Why AI Cannot Fix a Business It Does Not Understand

What happens when an AI agent begins influencing business decisions without fully understanding the systems, processes and dependencies behind them?

In this episode, I speak with Bert van der Zwan, CEO of Bizzdesign, about the gap between enterprise AI expectations and the results many companies are seeing in practice. Bert has spent more than 25 years in software and SaaS leadership, including executive roles at Webex, Bynder, Twinfield and Unit4.

Bert offers a candid assessment of the current AI market. He believes AI will have a lasting effect on businesses and society, but argues that expectations for near-term financial returns have become inflated. Many companies are spending money on tools and experimentation without reducing costs, consolidating software or producing new revenue.

That does not mean experimentation is a mistake. Bert sees it as a necessary stage. The harder question is how companies move from a growing collection of pilots to AI capabilities that can operate dependably inside the business.

One barrier is fragmented organizational context. Large enterprises have often grown through a combination of internal expansion and acquisitions, leaving behind disconnected applications, inconsistent data definitions and processes that cross several departments. An AI system working with only part of that picture may make a fast decision, but that does not make it a good decision.

Bert argues that AI needs an authoritative view of how the enterprise works. Systems, processes, ownership, dependencies, approval status and policy restrictions must be visible and consistently defined. Without that shared context, AI may reproduce existing silos or make them worse.

We also discuss the risks boards and technology leaders should consider as AI agents become involved in operational decisions. These include unreliable data, unclear accountability, legal exposure, weak governance and an incomplete view of the process being changed. Human oversight remains necessary, particularly when an automated decision could affect customers, employees or major investments.

Bert then introduces the idea of "bespoke from the cloud." Traditional SaaS products were built around largely standardized interfaces and workflows. AI-assisted development could make software far easier to personalize around individual customers and use cases. This may give users greater control, but it could also challenge long-term software contracts and the economics that have supported the SaaS market.

For leaders trying to connect AI spending with business results, Bert recommends beginning with visibility and a clearly defined outcome. Every initiative should be judged by whether it reduces costs, increases revenue or shortens the time required to deliver value.

If AI depends on understanding how a company actually works, have businesses invested enough in creating that shared understanding before adding agents to their operations? Listen to the episode and share your thoughts with me.