AI-Ready BI: The More Powerful BI Becomes, the Less Ambiguous It Can Afford to Be
I have spent a large part of my professional life thinking about reporting. And the longer I do this, the more convinced I become that the report itself is rarely the real problem. What matters is whether people understand the information quickly, whether they understand it in the same way and whether they can make a better decision because of it.
For years, Business Intelligence has become more powerful and more flexible. Users can create almost any visualisation, format information in countless ways and build highly individual dashboards. Technically, that is impressive. But I have never believed that more flexibility automatically leads to better communication. Quite often, the opposite happens.
If Actual is blue in one dashboard, black in another and green in the management presentation, the reader has to decode the visual language before thinking about the business. If the same KPI has different definitions depending on which report is open, no beautiful visualisation fixes the underlying problem.
This is one reason the thinking behind IBCS has interested me for so many years. The basic principle is simple: business communication benefits from a common language. Not because every report should look identical, but because things that mean the same should also be communicated in the same way.
IBCS 2.0 makes an important distinction between Notation and Composition. A common notation can reduce unnecessary interpretation. Composition still requires judgment, because no standard can decide what your actual message should be.
That is exactly how I think it should be. Standards should not remove thinking. They should remove the need to think about the same unnecessary things over and over again.
Why AI Makes Consistency More Important
Until recently, humans were surprisingly good at compensating for inconsistencies. We know that one dashboard works a little differently. We remember that Finance defines a KPI differently from Sales. If something is unclear, we ask somebody. It is inefficient, but it works.
AI changes that equation. We increasingly expect systems to interpret information, explain developments and make recommendations. With Agentic AI, we are beginning to expect them to take actions as well. At that point, ambiguity becomes much more than an inconvenience.
And the gap between those ambitions and the foundations underneath them is already visible.
Accenture surveyed executives at 2,000 companies in 15 countries for its 2026 research on AI-ready data. While 64% said their organisations had already moved beyond isolated advanced AI pilots, only 7% had reached the level of data readiness Accenture considers necessary to scale advanced AI. 72% still lacked trusted data of the right quality combined with standardised governance practices.
Deloitte sees a similar development around Agentic AI. Its 2026 State of AI in the Enterprise research shows that 74% of organisations expect to use AI agents at least moderately within two years. Yet only 21% currently report having a mature governance model for autonomous agents.
I find that combination fascinating. We are making systems more autonomous much faster than we are making the information underneath them unambiguous.
An AI agent will not magically resolve inconsistent definitions, unclear notation or missing business context. It may simply act on them faster.
Where TRUECHART+ Fits
This is exactly where TRUECHART+ comes in for me. We did not design it simply to add another visual to Power BI or Qlik. The idea was to make consistent business communication part of the everyday reporting environment rather than something users have to recreate manually in every report.
That becomes particularly important once reporting is no longer static. Users want to pivot tables, navigate hierarchies, collaborate, comment and increasingly work with planning and write-back. TRUECHART+ brings those capabilities together while keeping the reporting logic and visual language consistent across Power BI and Qlik.
For me, that is the real challenge. A standard should not disappear the moment somebody starts interacting with the data. It should still work when the structure changes, when people collaborate and when information moves from simply being viewed to being used.
And this is also where I see the connection to AI. If we expect systems to interpret and act on business information, we cannot leave all of that consistency to individual users. More of it has to become part of the software itself.
Powerful BI Needs a Clearer Language
The more powerful our systems become, the less ambiguity we can afford inside them.
For years, inconsistent notation or definitions mainly created friction for the people reading reports. With AI and Agentic AI, that same ambiguity increasingly becomes part of the input for systems that interpret information and potentially act on it.
That changes the role of standardisation. It is no longer only about making reports easier to read. It is about creating a business language that remains understandable when humans and machines are working with the same information.
If you are looking at how standardised reporting, IBCS and TRUECHART+ could make your BI landscape more consistent and better prepared for AI, book a 30-minute exploration call with us. We can look at where ambiguity exists in your current reporting, which standards and definitions would create the most value and how to embed more consistency directly into your BI environment.