From BI to decision intelligence
For years, data platforms were built with a fairly clear ambition. Collect the data, clean it, visualize it, and help people understand what happened. Business intelligence did exactly that, and in many organizations it did it well enough to become invisible.
AI changes the center of gravity. The question is no longer how well we report the past, but how reliably we can support decisions in the present. That shift sounds subtle. In practice, it changes almost everything, including what we expect from data, how we govern it, and how seriously we take adoption.
I’ve found it useful to think of this evolution as a maturity ladder. Not a theoretical model, but a way to recognize where an organization really stands, and why adding more tools rarely fixes the underlying problem. At the first level, data platforms focus on visibility. Dashboards, reports, KPIs. The data exists, it is consulted, and it supports conversations. This stage is often celebrated as a success, and rightly so. But it remains descriptive. It tells us what happened, sometimes why, and very little about what to do next.
The second level introduces trust and governance. Data definitions are aligned, ownership is clearer, access is controlled, and quality issues are addressed systematically rather than heroically. This is the unglamorous work that determines whether data can be reused safely. Without it, AI initiatives tend to amplify inconsistencies instead of insights.
The third level is where many organizations get stuck. They add advanced analytics or machine learning capabilities, but adoption remains uneven. Models exist, proofs of concept multiply, and yet decisions continue to be made largely as before. The gap here is rarely technical. It sits in workflows, incentives, and habits. If insights are not embedded where decisions are actually taken, they remain optional, and optional insights are easily ignored.
Decision intelligence emerges at the next level. Data, analytics, and AI are no longer separate layers, but part of how decisions are shaped, challenged, and executed. Recommendations are contextual, explainable, and aligned with governance rules. Humans remain accountable, but they are supported consistently rather than occasionally. This is where AI starts to change behavior, not just architecture. Reaching that level requires three things to move together.
Data must be reliable and relevant, governed in a way that protects the organization without paralyzing it. Governance must be clear enough to build trust, but pragmatic enough to allow experimentation. And adoption must be treated as a first-class concern, with attention to how people work, how decisions are framed, and how success is measured.
Tools matter, of course. But tools only accelerate what already exists. If data is fragmented, governance ambiguous, or adoption accidental, AI will scale confusion faster than value. What I see in organizations that make real progress is not a perfect platform, but a coherent approach. A clear sense of which decisions matter most, which data supports them, how responsibility is defined, and how people are expected to engage with insights.
That is the shift from BI to decision intelligence. It’s less about seeing more, and more about deciding better.
