Artificial intelligence promises a future where dashboards explain what went wrong, why it happened, and what to do next. One day, that vision will become reality. For now, the gap between expectations and results remains wide.
The expectations
Many believe that AI in business intelligence can automatically detect problems and opportunities, explain their causes, recommend actions, monitor outcomes, and then learn from the experience.
This is indeed how AI should operate — and how it eventually will.
In our current data analytics consulting projects, we are already experimenting with early forms of recommendation engines that extend beyond signal detection to the first steps of suggested actions.
The current reality
Today AI performs reliably only at the stage of identifying signals — the anomalies and deviations that mark the beginning of an analytical story. It can detect that sales have fallen, or that lead volume has decreased by seven percent compared with the previous week. What it cannot yet do well is explain why.
The transition from a signal to an insight still requires human context. AI may recognise that the drop correlates with a reduction in CPC leads and a lower conversion rate on a landing page, yet it struggles to connect this observation to the real business environment.
Where AI already adds value
Even with these limitations, AI brings a clear advantage: speed.
The time between a shift in data and the moment a human analyst notices it is often one hundred times longer than the reaction time of an AI system. The ability to detect such shifts early has measurable value, allowing teams to respond long before losses compound.
Where it still fails
Beyond detection, AI lacks the context that gives data meaning. To move from signal to recommendation, systems need a complete data catalog and a complementary context catalog that describe how the business operates, what activities occur, and how metrics relate to each other.
In our business intelligence consulting practice we build these catalogs into client systems. Once AI can access structured data definitions and contextual information—together with feedback on previous recommendations—it starts producing insights that are genuinely useful rather than statistically plausible.
Why building context is difficult
Creating such foundations takes time and discipline. Developing reliable data and context catalogs is a resource-intensive process that may take months before it delivers consistent results. Many projects fail because this groundwork is never completed, leaving AI models to make predictions in a semantic vacuum.
The bigger picture
AI for BI does not fail because it lacks power; it fails because it lacks understanding. Without context, algorithms simply make elegant guesses. The future of AI-powered business intelligence lies not in adding another chatbot to a dashboard but in teaching systems to comprehend the language, rhythm, and logic of a company’s decisions.
Interested in seeing how context-aware BI works in practice?
We can demonstrate how data and context catalogs are structured and how they enable truly intelligent analytics.
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