111. Människa + AI: så formas framtidens arbetsliv

Människor, organisationer och samhället står inför nya krav. I padden fråga forskaren kopplar vi ihop forskning med praktik för att driva utveckling som faktiskt fungerar. Vi på Sveriges hår förening står bakom padden där vi utvecklar ledarskap och organisationutveckling med forskningen i fokus. I det skits Sveriges hår förening och mer vålds stoltasponsor, akademiker förbundet SSR så är vi ytteråga forskaren tillbaka eller hur sortam. Jappelighet tillbaka heter det jämt, det är jätteroligt att vara här som vanligt och träffade juan, träffade jensvår tekniker och framförallt träffar våra splännande forskare som vi alltid har. Och vi lär oss tillsammans med våra lyssnare. Och vi har ju haft väldigt mycket fokus på AID senaste året. Och därför är det fantastiskt att kunna medel att tutta Isabel Special episode, Sorka. Ja, det är sorka. Det står Itza, Not Sorka. Det är förstås mindre. It's the first one in English and we are joined by the researchers who in 2025 received the HR Research Award from the Swedish HR Association. An honor that recognizes research with both high academic quality and strong relevance for HR practice. So please give a warm welcome to Katja, Enola, en violetta koreva. Sure, welcome. Very much Katja en violetta. That's so nice to have you here. And congratulations to the fine prize to start with. And we thought that perhaps we can start with that you guys can introduce yourself. So Katja, can you please start and give us just a brief introduction to yourself and to research area? Ja, som Katja, Enola, I'm working as an associate professor at Stockholm School of Economics. And my research spans from my experience in project and program management at Ericsson. Our global corporation is running IT projects. So to me, AI basically is a situation of the sort of technology that's being driven as a project organization and then all kinds of things start to happen and not all of them are happy. But a lot of them, it's fascinating world when you implement projects. So I'm interested in the grassroots phenomenon. What happens when something like big technology is introduced? What happens to people how they think? Yeah, my dilemma is kind of why do so many IT projects and projects fail? Because we know from research and practice that 80% of IT projects actually underperform or fail. So how could we perhaps understand these projects better to see then what we can do about it? If anything. Thank you very much. And we are left off. Please give us a short introduction. Hello, everyone. Thank you very much for inviting me to this podcast. And my name is Reolette Correba. I am a faculty member at Hansken School of Economics at the Department of Management and Organization. I've been for over a decade an assistant professor of management at this very school. And now I continue working as a full-time researcher. And, well, why research interests up very similar to what Katja has just discussed? I always focus on humans, on employees, on their feelings, emotions, experiences. What happened to these very people, to these very frontline employees, when various technologists come into place, digital technologists, agentech technologists, algorithmic technologists? So that's my research question. Wow. And how did you guys find each other? Because I mean, you have won this nice prize for the research you have done together. So just a short history. Have you found each other and how this project came along? Oh, we found each other. But nobody asked that because in academia, it's a certain debate, so often. So I was also working at some point at Hansken. I'm from Finland. So if Hansken is this Swedish language school in Finland, a business school like Hansel's in Stockholm, similarly. And Reolette and me, we are both outsiders, cultural outsiders a bit. So when that's the case, as you know, we may know, then people start bonding. And we bonded over coffee or a lunch and slowly building the trust to work on a research project. So we have been doing this together, which. It feels super good to be included in this way also to receive this prize in Sweden. That as also being outsiders again, a bit outsiders, then that you are actually doing this in English. And we continue on this same trajectory. So it feels extra good at least well for us to. To be included and to have this prize and to be here with you today. So I don't know, Rita, do you remember something else about our first encounters? I call her Vitta. Also, Reolette is her name, but everybody calls her Vitta, so I cannot in my brain. Reolette doesn't really work. Yes, I fully agree with what Katja has mentioned. I think our partnerships started about seven years ago. We had common research interests and we started working together researching this very topic. And then I would say that we also complement each other. We are very different in certain ways. Katja is an excellent idea generator. She can explain to you the hardest, the most complicated theory in very accessible way. And I am the person who is, you know, like a very good like secretary. I keep on structuring, pushing, maybe finding the right direction. And then if we have some rejections from some journals or somewhere else, we need each other. You know, we can complement each other. So I would say that this is like a perfect marriage that we've been together for already seven years. We have our moments, yes, of course. But at the same time, we have common goals, common interests, and we keep on moving forward together. Great. Great when you have that true partnership. That's across when it kind of. Exactly. Sorry, Katja. Shall we give the audience the yours motivation? Yes, I think we should do so because this is a really nice price that has been along for many years. And it's given every year for the research so to say there's a jury. And the jury's motivation goes like this. The jury notes that the research by Enola and Kordava is especially relevant for Asia professionals in an era where AI is rapidly reshaping tasks, decision-making, and leadership. It underscores the importance of strategic skills development, carefully planned implementation processes, and a cautious effort to the neutral organizational culture when new technologies are introduced. So this is the motivation for the price that you received. And I signed a stand from some of the papers that Katja shared with me. So your research shows that the future work is not just about humans or AI kind of, you know, it is more about the direction between these two and a lot of our podcasts that we have done so far with different other researchers. It kind of shows a little bit the same thing. It is defined the kind of the common ground. So because this kind of reshapes our roles, the relationships, experiences, everything is affected in a workplace kind of when we are introducing AI as a tool. So that is the standard. Some of your research is about this that there are some things that we need to consider more to be able to succeed. And perhaps be the 20% that succeeds as you say, Katja, because 80% of the technology IT projects, they are not successful. This is kind of, I think, give us an opportunity to hear from your research what we can do differently to be able to succeed. Is that correctly understood from our perspective? Yes, definitely. I think it's usually these are like small things people ignore, like when you put in a new system, there are emotions involved. So so people start fearing for their job. Some people are excited. Other, there's a commotion. There's something that's triggered in the organizations and people tend to think functionalist is so rationally. We do this type of system be implemented and there's a kind of linear plan and then output should be a system that works, but then then many times we forget that there are actually people and how to plan for those emotions. Like if there's a toxic culture or something really bad going on or poor relations between management employees. And so it's a different situation when everybody is new and excited and it's a startup and so there are different each company has its own scenario, which then oftentimes is forgotten when we start introducing this project. Det är en human, det är förgående. from the equation. So it becomes a project about the tech, not about humans who are supposed to use it. And that sounds silly, but we don't take that into account that humans are different from machines. Exactly, and this is really high relevance, Sorka. Yes. Great. It is research. And we will focus on a few questions or themes from your research. So I think let's get into it. AI changes jobs, but doesn't just replace humans. Any comments? Violeta? Yes, and I would like to respond to this question in a way as a boring, academic first questioning, what is actually AI? What is artificial intelligence? And many people, they believe that artificial intelligence, it's just a tool, it's a technological wave, while I would rather see it. And this is what Kasiya has just mentioned, that this is rather a foundational shift. Many compare AI to the internet, but in my opinion, that analogy it falls short. I think it's much bigger than the internet, for example. I personally call it when I explain this to students that this is the new electricity. 500 years ago, when human energy was limited, well, human energy was just limited to muscle or force power. And, well, energy was scarce, it was localized, but then came electricity. And suddenly every house had a socket. And you, as a user of this socket, you could plug in a device and you could start using the power. So the world transformed. But early on electricity, it was mysterious, it was, it was dangerous. People didn't know its purpose, they didn't know what to plug in. However, over time, we built electric lights, washing machines, radios, you name it. Electricity became invisible, yet it came also indispensable. Today, AI, in my opinion, is at a similar moment. The socket doesn't deliver energy, it delivers in intellect, artificial intellect. So AI, as you correctly pointed out, it becomes ambient, it's embedded in tools, workflows, homes, cities, everywhere. But we're still asking the right questions. What is it for? Is it safe? Does it replace us? And I believe these questions, the, the echo, the uncertainty of electricity's early days, answers will come with time, experimentation and obviously mistakes. So I would say that again, summing this up, AI is not just a tool. It's a general purpose capability. It does change or work, human potential. And just as electricity, democratized power, AI is democratizing intelligence. We are not just automating tasks. We are augmenting our minds. So yes, AI is bigger than the internet, because internet connects us. AI can empower us. So we are plugging into socket that thinks. And once we learn how to use it, everything might change. Wow, that was an interesting knowledge. I think really, because it kind of sets the scene you want to see it, because it opens up the definition of it, but also the, the dimension of the change it actually will have and has already in our society and workplaces. I think you wanted to say something. Yeah, just to open up a bit of the problematic, because, well, because humans have always been inventing, like, like inventing animal, I mean, we invented first the fire, like the fire, then tools and then many inventions, then dynamite and all kinds of nuclear fusion and all kinds of things that we keep on inventing things. So in that way, well, we are in that same continuum. So this invention inventive nature of humans is nothing new. So we tend to talk about revolution when it comes to AI, but we are just doing what we have always been doing as a specific type of animal that has this imaginative power and brain capacity to do certain things. But then again, there's a lot of debate about what is the nature of AI? So how we should not think it's a thing is our creation. So technology itself is our creation. So it's not a thing out there that lands upon us and we receive it. No, it's human knowledge is there. So why it is different is that from electricity or other inventions automation is that well, it starts combining ideas and acting as it as if it was intelligent, but we forget that these are actually, you know, electric charges algorithms. It's it's not thinking. So the moment human started to call artificial intelligence intelligence, we only frame it in a certain way. So whether the machine is intelligent is actually not so. So it's just the way we call things, it defines our imagination and it's too late to go back, but it's good to think about whether it's even intelligence, many people is alien intelligent or it's not even can a machine be intelligent. I don't know, it depends on your definition of what human is. The question about that we are being replaced, you see that already today, in for example, marketing, I think information that kind of jobs, I also read an article yesterday about system development and I heard from my youngest son is in that area, they are using it already extremely much when he works. What do you see today when it comes to different kind of jobs, but I think most jobs are like my job is our students, they, we have, I'm like fresh planning my horse from last year, no, because I don't want to keep on reading essays written by a touchy beauty, not, you know, then it the whole education, it loses its purpose journalism, I'm sure, and even HR hiring processes, they are being influenced. So it's like, if you're a hairdresser or a plumber, then I guess your job hasn't changed that much, but anything that has to do with knowledge work is impacted and we are, I think I most of us are confused. Confused about it because it changes so fast, like having to crash plan a course from last year, I have never seen that in my life so far. And so it's out, so it's out, what do you see? No, I just think that I think most jobs, it is, it goes back a little bit to what we are, let's get you saying, it's kind of how do you define human and how do you define AI and what it is, but I think there are so many things that AI is involved with that we already take for granted. But today, so today and then are some things that are new that affects knowledge workers and things like that, but I think it kind of affects quite many jobs today in our everyday life and the situation. So that's why I like, well, there's a knowledge to electricity because it's just kind of it's coming and it's getting, we are getting more and more used to it and we take it more and more for granted. And that is kind of happening and it's happening so fast so that that is kind of fascinating as I see it. But I find interesting from your relation from your research is also that just as you mentioned the different, I mean, there are different experiences of AI and organizations between, for example, the difference between leaders and employees. There's something about that because it's entering our work day life, it's obvious it has been doing so for a while and it's doing fast, but there are differences how this is experienced by leaders and employees. And that's also something that needs to be addressed as I think because just to be able to succeed with implementation, so can you please just give us some insights what you have reached, have shown. I can start responding to this interesting question. Well, I think many of us have experienced love triangles. So love triangle is a form of a relationship where we have free main actors, right? And when we talk about using the inception of AI and organizations, we also have free main actors. We have vendors, technology designers, right, who sell to the companies, their products, their AI tools. We also have leaders, managers, executives, those who lead the organizations and then the third actor, other employees, the people who actually do the real job. So we have a triangle here and how it usually happens and this is what we've seen with Cartier in our research that technology designers, vendors, they sell AI tools to the leaders, to the executives. And what do they usually talk about when they. sell their products they are talking about, intended, magical, if I may say so, at times illusionary, expected outcomes of the use of the AI tools. But in practice, leaders and executives are not the ones who are using those technologies, right? The users are the employees. And employees, they use the AI tools, which are far from being perfect, which are given to them in their present shape. They might be all some malfunctioning. And this is what we've seen in the one of the biggest Finnish media agencies that, and please, they were working with this at times malfunctioning AI technologies. And therefore, of course, this employees, they experienced such feelings as anger, frustration, fear, and so on and so forth. And at the same time, when we were having interviews with the executives of, and managers of those employees, managers were quite frustrated with them, please. And they were saying to us, why is it so difficult for our employees just to change from a manual to an automatic gear. While in practice for employees, it would be much more comfortable to use the manual, well-functioning gear rather than automatic one, which wasn't functioning that well. So this is, these are the experiences of AI and organizations. And this is a slot for young girls between vendors, executives, and employees. This is just one of the examples, which we have encountered. An interesting gap, definitely. Katja. Yeah, and then it's also like, I'm thinking to build on that, that within organizations, there are many, many interest groups. Like, let's say banks, the big banks, like banks are slowly transferring into technology companies. So actually, big banks have been hiring lots of tech people now. So there's a big technology back off is this technology platforms. And at the same time, this organization that used to be geared to customer support, like having physical banks and people responding to customers, those are fading out those roles and those people. And the people who work in the front lines, their jobs are changing. So the easy tasks are being automated to these chatboards and automated services. While the, you know, those people who remain in customer service, they get the tough inquiries, those that cannot be automated. And at the same time, these people's jobs are not necessarily being valued that much because the knowledge is at the same time baked into the chatboards. There is this whole new job category AI trainer, which is also kind of misleading. Like the AI, it means that there are certain people who used to be customer service people are now transferring the knowledge, are training so called the bot to act as if they were humans. And so it's a slow transfer of this implicit knowledge in humans, making it functional. And to be deliried by machines. And of course, it stripped out of this emotional component. Because I mean, the chatboards cannot be emotional, although they pretend they are emotional. So this way DC is like tricking people into thinking, you are like, there's some ethical concerns here as well that are not tackled by companies. Yeah. And my only reflection, because when you're giving the bank example, I think it's very, very interesting, irrelevant. And then I was thinking of one key, what should I say, partner, that one, it's the customer because you know, as a customer, banks are putting a lot of effort in the new technology, the new services, all of them are not humans. They are just kind of, you know, you should go to the, you don't have anybody to ask. So I was just thinking as employees, perhaps even you as a customer can feel that change is going too quickly and that you actually don't find your way around with the new technology solutions that a banks and other providers, service providers are offering. They're not offering that you can talk to somebody, but they are offering you the chatbots. And they are sometimes very well developed, but most time they are not came out. So they can answer the most easiest questions that you have that you can solve by yourself. But when you have these tricky questions, then you don't want to go to chatbots. You want to go to talk to somebody who has a higher level of knowledge, they can help you really. So that is also a, just whenever I was thinking of, when you said that, catch you as an example from the bank is, as a customer, it takes some time, not just for the employees, but also for the customers to manage the gap when you change how you provide the services to your customers and using the high tech technologies. But I was just thinking, I mean, just as you said initially, catch is kind of when you are, it's easy to think that when we are discussing AR, it kind of starts with the technology, with the products, with the vendors. What you can do for us, how it can make our life easier. And you said, but we often forget the emotions, every human's arts kind of, you know, emotional and things like that. I would like to come back to that kind of, because AI affects really kind of, you know, the emotions, the identity, the culture in our organizations. Can you give something that you find from your research regarding that? Because I think that is the kind of key. Well, we had this one project with Violeta that we actually were studying, you know, AI implementation, how do different employee groups, managers, middle managers, frontline employees, older, younger, whatever, how they would, how they were feeling about this implementation of this introduction of a quite simple AI chat, but because chat pots are always for a purpose. It's not, there's no generic chat, but they're always designed to booking something or to, it's very narrow there, focus. But actually what we found is that in that, because we spent a lot of time in that organization, it was before COVID, so we were hanging out there, is that there were lots of anger or frustration with lots of chains that had been going on for decades in the media industry. So digital transformation started there 20, 20, 30 years back. There's nothing new in this. It's just continuation of new projects. So certain groups of employees who had been around long enough were fed up with all this, again, a new tool, new thing, and I'm always stuck with dealing with the customers and also implementing new tool, and I don't have enough time. And my boss doesn't understand my job. They think, I mean, it's so simple and easy to be automated. I'm now stuck because of the poor planning of, you know, of this project, because it takes a long time to implement something like that. So it's this is emotions that popped up there. So, so AI amplified the problems between managers and employees, because it was poorly planned. So, so the existing frustrations and this, what we've said, love affair of the top management with this AI, wow, it's going to save our job. They are looking forward. They are looking like five years from now. They are not looking into today. I have my billable hours. I have my job to do. So it's like this type of bizarre, you know, emotions that get amplified within this, when you introduce something like that. So, so actually emotions do happen, but they don't, it's hard to capture them if you don't go and sit with these employees. If you run a survey or if you do a plan, you don't see emotions. So you have to be there to capture them, actually. So I would like to add to what Katja has mentioned, and this is absolutely what we've seen in this media agency and in other organizations, you have been studying and now going back to our bank project. It was very interesting, you know, like now we were talking about emotions. I would like to talk also, we found in this study that our sense of ourselves is changing when we work with AI tools. In academic terms, this is called subjectivity. So human sense of self. So when we, when we work with AI tools, what is also happening, and this is something very invisible and slow, it is that, first of all, it changes the relationships between us and the AI tools, because we start, well, asking more and more questions from the AI tools. We start creating knowledge together. We adopt those the way algorithms are working. And what we've seen in the bank, actually, that employees of the bank, they started to certain extent, evangelizing the board. They would believe what the board is saying, because everything the board would say the AI tool, it comes from the pan of the organization. So it's not self learning. Everything with the board is saying it's been told to the board by the organization by the bank. So human agents, they slowly become subjects of this non-human agents. It changes the relationships between humans and AI. Next, what is happening is that it also changes the interpersonal relationships, relationships between humans. It's harder and harder for us to maintain deep relationships, trust to resolve conflicts and so on and so forth. And for instance, in the bank agents, they are encouraged to ask the board when they have questions, rather than to ask another colleague. And then at the end, what is also happening is that our sense of self is changing, because we do see things, not as they are, but we see things as we, ourselves, are. This is not who I am, I didn't, I didn't come up with this phrase by myself. It's a French Cuban American writer and a sneeze who explored identity and emotional life. So she has mentioned that we don't see things as they are, we see things as we, ourselves, are. And now, because we are applying AI use so often in organizational life, that we start, we see ourselves also through the eyes of AI, because we are so often asking first AI before actually doing something. That's interesting, interesting, and you always see that in your private life. I mean today, AI, chef bots, or the notice the last year that they are the number one doctor, the number one psychologist, the number one relationship coach. So I think that's also affecting us a lot. And I think that's extremely interesting, because just as you are saying this with, I think you measure somewhere humanizing kind of, you know, the AI and have the changes yourself kind of awareness. And just as you are saying, how you see yourself, how you see yourself in the world. And just this also decreasing, which is a collaboration, colleagues in between, the human colleagues in between. So I think that's a kind of a bit of a, I would say scary, but kind of something that's really needs to be discussed. I think just as you're saying, going now on, implementing and working further with AI, I think there are so many more of these soft questions, if I say so, that we need to focus on some kind of relationship and how it's perceived for managers to employees, how I'm perceived and just this, what we are discussing, because I think it happens so fast. And we are still focusing on the technology solutions itself, the tools and not so much of all the reactions and emotions that all this changes. And especially the way we work and the cooperation and the social life in organizations, what do you see about that? So what I was, I've been thinking about this anthropomorphism, human, this is different technology for every other technology, because electricity we can understand and it's a physical phenomenon. We can, you know, it doesn't talk back to us. Electricity to charges don't back to us. They don't come up with own creative or so-called creative solutions. So this is why I'm like I'm listening to this podcast on Silicon Valley and people are like the biggest problem is that we don't know what we are doing. We don't understand because this thing called AI, it's being shaped as we implement it. So it's like what do we let it to do? How much do we let these decisions or combinations of new ideas? It comes up with influence our own thinking and emotions and lives. So that is unknowable. So it's something that the technology itself is being shaped by us. It's our making. So it's something we can even control and that's why we have to pay attention to the grassroots level of this project. So is AI projects have project managers and product owners in organizations, but it's very different because we see in our research that the closer to tech, more tech savvy that people are tech experts in, let's say banks, well they don't anthropomorphize these technologies from from them, it's a project, a thing and the far further away like customers or you know, the less savvy people are about technology, the more they tend to consider it to go together with this fantasy world from Hollywood where you have AI and machines and all kinds of movie imaginary, they think about it in such terms. So it creates differences in their field then. So I would be very careful as a manager about this anthropomorphizing of humanizing this technology. And if you let it lose, it's not cute and funny when you start talking about AI as a colleague, as a, you know, new junior assistant, it's actually doing something to us. So it's not innocent and cute game. It's very careful with how do you introduce this thing? Because it does change things in the field and that's a problem if you don't plan for it. If you do it on purpose, fine, but if you don't, then it's a problem, I will say. I would like to add what Katya has mentioned and well, that's why we are partners in crime with Katya because I couldn't agree more with her. Humanizing or in other words, anthropomorphism, there was a theory called, well, there is a theory called theory of uncanny valley. It was founded by a Japanese robotist who died about a year ago, a age of 97 or something. And well, he explained in his theory that usually why vendors, why managers of organizations tend to humanize certain tools is because they want to make them more human-like. So that employees tend to feel more, become feeling more familiar, more comfortable with the AI tools. And therefore, in place that, and we've seen this in our research, that in place, they start humanizing these AI tools, they call them bodies, babies, friends, colleagues, sound call them fools. But you as an employee, as a human being, you always have to remember that this is just a tool. This is not, this is nothing human. So be very careful with humanizing this kind of old checks. An important advice. And I mean, that's a trend too. I see it when you call it poly or holy or Freddie or you have your, you're really name your, your bots. And it's a part of it. Katja, would you like to continue? Yes. Yeah, I would like back to like manager, what would you do if you were now in charge of a company or department? Is that the other thing we see is that at that often managers, or the higher up you go, the less you know what the actual job is about. And then you, I mean, with our Nordic mentality, especially we tend to have this functional engineering mentality. So there's a purpose that job is like this. And it's, it's easy to forget their other aspects in any job that perhaps the top manager doesn't understand or doesn't care to learn about. And then I would invite people to be more humble about like if you want to automate a function, don't assume it. It's easily automated pull right away. Even if it's a very simple query, every simple algorithm, very simple function, well, there may be things that, because often that's a tech company to come in, it's the consultants to who automate these processes. But they, they haven't done the job. And often these people may be junior people, they may be consultants who come and go, they have zero understanding, let's say what it is to be a customer service person. So some stability in the personal who would work together with the people who actually work with the customers, for instance, actually do this job. So the design of the algorithm would be better at least more realistic. And it sounds quite arrogant that a top manager hires an outside company to then automate a task. And because they think it's super easy to do customer service for a bank, it is not easy. There's all kinds of customers. It's, my granny is different from my 16 year old. And my 16 year old doesn't even want to pick up the phone to speak to anybody anymore. So she doesn't mind bots. But my mom is different. And there are different life situations. And humans are emotional beings. You want to give a good customer service. You cannot like walk over emotional aspects of the customer service. And then customers tend to get angry. I'm, I let go, like banks have become so bad. So in personal, they just about business. They just about quarterly profits, which is actually true. You can say that there is one of the drivers of this project is, of course, to set people out to automate, to give them better service, but also to give better results, financial results. So it's a cold face of the business that comes true. You can think about it. And I remember sort of sort of that we had a guest maybe a year ago, something we talked about AI on the future. And he mentioned that how it affects us when we, when all the administration is gone, all the things that you sometimes love to do to put one hour in your Excel, Excel files. And in that, it's a routine work. And you do it every week, for example, every month. And you love it. You put on some music. And you do this routine work. And you can be calm down in that. You're everything is on the control. And if you put all that kind of work away, what will happen to the people? You remember? Yeah, yeah, it was also kind of from the Uppsala University, because she said that for us to function and to be able, so we can't be on the edge or the time to, you know, create, develop and being very kind of, I should say, on the edge, we need this kind of routine work just to kind of, you know, get some rest in there. but they feel that we do something like that. So she was very little bit the same things that you are saying, but in a different form, of course. But that it needs to be done just as her message and her research shows that we need to start with the people who are going whose work we will change because they are the ones. They need to be motivated and understand why it has to be done and how it can be done and ask them, "How can a bot help you in your work?" So because it's steered for me who is doing their job and not just the stock management on another level. So that is just the same thing as the few guys are saying. It is more like to really involve the people and involve me if we are going to do some of my work with help from AI that I need to be involved. How do I think it can help me the best way and what parts would it like AI to help me with and what parts would I like to keep for myself, so to say. So that is probably a little bit how do we do that and how do we find that. - It was like one year ago, so it's one hundred year ago. - Can we go back? Because I was just, I couldn't stop thinking of it because when you were talking about this, continue to say that people were tech heavy and the knowledge they wanted less time to be the one to humanize the technology. So I find that quite interesting. Because I think if you know how it works for the days, then you will never call this a colleague or something like that. But if you are far away from that, then you tend to do that. Do you know why that is so? Can you just give some explanation where you think that is the case? - I can and I think it's because of the complexity. Like even our student at Handels, I ask who is interested in technology? Rarely anybody lifts their hand because they think it's called the oh wow, it's the technical peak guys. It's a different, it's not my job. But to me, this is one of the pieces of advice I'd like to give to people is that get interested. Because it's in fact in you, your mental, your future, your job, everything. We need to get minimally interested in this development, get involved hands-on in these things. Because let's say, for instance, in physics, there is no such thing as a big bang or black hole. Physicians never would talk about a big bang or astrophysicist, but they have to invent this metaphors. So ordinary people would actually get some sort of glimpse into what this phenomenon is about because it's really so complicated that you know it's useful as to try to talk to late people. You lose them in the first sentence if you really talk about what these black holes really are about. So it's like we need to simplify the complex. And then we are given easy solution to this, which is the anthropomorphism. Like we think about the Cindy or Erica or the bot or whatever the terminator. I don't know, the Hollywood version of it. It's easy solution. Oh, okay, that's what it is. And the biggest problem is that people think of AI as a thing. Now it's different applications. It's different algorithms, different designs of different pieces of logical like steps, algorithms that are implemented for different. There's no an AI, there's no such a thing. There are many projects, AI applications in the field. So to protect people, this is obvious because they work with it. They know what it is. So they don't feel like, I mean, it may be their baby because the project really worked with something. They heavily, it's your baby. I mean, we run projects and they have names. And but we know they are not, you know, human things. So there are different ways of anthropomorphism. Like you say, my project is my baby, but you don't never think of it as in human terms. It's because you dedicate your heart to it and you spend maybe 50 hours a week working on a tent but you still don't think of it as in human terms. So it's complicated. - I wanted to add to what Kata has mentioned. And here I will add another analogy. I'm a big fan of playing chess and I often compare AI to one of the figures in chess so to the pawn. And the pawn is the weakest, the most basic unit of the game of chess. However, well, if you know how to play chess, you know that there is a rule of promotion and then actually the pawn can become the most powerful figure in chess. It can become clean or bishop or rook or knight or anything else, right? But then it's up to the player actually to decide whether the AI tool still remains being the pawn or at some point it becomes the queen. It's very important that it's still in the hands of the player to decide to make the strategic decisions of what is AI tool for you? Is it your baby or does it actually become the queen of your work? - And that's something you think needs to be kind of discussed when you're implementing these new tools. What is. - And it definitely I think to continue with Winsa because you can play with these words to say is it artificial intelligence or do you want it to become the alien in that grows? You know, you can think in terms of metaphors and to me that's the way to, you know, where you're gonna lose, having the control even understanding was going on, so it will run you instead. So the people, human beings, may become just things blocked to the machines. I would give an actually a presentation of a book. Me and Vita, we have cited, we have read this novella from 100 years back, artists are beautiful because they imagine this, like chaplain imaging in the machine early and the human doesn't fit into the machine in the movie modern times. When the Industrial Revolution was ongoing in 1930, so he was able to see ahead that people don't fit into the machine. He was making fun of the machine. It's this book called The Machine Stopps. It's a novella by EM Forster. It's the same guy who wrote the room with the view, the movie, the, I mean, romantic. He imagined what happens to this humans when humans stop thinking and they are all plugged into the machines and when the machine stops, it stops in the end, then humans presumably die because we are so dependent on machines, we have become completely, you know, slaves to the machine, but it lays out the thinking through the eyes of a person, of a family and individual human beings. It's a beautiful, beautiful novella, especially because it was written such a long time ago, over 100 years ago. So it was like, that's utopia. That's these images are something where we do not want to be going. - Shall we repeat the name, Cathia? - I think it would be-- - EM Forster. - EM Forster. - Machine stops. It's sort of novella. It's freely available on the internet. It's not even a long read, but it's kind of scary view of the future. And it has, it's like now the future is there. So we have, we have still in the chess game. We are still early on. So we can make the moves. I mean, some dice is only so. And each day goes by, well, things happen. Like right now in two days, well, things are happening already. Many things. But we can still kind of take control of what I can do in my work, what you can do in a world. You can do something about it. And that's all we can do. At least to be aware, I think about it, not just close to long and become like passive touchy PT users who brainlessly think we are saving time because you're right, it's like, doom and brain needs leisure. So you cannot just make people more productive so the boring task gets outsourced to the machine. We cannot produce creative ideas constantly without the slack. And we also have that in our interviews, like our other bosses in Sang, like what do you think I just can't create all, especially creative jobs. I can't just keep creating all the time. You cannot design out the slack or the simple task because they need to come in human biological intelligence. There needs to be the rest and the, you know, you just cannot keep on being top. It lists a burnout, depression, it lists to people who don't want it to work with each other, it's a competition, it doesn't lead to anything good, it's type of thinking. - Yeah, guys, the time is flying when we're having this wonderful discussions. I really love to have you here. But I would just like to post two questions to you before we end this session. And that would be kind of, what do you sell things? How would you sell your own research? What are the kind of main, which is a contribution of your research to our practitioners? What would you say that is? - I would say love technology, start loving learning, be more curious about how it works, about AI or other digital technologies in general. And mind the project when you implement them, you need to plan for things like competence transfer. You need to know what the people, their competence, what are the skills, what are they doing to then plan for the transfer of the automation or the AI implementation, to know what the people are gonna do in the future, what they do now, how do we try to plan for a bit more proactively and get closer to the field if you're a top management, like go, go, talk to the people, go sit with them, try to do their job. Or I mean, at least acknowledge that you just get a higher and external comfort to implement an AI and as crypt task or role into code, test without any consequences, that's nice thinking. So those are three things I would just say, I just came to my mind, Avita has more, I'm sure. - Again, just adding to what the Kata has mentioned, I will. say that like my piece of advice or my kind of a mission as being a researcher is that is to connect again I'm into triangles to connect three main players in the field to connect executives and managers and please and also governors regulators I think it's very important that they talk to each other and especially that they listen to each other listening I think is the most important part here so here it's like about you know like changing the glasses every time depending on who you are talking to so for instance I would say that when we talk with executives and managers it's very important for them to well maybe to change their minds slightly exactly as Scott has mentioned that implementing a new AI technology is not as easy as moving from a manual to an automatic gear at the same time it's very important to listen to employees to those people who actually do the actual work and here I would exactly like Scott has mentioned it's very important to share employees concerns here their stories you know be a little bit like Leo Tolstoy who was talking with common people with puzzles with everyone and by listening to them he was able to write his book books and at the same time the third actor here would be governors regulators we are very lucky with you that we live in the European Union and we have very strict laws and regulations related to the implementation of AI and some people will criticize the European Union for being so slow and attentive towards the implementation of AI but well I would be here on the side of the European Union and I would give you here another example so for instance in the mid 20th century the US dominated car manufacturing why because they had powerful engines their cars were visually very appealing attractive and only later on came German and other European cars and these cars they gained reputation for reliability for fuel efficiency for innovation in safety so I hope you understand why I'm giving you here this example so try your best to connect free players managers and police and governors make it so that they talk to each other and they listen to each other so that would be my advice thank you very much why do you take with you from this conversation with this it's a lot of thing actually in my head I love and catchers that get interested I will continue I put one hour every Monday to get deeper into AI but I still use that hour to common work and doing my ordinary work so I need to to shape up definitely and I think it's so interesting to talk about managers and employees the relationship between them and this that it's so much harder to maintain the deeper relationships that's something that affects me definitely I think it will affect health and well-being and we will see it pretty soon if not already so I think that's extremely important and this thing about humanizing the AI and you thought it's what's in your head oh it's so much coming up but I was just thinking kind of as Katsumiya let us say that what do we need to bring on now because the dice is thrown as Katsumiya said we can go back and now it's kind of what do we need to focus on forward when we are discussing for the implementation of AI tools and other things and what I take from this conversation with Katsumiya let us really just don't forget the relationships the emotions and the calibration between the employees and these triangle as Katsumiya is saying but I think we often forget we see still discussing it as a technology as tools and not so much discussing how it will change the relationships the emotions the way we work the collaboration between the employees the different functions and things like that that we really need to put much more emphasis on that than we normally do so to say so I think that is something that I really take with me is that we need to put some much more effort and planning and understanding these issues so I hear or understand that change projects from that angle instead of just the hardware stuff so to say because that is where we are and then of course it makes me a little bit afraid of this that it came up to fear the the less you know the more you get as a colleague with these things and that's kind of you know I mean I recognize myself so I think that's something it's a kind of awakening call for me in that sense to kind of really rethink that in a way so with that said I would really really like to thank you Yolette and Katya for being with us today and the sharing the research that you have won this nice price so thank you very much thank you so much thank you so much Katya and Yoletta this will be one hour of program forskare and podcast history I think and I hope we will have many listeners this episode is really interesting so thank you so much Katya and Yoletta and bye bye [MUSIC]