Uh, you're not allowed to be creative. You're not allowed to build things, uh, in the same manner that I like to do it. Philosophy. That was great, and I could think out loud and do the things I, I love to do. So, So back in, uh, I think during the law school, uh, years, uh, I found some fellow. Tech people that, uh, struggled getting a job after their, uh, um, computer science degree because this was.com or after the.com and, uh, I joined them, uh, part-time. Really, I didn't think that this would be something I'd do outside the, other than outside the studies and, and now I ended up being part of that industry from there.
Lari:
5:50
And that's remarkable. I I have to say that there's not many times in, in history when there's gonna be computer science majors who are looking for a job. So you, you, it sounded like you were in the right place at the right time. Yeah. Um, at the kind of disruptive early days of, of technology and, and reverse engineering. Uh, there's many other ways that you can learn things, but, but that, that does sound like it's really learning from the source. Um, Stick. Martin, In this episode we are gonna talk about advanced technologies, but before we get into those topics, let's talk about kind of the foundations here. Uh, many of the listeners will be in business operations in the Nordics, and these leaders will have organizations with different levels of data maturity. Right. Um, what is your take on data, digital maturity, uh, what are the foundations, do you think? Is it about the people or the technology or about.
Stig-Martin:
6:42
Well, of course there's a, there's a, there's a balance between all. But I, I mainly these days I think it's, uh, it's people that's the focus. And, and why do I say that? Because I think if we implemented the technology and what it allows us to do today, we would've a ramped up much, much faster than we are doing today. So, so it's about knowledge, it's about, uh, People knowing the art of the possible. Uh, and like you mentioned today, you wouldn't find any, any computer science people or, or, uh, majors, uh, looking for jobs were lacking people who has the knowledge lacking people who knows the realms of the possible. and also I'm afraid that a lot. Where we used to be and still are, is run by it, which isn't really about digital to me. There is a difference there, uh, when we enter into the business and the actual operations of things and not just the support function. So very much about culture, very much about people and knowledge, I would say. And that's, that's where we are. The technology is in many cases already far beyond what most people can imagine.
Lari:
7:55
Absolutely. And I, I like the word that you used the art of the possible. I think some people are realist and they, they see the, the cup half full. Um, they see bad data and legacy. It systems a lot of problems when it comes to digital. Um, now do you have any advice how you can kind of advance digitalization in those kind of realms?
Stig-Martin:
8:15
Yeah, it's a, it's a good question and, and honestly, uh, there are no silver bullets. Uh, and we're still in early days. Organizations are very different. Uh, I I, back to like the, it thought it's about standardization and, and buying off the shelf, which is a lovely expression that people love, uh, to, to use. But in reality, they're, they're all in bits and pieces. There is no standardization. There is no way to do this because it's been thought about as it, uh, and not about the, the actual business and operations and, and, and data. Data didn't used to be sexy. Now it is sexy again. Uh, to, to be quite blunt, I think, uh, people haven't understood what they did about the data before. Uh, There are many ways to look at this. So some, some tendon, A very traditional way to look at it is, is to do what we call data modernization. It's bringing these systems together, uh, and bringing, bringing, uh, the data up to up to par, so to say. Uh, making sure that the CRM talks with the, the other parts and, and, and so on and so forth. That tends to be very heavy. Uh, projects, um, and it needs to be done to some level of extent. But what we used to fail, uh, early on in industry here is to do that for three years or something and spend a lot of money, moving data and cleaning up data. And we didn't really think about the business outcomes at the same time. So what I would recommend. You have to do this in, in several work streams, but I definitely will recommend leading by the use cases and, and where you can see, you can create early on benefits optimization, new revenue streams, whatever, whatever it is, and then build a foundation just good enough to start showing those results. and the way I'd like to set this up, cuz that. Focus from the end result downwards all the time, drive it that way. It's painful sometimes. Uh, and it sure does not always solve everything at the first, uh, try. But you learn so much more and you keep focused on the outcome. The other advice would be get the organization involved. Don't treat it as a classical IT project where you just outsource it. Uh, I know a lot of people are in housing this stuff today. That could be good. Uh, but you also need someone who's done it before, who has the drive and the incentives to just get things done and not get the sucked up by the internal politics and, and meetings. Uh, so having that push from an external, uh, party that's. You know, actually it's a good thing that costs money to use consultants because that creates the incentive to get something done And at the end of the day, what I see a lot of people are also trying to do is transform their organization around data driven. Hmm. And that's the third part. So bring the organization, not just the techies, not just the, the people who are used to driving technology projects, but bring the forefront of business development forefront of, of, uh, of the, whoever runs the process inside the company, into this and get their hands dirty. It's the only way to learn. So let's,
Lari:
11:37
uh, double down on that. You mentioned data driven, uh, approaches. Um, Can a leader develop a data driven culture in people, or does it need to come from somewhere outside?
Stig-Martin:
11:49
It's a, again, no silver bullet. I don't have a, finished answer here, but honestly, depending on your organization, so if you're Spotify, Sure you can do it because you have the, you have the incentives. You started from the right spot. You're digital native, so, so to speak, and all of that. Most cases, I would say no, you either need to hire, uh, you probably both need to hire, uh, some of, uh, some people's been there before and knows what it means. Uh, and then you need some, some help to lift the organization. It's about upskilling, it's about getting your hands dirty. Uh, and, and also what is data driven. Uh, when I speak to sea level or VP or whatever, uh, they define it completely different and. Again, back to what I mentioned about the realm of possibilities. If you have never done data driven, how can you, uh, measure the outcome? How can you measure where you're going? How can you measure the KPIs if you're succeeding or not? So I think you need some help there. And I think the, the, the right mix is, is, uh, getting some people in that onset and has done it before. That would be ideal. You won't find many of those in the market, to be honest, So I think you need some external, uh, help as well. But, uh, Yeah, that balance is important.
Lari:
13:06
and that's a very interesting point, and when you reflect on it, um, a lot of companies might think that they're data driven, but they're actually data informed. So it's like everyone will have a different perspective and view on things, but, but bringing people onto the same page about what your definitions and goals are usually is a good starting point. Um, Steve Martin, you've worked a lot across the Nordic region. Um, do you have any kind of observations, juicy observations you'd like to share about the digital maturity, uh, or confidence uh, across your work?
Stig-Martin:
13:36
Sure, sure. Actually, in, and we've done some studies on this in the Nordics as well, and, and then quantify the maturity. Interestingly enough, what we see is that there's being overspend a lot of money in this area, in the Nordics, but the results aren't coming. Uh, so the, the return on investments isn't really there yet. actually that, uh, one of the things that I get met the most with, with my fancy title of artificial intelligence. And when I meet clients, uh, and, and, uh, look into. New possibilities, uh, together with them. One of the first things they meet me with is saying, We're not gonna have any ai, ai, we don't want any ai. We're not there. Which first of all, tells me that they don't know what AI is, which is fine. Uh, you need to define that as well. But, uh, uh, there was a period. Let's say three, five years ago where AI fair dust could be just, uh, sprinkled over anything and you still can't to some, uh, to some extent to get some funding and VCs in there and stuff like that. Uh, but then also broke the trust around this. so a lot of, uh, top leaders. Uh, kind of burnt themself, I think, on this stuff. I'm seeing that the market, the latest during Covid, I would say the last part of the covid, they're starting to ask the right questions. And a lot of things starts by asking the right questions, and I think, um, they're now asking, How can I be successful? What is the right scope of this stuff? And, and where should I really start? So we're starting to right. Ask the right question. We're still thinking a little bit of this, like a project, an IT project. We'll started in January and we finish in April. And, uh, and then it, it should, uh, uh, you know, get a, give an uplift. And I think that's where we need to get in the Nordic. A lot of, a lot of companies, a lot of leaders haven't understood that yet. Uh, that, uh, essentially whatever you're doing in your company, Is already, uh, a process. It's already run by algorithms, but the algorithms are way too often us. So I do this this way today because I've done it that way, or that actually works. Uh, and, and honestly that is the same thing as ai, cuz that's what AI can do. It can take that process, that recipe, but automate. Maybe do it faster, maybe do it at a 1% smarter, and then by keep reinvesting and, and tuning this and, and by the end of, uh, you know, two, three years, maybe it can do it a hundred percent faster and with a lot better results. Uh, and, and that's the transformation part, uh, where people don't get, they, they can't run this as a project. It's a, it's a change, continuous change. Those, uh, discussions starring barely in the Nordics, but we're, we're lagers for sure in the Nordics. Okay. Let,
Lari:
16:44
let's bring people up to speed. Let's, let's, let's start from the foundations, uh, and, and get them going. Mm-hmm. Um, now let's demystify a, a artificial intelligence a bit. You, we talked about very interesting topics there, but let's, let's go to the core basics of it. Yes. How would you explain artificial intelligence to your friends
Stig-Martin:
17:02
and family? Yeah, it's a good question. And the first thing I, I try to do is to take away this thought about Terminator or, or this, uh, almighty, uh, entity that's, uh, that's the know-it-all and all that stuff. It. There we will invent that, but that's not really what we're talking about in, in this context. Uh, but we're talking about applied ai and we're talking about the, like I mentioned, the processes and the recipes that we follow in our companies today and in our society today. That's what we're talking about. We're talking about taking what is. Common daily tasks. Uh, re reputable tasks, preferably, and, uh, making them automated. That's what we're really talking about. And driving a bit more on insights. So there are two types, main types of applied ai. It's either taking a process and, and making it automated or it's doing something with more insights. So understanding and processing. Um, large amounts of data, and I'm, by large amounts, I don't necessarily mean Google amounts, but you know, if, if you, if you come, um, to a larger Excel sheet, then at least I start losing control. So I think it's murdered that someone helps me, uh, digest that for me. And that's, that's basically the first parts of, of what I'm, I'm, uh, thinking about in ai. Uh, and I try. Explain that to people, but AI can be a lot more of those things. But for the purpose of a business, uh, leader, It's applied AI and it's about automating and making the things you do smarter and faster today. Uh, could also be creating new revenue streams in some extreme cases. Actually, we do see a little bit about that then in, in the nordex already, which is really cool. Yeah,
Lari:
18:53
and I think those, those use cases, sometimes they, they bring out their excitement, but then for, for many companies, they, they kind of create the hype as well. Yes. Um, For many companies, machine learning and applied AI is literally just taking away the boring, repetitive tasks or even parts of tasks in processes that most people will not see. Then end result of it. And it's a lot less exciting than, than the movies and science fiction may, may have your peer. Mm-hmm. Um. You've already hinted at these, and I'm interested to know what are the kind of most, uh, who should be most interested in, in a large or organization about artificial intelligence?
Stig-Martin:
19:31
I think, I honestly think it, it's the whole layer of things and that there, this is gonna be a little bit like when internet arrived and people said, uh, you know, that's a fat And we're still in those early days, uh, of, of an infancy of internet around AI and, and data driven. Organizations. So the C level should definitely be interested. The CFO should be extremely interested as this is about optimizing current operations. It's about taking down the risks. so a CFO should love this, a CEO. So should also be very mindful because it can run off your business and you can refocus your resources on new investments, uh, using people less on the, let's say, mundane tasks, if that's a, if that's a phrase we can use, but it also can bring a lot of insights. So the CEO and the CEO should all care for that because he can bring you insights to, of your own organizations and where to optimize. So, so those are the, those are the top level ones. And then of course, everyone else as well.
Lari:
20:38
And, uh, those make a lot of logical sense. I think what the kind of audiences we often talk to are business operations where finance, procurement, there's a lot of, uh, functions that have a lot of repetitive work. And, and in those cases, the, like you said, the process are, are some things that can be streamlined or automated or in some cases replaced. Um, if you have enough volume and if it's a big enough problem, then this could be something that can impact the whole company. But the, the, the financial leadership is definitely one that, that, that takes a lead. Um, what are some of the common mistakes you see in adopting ai?
Stig-Martin:
21:17
Oh. Um, I think the most, most common one is that they hire, uh, internally, and I'm not against that at all. And having internal resources that you work with is hands on and have understanding, but they hire some, typically the data scientists, one or two, maybe three, maybe four, uh, data scientists, and, and they. Then they just, uh, they just place them somewhere, uh, to, to, uh, figure out some AI So that's, that's a very common mistake. And, and still thinking about this as a, fairy dust that just will, uh, will appear. And, and the answer to that is, it's a whole line. Uh, capabilities and, and, and, uh, knowledge that's needed, uh, not the least about your current operations and current business. Uh, so a data scientist can do a lot of good things, but they need a whole team around them with the also other technical capabilities. You can't just do it with a data scientist alone. Uh, so that's, that's a very common. The other one is the one I spoke about and I alluded to earlier, that people drive them as it projects rather than a change of doing business, a change of running my company. Um, so they, they think about it as something they ramp up and then should ramp down and then it's just there. Um, that's not gonna happen. Uh, there are some edge cases that, that you can say that it can be used, but most of the time making yourself data driven and. AI is about the continuous change, which I'm sure most leaders don't like because, you know, another one of those So those are, those are the main common ones. Um, uh, the third, the third one, uh, which is coming more and more now because we're getting past the two first one. Uh, Or that the, the business, uh, side of the, of the operations isn't involved enough, they're putting this on a team that's either internally but outside of the daily operations and daily business model. Uh, or, uh, or with someone like me who's a consultant and, and they put it, uh, uh, as an outside project or an outside team, you have to integrate it properly. Uh, you have to. Use the momentum about some of these cases to train your organization, uh, in a different way of thinking, in a different way of operating. Uh, so that that's coming more and more clearly that then they, that we need to get past that as well.
Lari:
23:43
Yeah, and it's kind of, it's not magic dust. bad process doesn't become a good process just with the amount of AI you put into it. But it's a good advice that like, you know, when it comes to hiring data scientists and so forth, it's often better to just start with the core foundations. Is this a big enough business problem we, we can solve and, and assembling a team, whether it's external talent or internal talent to, to work on it.
Stig-Martin:
24:08
Yeah. And, and, and I think, you know, sometimes, and this is also the fault of the industry, uh, that I represent. We, we go to large, uh, and we're, we're, we're shooting for huge numbers or huge changes. Uh, find those few nuggets where we can spin, spin and show change, uh, at a pace where, where your organization is comfortable, but at the same time getting pushed a bit. so, so find those. Nuggets where you can, uh, prove ease relatively easily with, I don't know, six to nine months, a max kind of, uh, timeline to have some really outcomes and start telling that story and start building around it, onboarding more and more people and, and use cases around that, that, that's a better advice than saying, you know, I'm, I'm gonna change the way I do things from the, from today to tomorrow. Never gonna work.
25:01
Yeah.
Lari:
25:02
Incremental improvements that can be stacked is always gonna be better than a radical re-engineering of things, uh, with a bit of tech thrown into it. Um, stigma. Martin, let's talk about your new role or your role at cognisant. Many of people know Cognisant, uh, as a leader in the technology consulting space. Your focus is really broad, uh, if I'm honest, and your, your flavor to what you do across the Nordics. Uh, it has different spins, You work on ai, Internet of Things. Industry 4.0 and, and other aspects. you, you touched on the idea of hiring data scientists into your organization, and it's certainly something that, you know, in your role, you are doing a lot of hiring. Um, now do you think every company needs to be hiring machine learning experts or, what kind of advice can you give companies and leaders in organizations about the talent war that's ongoing or at the.
Stig-Martin:
25:55
Yeah, It's a really hard question. I wish I knew the answer to that one. Um, Cause it's the same for us, right? We need, we need to attract to talent and, and, and the war is really there. depends on the size and the scope of your company, but I, I think, uh, You if you're anything but a local shop, uh, at the end of the day, I honestly think you need to, to actually at some point invest around, uh, machine learning or something similar around data. Um, if that means that you need to hire all these people. Probably not, not for everyone. The, the main reason would be that it's, it's going to be hard for you to find that talent. I think one of the things we need to do is to recognize that this is all emerging and I try to bet more and more on, on the young and upcoming actually. And not just because there's a war on talent, but also because they have the mindset of, I'm jumping into this to learn now, which is a very good mindset, Yeah. Uh, and I think that's very important and I, I. I would give them a chance and sit really close to, to the young and hungry ones, uh, and try to, you know, uh, coach them Uh, but they will, they will really quickly understand a lot of things. They are very hungry, um, and they have a lot to prove, and I would use that energy to, to, to help me in this. Uh, that's for sure. Don't ask for the one with 15 years of experience every time, it's, it's this joke, right? where, uh, uh, basic, uh, CV ask is I want a candidate that has worked with the Power BI for 17 years and then Power Bay was just invented in 2015. So, and that happens all the time. Uh, and I, I don't think our associates, uh, are done learning and I don't think anyone will be. And I certainly need to learn every day cuz this is moving really, really fast. So, uh, Get a grasp, get your fingers dirty again. I think I get back to that all the time. Uh, don't do a PowerPoint presentation thinking that you now can hire the right people. Uh, don't go to a, uh, one of these, uh, breakfast shows or something like that and, and think that you can now you know what you're doing because you don't. So, so get some, uh, people to spar with you. I think that's, uh, best advice and then articulate. Outcomes and goals you really and value you want to create. Uh, a lot of the talent is very focused on value. Well, how can they be part of something and how can they, uh, be changed and, and contribute to the society in a, in a meaningful way through their work? Uh, and then they're really, really hungry to. to be challenged and to learn and to use their skills. Um, so those are, those are things you will have to answer if you get to get to the right talent.
Lari:
28:59
Yeah. And, and I, I think that what you touched on there, it is really a global, uh, war and talent where the likes of the Googles or Microsoft or Cognisant, really large players will have the same opportunity to, to address that talent, whether they're living in Norway or living in the Nordics or anywhere in the world. Um, the. The need is real. it's the game of every company to really apply what they're doing to this global challenge. Um, now I have to say that, you know, working in such a large organization, you have a pretty unique view on technology. Uh, like you said, Cognisent has over three and 350,000. Colleagues, and you probably have access to all the tools, all the fancy stuff, everything. Microsoft, Google, anyone provides, all the software imaginable. So, um, not many Nordic businesses will have that same, uh, access, uh, for investments and resources and tech. Um, what kind of, if you're advising Nordic businesses and looking really at the future, looking, looking at what's, what's coming up next, what kind of key trends would you anticipate, uh, for the next year and. Yeah.
Stig-Martin:
30:07
Uh, it is true. We do get access and exposure to a lot. Uh, and, uh, I make a point of, of, uh, stepping into it myself as well. Uh, cause like I said, learning every day. Um, I think, I think the key trends is going to be that we're going to have, uh, a more somber approach to a lot of these things we are going to, and the clients are going to the market and the whole are going to. Um, have to realize and have a sober approach to the fact that these people and these processes don't necessarily exist today. Uh, and then you'll see some people, uh, or companies being totally disrupted, um, and. And be marginalized. Um, so that's one trend you'll see. Another trend is, is from the software vendor side. So you spoke about the Microsoft and the Googles and the Amazons of the world. So you're also going to see that we are getting disrupted from the inside. They can't grow unless, I mean, data engineers, there aren't not enough data engineers. So that's one area where, where, uh, I, I would guess that, uh, our big, uh, global software vendors will go in and try to take away some of that pain. So you don't need so many data engineers because they can't grow if, if the market doesn't have enough data engineers. So you'll see areas. Where we have invested in people and skills that will disappear, be disrupted and probably way faster than than anyone ever seen before. and, and that will actually not just be within, you know, my field and my, my, uh, area. It will also be outside of that field. So doctors will be disrupted because AI will enter their domain. Lawyers will, uh, be disrupted because AI is the. So on and so forth. So knowledge work will be very, very disrupted in the next coming, uh, five to 10 years. So even less, I think. We will get used to have to change our way of thinking and our way of doing work and what life much faster than we're used to today. And, and again, that will spur a lot of debates, a lot of, uh, legislation around these areas because if it threatens jobs, that's when politicians usually, uh, get their act together and start creating, try to create some kind of a balance or, or something. So those are the really big, big trends I think that we'll see. Uh, and then of. Lastly, you will find AI in places where you never thought you, uh, found AI before. And not only in, in, in old factories and, and, uh, autonomous cars driving the roads, which people, some people still don't believe is happening, but is actually happening. Uh, but you'll find it in the dentist office. You'll find it with teachers, uh, and, and you'll find it probably plumbers and, and construction builders will also, cuz I know they're looking into this right now so that, that all of those things will happen. Uh, but at the end of the day, it's already happening right now. If you look at how dependent you become on your smartphone and how driven that is actually by analytics and uh, ai and how much we give it consent to run our life, either consciously or unconsciously. And, and that's what's gonna happen. We are going to get this increased curve or usage everywhere, and no one, including people like me, will have the full over. Uh, and then we'll just look back at some point and say, Whoa, we really changed. We as humans really, really changed and, and, uh, yeah. So it's exciting. Yeah,
Lari:
33:48
and I, I think you can see those changes with what you've mentioned just now with, with the internet. It's, it's a progress that happened first slow and then very fast. Yeah. With smartphones, things happened very slow and all of a sudden you don't even realize that you've been a part of the change. So people have a hard time imagining. Short term changes, but then in the long run, if you put perspective, AI is likely not gonna change anything in your life until you realize that it's already changed it. Exactly. Um, now if you're a business leader and you wanna build a business case for investing in ai, you wanna stay ahead of the curve, uh, what kind of tips would you have?
Stig-Martin:
34:27
Yeah, if you really wanna invest and back to my, uh, I know I'm repeating myself, but, but get your hands dirty. I, I, I was lucky enough, I've been lucky enough to meet a lot of, cool people, uh, in my job. And, uh, I was, uh, also lucky enough to meet, uh, the chief business officer of Google some years ago, and I asked him that question, How do you stay on top of all of this stuff in the most disruptive or one of the most disruptive organization that that exist? Uh, and he, I, I will never forget his answer because it was so somber. Um, and so, uh, realistic, uh, he said anyone that comes up to me will have to draw it on the whiteboard until I understand it. So j just spend some time and actually invest in understanding. You don't have to be the coder, you don't have to understand Python and machine learning to do it, but you have to understand the concepts and, Uh, you can't, again, realize the realm of possibilities if you have no clue what, where, where it is, and it's all darkness. Um, I hope we'll see more. Education or courses for leadership and, and mid management around technology in this area so they can, uh, understand better how to, to roll with it. I'm a little bit surprised how little there is actually other than the seminars and stuff that's more on a hallelujah level, um, and height level. Um, and then, and then I think find some trusted partner. Uh, and I don't necessarily mean being a consultant company. Us actually, I mean, look at your. Uh, neighbor or your, even your peers and competitors, what are they doing? Go together and, and talk about these issues together. Um, go to someone who's doing something completely different than you, but you know, they've come a bit further than you and have a conversation and, and start engaging. And I think partnering in that sense. It's underestimated, uh, not just, of course in AI that's relevant and everywhere, but you have to get the grips with the fact that there are too few resources and the ones who get them will accelerate at some point really, really fast if they succeed at, when they succeed. So partnering up is going to be way more crucial, than people are thinking about today. Um, and then don't do everything. Try to see if there's some, what, what is unique for you, uh, and is, what is your area of, uh, where you can excel and then leave everything else to standard stuff, right? Don't, don't try to do all, all things. But partnering, I think is number one, honestly.
Lari:
37:21
Partnering makes sense. When you think about it, it's, it's like any change. You, you don't want to go at it alone, especially if you're inexperienced. Um, and yeah, I, I can see that there is an increasing amount of, um, educational resources you can follow, but until that's widely available, then yeah. And accessing people who've been there, who've learned some from some of the mistakes just makes sense. You skip some of the bad stuff. Um, stick Martin, uh, we're getting to the end of the interview here. But I, I do wanna ask, um, do you have any recommended resources listeners should follow?
Stig-Martin:
37:53
Yeah, so, I, I actually, um, I use um, medium to com quite a lot. Uh, there are several, uh, areas around there where you can follow. So that depends a little bit on the level you are at. Of course, a lot of that can be very, Uh, detailed. Uh, but there are some high level stuff that you can follow and, and read, uh, and keeps you updated. Uh, and a lot of it is also sharing about what's gone wrong. I think that's important. Uh, I would also recommend those who just want to get a. For its grasp to invest in, in, uh, in the chief, uh, decision officer of Google's YouTube, uh, series. It's free and all of that stuff. And it's, it's, she's made it into something that's quite. Um, tangible and understandable, uh, and it doesn't require you to, spend hours and hours. It's quite easy to get it, and it's, I would say very good, very up to date. And she touches on what's important and tries to take away the, hype basically. So that would be a, a good advice. And then there are good pod podcasts out there, uh, interference from from Finland as as one which I actually follow. Uh, and then leadership posts, podcasts, uh, which you can listen to in between. I think that's a very useful, uh, useful way of looking at it and. I know that's outside. Uh, um, but for perspective, I think it's also useful to, to read the Hararis books. Uh, the human, uh, Davis is the, is the latest one I believe. Uh, and it gives you perspectives on, on, on where we're going. It's very thought provoking and not necessarily correct in every way, but it gives you perspective and it touches on a lot of the things that will change us as a society around this. And, and it also gives you the perspective on, on how thoroughly, um, through and through the change. So I think that's very helpful. Um, so that's from a leadership, uh, angle, but I.
Lari:
40:01
Stig-Martin, that sounds great. I think that was a, a good variety of, uh, resources there. We'll make sure to include in the show notes of the podcast when it's released. Um, so Stigma Martin Stu Tuck, it's been absolute pleasure, absolute pleasure to hear your insights. It's time to wrap up here. So this has been The Wonderful Work Podcast produced by Work Fellow. Um, thanks for your time. Subscribe and stay tuned for more views and insights that make the world of business operations just a bit more wonderful. Goodbye
