The Better Boards Podcast Series
The Better Boards podcast series is the podcast for Chairs, CEOs, Non-Executive Directors, Company Secretaries, and their advisors.
Every episode is filled with practical insights and learnings from those inside the boardrooms. We tease out what really matters and highlight actionable steps you can take to enhance the performance of your board.
The Better Boards Podcast Series
Can AI make better business decisions?
In this episode of the Better Boards Podcast, Professor Katja Langenbucher explores how boards can embrace AI to future-proof their decision-making.
Dr. Sabine Dembkowski speaks with Katja, a law professor at Goethe-University in Frankfurt and affiliated with SciencesPo, Paris. She serves on the supervisory boards of BaFin and IEP and brings extensive boardroom and academic experience.
Making Better Judgements: Why Boards Must Embrace AI
AI is rapidly reshaping industries—from pharmaceuticals to finance—and boards can no longer afford to stand still. Katja outlines why boards must move past hesitation and actively integrate AI into their processes.
She explains how leading organisations embed AI into strategy, what this means under the business judgment rule, and why AI should challenge—not replace—human insight.
AI Isn’t a Trend—It’s Becoming a Legal Expectation
AI may still seem opaque to some directors—but that view is increasingly out of step with governance expectations. In jurisdictions applying the business judgment rule, directors must demonstrate informed, reasonable decision-making. AI is becoming part of that expectation.
“Very soon, you cannot claim to be well-informed without consulting an AI.”
Boards have long leaned on expert input for board evaluations and strategic oversight. Going forward, AI must be part of that toolkit—or boards risk falling short of legal standards.
From Coffee Chains to Capital Markets: The Real-World Power of AI
Katja cites practical use cases—like how Starbucks applies AI to optimise store locations using behavioural, geographic, and competitor data.
“You can use AI to identify an M&A target, spot a hostile takeover risk, or even test how markets might respond to your messaging.”
Yet, she observes that AI is still rarely referenced in board evaluations or agendas, despite its ability to surface risks, run scenario models, and sharpen decision-making.
The New Role of Company Secretaries
Company secretaries are ideally placed to help boards adopt AI meaningfully. Katja is clear: directors don’t need to code—they need to ask better questions.
“Nobody is asking directors to code—but boards must ask the right questions.”
Understanding a company’s proprietary data and strategic priorities is a governance task. AI experts deliver the tools, but boards must frame the questions.
Challenging Groupthink and Elevating Debate
Groupthink continues to undermine board effectiveness. Katja shares a compelling example of using AI to simulate press responses—ranging from neutral to harsh—on a sensitive issue.
“Seeing a mock ‘nasty article’ on the big screen challenged the entire board’s thinking.”
Used this way, AI becomes a catalyst for challenge and debate, broadening the board’s perspective.
AI as Induction, Humans as Interpretation
AI and human judgment are not competing forces—they are complementary. AI finds patterns. Humans interpret them.
“A good strategic decision is always a combination of AI and human thinking.”
Board evaluation frameworks must reflect this dual approach. AI accelerates insight; humans weigh impact.
Three Key Take
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Will AI make the better business judgment? Welcome to the BetterBots podcast series. The podcast for chairs, CEOs, non-executive directors, company secretaries and their advisors. Every episode is filled with practical insights and learnings from those who are sitting around the table. or those that are very close to this table. We discuss what really matters and highlight actionable steps you can take to enhance the performance of your board. Join us this time where we explore how artificial intelligence is transforming decision-making in the boardroom. As technology rapidly advances, AI is becoming an essential tool for board members to cope with risk and uncertainty. But what I'm seeing actually when I analyze board papers is I can't see very much that AI is explicitly on the agenda. But hey, it might change after people listen to this episode. When you ask AI for guidance, the process can look very different from consulting a human expert. I will analyze, can analyze vast amounts of data sets, surface hidden patterns, and generate recommendations that might never occur to even the most seasoned board professional. Still many board members are hesitant, and I see a lot of this hesitation, especially as to rely on so-called black boxing. What is it actually? What does it do with the information I feed? How is my usage tracked, et cetera? Discover why this lack of explainability can be daunting. But why it is important to recognize that boards routinely make high-stake decisions with incompatible, incomplete information? Will future-proofing board decision-making mean embrace AI while remaining vigilant about its limitations? Join me in a conversation with Professor Katja Langbucher. Katja is law professor at the Goethe University in Frankfurt and at Science Po in Paris. Each year she teaches also a course at U.S. law schools such as Columbia, Penn, Fordham, New York University. Katja is also sitting on various boards. For example, she is a member of the German BaFin supervisory board and was for a long time a You are in for a really interesting conversation. And because we record this introduction actually after the main part, what I really like about this podcast is Katja makes it really relatable. And there are some really great examples in here. So I hope you enjoy listening. Katja, fantastic to have you on the Better Boards podcast series. Thank you so, so much for contributing.
Guest Speaker - Katja Langbucher:Sabine, thank you for the invitation. I'm really happy to be here.
Sabine:Fantastic. Let's jump straight in. I hear some noises that boards are starting to use AI for their decision-making. Can you give us some examples of what you have seen, how an AI tool might be valuable to boards?
Guest Speaker - Katja Langbucher:Yeah, and I've seen a couple of dashboard-like things. The one I like because it's such an easy example to explain is also available online if people want to double check it, is on Starbucks. And they use AI to figure out the best place for store location, right? So where should I open? Yeah, it's like, where should I open a new one? Where would I close an existing one? And I mean, what's the challenge? Of course, you need to predict the success of potential locations. And also you should be able to more or less assess performance of the existing ones in real time. And what traditionally Starbucks and other coffee shops have been doing is, you know, sort of a human mix of a couple of demographic studies, food traffic analysis, expert judgment, stuff like that. And then they've started to develop a machine learning model. And this analyzes, of course, an enormous data set, right? So as to demographics, it's age, it's income level, it's lifestyle, it's what you prefer. Then traffic, you look at pedestrian movement, you look at vehicle movement You include competition. Where are the coffee shops located? Can you figure out where they come from, where they're going to open new ones? You look at the local ecosystem. Is there a university? Is there an office? What about the tourist spots? And what's also really interesting, you include behavior. Customer behavior. And we're already, from a European point of view, getting into tricky waters here. But you figure out their purchasing patterns, previous sales data, stuff like that. Their heat maps that show you where are the potential areas which would really make a lot of money. So it's really, really a significant improvement. And it's just one sort of easy, small-scale example. And if you think about where you could use that, you know, think about geopolitical risk. That's something, for instance, a Spanish bank does. Yeah. But you can also identify an M&A target or a hostile takeover threat or an IPO price, you know, all kinds of things. So it's interesting that we see small steps right now, but there is Incredible potential at the horizon. I
Sabine:mean, there is this enormous potential, but I can tell you when I analyze the board agenda as part of a board evaluation, and I can say in the last 12, 24 months, I have seen, I can't actually even recall an example where AI was explicitly on the agenda and discussed how the board could make use of it.
Guest Speaker - Katja Langbucher:Yeah, and that is interesting. So, I mean, there are a couple of studies out, and in my experience, putting it on the agenda is something that at least I have seen all over the place. But of course, there's more to be done than putting it on the agenda, right? The really big gap we're seeing is between kind of the normal middle of the road corporation in the EU or in the UK and the big tech players is so big because the big tech players have early on understood how important it is to put all the data that this corporation produces, collects samples in sort of one big data pool, metaphorically speaking, in the middle of the corporation. And every part of the corporation has access to that data pool and can use it for whatever its task is sort of in this world of the corporation. And I mean, a lot of it might be culture. A lot of it might be due to board members having a certain age. We don't know. But what is clear is that The future is we will need it. We should use it both for business reasons and maybe also for legal reasons, because at some point the law is going to expect you to use it.
Sabine:That's interesting when you say this, because here where I'm sitting in London, a lot of people are seeing also the dangers. What does AI do and how do we need to contain it? So you say now almost the opposite, that the law expects us to use it.
Guest Speaker - Katja Langbucher:Yeah, I do say that. And I mean, of course, lawyers, you know, you're all often the killjoy. But if you think about what the law says about board decision making, in many areas, we have an explicit kind of safe harbor from board member liability, that's called the business judgment rule. You know, codified in many jurisdictions, not in the UK, but the UK courts really do the same thing without having kind of it written in the text of a law. And so the idea behind that is, if there is a business judgment on the table, and that's more or less everything, it's geo strategy, M&A, takeover, IPO pricing, whatever it is, the law really does two things. It says, look, I'm not interested in a judge second guessing a management decision. I would like you board member to kind of act with due care and good faith and take decisions you reasonably believe in, right? But what does that mean for the board? If we're saying you need to act with due care, What boards have been doing in the past is, of course, pick experts, human experts, to inform them, challenge them, etc. And in the same way, in my opinion, boards will need to understand that Very soon, you cannot claim to be well enough informed in that do care part of the law without an AI. And so an AI, in my view, is going to become very soon market standard. And then the question a court might, after all, ask you is, why didn't you ask the AI that all your competitors have started using? So
Sabine:what do you think? What's the timeframe on this? You say it soon becomes a standard. What do you think? How long are we talking about it until it is a standard in boardrooms?
Guest Speaker - Katja Langbucher:I mean, it depends, of course, on what the corporation does, right? If we're talking about tech corporations, I think we're already there. If we're talking about areas of what the corporation does, which has to do with huge data pools that need to be gathered, evaluated, and used towards some corporate purpose, I also think we're already there. So the question is more, why and where are we not there? And it might be really traditional industries, but you look even there. A couple of months ago, I was visiting an automotive factory. And of course, they have robots all over the place. And this is AI too, right? And then the question, what kind of AI are we going to use to, for instance, predict maintenance? It's just better. than humans, especially if you're able to meaningfully combine the two.
Sabine:I mean, what I'm saying, of course, companies, secretaries are using AI tools or teams are starting to use AI tools. I think also directors use it as do their searches. But what I'm not seeing enough on the agenda is really how do we do it as a group? How do we deal with it as a board? And I feel really it should be on the agenda.
Guest Speaker - Katja Langbucher:Yeah, I can totally agree. And The fun thing is you say, as a group. And part of my research outside AI has been to look at group decision-making on corporate boards and think about corporate scandals such as Enron years ago in the US or Wirecard, which was a German corporate scandal, accounting scandal. And you know what? Groupthink is something that many have identified as one of the problems that plague corporate boards. And now you draw the link to AI and you can say, look, if I have a meaningful, well-trained AI, this is one way in which I can use an AI. I can have the AI challenge me. A friend of mine, for instance, who is sitting on a bank board lately told me, look, what we have been doing is we've been discussing a bit of a problem we have in the bank. And we have, while we were all sitting around the table, asking an AI to draft a a press statement and a couple of media, potential media articles talking about the scandal. And you can do that on a range, right?
Sabine:Absolutely.
Guest Speaker - Katja Langbucher:have it draft a nice kind of article, have it draft a really nasty article. And then suddenly you sit there around the board table and you see this truly nasty article on the big screen. And this is going to challenge what you've done before maybe. And then you assess it, review it, and you say, well, could that be? What could we do if this really happens? So it's not in any way saying, oh, we're all going to go back outside the boardroom and only the AI is taking over. This is not what I'm talking about. I'm talking about meaningfully using an AI to challenge, review, critique, and integrate it in board decision making.
Sabine:And where do you really see the difference to these traditional support tools?
Guest Speaker - Katja Langbucher:Yeah, and that's a good question. In my view, it's really different in two ways. So The first one is, well, of course, data, right? So depending on whatever it is, the Starbucks example, but also just reviewing documents, which if you're a board member, of course, you get enormous amounts of documents you have to review before you go into the board meeting. Mostly, you're going to ask your staff to do that. increasingly, you might be able to ask an AI to do that, which can very quickly answer concrete questions you might have because it's so much easier for the AI to run through these thousands and thousands of pages. I mean, the second way is maybe even more interesting and might require a short explanation. So, What an AI does is called pattern recognition, right? So it's really a learning machine. If you like metaphors, it's sort of like taking a walk through data. We feed it with. And the more sophisticated ones do it without our help. You just simply program them to respond to one initial question. It's called a loss function. And then they take this loss function and walk through the data and try and find correlations between all the different data points. And if you compare it to a human expert, I mean, we start usually with a hypothesis. Should I open the Starbucks in this area or somewhere else? Or should I price my IPO here or there, right? It's sort of a conceptual, theory-driven type of thinking. And the AI doesn't do that at all. And many have said, oh, an AI, and I think it's a nice word, is an induction machine. You know, it scours data and data and data. And what it does is identify a correlation. And, you know, I mean, if you think about the IPO example, of course, the human expert is going to talk about numbers, right? And about business plans and stuff. But who knows, the AI might add stuff you would have never thought about. Maybe, and that maybe you would have thought about a coverage in social media, but maybe also data. very specific words that were repeatedly used during roadshows. There's a couple of studies on that, for instance. Or maybe the good looks or the bad looks, interestingly, there are studies on that also, the bad looks of a CEO. Right. And so you're suddenly like, what? What does that mean? Right. And then you need to process it. And this is where kind of the classic board comes back in and the classic type of thinking, usually human thinking, causal thinking. Right. And the really interesting thing I focus on in my own work is thinking. to come up with ideas on how to bring together these really different ways of thinking, so to say, even though, of course, the AI in itself doesn't think. If it does that, it's sort of like a black box maybe, right? It spits out its prediction. It doesn't explain it in the human causal way. And if you think about it, it makes perfect sense. The AI identifies correlations and correlations only. And we all know correlation is not causation. So it's just two different ways of approaching a problem.
Sabine:So if people are listening now, to this podcast? And they say, gosh, yes, we use it a little bit, but we are on the fringes. We are still on the fringes of it and haven't really integrated it into our board meetings and our decision-making processes. What have you seen working? How can boards really get started? I
Guest Speaker - Katja Langbucher:mean, as I say, it depends very much on what you would like to do with the AI and what is the context, you're asking it to provide meaningful input, right? So one question is, or one example I like to use because it's so easy to get it, there is AlphaFold, which is a really well-known foundation model AI that has been programmed to identify, so that's very medical, protein folding structures. And why is that important? Why could that be important for a pharmaceutical corporation? Because if you use a medical drug, you need to understand how it works well enough to kind of click the drug to the body, right? To the cells in the body. So if I'm a board, for instance, and I'm interested in identifying new drug development options, that's a mini input an AI gives me. It only triggers an idea. And then The classic board stuff starts, right? Understanding it, understanding I'm evaluating a known unknown. Maybe it's wrong. Maybe it's not going to work. Let's assess it. Let's evaluate the probability that this might not work. What would be the error costs? How much do we want to invest in that and stuff? So that's one very specific one. And AI could be used as kind of generating a hint, right? But they're very different examples. You could also use it, as I explained before, as sort of a discussion partner asking it, for example, for the press statement or asking it to challenge what the board came up with in a certain situation or ask it, how would a US, a German or a Chinese market respond to what we just said? So, you know, there's a million ways in which you could use an AI. So it's hard to say, look, this is the one way to do it. The question is more Where could you see using it? I think this is the first question the board needs to ask itself. Where is an opportunity that an AI might help us? And from there, it would move on and say, okay, if this is what we would like the AI to do, what should the AI look like?
Sabine:Now people are listening and go, oh my God, yet another thing for us to do. We don't want yet another thing. You know, the board agenda is already full as it is and God knows what problems we all have, you know, also on the operational side at the moment. Still the question, sorry to repeat. Where to start? How to make sure that we are not bloating up the agenda even further, rather use it to save time? What's the best way to do it?
Guest Speaker - Katja Langbucher:Well, I mean, you know, AI itself is going to help you to reduce the agenda. Because another example, there is a type of AI that is going to analyze your board decision and the discussions you had. And then it's going to do what's called argument mapping. So it's going to show you very quickly, okay, these were the one, two, three arguments, and this is how I would rank them, and now you decide. There's also another company I've seen. They're going to front load. A lot of board discussions. So how do they do that? Sort of like you and me, there are pre-discussions you record, and then you feed those in the EAI, and then the EAI does just what I explained. It's going to draw out the relevant three or four arguments, rank and contrast them, and then it's going to be shorter rather than longer, the board decision. Because as we all know, often quite a bit of board discussions Board member meetings are about who gets to speak first, who gets to speak second. And, you know, everybody wants to make space for him or herself. And so AI can really be efficiency enhancing rather than bloating it up.
Sabine:So is it actually a task for the company secretaries to suggest to the board, here is where you could use AI on this board meeting?
Guest Speaker - Katja Langbucher:Absolutely. Absolutely. That would be a very good way forward to identify things that specifically for this corporation might be relevant and where AI could be value enhancing. And it could be about board meetings only. That could be about critically challenging what directors are proposing, right? That could be even a compliance question. It's super specific, right? and requires a deep understanding of what that corporation does and where you could actually enhance what it does and do it better. And part of that is really the data, you know? And the AI, I think what I've seen in practice mostly is that people are afraid and they're like, oh, AI and it's technique and I don't understand it, right? But this is not, yeah, but I mean, this in many ways is kind of the wrong fear because nobody asks the board to sit down and code machines. What the board is asked to do is to say, look, We have this specific data pool and this is what we have. This is our proprietary data. And this is where we can use it and make it interesting and get a competitive edge. And the coder is not going to know about that, right? So that's the unique expertise that the board is going to bring to the table. I mean, Amazon has been a, you know, often quoted example, started out as a book selling business, right? And what was their unique data pool? Well, they had access to addresses of people and to their preferences as to books. And that's what I was talking about earlier. If you put this data pool kind of in the middle of the corporation, and then all kinds of departments of your corporation can kind of ask that data pool questions. And so if you know that a lot of people are interested in cooking books, you might as well send them an email, would you be interested in buying a set of pot and pans, right? And so that's why I'm saying it's corporation specific. The question you need to come up with, you're interested in, you wanna have the AI give you an answer to, That's the stuff the board must think about. And that's nothing an AI expert can tell you. The AI expert takes the next step. You tell the AI expert, look, this is what we would like to know. Is there any way to get an AI to do that?
Sabine:Great. We could talk a lot longer, but I'm eyeing the timing here on our recording. And still to come, our main question at the end. What are really the three key takeaways our listeners, what should they take away from Yeah, I
Guest Speaker - Katja Langbucher:mean, you won't be surprised that my first takeaway would be don't be late to the party. Using an AI one way or another will become market standard and you want to be ahead of the curve. And then the second one maybe is a good strategic decision. is always a combination of the sort of thinking style of an AI and the thinking style of a human. And then, you know, taking these together, the third one might be pick the right AI and understand your data suited to your corporation's specific use case. And the law is going to encourage, even urge you to do
Sabine:it. Fantastic. Katja, thank you so, so much for contributing to the Better Bots podcast series.
Guest Speaker - Katja Langbucher:Thank you so much, Sabine. It was a real pleasure to be here.
Sabine:If you want to know where you stand in terms of the readiness, your readiness for AI, reach out to us. Together with the Cantellus Group, we developed an AI readiness questionnaire. So if you would like to have access for this or like to have information, reach out to us. We are very happy to share it and to work with you. As always, we love your feedback on this episode. If you have any ideas for topics you would like to see covered, please do get in touch. If you would like to hear more about our work, which is our day-to-day job to do board effectiveness evaluations, reach out.
