Latest / The Better Boards Podcast Series / Can AI make better business decisions? | Professor Katja Langenbucher, Goethe University, Frankfurt

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Can AI make better business decisions? | Professor Katja Langenbucher, Goethe University, Frankfurt · The Better Boards Podcast Series

• 0:02
  Sabine: Will AI make the better business judgment?
• 0:04
  Sabine: Welcome to the BetterBots podcast series.
• 0:07
  Sabine: The podcast for chairs, CEOs, non-executive directors, company secretaries and their advisors.
• 0:14
  Sabine: Every episode is filled with practical insights and learnings from those who are sitting around the table.
• 0:21
  Sabine: or those that are very close to this table.
• 0:24
  Sabine: We discuss what really matters and highlight actionable steps you can take to enhance the performance of your board.
• 0:31
  Sabine: Join us this time where we explore how artificial intelligence is transforming decision-making in the boardroom.
• 0:39
  Sabine: As technology rapidly advances, AI is becoming an essential tool for board members to cope with risk and uncertainty.
• 0:48
  Sabine: 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.
• 0:58
  Sabine: But hey, it might change after people listen to this episode.
• 1:02
  Sabine: When you ask AI for guidance, the process can look very different from consulting a human expert.
• 1:09
  Sabine: 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.
• 1:21
  Sabine: Still many board members are hesitant, and I see a lot of this hesitation, especially as to rely on so-called black boxing.
• 1:30
  Sabine: What is it actually?
• 1:32
  Sabine: What does it do with the information I feed?
• 1:35
  Sabine: How is my usage tracked, et cetera?
• 1:38
  Sabine: Discover why this lack of explainability can be daunting.
• 1:42
  Sabine: But why it is important to recognize that boards routinely make high-stake decisions with incompatible, incomplete information?
• 1:52
  Sabine: Will future-proofing board decision-making mean embrace AI while remaining vigilant about its limitations?
• 1:59
  Sabine: Join me in a conversation with Professor Katja Langbucher.
• 2:04
  Sabine: Katja is law professor at the Goethe University in Frankfurt and at Science Po in Paris.
• 2:10
  Sabine: Each year she teaches also a course at U.S.
• 2:13
  Sabine: law schools such as Columbia, Penn, Fordham, New York University.
• 2:19
  Sabine: Katja is also sitting on various boards.
• 2:22
  Sabine: 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.
• 2:37
  Sabine: And because we record this introduction actually after the main part, what I really like about this podcast is Katja makes it really relatable.
• 2:47
  Sabine: And there are some really great examples in here.
• 2:50
  Sabine: So I hope you enjoy listening.
• 2:51
  Sabine: Katja, fantastic to have you on the Better Boards podcast series.
• 2:56
  Sabine: Thank you so, so much for contributing.
• 2:58
  Guest Speaker - Katja Langbucher: Sabine, thank you for the invitation.
• 3:00
  Guest Speaker - Katja Langbucher: I'm really happy to be here.
• 3:02
  Sabine: Fantastic.
• 3:02
  Sabine: Let's jump straight in.
• 3:04
  Sabine: I hear some noises that boards are starting to use AI for their decision-making.
• 3:10
  Sabine: Can you give us some examples of what you have seen, how an AI tool might be valuable to boards?
• 3:18
  Guest Speaker - Katja Langbucher: Yeah, and I've seen a couple of dashboard-like things.
• 3:22
  Guest Speaker - Katja Langbucher: 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.
• 3:33
  Guest Speaker - Katja Langbucher: And they use AI to figure out the best place for store location, right?
• 3:40
  Guest Speaker - Katja Langbucher: So where should I open?
• 3:41
  Guest Speaker - Katja Langbucher: Yeah, it's like, where should I open a new one?
• 3:43
  Guest Speaker - Katja Langbucher: Where would I close an existing one?
• 3:46
  Guest Speaker - Katja Langbucher: And I mean, what's the challenge?
• 3:49
  Guest Speaker - Katja Langbucher: Of course, you need to predict the success of potential locations.
• 3:52
  Guest Speaker - Katja Langbucher: And also you should be able to more or less assess performance of the existing ones in real time.
• 4:00
  Guest Speaker - Katja Langbucher: 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.
• 4:14
  Guest Speaker - Katja Langbucher: And then they've started to develop a machine learning model.
• 4:17
  Guest Speaker - Katja Langbucher: And this analyzes, of course, an enormous data set, right?
• 4:22
  Guest Speaker - Katja Langbucher: So as to demographics, it's age, it's income level, it's lifestyle, it's what you prefer.
• 4:28
  Guest Speaker - Katja Langbucher: Then traffic, you look at pedestrian movement, you look at vehicle movement You include competition.
• 4:35
  Guest Speaker - Katja Langbucher: Where are the coffee shops located?
• 4:37
  Guest Speaker - Katja Langbucher: Can you figure out where they come from, where they're going to open new ones?
• 4:40
  Guest Speaker - Katja Langbucher: You look at the local ecosystem.
• 4:42
  Guest Speaker - Katja Langbucher: Is there a university?
• 4:44
  Guest Speaker - Katja Langbucher: Is there an office?
• 4:45
  Guest Speaker - Katja Langbucher: What about the tourist spots?
• 4:46
  Guest Speaker - Katja Langbucher: And what's also really interesting, you include behavior.
• 4:51
  Guest Speaker - Katja Langbucher: Customer behavior.
• 4:53
  Guest Speaker - Katja Langbucher: And we're already, from a European point of view, getting into tricky waters here.
• 4:58
  Guest Speaker - Katja Langbucher: But you figure out their purchasing patterns, previous sales data, stuff like that.
• 5:04
  Guest Speaker - Katja Langbucher: Their heat maps that show you where are the potential areas which would really make a lot of money.
• 5:10
  Guest Speaker - Katja Langbucher: So it's really, really a significant improvement.
• 5:14
  Guest Speaker - Katja Langbucher: And it's just one sort of easy, small-scale example.
• 5:19
  Guest Speaker - Katja Langbucher: And if you think about where you could use that, you know, think about geopolitical risk.
• 5:24
  Guest Speaker - Katja Langbucher: That's something, for instance, a Spanish bank does.
• 5:26
  Guest Speaker - Katja Langbucher: Yeah.
• 5:27
  Guest Speaker - Katja Langbucher: But you can also identify an M&A target or a hostile takeover threat or an IPO price, you know, all kinds of things.
• 5:36
  Guest Speaker - Katja Langbucher: So it's interesting that we see small steps right now, but there is Incredible potential at the horizon.
• 5:44
  Guest Speaker - Katja Langbucher: I
• 5:44
  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.
• 6:09
  Guest Speaker - Katja Langbucher: Yeah, and that is interesting.
• 6:10
  Guest Speaker - Katja Langbucher: 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.
• 6:21
  Guest Speaker - Katja Langbucher: But of course, there's more to be done than putting it on the agenda, right?
• 6:26
  Guest Speaker - Katja Langbucher: 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.
• 7:00
  Guest Speaker - Katja Langbucher: 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.
• 7:10
  Guest Speaker - Katja Langbucher: And I mean, a lot of it might be culture.
• 7:13
  Guest Speaker - Katja Langbucher: A lot of it might be due to board members having a certain age.
• 7:19
  Guest Speaker - Katja Langbucher: We don't know.
• 7:20
  Guest Speaker - Katja Langbucher: But what is clear is that The future is we will need it.
• 7:25
  Guest Speaker - Katja Langbucher: 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.
• 7:35
  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.
• 7:42
  Sabine: What does AI do and how do we need to contain it?
• 7:47
  Sabine: So you say now almost the opposite, that the law expects us to use it.
• 7:52
  Guest Speaker - Katja Langbucher: Yeah, I do say that.
• 7:54
  Guest Speaker - Katja Langbucher: And I mean, of course, lawyers, you know, you're all often the killjoy.
• 7:59
  Guest Speaker - Katja Langbucher: 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.
• 8:16
  Guest Speaker - Katja Langbucher: 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.
• 8:27
  Guest Speaker - Katja Langbucher: 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.
• 8:43
  Guest Speaker - Katja Langbucher: It says, look, I'm not interested in a judge second guessing a management decision.
• 8:49
  Guest Speaker - Katja Langbucher: I would like you board member to kind of act with due care and good faith and take decisions you reasonably believe in, right?
• 8:58
  Guest Speaker - Katja Langbucher: But what does that mean for the board?
• 9:01
  Guest Speaker - Katja Langbucher: 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.
• 9:14
  Guest Speaker - Katja Langbucher: 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.
• 9:31
  Guest Speaker - Katja Langbucher: And so an AI, in my view, is going to become very soon market standard.
• 9:36
  Guest Speaker - Katja Langbucher: 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?
• 9:45
  Guest Speaker - Katja Langbucher: So
• 9:47
  Sabine: what do you think?
• 9:47
  Sabine: What's the timeframe on this?
• 9:49
  Sabine: You say it soon becomes a standard.
• 9:51
  Sabine: What do you think?
• 9:52
  Sabine: How long are we talking about it until it is a standard in boardrooms?
• 9:57
  Guest Speaker - Katja Langbucher: I mean, it depends, of course, on what the corporation does, right?
• 10:00
  Guest Speaker - Katja Langbucher: If we're talking about tech corporations, I think we're already there.
• 10:05
  Guest Speaker - Katja Langbucher: 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.
• 10:24
  Guest Speaker - Katja Langbucher: So the question is more, why and where are we not there?
• 10:28
  Guest Speaker - Katja Langbucher: And it might be really traditional industries, but you look even there.
• 10:33
  Guest Speaker - Katja Langbucher: A couple of months ago, I was visiting an automotive factory.
• 10:38
  Guest Speaker - Katja Langbucher: And of course, they have robots all over the place.
• 10:41
  Guest Speaker - Katja Langbucher: And this is AI too, right?
• 10:43
  Guest Speaker - Katja Langbucher: And then the question, what kind of AI are we going to use to, for instance, predict maintenance?
• 10:50
  Guest Speaker - Katja Langbucher: It's just better.
• 10:52
  Guest Speaker - Katja Langbucher: than humans, especially if you're able to meaningfully combine the two.
• 10:59
  Sabine: I mean, what I'm saying, of course, companies, secretaries are using AI tools or teams are starting to use AI tools.
• 11:06
  Sabine: I think also directors use it as do their searches.
• 11:11
  Sabine: But what I'm not seeing enough on the agenda is really how do we do it as a group?
• 11:17
  Sabine: How do we deal with it as a board?
• 11:20
  Sabine: And I feel really it should be on the agenda.
• 11:24
  Guest Speaker - Katja Langbucher: Yeah, I can totally agree.
• 11:26
  Guest Speaker - Katja Langbucher: And The fun thing is you say, as a group.
• 11:29
  Guest Speaker - Katja Langbucher: 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.
• 11:48
  Guest Speaker - Katja Langbucher: And you know what?
• 11:50
  Guest Speaker - Katja Langbucher: Groupthink is something that many have identified as one of the problems that plague corporate boards.
• 12:01
  Guest Speaker - Katja Langbucher: 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.
• 12:13
  Guest Speaker - Katja Langbucher: I can have the AI challenge me.
• 12:16
  Guest Speaker - Katja Langbucher: 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.
• 12:24
  Guest Speaker - Katja Langbucher: 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.
• 12:40
  Guest Speaker - Katja Langbucher: And you can do that on a range, right?
• 12:43
  Sabine: Absolutely.
• 12:44
  Guest Speaker - Katja Langbucher: have it draft a nice kind of article, have it draft a really nasty article.
• 12:49
  Guest Speaker - Katja Langbucher: And then suddenly you sit there around the board table and you see this truly nasty article on the big screen.
• 12:56
  Guest Speaker - Katja Langbucher: And this is going to challenge what you've done before maybe.
• 13:00
  Guest Speaker - Katja Langbucher: And then you assess it, review it, and you say, well, could that be?
• 13:04
  Guest Speaker - Katja Langbucher: What could we do if this really happens?
• 13:06
  Guest Speaker - Katja Langbucher: 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.
• 13:13
  Guest Speaker - Katja Langbucher: This is not what I'm talking about.
• 13:15
  Guest Speaker - Katja Langbucher: I'm talking about meaningfully using an AI to challenge, review, critique, and integrate it in board decision making.
• 13:25
  Sabine: And where do you really see the difference to these traditional support tools?
• 13:30
  Guest Speaker - Katja Langbucher: Yeah, and that's a good question.
• 13:32
  Guest Speaker - Katja Langbucher: In my view, it's really different in two ways.
• 13:35
  Guest Speaker - Katja Langbucher: So The first one is, well, of course, data, right?
• 13:40
  Guest Speaker - Katja Langbucher: 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.
• 13:55
  Guest Speaker - Katja Langbucher: Mostly, you're going to ask your staff to do that.
• 13:58
  Guest Speaker - Katja Langbucher: 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.
• 14:12
  Guest Speaker - Katja Langbucher: I mean, the second way is maybe even more interesting and might require a short explanation.
• 14:20
  Guest Speaker - Katja Langbucher: So, What an AI does is called pattern recognition, right?
• 14:25
  Guest Speaker - Katja Langbucher: So it's really a learning machine.
• 14:28
  Guest Speaker - Katja Langbucher: If you like metaphors, it's sort of like taking a walk through data.
• 14:33
  Guest Speaker - Katja Langbucher: We feed it with.
• 14:35
  Guest Speaker - Katja Langbucher: And the more sophisticated ones do it without our help.
• 14:39
  Guest Speaker - Katja Langbucher: You just simply program them to respond to one initial question.
• 14:45
  Guest Speaker - Katja Langbucher: It's called a loss function.
• 14:48
  Guest Speaker - Katja Langbucher: And then they take this loss function and walk through the data and try and find correlations between all the different data points.
• 14:58
  Guest Speaker - Katja Langbucher: And if you compare it to a human expert, I mean, we start usually with a hypothesis.
• 15:05
  Guest Speaker - Katja Langbucher: Should I open the Starbucks in this area or somewhere else?
• 15:10
  Guest Speaker - Katja Langbucher: Or should I price my IPO here or there, right?
• 15:14
  Guest Speaker - Katja Langbucher: It's sort of a conceptual, theory-driven type of thinking.
• 15:17
  Guest Speaker - Katja Langbucher: And the AI doesn't do that at all.
• 15:21
  Guest Speaker - Katja Langbucher: And many have said, oh, an AI, and I think it's a nice word, is an induction machine.
• 15:26
  Guest Speaker - Katja Langbucher: You know, it scours data and data and data.
• 15:30
  Guest Speaker - Katja Langbucher: And what it does is identify a correlation.
• 15:32
  Guest Speaker - Katja Langbucher: And, you know, I mean, if you think about the IPO example, of course, the human expert is going to talk about numbers, right?
• 15:40
  Guest Speaker - Katja Langbucher: And about business plans and stuff.
• 15:41
  Guest Speaker - Katja Langbucher: But who knows, the AI might add stuff you would have never thought about.
• 15:47
  Guest Speaker - Katja Langbucher: Maybe, and that maybe you would have thought about a coverage in social media, but maybe also data.
• 15:53
  Guest Speaker - Katja Langbucher: very specific words that were repeatedly used during roadshows.
• 15:58
  Guest Speaker - Katja Langbucher: There's a couple of studies on that, for instance.
• 16:00
  Guest Speaker - Katja Langbucher: Or maybe the good looks or the bad looks, interestingly, there are studies on that also, the bad looks of a CEO.
• 16:09
  Guest Speaker - Katja Langbucher: Right.
• 16:10
  Guest Speaker - Katja Langbucher: And so you're suddenly like, what?
• 16:11
  Guest Speaker - Katja Langbucher: What does that mean?
• 16:13
  Guest Speaker - Katja Langbucher: Right.
• 16:14
  Guest Speaker - Katja Langbucher: And then you need to process it.
• 16:16
  Guest Speaker - Katja Langbucher: And this is where kind of the classic board comes back in and the classic type of thinking, usually human thinking, causal thinking.
• 16:25
  Guest Speaker - Katja Langbucher: Right.
• 16:26
  Guest Speaker - Katja Langbucher: And the really interesting thing I focus on in my own work is thinking.
• 16:32
  Guest Speaker - Katja Langbucher: 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.
• 16:45
  Guest Speaker - Katja Langbucher: If it does that, it's sort of like a black box maybe, right?
• 16:48
  Guest Speaker - Katja Langbucher: It spits out its prediction.
• 16:49
  Guest Speaker - Katja Langbucher: It doesn't explain it in the human causal way.
• 16:54
  Guest Speaker - Katja Langbucher: And if you think about it, it makes perfect sense.
• 16:56
  Guest Speaker - Katja Langbucher: The AI identifies correlations and correlations only.
• 17:00
  Guest Speaker - Katja Langbucher: And we all know correlation is not causation.
• 17:03
  Guest Speaker - Katja Langbucher: So it's just two different ways of approaching a problem.
• 17:07
  Sabine: So if people are listening now, to this podcast?
• 17:10
  Sabine: And they say, gosh, yes, we use it a little bit, but we are on the fringes.
• 17:15
  Sabine: We are still on the fringes of it and haven't really integrated it into our board meetings and our decision-making processes.
• 17:24
  Sabine: What have you seen working?
• 17:26
  Sabine: How can boards really get started?
• 17:28
  Sabine: I
• 17:28
  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?
• 17:40
  Guest Speaker - Katja Langbucher: 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.
• 18:00
  Guest Speaker - Katja Langbucher: And why is that important?
• 18:02
  Guest Speaker - Katja Langbucher: Why could that be important for a pharmaceutical corporation?
• 18:05
  Guest Speaker - Katja Langbucher: 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?
• 18:17
  Guest Speaker - Katja Langbucher: To the cells in the body.
• 18:18
  Guest Speaker - Katja Langbucher: 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.
• 18:29
  Guest Speaker - Katja Langbucher: It only triggers an idea.
• 18:32
  Guest Speaker - Katja Langbucher: And then The classic board stuff starts, right?
• 18:36
  Guest Speaker - Katja Langbucher: Understanding it, understanding I'm evaluating a known unknown.
• 18:41
  Guest Speaker - Katja Langbucher: Maybe it's wrong.
• 18:42
  Guest Speaker - Katja Langbucher: Maybe it's not going to work.
• 18:44
  Guest Speaker - Katja Langbucher: Let's assess it.
• 18:45
  Guest Speaker - Katja Langbucher: Let's evaluate the probability that this might not work.
• 18:50
  Guest Speaker - Katja Langbucher: What would be the error costs?
• 18:52
  Guest Speaker - Katja Langbucher: How much do we want to invest in that and stuff?
• 18:54
  Guest Speaker - Katja Langbucher: So that's one very specific one.
• 18:57
  Guest Speaker - Katja Langbucher: And AI could be used as kind of generating a hint, right?
• 19:01
  Guest Speaker - Katja Langbucher: But they're very different examples.
• 19:03
  Guest Speaker - Katja Langbucher: 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?
• 19:28
  Guest Speaker - Katja Langbucher: So, you know, there's a million ways in which you could use an AI.
• 19:32
  Guest Speaker - Katja Langbucher: So it's hard to say, look, this is the one way to do it.
• 19:35
  Guest Speaker - Katja Langbucher: The question is more Where could you see using it?
• 19:39
  Guest Speaker - Katja Langbucher: I think this is the first question the board needs to ask itself.
• 19:42
  Guest Speaker - Katja Langbucher: Where is an opportunity that an AI might help us?
• 19:47
  Guest Speaker - Katja Langbucher: 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?
• 19:56
  Sabine: Now people are listening and go, oh my God, yet another thing for us to do.
• 20:01
  Sabine: We don't want yet another thing.
• 20:04
  Sabine: 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.
• 20:13
  Sabine: Still the question, sorry to repeat.
• 20:15
  Sabine: Where to start?
• 20:16
  Sabine: How to make sure that we are not bloating up the agenda even further, rather use it to save time?
• 20:24
  Sabine: What's the best way to do it?
• 20:26
  Guest Speaker - Katja Langbucher: Well, I mean, you know, AI itself is going to help you to reduce the agenda.
• 20:31
  Guest Speaker - Katja Langbucher: Because another example, there is a type of AI that is going to analyze your board decision and the discussions you had.
• 20:43
  Guest Speaker - Katja Langbucher: And then it's going to do what's called argument mapping.
• 20:46
  Guest Speaker - Katja Langbucher: 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.
• 20:54
  Guest Speaker - Katja Langbucher: There's also another company I've seen.
• 20:57
  Guest Speaker - Katja Langbucher: They're going to front load.
• 21:00
  Guest Speaker - Katja Langbucher: A lot of board discussions.
• 21:03
  Guest Speaker - Katja Langbucher: So how do they do that?
• 21:04
  Guest Speaker - Katja Langbucher: 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.
• 21:14
  Guest Speaker - Katja Langbucher: 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.
• 21:26
  Guest Speaker - Katja Langbucher: 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.
• 21:36
  Guest Speaker - Katja Langbucher: And, you know, everybody wants to make space for him or herself.
• 21:40
  Guest Speaker - Katja Langbucher: And so AI can really be efficiency enhancing rather than bloating it up.
• 21:47
  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?
• 21:57
  Guest Speaker - Katja Langbucher: Absolutely.
• 21:58
  Guest Speaker - Katja Langbucher: Absolutely.
• 21:59
  Guest Speaker - Katja Langbucher: 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.
• 22:11
  Guest Speaker - Katja Langbucher: And it could be about board meetings only.
• 22:13
  Guest Speaker - Katja Langbucher: That could be about critically challenging what directors are proposing, right?
• 22:19
  Guest Speaker - Katja Langbucher: That could be even a compliance question.
• 22:22
  Guest Speaker - Katja Langbucher: It's super specific, right?
• 22:25
  Guest Speaker - Katja Langbucher: and requires a deep understanding of what that corporation does and where you could actually enhance what it does and do it better.
• 22:36
  Guest Speaker - Katja Langbucher: And part of that is really the data, you know?
• 22:39
  Guest Speaker - Katja Langbucher: 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?
• 22:49
  Guest Speaker - Katja Langbucher: 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.
• 22:59
  Guest Speaker - Katja Langbucher: What the board is asked to do is to say, look, We have this specific data pool and this is what we have.
• 23:08
  Guest Speaker - Katja Langbucher: This is our proprietary data.
• 23:10
  Guest Speaker - Katja Langbucher: And this is where we can use it and make it interesting and get a competitive edge.
• 23:15
  Guest Speaker - Katja Langbucher: And the coder is not going to know about that, right?
• 23:18
  Guest Speaker - Katja Langbucher: So that's the unique expertise that the board is going to bring to the table.
• 23:24
  Guest Speaker - Katja Langbucher: I mean, Amazon has been a, you know, often quoted example, started out as a book selling business, right?
• 23:31
  Guest Speaker - Katja Langbucher: And what was their unique data pool?
• 23:33
  Guest Speaker - Katja Langbucher: Well, they had access to addresses of people and to their preferences as to books.
• 23:40
  Guest Speaker - Katja Langbucher: And that's what I was talking about earlier.
• 23:42
  Guest Speaker - Katja Langbucher: 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.
• 23:53
  Guest Speaker - Katja Langbucher: 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?
• 24:04
  Guest Speaker - Katja Langbucher: And so that's why I'm saying it's corporation specific.
• 24:09
  Guest Speaker - Katja Langbucher: 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.
• 24:19
  Guest Speaker - Katja Langbucher: And that's nothing an AI expert can tell you.
• 24:22
  Guest Speaker - Katja Langbucher: The AI expert takes the next step.
• 24:24
  Guest Speaker - Katja Langbucher: You tell the AI expert, look, this is what we would like to know.
• 24:28
  Guest Speaker - Katja Langbucher: Is there any way to get an AI to do that?
• 24:31
  Sabine: Great.
• 24:32
  Sabine: We could talk a lot longer, but I'm eyeing the timing here on our recording.
• 24:37
  Sabine: And still to come, our main question at the end.
• 24:42
  Sabine: What are really the three key takeaways our listeners, what should they take away from Yeah, I
• 24:48
  Guest Speaker - Katja Langbucher: mean, you won't be surprised that my first takeaway would be don't be late to the party.
• 24:52
  Guest Speaker - Katja Langbucher: Using an AI one way or another will become market standard and you want to be ahead of the curve.
• 24:58
  Guest Speaker - Katja Langbucher: And then the second one maybe is a good strategic decision.
• 25:03
  Guest Speaker - Katja Langbucher: is always a combination of the sort of thinking style of an AI and the thinking style of a human.
• 25:10
  Guest Speaker - Katja Langbucher: 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.
• 25:21
  Guest Speaker - Katja Langbucher: And the law is going to encourage, even urge you to do
• 25:27
  Sabine: it.
• 25:27
  Sabine: Fantastic.
• 25:28
  Sabine: Katja, thank you so, so much for contributing to the Better Bots podcast series.
• 25:33
  Guest Speaker - Katja Langbucher: Thank you so much, Sabine.
• 25:34
  Guest Speaker - Katja Langbucher: It was a real pleasure to be here.
• 25:36
  Sabine: If you want to know where you stand in terms of the readiness, your readiness for AI, reach out to us.
• 25:43
  Sabine: Together with the Cantellus Group, we developed an AI readiness questionnaire.
• 25:49
  Sabine: So if you would like to have access for this or like to have information, reach out to us.
• 25:54
  Sabine: We are very happy to share it and to work with you.
• 25:57
  Sabine: As always, we love your feedback on this episode.
• 26:01
  Sabine: If you have any ideas for topics you would like to see covered, please do get in touch.
• 26:06
  Sabine: If you would like to hear more about our work, which is our day-to-day job to do board effectiveness evaluations, reach out.