The sheer number of variables in a consumption model—individual customer seasonality, new bookings, timing, and rep forecasts—creates a level of complexity that is nearly impossible for humans to manage effectively. AI is becoming essential to aggregate and analyze this data to produce a reliable forecast.
Aggregate consumption revenue is often stable and predictable at the macro level, making it manageable for a CFO's office. However, for individual sales reps forecasting specific customer usage, the process is highly volatile and difficult, akin to predicting sporadic umbrella purchases versus a steady stream.
AI overcomes the difficulty of forecasting individual consumption by not looking at reps in isolation. Instead, it groups them into cohorts based on shared characteristics (e.g., channel, type). This allows the model to learn from collective patterns and apply those insights to correct and improve individual forecasts.
By providing a more objective, data-driven forecast that learns from collective behavior, AI depersonalizes inaccuracies in sales predictions. This can fundamentally change the organizational dynamic, moving the focus away from blaming individual reps for missed targets and towards a more collaborative and trusting environment.