Herewith, a video and transcript of my inaugural lecture, on November 4th.

Chasing Scarcity in Teaching: Business Education in a Time of AI Disruption

Inaugural lecture of Teaching Professor Espen Andersen, BI Norwegian Business School, 4 November 2026

Lightly edited transcript:

Well, hi everyone. I’m not usually nervous when I teach, but I am now. Thank you all for coming. I was actually made a teaching professor a year and a half ago, but I got sick, so the whole thing got delayed. But thanks to all of you for coming here – family, friends, colleagues, former colleagues, members of Comunita, and members of my former class here at BI. We’re having a party this Friday, so I’m looking forward to that.

What I’m going to talk about is this notion of AI – what it is and how it affects teaching. And I’ll start by telling a story, which I tend to tell in all my courses as a sort of final story.

The best essay on innovation I have ever read was written by Elting Morison and published in 1950 – I think it was written some years before. It’s called “Gunfire at Sea”, and it is the story of a captain on a US Navy ship who made certain modifications to how they fired the guns and improved the accuracy from 8% to 74%. Which I think you’ll agree is pretty good. It took 30 years before the Navy adopted his innovations, and there were many reasons for that.

At the beginning of the essay, Morison tells this story about the guns. In the summer of 1940, France had fallen, Britain was basically fighting Germany on its own, and the danger of Hitler coming across the Channel was imminent. At the same time, Britain didn’t have weapons, because most of its heavy machinery and heavy weapons had been lost in France before the evacuation at Dunkirk. So they had to do something. And they had all these old guns, which looked like this – they’re called howitzers. So they pulled them out of storage, hitched them up to trucks, drove them down to the coast and pointed them towards the English Channel.

And then they said, “Wait a second, maybe we can improve the rate of fire using modern methods.” Now, modern methods at that time meant something called time and motion studies. You use film and a stopwatch, and you try to take away unnecessary movement. So they set up a gun, with eight men to man it and a sergeant to command. They followed the routines, fired the gun, and filmed it. Then they sat down and started to analyse the film.

And then the scientists saw something very weird. Right before the gun was fired – about three seconds before – two of the soldiers stood to attention, like that. And they stood to attention until the gun was fired, and a little while after. In terms of firing a gun as rapidly as possible, that is a fantastic waste of manpower.

So they thought, okay, we have to do something about that – but we are not going to change anything until we know why it is there. So they went to the sergeant, and he said, “Well, it’s procedure.” They followed the chain of command up, and finally they got to this old colonel. He should have been pensioned off a long time ago, but there’s a war on. He had actually been in the Boer War and in the First World War, when these guns had been used. So they dragged him in, plunked him down in front of the film projector, showed him the movie and said, “What’s going on here?” He looked at the movie and said, “Run the movie again.” He looked at it again and said, “I’ve got it. They’re holding the horses.”

But there aren’t any horses! And you can imagine: what are we going to do with those two soldiers for those seconds? Well, there are no horses to hold, so they’ll just stand there like this. Rather than changing what we do, we just continue to do what we’re doing and adapt it a little bit. Remember that story. I’m going to come back to it.

I’m going to talk a little bit about theory – very little theory. In fact, it’s not even theory. This is my framework. I made this, and I haven’t published it anywhere – maybe I should. But this is what I teach my students, and it goes like this: Technology changes the business environment. In fact, changes in technology are the primary change agent when businesses change. And when the business environment changes, we have to change our strategy. And I tell my students and executives, and anybody who wants to hear: wouldn’t it be nice to understand how technology changes, so that we can change our strategy before we’re forced to by the environment? That’s why I teach technology.

The trouble is, we don’t really know how technology influences organizations. When I came back from doing my doctorate and working in the States in 1996, I got into a debate about the productivity of information technology. Just as we now have the idea of AI and productivity – we see AI everywhere, everybody’s talking about how fantastic it is, but do we see any change in the productivity numbers? No. We had precisely the same debate over PCs back in ’96.

I got into a discussion with a researcher in Norway who had written in Aftenposten that there was no point in buying PCs, because he had timed how fast you wrote on a typewriter versus on a PC, and the difference was 10%, so the PC was not worth it. I wrote back and said: “I’m actually sitting on an airplane with my portable computer, typing this answer, and you’re absolutely correct – I can’t type much faster. But when I’m done, I can email this to Aftenposten and they can take it straight in.” Meaning that the productivity impact shows up in other places than where you measure it.

However, Aftenposten had something called typographers, and they had a union, and the union demanded that everything that came in electronically had to be retyped. So Aftenposten didn’t get the productivity benefit until they got rid of the typographers. Which teaches us that productivity shows up somewhere else in the whole supply chain, and you have to change your organization in order to reap the benefits.

Okay, that was the theory. Let’s talk about the technology that is changing things, and that technology is artificial intelligence. Trying to define artificial intelligence is more of a philosophy question than anything else. But I tend to divide it into three things – or most people do. We talk about analytical AI, generative AI and agentic AI. Analytical AI is sometimes called machine learning – think of it as the Excel department. Generative AI is the Word department, or the picture department. And agentic AI, where you have electronic agents doing something on your behalf, is perhaps the Teams department.

All of these are disruptive innovations in their own right. The main thing about a disruptive innovation is that the existing players in an industry are the least adaptive to the new technology.

So if you look at analytical AI, it challenges the traditional scientific method. In the traditional scientific method, Popper and other philosophers of science would say you have a theory, and from the theory you develop testable hypotheses. Then you collect the data and test the model, and maybe you validate it – but very often we don’t. Now, machine learning works like this: You have some data. You take 20% of the data and put it over here. Then you take the remaining 80% and tell the machine to find a model. And once it has found a model, you point it at the data it hasn’t seen yet and test how well it does. Can it predict whether there is cancer in this lesion? Can it predict how much this customer will buy? Can it predict how much we should pay for an ad on Google? The validation happens out of sample.

And once we have a model developed, we don’t have a theory. The reason we don’t have a theory is that, especially if you use neural networks, the model is so complicated that it’s humanly impossible to explain how it derives its decisions. And this is a challenge, because the skills that were good here – theory formation and so on – are not that good here.

How about generative AI? That’s what people mean when they talk about AI – ChatGPT and friends. There are many ways to think about it, but the way I look at it, it is a way of predicting the next word in a sentence, and a way of constructing something according to what is called a reward function. And the reward function tends to be what you’re happy with. I had a prospective doctoral student who told me that she was thinking of applying to BI because her AI therapist had told her to. And the AI therapist obviously tells her whatever she wants to hear.

I refer to it as hyper-efficient mediocrity, because AI can only do that which has been done before. It can do it very efficiently, and you can have an army of agents that do it for you, but it can’t come up with something that is genuinely new. So rather than try to explain it myself, I decided to just ask: what is hyper-efficient mediocrity? And it says that generative AI creates hyper-efficient mediocrity by automating routine white-collar tasks, such as answering basic questions, summarizing text and generating standard documentation of all kinds. And it’s very good at that. Where did it find that? Well, I wrote an article about it. And rather than summarize it myself, I just asked Google, and it summarized it for me. That’s what generative AI is good for. It does the simple work, leaving us to do the really valuable, really creative, really judgmental work. But it does it very well – and better and better.

One of the things it does is automate the simple work, and we’re now beginning to see that in the productivity numbers. This came from Erik Brynjolfsson at Stanford last week, I think, saying that if you now look at industries that are exposed to AI, we see lower hiring in the lower age brackets. Because the routine work normally done by younger people is increasingly being automated.

That challenges us, because it challenges the educational trajectory. Education used to look like this: We have the basics – you go to school. Then we have the specifics – you go to higher education. And once you come out of higher education, you have the tools and the theoretical knowledge, but you don’t yet have enough skill to apply it. That you have to learn as a junior person in a company. I’d say 95% of my students start in consulting, and they start at the lower level, doing very routine, hard work. And through doing so, they learn the skills that allow them to get more responsibility, experience and judgment.

Well, now we have a problem, because now we have AI and YouTube. I think it was ten years ago that I heard one of the people in the top management group say he had overheard students behind him on the subway saying, “I don’t go to classes. I just go to YouTube.” When I want to learn something new, like how a language model works, I go to YouTube. That’s what we’re competing against. And then we have AI, where students can now produce beautiful reports very, very quickly.

So you get the basics from school – but increasingly we see students dropping out of school. My wife Kristine is a real expert on people leaving school. (My clock talks to me.) It’s very complicated, but it seems that fewer and fewer people can deal with it. The specifics, higher education – well, increasingly online, and increasingly students are not getting the education they need, because they basically ask AI to do it. And now they can no longer get jobs. So where are they going to get the professional skills? Where is the next generation of experience and judgment going to come from?

There’s a little hint of an answer here: I think we need to move this training into what we do. There are two reasons for that. One is that these young people need to be educated to perform at a higher level when they come out, because companies can no longer afford that training period. But also, we need something more to do, because we’re being outcompeted by YouTube and AI and all that stuff. So we need to make what we do more real.

And then the last one: agentic AI, which does things on your behalf. What does that mean? We sort of divide AI into augmenting what you do – helping you get better – and that’s what most of us do. Most people here have grey hair; that’s what we do. But you can also use AI to replace people. Rather than hiring humans, we hire digital employees who go out and do things for us, and more and more people do that. And this could be the situation: “I’ll have my people talk to your people.” Well, they’re down here – some of them are human, some of them are digital.

By the way, this is a New Yorker cartoon, right? No, it’s not. I asked Gemini to make it. I specified it to Gemini. The signature you see down here is a mix of three or four artists who draw for The New Yorker. I should have put my own signature on it, but it didn’t feel right. That’s pretty good. I specified the joke, but it made the drawing.

How do you know that something is an agent? How do you know that you’re talking to a human? I’ve had that experience now – people calling me, and I have to figure out: are they real, or is this a fake? You’ve probably had that too. That was the woman I interviewed, because I thought she was fake. Turned out she was real – except she talked to her agent.

Now, we can have some sort of tests. Everybody has pressed this one: you click on it, and it figures out you are not a robot. It does that by analysing the way you move the mouse, because we cannot move it as precisely as a machine can. But sometimes you meet somebody… This is a friend of mine on Facebook. Her name is Tina Underhill. She lives on a farm in Suldal, in Rogaland. She works for a small software company called Aadnøy Labs, where she does all the accounting and things like that. She sometimes puts out pictures – when they have new lambs on the farm, or when she goes for a walk in the mountains. She’s not real.

She’s made by Alf Aadnøy. Alf is a member of the network of managers that I run with Haakon Gellein. He has programmed her and named her – his farm is named Underbakke, so she’s named Underhill. She does a lot of work for him, answers calls and does boring things like that. And he says that if he were making her all over again, he wouldn’t have made her so attractive, because he has to fend off a lot of not very welcome attention.

This is important for many companies. I’m not going to go into it – it’s not part of this – but a lot of competitive advantage online has been due to what we call network effects: consumer platforms. Take Finn, which has a 95% market share, and they get really antsy if their EBIT is below 40%. Then there’s a crisis in the house. I wish I was that lucky. Now they have a problem. We go to Finn because they have a 95% market share: if we want to sell something, that’s where the buyers are, and if we want to buy something, that’s where the sellers are. And we don’t bother with the smaller platforms. But if I have an agent that goes out and visits for me, I could go to Finn, I could go to Golsfjellet kjøp og salg, I could go to Facebook Marketplace – I could go anywhere, because the agent has infinite time and can just go out there. And that erodes something we thought was pretty permanent in the competitive world of the internet: network externalities.

So that’s that part: the impact on higher education.

That’s sort of the title of this whole thing: scarcity in teaching. What does that mean? Well, you can’t make money – money above normal profits – selling something that is available in abundance. You have to sell something that’s scarce. And AI can’t do creativity and judgment. It can’t come up with things that are truly new – yet. It can, through a process of self-complication, but it’s hard for it to come up with new things, and it’s very hard for it to exercise judgment. So we have to teach that. That’s a scarce skill we need to teach. How do we do that? That’s not easy.

The way I see it, it’s not easy because it requires a few things. It requires not just teaching students, but challenging them in an environment that is a simulation of what they’re going to meet after they’re done. It requires unpredictability. It requires that we don’t tell them what they’re going to learn, we don’t tell them how they’re going to do their exam, and we don’t tell them how they got their grade. Jan Ketil Arnulf has written an article called “Without unpredictability, higher education dies”. So we have to be unpredictable. And it requires making the classroom a place where the students and the instructor want to be – which I also stole from Jan Ketil.

So how do we do that? Well, I can only talk about what I do, because that’s my experience. But I have had students engaged. I have students who show up. I have students who take my courses. I don’t have drop-off in who comes to class. Quite a few of my colleagues do this as well, but they have different techniques. I can only talk about what I do.

And I prioritize having fun. That does not mean that the students have fun in the classroom – actually, they’re quite nervous. But I have fun. There are a couple of things we need to rethink. Students are product, not customers. Students come to me and say, I would like to do this by myself, I would like to do that. I had a student who came to me and said, “I have to leave early every Monday. Will that hit my participation grade?” I said, “Yes.” And he said, “But I have to go to work. I’m a professional football player.” I said, “No, you misunderstand. Every Monday, you will be 45 minutes late for practice.” And he said, “Can you speak to my trainer?” And I said, “No, I’m not your father. You do it yourself.” He actually showed up, and he became my teaching assistant the next year. Students respond to challenges, and we have to challenge them.

The second thing we need to think about is that BI is a health club, not a tanning salon. I’ve stolen that one from Øyvind Revang, who used to teach here. Students think we’re going to come in and sit down and sit like this, and then we’re going to get a nice tan and a diploma that shows we have a tan, and then we can go. That’s not how it works. BI is a place where you can train and practise and get better, and it’s going to be painful and require something.

For a long time I have wanted to write an article called “What can higher education learn from Kompani Lauritzen?” For those who are not Norwegian, this is a TV programme where they take civilians in. It’s a reality show where they put them through a boot camp, and they meet this guy – I’ve forgotten his name, but he’s known in Norway as Fenriken. That’s with a capital F; it means the second lieutenant. He comes in and tells the people who are there what they’re going to do, how they’re going to do it, what the limits are, and what is considered acceptable and unacceptable behaviour and performance. He does it in a supportive but firm way, and he says wonderful things. My favourite one is this: people complain, “But I did wash my room,” and he says, “We’re not looking for the activity of washing. We’re looking for the result: clean.” I think we can learn a lot from Kompani Lauritzen.

I know one guy who has this attitude – my colleague Hanno Roberts. He is frequently voted the best professor in our Chinese programmes and others. When he comes in, he has a T-shirt on, and on the T-shirt it says “No pain, no gain”. That’s the right attitude, and that’s what we need to do, I think – or at least it has worked for me.

So how do we do that? Well, you basically have to force students. You have to force them to participate. You have to force them to prepare. I have something called prepared presence. In my courses you have to show up for 70% of the classes in order to qualify, but it is a prepared presence. So I can say, “Birgitte, please introduce this case for us” – she’s my boss, by the way – and if she says, “I didn’t read the case today,” I say, “Okay, you weren’t here today. You’re marked as not present.” Because you have to force people to prepare, and they do instrumental work.

You have to force them to work in groups. You get students who say, “But I want to do this project by myself.” And I say, “No, that’s not an option. It’s written in.” And we force students to solve real problems. Every year, when Chandler and I do our machine learning course, we have students who say, “But I want to work with a data set that’s already available.” And we say, you have to work with a real data set, because real data sets are not clean or well defined, or even available. Half the work for a data scientist is actually getting the data into a shape where you can analyse it. You have to force them to touch the dirty reality, and there are no exceptions.

Here’s a technique I learned from a colleague – I’ll get back to him later. When the students choose their term papers, this is the way we do it. Every student makes a handwritten poster where they present what they want to write about: which company, which problem they want to solve, and so on. They all present; it takes a minute or two each, depending on how many students we have. As soon as you’ve finished presenting, we glue the piece of paper up on the wall, so we have all these posters along the wall. When everybody’s done, everybody goes up and stands in front of their poster. I ask people to notice where everybody is, and then I declare the market open. And you have to find a group, and you have to do it there. That’s engagement. When I do that, two minutes later the classroom looks like this. And that’s what you want to see: students actively engaging, finding problems. And you have to force them to do it.

Another thing I do is that I tend not to emphasize theory, because theory you can find in other courses, and you can also find it online or elsewhere. It’s kind of normal to have a guest lecture or two. I do a lot more. This semester I have four courses, and these are the guest lectures booked so far. I go out and recruit all these business people and have them come in, and I don’t ask them to teach. I ask them to talk about what they do. So I asked Geir Karlsen, what’s it like to run Norwegian, and how did you manage to turn it around? Or I asked Torstein Berg how he’s been able to start and fund three or four very successful companies. Or I talked to Andreas Solbakken about his serial entrepreneurship since he was 16 years old. And these people come back, because they’re asked not to be teachers but to be business people. The whole thing is about providing an experience in the classroom that you can’t get online or through AI.

And then I collaborate, and I experiment with masters. I never teach alone. That’s expensive, again, so you’re going to have to fund this. But courses get much better if there are two of you. If you’ve ever listened to a podcast, you know there are two people, because they talk to each other and it becomes a conversation. And I find masters I can collaborate with.

So I have a master of interaction: the best case teacher I’ve ever met. His name is Bill Schiano. He’s not here; he’s at Bentley University. In 1996 we put together a course where we did video teaching across the Atlantic and had the students form groups across the Atlantic to solve business problems. That got us into Aftenposten, and eventually we wrote a book together called Teaching with Cases, with all sorts of tips and tricks for how to get this engagement.

Then I have a master of relevance – somebody who really knows business and can provide relevance. Her name is Berit Svendsen. When Berit resigned her job as CEO of Telenor Norway, I heard it on the radio, and I stopped the car and called Inge Jan Henjesand, the former president, and said, “Hire her.” And it’s the only time he has listened to me. We do a course called Det verdiskapende styret together, and I shamelessly use Berit for her relevance and her business connections. The first class we taught, she said, “I have to leave early, because there’s something going on.” We were teaching board management – and the day after, it was in the newspaper that she had become Board Chair of the Year.

I have a master of explanation. His name is Chandler Johnson. I didn’t know anything about machine learning – or I knew a little bit; I knew statistics – but I wanted to learn. And the best way to learn something is to do a course in it. So I set up a course in analytics, which gave Chandler access to business people and me access to Chandler. Now we’ve done it ten times, and now I can teach machine learning. That’s how you do it.

I have a master of diplomacy. His name is Stein Gjessing. For seven years I wanted to do a course that mixed real technology – computer science from the Department of Informatics at the University of Oslo – with the business part delivered by BI: the biggest university in Norway and the biggest business school, two stops away from each other on the train. How hard can it be? Turned out, very hard. But Stein shepherded this through a very sceptical faculty at the University of Oslo. I was not so successful shepherding it through a very sceptical organization here locally. So we had to do it at the university, and we called it Digital Economics and Management – first a bachelor’s, then a master’s – and it became Norway’s most popular programme: 3,000 applicants for 27 places. I got calls from parents saying, “Can you get my daughter in?” It’s still the most popular programme at the institute, and we made the front page of Dagens Næringsliv.

And last but not least, I have a master of innovation, and his name is Ragnvald Sannes – a master of innovation in teaching. We have done a lot of things together. He’s an expert in the process of setting up a course, and this whole process I use, of having students choose a project early and shepherding them through the whole class, I learned from him – including this trick with all the posters. If you want to see a really nice classroom, go and look at the Ragnvald Sannes auditorium, which is just over here. It hasn’t got that sign on the wall yet, but it will.

We have created lots of courses. The one that has been the most fun was the course we ran for 20 years called Strategic Business Development and Innovation. We did that together with Accenture in Sophia Antipolis, a technology park in the south of France. The side effect, of course, was that every October we had to go to Antibes for a week or two – and Egil was paying for it, or his predecessors – and we went with our wives and with Ellen Heggelund, who is a master course arranger. We did that for 20 years. It was a pain, but somebody had to do it…

What should higher education and BI do? Well, here’s a little list to think about. Here’s the thing: BI is already doing a lot. I’m not as worried about BI as I am about other institutions of higher learning. And the reason is very simple: we do not have two billion kroner in foundational funding. We have to – and we’re doing quite a bit.

We have a word called employability, which Egil has put on the agenda. That means we need to care a lot about our students getting jobs, because that is our contract. If you don’t know what to do, go to BI – you will get a job. That’s our unwritten contract. So employability is now on the agenda. What it is, I don’t know, but it’s on the agenda.

Secondly, we’re doing something called Doing Sustainable Business. I was going to be involved with it, but I got sick, and that really saddens me, because it is a fantastic course. It’s the final course at the end of the bachelor programme. It has three and a half thousand students doing cases in groups across study disciplines. And here’s the thing: I was at Harvard Business School about three years ago, discussing case teaching with them, and they talked about this programme and said nobody is doing that. We’re the only school in the world that does that. 70 faculty are involved. This is great. It’s expensive, Egil, so you’re going to have to continue funding it, because it’s important.

We have this building. This building has classrooms where the students can see each other. This building is beautiful. This building is interactive. This building is clean. When I go to certain other institutions, I have to bring my own markers, because there are no markers in the classroom. Here, people test the markers every morning to make sure they work. We have the infrastructure. People go to other institutions in Oslo, look around at brutalist concrete buildings, and then they come here and think, “Hmm – the next five years of my life.” This building is, if not the most important, at least the most visible competitive advantage we have. I’ve tried to do case teaching in classrooms where students can only look straight ahead. That’s not easy. We have U-shaped classrooms. We have the technology, we have the infrastructure, and we understand the severity of the problem. And that’s important.

But we need to change things, and here are the things I think we need to change. We need to change recruitment and rewards. We need to reward teaching, teaching innovation and teaching development as much as we do research. Why is it called “undervisningsplikt”? Get rid of that word. It’s something you do – it’s the main thing we do.

We must change the innovation process from stage-gate to experimental. When I teach innovation – or rather, when Ragnvald teaches innovation – we talk about old processes, where you come up with an idea, present it to a committee, and they decide yes or no; and more modern processes, where you develop a prototype and test it in the market. And it’s kind of weird to me that we are using the old model and not the new one. Because almost all innovation at BI has happened because somebody just did it. One of the reasons we rank so high is that we have a Chinese programme, and the Chinese programme was done by a couple of faculty who just went there and did it. There was no formal gate process. So we have to be able to experiment.

We must oppose regulation that shackles innovation. One of the problems we have with AI is that we need to change the way we do evaluation. If you go to Harvard, MIT, IMD, INSEAD, Darden or London Business School, you’re evaluated based on what you say in the classroom. In Norway, we’re not allowed to do that. The best universities in the world do it. We must do something about that. We must stop this notion that grading is a controllable and measurable process. It is opinion.

And we must tolerate innovation even if we don’t like it – even if we don’t like the person. Remember that article about the guns? One of the reasons it took 30 years was that the guy who improved gunnery from 7% to 80%, or something like that, was an arrogant bastard. Nobody liked him. Another reason was that he had the gunner decide himself when to fire, rather than an officer saying “fire”. You had to compensate for the ship rolling, and the gunner could actually do that – which meant transferring the decision right from the people who traditionally had it. We can’t do that. We have to test in the market and then do what works.

So what we essentially have to do – and I’ve used this term many times, but I think it’s very, very important – is protect the future from the past. Otherwise, we end up holding horses.

Thank you very much!