Decision intelligence

Decision intelligence is a discipline that treats a decision itself, not just the data or model behind it, as the thing to be designed and engineered. It draws on data science, optimization modeling, behavioral science, and process design to map out how a decision gets made, what information feeds it, who or what makes the call, and what happens afterward, then looks for ways to make that process faster, more consistent, or more accurate.

It sits a level above prescriptive analytics, which recommends a specific action for a specific problem; decision intelligence is broader, covering the surrounding workflow, including how automated recommendations, such as next-best-action systems, and human judgment interact, and how outcomes are fed back to improve future decisions. It is closely associated with augmented analytics, since many decision intelligence platforms use AI to surface options or scenarios rather than fully automate a decision.

Decision intelligence matters for recurring, high-volume decisions, pricing, inventory, staffing, where small improvements in decision quality compound over many repetitions, and for high-stakes decisions such as scenario planning for a major investment. A common pitfall is applying heavy decision-modeling infrastructure to one-off, low-stakes decisions where the overhead outweighs the benefit, or building a decision system without a clear feedback loop to check whether its recommendations actually improved outcomes.