AI and data that create real impact

AI is fundamentally changing how businesses work, create value and compete. Used correctly, it raises both the pace and the quality of decisions that previously took days of manual groundwork. But real impact takes more than technology.

We see the same pattern at company after company: the AI tools are already in place. Employees use them every day to summarise text, draft documents and clear the inbox. What's missing is the connection to the organisation's own numbers. As soon as the question concerns the business itself – which customers are we losing, what has happened to the margin, how does inventory look – the AI has nothing to work from and starts guessing.

The difference between a pleasant experiment and something that actually changes how people work almost always lies in the data underneath. Not in which model you chose.

The fact that most AI investments never take off isn't just our gut feeling – it's well documented.

95 percent with no measurable impact

MIT's NANDA initiative reviewed just over 300 AI initiatives in the report The GenAI Divide: State of AI in Business 2025. The conclusion: despite an estimated USD 30–40 billion in investment, 95% of the initiatives reviewed produced no measurable impact on the income statement, while only 5% created significant value. The authors don't point to model quality as the cause, but to the gap between tools and real workflows (Forbes on the MIT report). One caveat: the figure applies specifically to measurable impact on results, and many pilots lacked a baseline to compare against. It should be read as most initiatives not being possible to substantiate – not as AI not working.

Gartner puts its finger on the same issue from the data side. In a survey of 248 data leaders, 63% said they either lack the right data management for AI or are unsure whether they have it, and Gartner predicts that through 2026 organisations will abandon 60% of AI projects that are not supported by AI-ready data (Gartner, February 2025).

What the underlying data looks like in most organisations

The pattern holds true in reality. An AI answer is never better than the data it's built on, and in most organisations that data looks roughly like this:

• the business data sits in the ERP system, in tables built for transactions
• key concepts lack a shared definition, so "revenue" means different things
• parts of the truth live in spreadsheets that act as master data
• history is incomplete, because the system overwrites instead of preserving

Connect an AI assistant straight to an environment like that and you get answers that sound good but don't hold up. And an answer that sounds good but is wrong is more dangerous than no answer at all – it's rarely discovered until someone has made a decision based on it.

That's why we argue that the AI question is fundamentally a data question. The order matters: structure first, prompt second.

The path there doesn't have to be long, but it does have to be the right one. In short, it's about:

• gathering the business data from the systems where it actually originates
• defining the concepts once, so that everyone calculates margin the same way
• quality-assuring the KPIs in a model that both reports and AI read from
• opening up the model to AI, instead of letting the AI dig through raw tables
• controlling access, so that each user only sees what they should see

That's exactly the sequence Vellox is built for. Your data is retrieved from the ERP system, structured into a semantic model and then used by both your reports and your AI assistant. Same figures, same definitions, no matter where the question is asked.

1. Lay the foundation – a semantic model of your business

We pull data directly from Business Central, Jeeves, Monitor, Pyramid or Visma Business and structure it in the Vellox data warehouse. The model covers sales, purchasing, inventory, production and finance, and it comes pre-configured for your ERP system from day one.

2. Prompt your data

With Vellox MCP, Claude or ChatGPT connects directly to your semantic model. The assistant understands your modules and KPIs and retrieves current figures instead of guessing. Questions that previously required an analyst and a few hours are answered in seconds – with a basis you can go back to and review.

3. Build your own on top

Our REST API serves the same verified data that powers Vellox modules. That makes it possible to build customer portals, embedded dashboards, automations and your own AI agents without building a new data foundation for every initiative.

4. Keep it responsible

AI against operational data raises reasonable questions about security and governance. The connection to Vellox is read-only – no write operations are possible – and you decide which parts of the model are exposed and to whom. With the EU AI Act in place, it also becomes increasingly important to be able to show where an answer comes from. A model with traceable definitions makes that question easy to answer.

5. Make it part of everyday work

The value emerges when the controller, the sales manager and the production manager ask the questions themselves. We help with the first use cases, show how to ask and make sure the answers turn into decisions rather than yet another report no one opens.

A few examples of questions our customers put directly to their own data:

• Which ten customers did we lose the most revenue from last quarter?
• How much of our suppliers' price increases have we passed on to customers?
• Which items are tying up capital in inventory without having moved in six months?

What makes the answers useful isn't that they arrive quickly. It's that they're based on the same figures as the management report – and that whoever gets the answer can ask the follow-up question straight away instead of ordering a new analysis.

Want more examples to try right away? We've put together ten questions you can ask your own business data.

If AI feels like something that ought to deliver more than it does today, the model is rarely the problem. More often, it simply has nothing to work with.

Book a demo and we'll show you how Claude or ChatGPT answers questions about your specific business – using your own data. If you have a question before then, feel free to get in touch directly.