How Real Leadership Turns AI Initiatives Into a Sustainable Competitive Advantage

🎙️ ABOUT THIS EPISODE
Most SaaS companies are rushing into AI without a clear strategy—and many risk becoming irrelevant. Dietmar Walther, a seasoned board advisor and investor, pulls back the curtain on how startups and scale-ups can turn AI from a tactical experiment into a strategic advantage that drives growth, profitability, and defensibility.
In this eye-opening episode, Dietmar reveals the critical mistakes companies make when adopting AI—like treating it as a side project or ignoring data quality—and shares how a deep alignment between leadership, data, and mindset is key to success. Discover the three growth phases of AI adoption in B2B SaaS, from doing nothing to becoming all-in, and learn how the best companies turn AI initiatives into competitive moats.
You'll find out why the most successful AI strategies are rooted in strong leadership, clear goals, and organizational readiness—rather than shiny new technology alone. Dietmar shares practical stories of failures and wins, illustrating how a growth mindset, cross-functional collaboration, and strategic governance can make or break an AI project. Beyond technology, he emphasizes that AI is a leadership and culture challenge that requires soft skills as much as hard skills.
And for boards, Dietmar explains how they can become strategic enablers—not bottlenecks—by setting the right context, aligning expectations, and fostering clear communication. He also guides founders on how to ask the right questions before pitching AI to investors: Is your approach defensible? Are the right people in place? And does your AI directly impact key business drivers?
Perfect for SaaS founders, investors, and board members committed to building resilient, AI-enabled businesses, this episode offers a blueprint to move beyond the hype and embed AI as a core, strategic driver of growth and differentiation. Whether you're scaling fast or just starting out, you'll walk away with actionable insights to elevate your AI journey—and avoid costly pitfalls.
The future belongs to those who lead with intent. Tune in to learn how to make AI your strategic advantage—and build a SaaS business that thrives in the age of AI.

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LinkedIn: https://www.linkedin.com/in/dietmar-walter

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Transcript

SPEAKER_01 0:00

It's about the mindset of the leadership team and the board. Do they have a gross mindset or are they stuck in their ways and saying that's where I've been doing it for the last 15 years? And that's how it works here. It's very much about the mindset. Second, look at the relationship between the board and the leadership team. If there is not a constructive working relationship between these two um groups, then nothing is gonna nothing's gonna come good out of that one. But specific to AI, I want to understand the AI experience at board level, because again, the board at least needs to have a base level of understanding. Otherwise, the relationship between the board and the leadership team is not gonna work for AI strategy. Do they have an AI vision? Meaning, do they have an overall AI strategy which communicate communicated clearly across all stakeholders? Then I look at the execution play. Do we actually have a plan in place which is a credible one that can execute on what they say you're gonna do?

SPEAKER_00 1:18

What does it take to scale a software company in the age of AI? And what are the lessons we can learn from the people who see it from every angle, the investors who fund it, the founders who build it, and the board members who govern it. Welcome to the Anaya Algorithm, the podcast for leaders who want to build the future intentionally. I'm your host, Magdalene Amigashti, founder of Anaya Group, a consultancy where we provide the frameworks to help enterprise leaders do just that. Today, we are privileged to have a guest with a truly unique 360-degree perspective on this challenge. Deed Marter advises founders and investors on scaling technology businesses and achieving successful exits. His specialist focus is on B2B SaaS and data analytics companies. In this conversation, we do construct the playbook for AI-driven SaaS success. We explore the questions investors are asking, the mistakes founders are making, and the new rules of governance that boards must understand. This isn't just theory, it is a masterclass in what it really takes to win. So please join me for this incredible insightful session with Dietmar Wouter. Dietmar, with your incredible perspective, sitting on boards, advising founders, and investing in the next wave of technology. Thank you for being here. Welcome to the Anaya algorithm.

SPEAKER_01 2:54

Well, thanks for having me. It's great to be in a podcast, and I'm certainly looking forward to our discussions.

SPEAKER_00 3:00

Absolutely. Would you mind introducing yourself first?

SPEAKER_01 3:03

Well, I work with founders and investors of tech companies to scale the businesses and guide them to exits. And my specialty is in B2B SaaS and data analytics businesses. I started my career at Gartner, and Gartner being a leading or the leading IT research and consulting business gave me a great foundation to understand the IT and tech ecosystem. But then I decided to take a blanch into the tech starter world, and that's when I lost all my hair in the process of scaling and exiting B2B SaaS and data analytics businesses. For the last 15 years, I've been predominantly working as a non-executive director, board advisor, and also as a mentor to founders of SaaS businesses.

SPEAKER_00 3:53

Fantastic. So let's start there. When you work with SaaS founders, what are the most common approaches you see them taking to adopt AI? And what's the biggest difference between the ones you succeed and the ones you build?

SPEAKER_01 4:10

Right. I just want to emphasize, going to focus on B2B SaaS businesses, which use AI to scale. I'm not necessarily talking about AI native SaaS businesses, because my experience is more in the former and predominantly the majority of SaaS businesses still falling into the B2B traditional SaaS model, which now are adopting AI. When it comes to adopting AI for B2B SaaS businesses, I will classify, categorize them in three buckets. One is they do nothing. The second one is they do something, and the third one are the all-ins. So what do we mean by that? They do nothing. A diminishing minority of companies, SaaS businesses, decide actually not to take on AI at all. And the reason my warry, it could be that they say, well, AI is just another hype, not relevant for us. Others say, we are so special, we're so unique AI, it's just not going to work for us, not relevant. One CEO told me, because of the environmental impact of AI, we have decided not to take on AI. Others haven't got the financial means or the expertise to take it on. So it's fair to say, regardless of the reasons, these companies risk to become irrelevant. For sure, they will struggle to keep and maintain talent. So in the second bucket, they do something. This is where the majority of businesses fall into. And they're typically businesses which have got AI projects in isolation, almost like as a side project, as an RD, not necessarily at a strategic leadership level where they take a sort of an approach for the entire business. And often these are companies where pushed by the board to do something with AI, or the initiatives taken by the CTO and the development team think about using a co-pilot for the programming or doing some RD work, or second line, typically are then the customer success team, customer service team who are tinkering with some AI chatbot for customer service. What's very interesting is that this category is often the employees are driving the AI agenda. In fact, there's a very interesting report by McKinsey who's been published recently, which shows that leadership teams tend to underestimate how widely AI is already used in the business. And they tend to underestimate the readiness of employees to adopt AI. Now that's a big challenge for the leadership team to take ownership and roll out an overall AI strategy and tap into the opportunities, but certainly deal with the risk side of it. Because just to give you one example, uh recently a sales director came to me and said, quite proudly, Did my are using chatbots to customize my service level agreements. I mean, it doesn't say, you know, I don't need to wait for days for the legal team to come back with that. I mean, talking about risk exposures here, right? And then we come to the all-in. And these are the companies increasing B2B size businesses which realize, okay, in order to stay relevant, in order to stay competitive, AI is not optional. AI needs to be become the fabric of what we are doing and how we are doing things. And typically they raise the AI discussion at the leadership and board level. They become very clear that AI is not only to gain efficiency, but also trying to find ways to have a competitive advantage. And it becomes a cross-functional exercise, not a sort of an RD project on the side. So, in essence, we have got to do nothing, which risks to become irrelevant, to do something, which gain some time, but they increase the risk profile if they're not careful. And then the all-in because realized, okay, in order to increase the chance to be successful in the future, we need to look at AI in a serious way.

SPEAKER_00 8:27

Fantastic. Thanks for sharing that useful insight in terms of the research from McKinsey. And that's a perfect breakdown, Titman. That do something category is where we see most of our clients also starting. They're caught in what we call a random act of AI. Well-intentioned projects that lack a central unifying strategy. And it's a clear sign that they need to move from tactical experiments to an intentional AI blueprint.

SPEAKER_01 8:57

Yeah, absolutely.

SPEAKER_00 8:59

So often we learn more from failures. Could you share a real-world anonymized story of a company whose AI adoption strategy went wrong? And what was the core mistake they made? And what is the key lesson for other founders?

SPEAKER_01 9:15

Great questions. There would be quite a few of stories about failures about success as well. But I'm going to focus on one in specific because it's a great example how a company turned a failed do-something project into a successful all-in one-on-one. In this case, we're talking about a B2B SaaS business, which has been growing for the last five years, double-digit growth. So they're doing phenomenally well. They're showing some modest profit levels and their cash flow positive. And one of the key competitive advantages is their customer service and customer success function, the quality of their function. But they have been tasked, the leadership team have been tasked with increasing the profit margins because the board decided to go to an exit in the next 12 to 24 months. And they know that with increased profit margins, they are able to request or demand higher multiple for evaluation at an exit. So one board meeting, the board said, come on, guys, we are an IT company, do something with AI here to increase the profit margin. It couldn't be that difficult, can it? Which, by the way, is typical for companies, if they don't have an overall AI strategy, rightly or wrongly the board steps in, in most cases, wrongly. That's a great example. So the leadership team felt pressured, and what they did is they introduced a customer service AI chatbot. Well, after two weeks they pulled it black because the project totally failed. But what this company did differently is they went to now into deep dive and post-mortem analysis because very often companies, when they're not succeed with a project, they just blame the AI platform saying, let's try another AI model because this one clearly didn't work. What these guys did is they analyzed what went wrong and they realized that such a project cannot work in isolation. Such a project needs to be dealt at the leadership level. They realized it cannot be dealt with the customer service team on their own. It has to be a cross-functional exercise. And then they looked at the basics and said, okay, do we actually have the right data in place? Then they looked at the process of customer service and customer support, broke it down in steps and tasks, and looked how AI could help each of these tasks and steps. And then they looked at the training and needs, and do we actually have the internal capacity and capabilities in house to pull this one off? So, do you notice there's nothing about technology, it's about people, it's about processes and attitude. And as a result, what they did is they did the full assessment of the data. They looked at the quality of the data, what data is missing, they looked at the different silos. They have functional silos where the data is placed and how to bring that together. Then they created a cross-functional team led by the CTO to implement a knowledge-centric service approach. What that means is they basically created a digital knowledge repository where they collected all information about how customer inquiries are dealt with. So every time the cost of customer inquiry or ticket comes in and how it's been answered, it's been added to this ever-growing digital knowledge base. So that created the perfect conditions of the quality of the data and an ever-growing knowledge repository to train an AI model. And that's exactly what it did. So they trained an AI model, but for the internal customer service representative to make them more effective. So what they ended up with, a human-centric customer service approach augmented by AI. They kept on improving and training it to make the customer representative more effective, but only gradually and slowly they started opening up this AI model to be in direct contact with the customer. So I think it's a great example of to a side project, to do something pushed by the board, reflect about it, and then work on it and create a successful one. And again, I repeat myself here. In most cases, it's not about technology, it's about data, it's about the process and the mindset, the attitude that people have to it.

SPEAKER_00 14:05

That's awesome. Thanks for sharing that story. Loved it. On the flip side, could you also tell us about a company that got it right? What was their best practice, the best, you know, the specific thing they did when adopting AI that truly set them up for successes?

SPEAKER_01 14:24

It comes pretty much to if we go back to this example, the way they made it successful, that's how others are also successful as well, is to raise the AI discussion at the leadership strategic level, where companies fall short very often when it comes to AI execution is on the data side of it. They go, they rush to imply or to train the AI model, and only realizing later that the output of the AI model is just not good enough. But the reason the output of the AI model is not good enough is because the data is just not good. And that is where a lot, a lot of companies actually fall short. They want to get this one right, they have a much better chance than to have a successful AI model working for their advantage.

SPEAKER_00 15:09

Absolutely. Couldn't agree more. Thank you for sharing that. It's a totally powerful story because it highlights that the most critical factors wasn't the initial technology choice, but the leadership growth mindset to learn from the failure. And it proves that the first attempt is really the final answer. The ability to iterate is the real competitive edge as well.

SPEAKER_01 15:34

Yes. And to add to that, what have experienced company going to write, they they realize it's not just even if the data write, then they train the I model and they think, okay, we train the I model once, and then okay, off we go, we are we're all good. No, no, it's a lot of work, ongoing work of improvement, of training, of fine-tuning. It's almost like if you hire somebody, a human, you hire, you have to train them, you have to help them, you have to monitor them in terms of what needs is help, what's needed for them to be successful. And very similar, you should look at when you train an AI model. So I found that comparison quite quite compelling.

SPEAKER_00 16:14

Love that. Thank you. So now let's move to the boardroom. A lot of the work we do at an IR Group involves helping leaders build frameworks for intentional governance. From your perspective on the board, what do you see as the board's proper role in that AI journey? And how can a board be a strategic accelerator for AI adoption rather than just a governance roadblock?

SPEAKER_01 16:41

Right. That's a great question. I the quality of discussion, board discussion related to AI, and the usefulness of board decision for the leadership team related to AI varies tremendously. On one side, you have boards which say, well, this is a tech discussion, the tech team should get on with it, do some RD projects. And on the flip side, you have got boards who take it so deep they have ongoing workshops, AI workshops, and go as far as hiring AI experts to the board. The role of the board, as you said, is absolutely critical, not only from a corporate governance perspective, but also to give the leadership team the strategic direction, the mandate, almost like the entrepreneurial freedom to progress with the AI approach. And what I have seen boards who get it right is they follow some very basic steps. One is they set the context. By setting the context, meaning they as a board, they make sure that everybody on the board in the leadership team has got at least the same base level of understanding how AI is reshaping the industry and what opportunities and threats this can bring to the business. Then they set goals. They want to get it right, they are very clear what they should expect by implementing AI. Should it be gaining efficiency? So they are very specific in what area and what aspect of the business, and should and or should it be gaining competitive advantage by embedding AI into the product and services. And then they do a very important step. They make sure there is a readiness assessment. They make sure that at leadership and at board level, they look at the data structure, they look at the tech layer, and they look at the human layer and say, do we actually have the right people to pull this one off? And then very clearly they say focus on one project to prioritize. Very often, companies get too excited and have several projects running on the side. No, no, they focus on one project because they want to learn as much as they can from this project so they can then implement the learnings uh further on. Being a board, as you already mentioned, that corporate governments, as a board, you need to then look at the uh the risk and compliance aspect and build a corporate governance framework around it. That's quite a given. But that's the last point, but an equally important one, important to get this one right, and that is to create a communication strategy. Absolutely critical, not only between to have the right communication and between the board and the leadership team, but all stakeholders, the employees. Because if there is not a clear communication to the employees, employees might get nervous about, well, we're not progressing here fast enough, or they might think, oh, should I be worried that with this in AI initiatives, my job is at risk? So there needs to be communication strategy for the employees. There needs to be communication strategy for the clients. Clients should know what to expect for the AI initiatives, but also for the investors, because investors want to know, okay, if we invest or our money's been taken now to invest in AI, what measurable business outcome should we expect? Yeah. So these are basic steps, if you think about it, but not all boards follow them, but the boards who get it right follow these basic steps and it shows off.

SPEAKER_00 20:21

Thanks for these fantastic, actionable steps. It sounds like the board's role is to elevate the conversation from what we're doing with AI to what is our AI-enabled business strategy. It's about ensuring governance isn't a brick paddle, but rather the guide rails that allows the company to thrive.

SPEAKER_01 20:43

Very much so, yeah.

SPEAKER_00 20:45

Awesome. So when you join a board, what are the first questions you ask the leadership team about the AI strategy? And what are you trying to uncover with those questions?

SPEAKER_01 20:57

Well, there's some for going specific about the AI, there's some general ones as part of my due diligence, and that is firstly, is about the mindset of the leadership team and the board. Do they have a gross mindset or are they stuck in their ways and saying that's where I've been doing it for the last 15 years, and that's how it works here. So it's very much about the mindset. Second, look at the relationship between the board and the leadership team. If there is not a constructive working relationship between these two groups, then nothing is gonna nothing's gonna come good out of that one. But specific to AI, I want to understand the AI experience at board level, because again, the board at least needs to have a base level of understanding. Otherwise, the relationship between the board and the leadership team is not gonna work for AI strategy. But then I want to understand specific to AI. Do they have an AI vision? Meaning, do they have an overall AI strategy which communicated clearly across all stakeholders, as I mentioned earlier? Then I look at the execution plan. Do we actually have a plan in place which is a credible one that can execute on what they say you're gonna do? The third one, because risk and compliance, so many companies get caught out on that, don't think, oh, let's deal with that once we have implemented it. No, no, no. If they don't have a corporate government structure or risk and compliance around it, that's a red flag for me. And the last one is how defensible, how credible is their AI strategy? Is it just a copy kind of like that blagging into ChatGDP, or we're talking something which is, you know, stands in their own leg and it's not easily copied by competitors? So these are the aspects I typically look at uh when I engage uh before I even engage with a board.

SPEAKER_00 22:50

Yeah. Wow. I'm just to start with, I'm so glad you focused on the mindset and the team, right? At the top level, not just the tech. It shows you're assessing the organizational readiness, not just the technical readiness. And I always say a brilliant strategy with a team that lacks the right mindset or skills is a strategy that's destined to fail. It's a crucial and often overlooked part of the due diligence. It's great that you shared that.

SPEAKER_01 23:20

Yeah, very much so. It comes down to the example I mentioned earlier, to the case study. You know, these guys first they failed, they could have blamed the technology, they could have gone back to the board saying, look, you told us to do something, we did something, it didn't work, uh go away. No, they they they reflect it, and then you can see it's a classic, a typical good example of a growth mindset to understand what went wrong, learn from it, and then make it better. So that's a great example.

unknown 23:47

Yeah.

SPEAKER_00 23:48

Thank you. And I know you kind of alluded to some of the red flags you pick up at early stages, but could we just go a bit further into that conversation? So when we we advise our clients, we often see a disconnect between the tech team and the board. So in your experience, what would be a red flag at that board level that signals their approach to AI might be flawed? And is there a particular metric, statement, or attitude from the exact team that makes you concerned as well about their approach?

SPEAKER_01 24:22

What I wouldn't say, great question, by the way, I wouldn't say there's a metric, um, but certainly some attitude. Um, well, firstly it comes down what's already mentioned is the relationship relationship between the board and the leadership team. But given that is if there is no high understanding of board level, there's a big risk. And I've seen that more than once, that the CTO takes an approach. Well, the board doesn't understand it anyway. So what's the point in telling me what we are doing with AI? Or to say, well, the board doesn't understand anything about AI. I can tell them what they would like to hear, but I I do whatever I think is right. So that is the risk there, and there's a very genuine risk which needs to be addressed head-on. So that's the the conflict between potential conflict between the tech team and the board. The next red flag for me is when I sense that the CEO has not given a strategic overall AI strategy. And the CTO works in isolation. It's like, as I mentioned earlier, like a side project which doesn't roll up into an overall part of an overall AI strategy. You sense that very quickly and at the board, and that is certainly something which needs to be dealt with. At board level, the other red flag for me is the so-called bottom-up approach, which I mentioned earlier, when AI initiatives are mostly driven by the employees without the awareness or the control or the guidance from the leadership team. And if I add one more, is when the board gets asked for big budgets for AI initiatives, and the leadership team and CTO is not able to link it back to measurable business outcomes. That's certainly a big red flag.

SPEAKER_00 26:15

Wow, thank you. These red flags are what the tech team working in isolation is one we see often.

SPEAKER_01 26:22

Yeah, yeah.

SPEAKER_00 26:23

It's a symptom, I always say it's a symptom of a leadership vacuum around technology, and it underscores the need for a leader or an advisory partner like our naya to act as that strategic translator between the board, the CEO, and the tech team to ensure everyone is aligned and speaking the same language.

SPEAKER_01 26:44

You're absolutely right. I I very much like your analogy about a translator. They they speak different languages because the board is speaking uh uh in business terms of shareholder value, longevity of the business. And if the CTO is not really when they present their plan, they they they need to customize the message for the board because otherwise they speak two different languages and a friction can happen, which is very, very unhelpful.

SPEAKER_00 27:14

Absolutely. So putting your investor hat on, because you have many hearts, when ASAR scale pitches to you, what are your core expectations for the impact of the AI? And are you looking for cut savings, new revenue streams, a competitive mod, or something else entirely?

SPEAKER_01 27:36

Well, all of the above. No. Um, well, the relationship between AI and investors is quite an interesting one because it went a few years ago from we west everything which has got AI in it. I'm exaggerating here a little bit, to now, well, you're talking about AI, okay, fine, but show me the impact of your environment as measurable business outcomes, but also to what extent are you able to create competitive advantage? Having said that, the majority of the focus today is still very much of linking AI to gaining efficiency. In fact, there's a very interesting report by SaaS Capital, our US finance business, which analyzed small and medium-sized B2B SaaS businesses and showed that typically SaaS businesses which invest in AI reduce cost in the RD side of it because the developers are just more efficient in the programming side of it. They see reduced cost in administration costs because companies start implementing automated processes with AI for their administrative parts. They see a slight increase in sales and marketing costs, interesting, because when you try to push the AI message to the market, you need to invest more in sales and marketing. And they see a slight increase in the cost of goods sold because as soon as you put AI into the product mix and the service mix, the customer service team have to do a better or a higher job in onboarding, explaining the advantages of AI to the clients. So they need to invest, the companies need to invest more in customer service and customer success teams. So, with that all said that, the relationship between AI investment and gaining efficiency from an investment perspective, that's a given one. Now you need to prove as an investor, sorry, as an entrepreneur, you need to improve that to their investors. So that leads into the question about competitive advantage. And that becomes increasingly more important one. But the question is investor then comes right, okay, how does your AI initiatives move the key value driver of, in this case of Assinus, which is retention, conversion, custom acquisition costs. And to what extent is this unique, is this defensible, or can it be easily copied by competitors? And as in being involved in investment discussions, I've seen the competitive advantage can be stabilized around three areas. One is the quality of your data. With better quality of data, you can train better AI models. Then it comes to the domain expertise you have, and to what extent you can encode this domain expertise into the AI models. The third one, to what extent are you able to reshape your business model by bringing AI into the product mix? Ideally, you have a combination of all three, but if you get one of these right, you are increasing your chance you have sort of a defensible AI strategy, which cannot be easily copied by other competitors. And that as an investor, certainly you're going to look at that.

SPEAKER_00 31:09

Well, that that's really great. Um, you everything you shared, one of the pieces that I picked the most is that there's a critical shift in the market. The age of AI for AI's sake is over, and investors now demand a clear line of sight between the AI initiative and a core business driver, right? And it forces a level of discipline that's incredibly healthy, making sure every AI project has a clear why tied to the measurable value.

SPEAKER_01 31:40

Right, absolutely.

SPEAKER_00 31:42

So, yeah, so every founder says they are AI powered now, right? How do you cut through the noise? And what specific proof points or data do you look for to validate that a company's AI is creating real defensible value?

SPEAKER_01 32:00

Right, it comes to credible. Basically, as an investigator, how credible is your laboratory? And this credibility comes not just what they're saying, but what they can prove. And there's a lot you can prove with data, meaning you can run as an entrepreneur, you can run some experiments and collect data and see how is this my application which I'm gonna work with impact certain business metrics. And if you even if you do that as a as a experiment, a test phase, that's great because then you can go into investing saying, look, this is what we have tried out, this was the experiment, these were the results. And hence, if we invest in that, we know this will have a measurable impact.

SPEAKER_00 32:43

So the buzzwords are meaningless without the metrics, and it's not about being AI powered. It's about proving that your AI is moving a key value driver.

SPEAKER_01 32:53

Yes. If if you come to an investor discussion and uh speak about AI this and AI that, and are not able to link that to a measurable business outcome, the discussion is over very quickly.

SPEAKER_00 33:03

So finally, what is the one question you wish more founders would ask themselves about their AI strategy before they even walk into an investors meeting?

SPEAKER_01 33:16

It comes back to the point um mentioned earlier, and that is they need to ask themselves, how defensible is my AI strategy? How easily can it be covered? And what's unique on my approach? And the uniqueness, I remember, in my experience, this comes down to the data and to the domain expertise and the business model. So it's really the question, how credible am I with my AI strategy? And the credibility, as you rightly pointed out, just right now, comes back, can I link this to business drivers, uh, impact of AI and business drivers? That it comes down to that. But I would what I would love, that's the second question I would love they should ask themselves because of investor pitch, and that is do I have the right people in the organization to pull this one off? Um, because very often they have maybe the founder is in a great position and uh have the right mindset and they do it all right. But it comes to the actual execution, that's a different ball game. If they haven't got a sort of a credible story behind, I have a strong team here, then as an investor, say, okay, that's great on paper, and you've you've done your homework, we believe you, but can you actually execute it? And that is where I wish founders would ask the question before they're going to invest the pitch how strong, strong you feel, do I feel I have got the right place, uh right team in place here?

SPEAKER_00 34:38

Wow, those are two powerful and humbling questions. They force a founder to move from just storytelling to evidence and also from ambition to a realistic assessment of their capabilities. So answering those two questions honestly is the first step in building a credible investor-ready AI strategy. So, Deetma, looking across these three roles, founder, board member, and investor, what is the single most important piece of advice you'd give to create alignment between all three on the AI journey?

SPEAKER_01 35:13

That's a good one. Um, I would say it's the caliber of the chair of the board, plays an incredible important role. Why? Firstly, the chair of the board is responsible to make sure there is the right board in place, also linking to have AI understanding. This board, then the chair is also then responsible to make sure that the chair provides the strategic direction to the leadership team and gives them the mandate to execute an unagreed strategy. So the chair is then also responsible to make sure that the corporate governments is in place for the risk and compliance assessment. But also then for the linking all three stakeholders, board, leadership team, and investors, the board, so the chair in this case needs to make sure there is a communication directly with the investors to reassure the arrest and investors that the AI investments will have a measurable business outcome. So I would say it's not as much technology again. In this case, to bring this all together in one, the chair of the board plays a tremendous important role.

SPEAKER_00 36:26

Thank you. That relationship is the central nervous system of the entire strategy, right? And it highlights that AI adoption is ultimately not a technology challenge, but a leadership and communication challenge.

SPEAKER_01 36:40

Absolutely. Absolutely, yes.

SPEAKER_00 36:42

Thanks for sharing this. It's been awesome. So looking out over the next three to five years, what is the white space or the biggest opportunity you see for new AI-native SaaS companies?

SPEAKER_01 36:55

AI-native businesses have a huge advantage because they need less money, less capital, less people, and less time to disrupt entire segments. Indeed, if you look at B2B, traditional B2B SaaS businesses, they find themselves somehow squeezed in the middle. On one side, they have the large IT companies who keep on innovating with AI through acquisitions. On the other side, you have the AI-native SaaS businesses which innovate and go to market much faster because they don't have the technical debt baggage traditional SaaS businesses have. And I expect that certainly to continue. But having said that, I feel very optimistic, actually very excited, because in front of our eyes, we see a huge democratization process happening. Because AI is lowering the barrier for digital creativity. You don't need to be a programmer anymore to create digital products or services, or even digital art and entertainment. So exciting times indeed.

SPEAKER_00 38:13

Absolutely. What a fantastic and inspiring vision to end on. It brings us full circle. The technology, as it becomes more accessible and powerful, doesn't diminish the need for great ideas and intentional leadership. It rather amplifies it.

SPEAKER_01 38:30

Absolutely right. Yes.

SPEAKER_00 38:36

Thank you for sharing your comprehensive insights with us today. It's been incredibly valuable.

SPEAKER_01 38:42

Well, thanks for having me. It's great talking to you.

SPEAKER_00 38:45

Thank you so much, Deetma. And I'm hopeful that we can have you on the show again soon.

SPEAKER_01 38:50

Anytime.

SPEAKER_00 38:52

Thank you. The power of that conversation with Dietma is his ability to connect the dot from the investors' thesis all the way down to the founder's reality. My key takeaway is that in the AI era, strategy, technology, and governance are no longer separate conversations. They are one and the same. To connect with Dietma, you can find his details in our show notes. And if you are a leader grappling with the challenges of building a trustworthy and effective AI strategy, and you would like to learn more about the frameworks we use at Anaya Group to help companies like yours, you can visit us at anaia.org or connect with me directly on LinkedIn. If this episode gave you a new perspective, the best way to support our work is to follow the Anaya algorithm on Apple Podcasts, Spotify, or wherever you get your podcast. Until next time, keep leading intentionally. Thank you.