Dashboards show data. Decision systems recommend actions.

Business intelligence answers "what happened?" Decision intelligence answers "what should we do about it?" BI takes the same data, summarizes it into dashboards and reports — useful for understanding the past, insufficient for shaping the future. Decision intelligence adds causal modeling, scenario simulation, and prescriptive optimization. It tests strategies against thousands of scenarios before you commit resources. Quantexa focuses on entity resolution and network analytics; Aera Technology on cognitive automation; Peak.ai on supply chain decisioning. This platform spans all strategic decision types — M&A, capital allocation, market entry, pricing — with domain-agnostic scenario simulation.

Keep them. Tableau and Power BI are excellent at descriptive and diagnostic analytics — showing what happened and why. Decision intelligence starts where they stop. When your Tableau dashboard shows Q3 revenue dropped 8% in the Midwest, the next question is: do we adjust pricing, reallocate sales territory, launch a regional promotion, or accept the decline? That question requires scenario simulation, not another chart. Decision intelligence consumes data from your existing BI stack and produces actionable recommendations with quantified trade-offs.

Predictive Analytics generates probability estimates — this customer has a 73% churn risk, this equipment has a 14-day failure window, demand next quarter will be 5,200 units. Decision Intelligence consumes those predictions as inputs. Given that churn risk, what intervention maximizes retention ROI? Given that failure prediction, should we schedule emergency maintenance, stock replacement parts, or redistribute load to redundant equipment? Prediction tells you what will happen. Decision intelligence tells you what to do about it. The two systems compound each other's value.

Every strategic decision involves incomplete data — that is what makes it strategic instead of operational. The system handles uncertainty explicitly. Unknown variables get probability distributions instead of point estimates. Sensitivity analysis shows which unknowns matter and which do not. If the best decision is the same across 80% of scenarios regardless of a particular assumption, you do not need to spend three weeks researching that assumption. If a single variable flips the recommendation, the system highlights it: get better data here before deciding.

Pre-built decision templates — M&A evaluation, market entry, capital allocation, vendor selection — take 1-2 weeks to configure with your data sources and organizational constraints. Custom decision models for novel strategic questions take 4-8 weeks. The initial setup involves mapping your data landscape, defining decision variables, and calibrating the causal graph with domain experts. Once configured, new analyses on the same decision type run in hours, not weeks.

AI Agents handle the data assembly and analysis preparation that traditionally consumes analyst time. An agent monitors market conditions continuously, flags when a decision trigger is reached (competitor price change, regulatory announcement, threshold metric breach), automatically updates the decision graph with new data, and drafts initial scenario parameters. The human still makes the decision. The agent ensures the decision model is current when that moment arrives — instead of scrambling to build an analysis after the fact. Agents reduce the time from trigger event to decision-ready analysis from weeks to hours.

No. The math scales down. A 50-person company making a $2M market entry decision benefits from scenario simulation just as much as a Fortune 500 company making a $200M acquisition. The decision framework is the same — define options, model uncertainties, simulate outcomes, rank trade-offs. What changes is the data complexity and the number of stakeholders. Mid-market companies often extract more value per dollar because they lack the 20-person strategy teams that large enterprises use to build these analyses manually.

Three metrics. First, decision cycle time: how many days from question to commitment? Organizations typically see a 50-60% reduction. Second, forecast calibration: when the system predicted an 80% probability outcome, did it actually occur roughly 80% of the time? Brier scores track this rigorously. Third, outcome variance: are actual results closer to projections than they were under the previous decision process? This takes 6-12 months to measure meaningfully. The system logs every prediction and every decision, creating a feedback loop that most organizations have never had — and that makes every subsequent decision better informed than the last.