Forecasting case study
Improving Forecast Accuracy with AI
Perspective: Forecasting and Analytics Leader
Forecast accuracy does not improve because a model is impressive. It improves when the model is connected to the decisions people actually make. In commercial operations, the forecast is not a math contest. It is a planning tool for inventory, pricing, capacity, sales execution, and executive tradeoffs.
The forecasting problem
At Dell Technologies, my work touched commercial strategy, planning, and AI analytics in a server revenue operations environment with $16B+ in revenue scope. The challenge was not simply predicting a number. The challenge was integrating signals that changed at different speeds: pipeline movement, deal timing, pricing actions, product mix, supply constraints, historical seasonality, and executive assumptions.
The method that mattered
The useful system combined three layers:
- Signal integration: bring commercial, historical, and operational signals into one planning view.
- Exception detection: identify where the forecast was drifting and why.
- Decision translation: connect forecast movement to the planning action it should trigger.
The 90%+ accuracy journey
The path to 90%+ forecasting accuracy came from narrowing the gap between analytics and the operating rhythm. Forecast reviews needed to explain not only what changed, but which assumption changed, which signal created the movement, and what decision should follow. AI helped most when it reduced noise and surfaced the few changes that mattered.
What leaders should measure
Accuracy is necessary, but incomplete. I would also measure decision latency, exception resolution time, confidence by segment, and how often the forecast changes the plan. A forecast that is accurate but ignored is not a business system.