Why AI Cannot Fix a Business It Does Not Understand

[00:00:04] What happens when AI stops simply carrying out tasks and starts influencing the decisions that shape an entire business? Well, that's a shift that's already underway. And while the opportunities are enormous, so are the risks. And my guest today is the CEO of a company called BizDesign. And he works with some of the world's largest organizations and helps them make sense of an increasingly complex technology environment.

[00:00:33] And we'll explore together why enterprise architecture has become more important than ever. And what business leaders need to get right before they can deliver on those big promises that AI is making right now. But enough scene setting for me. Let me introduce you to my guest right away. So a massive warm welcome to the show. Can you tell everyone listening a little about who you are and what you do?

[00:01:00] So Bert van der Zwang, CEO of BizDesign, ever since the last three years and a bit. Actually joined it as a former chairman of the board. I am in SaaS software forever, I would say. Started my career at WebEx back in the days and have stayed in SaaS forever. Both as a manager, CEO like today, as well as on board seats with several private equity firms.

[00:01:30] So I've done a lot of growing, a lot of like M&A, a lot of integrations and a lot of exits. That's who I am. And for people listening, hearing about BizDesign for the very first time. I know you work with some huge, huge names out there. But can you tell everyone listening a little about how you're helping organizations, the problems that you solve and everything that you do there? Well, BizDesign is a market leader, global market leader actually in, I call it enterprise architecture and beyond.

[00:01:59] So enterprise architecture is our core business, but adjacent categories such as application portfolio management, solution architecture, strategic portfolio management. We also cover within our product suite. And translating that into human language, we provide tools and services for large organizations, large enterprises to guide them around transformation in the widest sense of speaking.

[00:02:27] Organizations, large organizations are often a combination of organic growth in the past and acquisitions, M&A growth, so to say. And with that, it is very difficult to run your business processes, to run your software applications in an efficient manner. And our services, our products provide you a framework to get that done with more visibility, quicker and with lower costs. Awesome.

[00:02:55] And as CEO of BizDesign, I'm curious, how are you seeing a genti care like just move from task execution into real participation in enterprise decision making? And ultimately, what does that mean for executive accountability? I think that to the first part of your question, when it comes to the changes of AI, it definitely is not a buzzword or a hype. It's going to be a trend to stay. And with that, I'm stating the obvious.

[00:03:24] But in reality, I do think that we are in a bubble. Also, stating the obvious. But I am not so much talking about valuation of companies with AI, but more about the expectations on the results as a result of AI in the short term and the midterm. I think that is inflated. And we expect too much out of it. Because I talk with many CEOs that are in similar organizations, different industries.

[00:03:54] And if you really ask them, like you can ask me myself, is what did AI bring you until now in your own organization? Well, the only thing is a cost increase, right? Yeah. Because we are creating a playground where we start experimenting with all kinds of AI tools, which is the right thing to do, by the way. We ourselves, we use Languedoc. We use SalesLoft. We use a lot of other tools.

[00:04:21] But when it comes to monetizing it, then I ask my colleagues, what is it? What it does bring you? Can you go with the reduced headcount? Or can you use less different tools? They all say, no, no, no, no. So I'm okay for now, but not in future. So coming back to your initial question, what will it bring you over time? For my own organization, I do not know.

[00:04:44] When it comes to the impact of AI and AI agents, and when it comes to accountability of the execs, I think it's going to be crucial that rather than like doing the routine automatic work, you now see that AI is starting to support enterprise decision processes in large organizations.

[00:05:08] So what is very important for anyone, including the execs, is data authority. It is transparency. It is visibility into your processes. Because large organizations normally have an application landscape that is like all over the place. Large organizations grow by organic grow, as well as M&A grow.

[00:05:33] And with that, they have very fragmented processes, applications, and so forth. So getting to a point where you can cope with that, you have the visibility, the full visibility on an enterprise level is the most important thing for the execs to work with. A few years ago, there were comparisons with blockchain and tulip mania. Do you see any parallels with AI? No. No. Well, blockchain, like I have a strong fintech ERP background.

[00:06:03] So blockchain, the distributed general ledger, so to say, never to be heard of again, right? Was also like the promising kid on the block a couple of years ago. Never to be heard of again. You had also the smart contracts as a result of it. That ain't gonna happen with AI. AI is here to stay. It will definitely reshape our category, our industry, and our entire society. But to what extent? I don't know.

[00:06:32] I truly believe that the human aspect will never be replaced in totally. The current issues with the AI are that the hallucination aspect is a big one, right? Of course, that will improve over time with the next 20 iterations of entropic or cloud or what you have. But still, always, I think there will be a human aspect needed to that. And I think that the expectations about AI running by itself are inflated.

[00:07:01] Coming back to my initial end of course, we're in the middle of it. But in the long term, I do not know how that is going to work out. And AI is here to stay. We both agree there. So what emerging risks do you think boards and CIOs listening should be considering as AI agents begin influencing strategic and operational decisions? What are you seeing here? Any risks or warnings that you'd offer? Oh, many. Many.

[00:07:27] It's, as I just indicated in the large enterprises, there is no coherent data model. You know, that covers it all in a very reliable manner. So the authority of data, the lack of visibility in processes, if AI starts working to redefine the process, are they looking at the whole process? Are they only looking at parts of it? The governance is a very big risk area as well.

[00:07:55] The legal aspects in a result of the governance aspects are there as well. Technology risks. So I can keep going for a while. But many risks I do see for AI as being part of the enterprise play moving forward. When researching you online, I read a little how you often reference decision friction inside large organizations.

[00:08:15] And I've got to ask, what do you see as the main bottlenecks that are preventing strategy from translating into execution and improving business value and generating better business outcomes? When you start with, again, the fragmented visibility that organizations have. And whether you talk enterprise architects or you talk business owners or CIOs, another risk is that you put initiatives in place that are too siloed.

[00:08:40] The complexity of the complexity of the complexity of the complexity of the complexity of the technology can be a risk. The risk of the decisions, decision cycles, speed up. But if you do not have the oversight and not identify the risks and the visibility that I just indicated to, you're going to be in big trouble.

[00:09:09] And it will basically backfire to you, not leading to better and more efficient or less costly processes or applications. It will be the other way around. And another topic I know you're passionate about is fragmented organizational context. Tell me a little bit more about that and how you see it undermining AI initiatives, even when the technology itself might be sound. Yeah. Let me say that the short answer is that AI is only as good as the context it operates in, right?

[00:09:39] The technology can be strong. But if AI is working against fragmented enterprise context, it will produce fragmented outcomes. AI does not operate in vacuum. It depends on understanding how data, systems, processes, ownership and business priorities connect across the entire enterprise. And that organizational context already act as a blocker or delay point for many transformation of efforts.

[00:10:09] AI simply amplifies the issue because it needs contact to reason. Do you struggle to keep your AI agents from acting outside of compliance? Well, Denodo provides an AI data layer connecting your data systems, keeping guardrails consistent across all of your data platforms. So start scaling your business carefree with Denodo.

[00:10:36] And you can do that by visiting Denodo.com to learn more. But now on with today's show. So in working with organizations such as NHS, HSE and Airbus and so many other household names, I'm curious, everything you're seeing and hearing here, what patterns do you see in companies that successfully align enterprise architecture with AI deployment? What are you seeing here?

[00:11:00] So across the large organizations we work with, AI is very clearly at the top of the agenda. Organizations are experimenting quickly, but the real pressure now is to understand what works, what can be scaled in production and how AI can advance enterprise goals. Many organizations are still working through the move from promising pilots to AI capabilities that can be used reliably in day-to-day operations.

[00:11:29] The pace also varies significantly by sector. Governmental organizations and highly regulated industries often have to move more carefully with stricter approval processes and clearer rules around where AI can and cannot be used.

[00:11:47] Technology companies, on the other hand, and consulting organizations have more freedom and can move quicker and test new models of working quicker than, like I just called out, the governmental organizations. Across sectors, the organizations are making progress. The progress they tend to make have a few things in common. They have clarity about the enterprise, how the enterprise actually works, which capabilities matter most, which system support, and where key dependencies sit.

[00:12:16] They are clear about governance, decision rights, accountability, and where human oversight is required as AI becomes part of the operational workflow. And I think when we look at a lot of organizational structure and the technology in place, much of it was almost built for a completely different time. So I'm curious what you're seeing here. How is AI forcing enterprises to maybe rethink their long-term technology roadmaps and business operating models?

[00:12:43] Yeah, well, AI is pushing organizations to rethink both their technology roadmaps and their operating models. Because intelligence is starting to influence how work gets done across the entire enterprise. One of the biggest shifts is the speed of change. In many organizations, long-term AI roadmaps are becoming harder to define with confidence because the market is moving so quickly. Leaders are trying to keep their options open.

[00:13:10] They want flexibility around vendors, models, and tooling. Because what looks like the leading approach today might be looking very different in six months or a year from now. And let me try to elaborate a little bit on this with probably a silly example. But back in the days, I can almost say, then for me, the ultimate example of a scalable SaaS solution is Netflix.

[00:13:35] And although that one is business to consumer and not business to business, I still want to use the example. So if I, as a user of Netflix, would say, you know what, dear customer support manager at Netflix, I would like to have a little bit more horror in my home screen and a little bit more comedy in the second screen. They laugh at you and they say, you know, it's one size fits all. This is our user interface and this is the content behind the user interface. End of story.

[00:14:05] With AI coming into play now, I think it's going to happen. I think that in, is it a half year, a year, that you can configurate whatever you want to have at Netflix. And that is the deal of, I would almost say, the end of SaaS. We're going back to Bespoke. Because it is so easy to develop apps in order to support a certain use case that you can almost do that on a per customer, per user basis.

[00:14:34] So I call that, and I don't know if it's an appropriate word or not, it's going to be bespoke from the cloud. And that has big implications on models as well. As I just indicated, organizations will less and less be prepared to make long-term commitments because they do not know what the world looks like tomorrow. So we have to go back to less committed, less long relations with your customers. I mean, contractually. Hopefully the relation is as long as it used to be, but without a committed contract.

[00:15:04] And it will be much more tailored towards the specific needs of the use case. It's not going to be one size fits all anymore. It's going to be back to Bespoke. And I also think we're moving away from the debate of AI versus humans and all that doom and gloom we see on our news feeds. And now it seems to be much more about how can humans and AI work alongside one another seamlessly. So how do you see collaboration-ready infrastructure looking like this year and beyond,

[00:15:31] especially in environments where humans and AI agents will be challenged with working side by side? How do you see this playing out? Yeah, collaboration-ready infrastructure is becoming the foundation of an AI-native enterprise, where people and AI can operate from the same trusted understanding of how the organization actually works. In these emerging AI-native environments, both humans and AI agents rely on the same enterprise complex.

[00:16:00] That means a clear, shared view of the systems, the processes, the dependencies, ownership, and the relations that shape how the business operates. The data behind that contact must be authoritative and governed. When enterprise concepts are defined differently across systems or teams, AI reasoning quickly becomes inconsistent and decisions become harder to trust.

[00:16:25] Governance also has to be embedded into the architecture and data models themselves. Life cycle status, ownership, approval states, and policy constraints need to be enforceable. So AI operates within the same boundaries that guide human decision-making. And for any leader listening who wants to avoid stalled AI projects, falling into pilot purgatory,

[00:16:51] what practical steps should they be taking to connect strategy, architecture, and measurable business outcomes? Any advice that you would pass on to people listening there? Well, the starting point, in my view, is clarity about where AI will create value and whether the organization has the foundation to scale it. There are a few practical steps leaders can take. First, build visibility before you scale. Understand the entire landscape across the systems, your data sources, your processes,

[00:17:21] and dependencies, the AI initiatives touches. The second point is, let's start with the business outcome, the required business outcome, so to say. Be explicit about what your problem should be solving, what's the problem that you're solving, and how success will be measured in operational or financial curves. I'm always all about financials. I didn't tell that to you, but my educational background is a CPO.

[00:17:49] So one of my burdens is that I translate everything into money. So whatever you do, whatever you buy, an application, a consultancy, what does it mean for me, money-wise? Does it decrease my costs? Does it increase my top line? Or does it probably shorten my time to market, my time to execution, my time to value, or probably a combination of those three? So everything should be held against those lines.

[00:18:15] And that's actually the most important things of what I wanted to state in this area. And from your point of view and business design, there's so many opportunities on the horizon. What excites you about the future? Many things. Many things. Because if you look at that we as a society, and I don't want to be too philosophical here, but I truly think that we are in unknown territories. And that means that people, businesses, companies,

[00:18:44] that are really entrepreneurial, open to changes, are in for innovations. There's lots of opportunities. Opportunities that are significantly bigger than that we've had seen until now. Obviously attached to the risks that comes with it. But the opportunities I see are significantly bigger than the threats I see coming alongside with them. I think that is a great moment to end on. And for anybody listening that would just like to find out more information

[00:19:13] about anything we talked about today, your work at BizDesign, how you're working with big names from NHS, HSBC to Airbus, where can they find more information? Obviously our website, bizdesign.com, it's quite a comprehensive oversight of what we're doing and who we are. But on top of that, we obviously also cover all the socials, the business socials, LinkedIn, where we are big on other stuff.

[00:19:43] So it should be fairly easy to get in contact with us. Well, I think as Agentic AI moves from just executing tasks to participating in decision-making, I think we covered today the emerging risks and opportunities and some of the bottlenecks of decision friction and fragmented organisational context, all the things that prevent an effective strategy execution. But most interestingly for me,

[00:20:11] I'd love talking with you about how AI is forcing businesses to rethink their technology plans and transform business practice and how their infrastructure can stay collaboration ready. And for anybody listening that would like to carry on that conversation with you, I will include links to everything you just mentioned and add them to the show notes over at techtalksnetwork.com. There'll be a blog post associated with this and a useful link section where I will include links to everything. But more than anything,

[00:20:41] big thank you, Bert, for taking the time to sit down with me today, share your insights and everything you're seeing and hearing out there. Thanks. Great. Thank you as well. I think today's conversation was a timely reminder that successful AI adoption isn't determined by the latest model or the biggest investment. It actually starts with understanding how your organisation works, how decisions are made and whether your data, systems and people are connected in a way that AI can actually support.

[00:21:10] And Bert also offered somewhat of a reality check. Yep, AI is moving at remarkable speed, but trust, governance and human judgment, all these things are equally as important as ever, if not more so. But I'd love to hear your thoughts. Is your organisation ready for AI to influence decision-making? Or is there still work to do before those foundations are in place? techtalksnetwork.com, let me know your thoughts and keep listening, please.

[00:21:38] I'll be back in your podcast feeds again tomorrow. Hopefully, I'll speak with you then. Bye for now. Bye for now. Bye for now.