Welcome to Work and the Future, a podcast about tomorrow with your host, Linda Nazareth. Well, hello, and welcome to a new season of Work in the Future. I took a little time off from doing this podcast over the summer. I wanted to kinda regroup, but now I'm back and I'm excited to explore some of the things going on, specifically some of the things that are really changing the world of work. And top of that list really is artificial intelligence. To talk about some of this, I'm joined
on this podcast by Anders Hogito. Now he's the founder of Iterate. If you're a long time listener, you might recognize his name. He was one of my first guests in 2020. And at that time, we talked about how to create a great environment for innovation, what that means for work. Seems like a long time ago. Lots has changed since then, even though that's an important topic. Now when we talk about innovation or anything else, we talk about it while also talking about what AI is doing to change it.
And Anders has a lot of thoughts on that. I spoke to Anders about AI, the way it's changing organizations, and specifically what it means to work and to knowledge workers. If you remember at one point, and it wasn't that long ago, we talked about technology and AI, and the narrative was kind of, well, you know, what's being threatened is fast food jobs. We're gonna have a lot of robots or whatever.
Now we know that we are talking about all jobs being disrupted and knowledge workers, they're pretty high on that list because of the things that you can do with artificial intelligence. Now, Anders didn't shy away from all of that. He talked about the disruption ahead, but he's also really positive about the ways that AI will change jobs and create opportunities for the better.
That might particularly be true in small organizations who'll be able to use the technology to maybe compete a little better, maybe create opportunities for workers. So it was a really great discussion. You know, we touched on some, I would say disruptive or scary things, but things that are really changing, how we look at work. And I enjoyed the discussion. So please stay with us to hear it. Well, will knowledge workers lose their jobs to AI? To talk about that, I'm joined by
Anders Mukito. He is the founder at Iterate. Anders, so nice to see you again. We spoke few years ago now. Yeah. Nice to be back. You know, remind everyone about your work and why you're interested in this area.
Well, we started Iterate, you know, it comes comes comes from the name, I suppose, but it to with the goal to become a learning organization, and to enable people to figure things out faster and get faster to better solutions and to, hence, by the name, work more in iterations and collaborate and be a place to really do complex problem solving. And this has been a long journey. We started, almost 2 decades ago. We've gone through many phases.
But now with the advent of generative AI and generally the big, progress that's made in the field, We've started looking into what how this will impact organizations, and knowledge workers. And, I think it's some exciting times ahead. It's exciting times, but it's really hard for people to hear about the things you can do with AI and not feel insecure about, you know, their
future. Right? So if you could summarize it and we'll go into this in detail, but if you could summarize how you see this rolling out over maybe the next 5 years and maybe even less time, What would you say it's going to look like for people? Well, I think one way to think about this is that if you look at the previous 5 years up until this point and you ask yourself, like, there have been moments during those years where you have been super productive.
You've done some really, really important work, and you can kind of see how it plays out. When was that? And what did that look like? And perhaps even more important, how often did those situations occur? Because I think when you ask the average knowledge worker about this, they will have some stories, but they will also say that, you know, there are a lot of other things we've been doing too that maybe didn't make that much sense. We did it because we should do it.
It's the way we work. There are always explanations for it, but it didn't really lead up to anything very valuable or very important. So when it comes to this question, you know, am I going to lose my job as a knowledge worker because AI is taking over? I think that the kind of catchy answer is that, yes, you will probably lose your job. But, there's good news. You will get another job, which is probably going to be a lot more exciting and a lot more rewarding.
Because if we can have help, remove all these unnecessary things that we do, we will have more time to do what humans are very, very good at, which is complex problem solving and being creative and collaborate. So I think, I choose the optimist route, on this one. And there are some early signs also, I think, that speak to this direction. Well, let's bring it back to the productivity part of this because it's a huge debate. Right? How to raise productivity in a lot
of countries. I live in Canada. It's always something that's talked about. How do we become more productive? You're arguing, and I know you wrote a piece for Medium on this, that we're at a point where productivity in general has not is not at a place where it should be, that it's maybe falling in a lot of organizations. Why is that? Well, it's it's puzzling. I mean, when we started Iterate, our basic approach was to go into software teams with customers and just see
if we could iterate and learn faster. And the more we tried to do this, the more we learned about the surrounding organization. And when I started looking at what other people were saying about this, this was kind of always the conclusion we came back to. You could go only so far with the teams, but then there would be factors around the teams that would slow things down.
And there are people who have looked into this from a more macroeconomic level, and they they can show that, okay, yes, the economy has become more complex over the last 50 years, but the organizations have become far more complicated. It doesn't correspond to the growth in complexity. And there are so many layers, so many rules, hierarchy
and procedures. And I think many organizations tend to maybe just do this because it's all what they've always done or because that's what the other organizations are doing. And there's a lot of this inheritance which is going on. And I think that really, impacts productivity. Why have organizations become so much more complex than the economy? I know you said it's hard to explain, but give us an example of how this works in different industries.
Well, you know, there are different aspects, but you could take your cost control as one example. So IT and the software, when it emerged, it kind of became at some point, I don't know, maybe during the seventies eighties, people started realizing that this is something we need. So they started investing in it, but they didn't have really much control over these projects.
And then there will always be some company that pulled off something really important and just made a leap from the competitors. But most companies, they were wasting a lot of money and had no idea. It was like a black hole. You just put money into it and you pray that something comes back. And then they kind of overcompensated and started these very rigid project models to kind of control, software development and by extension, also innovation.
And, it created a lot of this complicatedness, I think, which, hurts productivity. You will have to have rigorous plans, which means you'd have to, think, you know, speculate really a lot about what's going to work and what's not going to work. Slow adaptability and the wrong incentives. And then you had all of these people that would have to manage all this additional process and bureaucracy. And that's kind of just gives give gives the whole organization a lot of extra
weight, which in itself is a cost. But the secondary effect is perhaps bigger that the organization goes slower. And then there, you know, gradually there was a movement to try to, remove some of this and and get to smarter ways. But this is a pendulum and it swings a little bit back and forth, and I think we're still pretty much in that control.
You know, the the this way of thinking that, you know, split things up into smaller parts that we can control, whereas a lot of the the real productivity and the real innovation happens between the parts. And that's when you have a big organization on top, it's really really hard to break through this. So now we're introducing AI to these complex organizations and this economy that's changed so much. Well, it's a big question, but how will this change organizations?
Well, I had this realization when I came back to this old book, which is called the mythical man month. I think it come came in the seventies, written by Fred Brooks. And he he realized that, you know, he he was managing these software projects. And, the kind of project that could be an innovation project has the same type of complexity. And every time, you know, they were falling behind, they would always try to add more people to the project, which usually had the impact
that they went even slower. And out of this, he formulated what's known as Brooks Law today, which is basically that you add more people, well, then you have more people that need to communicate. And because we're doing this complex problem solving, everybody directly or indirectly will have to communicate with each other. So the need for size and the growth in size and number of people in an organization leads to an organization or exponential growth in organizational complexity.
So then I started looking at startups and what are startups doing today now with the advent of AI and generative AI and all of these tools? And it turns out that even though they're at the very early stage now, they can really remove a lot of this repetitive administrative type of work that humans are doing. And it occurred to me that, well, doesn't that mean like if that works, doesn't that really mean that we can have, we can do the same with fewer people?
And if we can just reduce the number of people in an organization by, let's say, 30%, that's a huge reduction in complexity because that has a that's exponential to the the number of people. So you can see, say, in areas like sales and marketing, for instance, the work you need to do to just find customers, like get their attention and get to that first meeting, that's a lot of work that is carried
out by humans. But I would argue that the most valuable work a salesperson, for instance, does is in that direct communication with the customer. And if they can have help to find the customer and get to the right person at the right time and do that much faster, that will turn that salesperson into someone who's much more productive. At the same time, the organization will need fewer people. That's a really strong statement though, when you said 30% reduction.
30% reduction across the board is, anyway you look at it, a huge increase in unemployment. Yes. But then the next question becomes, what will these people who maybe then never got that job in the first place? What happens to them? But what I think will happen is that they will have opportunities with other similar companies who have the same rig or something similar, smarter, more innovative companies.
But because of this reduction in complexity in the organization, you essentially reduce also the risk of whatever it is that organization is trying to achieve. And then you can speed up innovation, and it will be easier to find jobs, and it will be easier to run companies that are more innovative. So that brings us to the, yes, you will lose your job, but new jobs will be created cycle.
And it's very, it's mind blowing to think about what can happen if we are able to lower the risk of innovation. What will happen? Because there's so much money and so many resources now that go into preserving the old companies, even though we have the technologies and the ideas that we need to really solve whatever these old companies are doing in new and much better ways. But still, it's so much preservation going on, and I think it has to do with the risk of innovation.
And if we can lower that risk, we will see more innovative companies. It's an interesting concept because you're talking really about leveling the playing field in some way, right, against the larger companies. There are huge repercussions, not just for work, but for the valuations of large companies. Correct? Absolutely. And that's where you can say, if this happens, I would assume that we will see migration of capital. Still, we talk a lot about venture capital.
It might be in many areas the most visible type of capital there is, but it's still very small, in the capital markets. It's a small part of it. And when it comes to building better societies, we need that kind of capital. We need that kind of risk taking to get to better places. And if more people can have the opportunity to do this, it will have large repercussions, both in the economy and also the development of societies. It's a little frightening, but interesting, optimistic.
Let's talk about guided AI workers. What does that refer to? Well, the idea of an guided AI worker is that you it's an AI agent that you task with some kind of objective. And, the difference between this and any kind of, you know, normal software tool you'd use today is that the guided AI worker is really out to achieve a business outcome on your behalf.
So it's not just about producing something or making a process smoother or faster, but it's to achieve, say, a new customer or a new leads into a sales pipeline, or it could be any kind, production of content, but things that you can put out in the world. You can get feedback on it, and that feedback can in turn go back to the guided AI worker to make it better. But you have basically these companions, which are AI, but they help you achieve business outcomes.
So the guided worker is the human being, or is it the AI part of it? It's the AI part of it, but it's guided by the human being. And that's got yeah. And the guiding is very important because it's not like you're just gonna send them off to a mission and you'll check-in on them, you know, a year later. It's very interactive, and you are still in charge. You are the one who are telling you are the one who's guiding it. You're giving it the brief. You're following up. You're tuning all the time.
But it does all the heavy lifting in terms of repetitive tasks, time consuming tasks, and it creates more space for you to be creative and do the human part and maybe even make your organization a little bit more human. I know you're in Europe. You're in Oslo. Right? Are there examples of these being used in a large way where you are or that you've seen? I haven't heard a whole lot about it in North America.
No. It's still emerging. But so if you really want to see someone who's doing it, you'll have to, you'll find most of them in the startup scene. But we do see that there are also other types of companies who are trying it out. So, for instance, when it comes to marketing, sales prospecting, this is one area where it's reached a maturity, where it actually is now possible to operationalize it and get value from it. You still have to do a lot of the manual work, but
this field is moving so fast. So if we are here already, imagine where we'd be in just a year or 2. So that's one area. Another area where you can see it is in customer support. You interact with these chat bots. I've had the experience myself with 1 company, and it was kind of clear to me that it looks like I'm interacting with a bot now. But it so it's an AI worker. But okay. The the the the the messages I get, they make sense. I'm actually getting the type of help that I need.
And then I I saw at one point, it seemed like a human took over. So they were monitoring and using them. But once it failed to help me all the way, the human being was there to take over. But it means that this customer service worker could probably serve many more in parallel than they would have had to do. And maybe also when they do take over, it's not a rudimentary task any longer. It's more exciting, right? Because the AI worker
can only go so far. And maybe that makes work even more interesting also on the other part. So a 2 part question. One is on the organization side of it. If you're an organization and you want to profit from AI, you wanna reform your organization, what are the skills you should be hiring for? And then on the other side, if you're a worker, what are the skills you should be acquiring?
Well, if you're the organization, I think there's something very democratizing that's going on now with generative AI because you don't need to be a data scientist. You don't need huge data sets and to train algorithms and all of this. This this is also happening. But with generative AI, it's very accessible.
So really, the type of skills you're looking for is creative problem solving people who have a strong public development mindset, who can, in a way, go a little bit wild with these tools and try them out and experiment and figure out ways they can be utilized in the organization. And, flip sides, the knowledge worker. I mean, this is just, I think, wise for for any knowledge worker to just be curious about these things.
Try out different tools that are coming, and try some use cases, see how how far you can go. A lot of fantastic content on this, you to be found, YouTube videos, a lot of lectures and things. If you start just Googling or searching for AI workers and AI agents, you will find a lot of people are talking about this. And it's really an emerging field where we just collectively try to figure out
where this goes. And I just want to add that that's something that excites me about this technology, that it's in a way very serendipitous because I've been trying to use it to solve a given problem that I had. And many times what happens is that it doesn't really solve the problem I intended it to solve, but it went ahead and solved a different problem.
So we're and I think it's something about it's very new and we're not used to it yet, but there's also so many inherent capabilities in these algorithms. And you can only find them by exploring and experimenting and trying out and connecting with other peoples who are doing the same. Very exciting times Anders. Thank you so much for talking to me today. Great being here, Linda. Anders Ageko is founder at Iterate.
Well, that's it for today. If you wanna know more about Anders' work, please check out our podcast notes. You can find them on relentlesseconomics.com as well as a lot of podcast platforms. You can also contact me through relentlesseconomics.com. You can find me as well on x@relentlesseco, and I'm also on Instagram at lindenasworth keynote speaker.
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You can also contact us at comments at the work and the future podcast.com. The Work and the Future podcast with Linda Nazareth is a relentless economics production.