Laatst zag ik een filmpje van twee influencers in Dubai. Terwijl er onrust was en er raketten vanuit Iran richting de regio kwamen, vroeg de een aan de ander: “Is er nu oorlog?” Het antwoord van de dame was even opmerkelijk als illustratief:
According to ChatGPT, officially not.
That sentence stuck with me. According to ChatGPT, officially not.
As if a language model determines what reality is. As if something only becomes true when a chatbot confirms it. And perhaps even more importantly: as if an answer that sounds confident is automatically correct.
That is precisely where one of the biggest misunderstandings about AI lies today.
The temptation of a convincing answer
In my conversations with clients, I increasingly hear the same thought: “Why would we still need a specialized data provider for this? I can just ask AI for that information, right?”
It’s often about business information that seems straightforward at first glance. How is a parent–subsidiary structure organized? Which legal entity belongs to which group? Who is ultimately responsible? Who is the UBO?
These are questions you can easily ask ChatGPT or another model. You’ll usually get a quickly formulated, well-structured answer in return.
But fast and well-formulated is not the same as correct.
AI is not a truth system
A generative AI model is not a truth system. It is a language model that recognizes patterns in text and, based on those, generates the most probable answer.
It often works impressively well. Sometimes so well that it seems as if the system actually knows what is true.
But that is not the case. There is no source registry. No verified database. No legal validation. Only a model trained on vast amounts of text that generates a plausible output from that data.
Interesting read: From AI FOMO to smart sales: why good data and MDM are crucial
The risk in a business context
In business, plausibility is not enough.
Definitely not for topics such as:
- ownership and corporate structures
- UBO registrations
- compliance checks
- client acceptance
- risk assessment
In those areas, you don’t want an answer that merely sounds right. You want an answer that is correct and that you can trace back to its source.
You want to know where the information comes from, how up to date it is, and which specific entity it refers to.
AI as an interface, data as the foundation
That is why it is important to clearly distinguish between AI as an interface and data as the foundation. AI is strong in making information accessible. It can summarise, structure, identify connections and make complex information understandable. There is a lot of value in that.
But once AI is treated as a source of truth, trust shifts from data to the persuasive power of the model. And that is exactly where things can go wrong.
Why authentic data is becoming more important, not less
The real value of AI does not lie only in the model itself, but especially in the quality of the data it is allowed to build on. A generic model without controlled, up-to-date and verifiable data can still sound convincing, but it lacks reliability.
That is a fundamentally different approach from relying on a generally trained model without source verification.
The real question for organizations
That is why the discussion is not only about which AI tool you use.
The question is mainly: what do you let AI rely on?
On open web information, training data and probability?
Or on authentic, up-to-date, verified business data specifically intended to support business decisions??
That difference is significant.
A general-purpose model can help with searching, summarising and exploring. But once you need to know who the ultimate beneficial owner is, how a corporate structure is legally organised, or which entity you are actually doing business with, you do not want probability. You want verification.
The future is AI built on trusted data
AI is playing an increasingly large role in how we work and make decisions. But precisely for that reason, the quality of the underlying data is becoming more important than ever.
The future is not AI versus data. The future is AI built on reliable data.
Interesting read: Agentic AI: from hype to practical reality
Conclusion: does it hold up, or does it only sound good?
So yes, feel free to ask ChatGPT your question. Use AI where it adds value. But never confuse a fluently formulated answer with reality itself.
The organizations that will use AI most effectively are not the ones that make the model speak the loudest. They are the ones that ensure the model has access to the right source of truth.
Do I want an answer that sounds good? Or do I want an answer that is correct?
