The gap between published research on AI's economic impact and what a CFO needs on a one-page report is enormous. Closing that gap is most of what we do.
The Eclipse Method — our internal name for the pipeline that takes research into the platform — has three stages. We start with the underlying studies: task-level automation baselines, workforce impact forecasts, and the increasingly rich body of empirical work on AI's productivity effects. Then we translate those baselines into a set of conservative multipliers we can apply to specific roles and functions. Finally, we anchor those multipliers to the customer's own adoption and spend data, producing per-vendor attribution.
The word 'conservative' is load-bearing here. A lot of published studies report headline effects under ideal conditions. We discount those aggressively. The goal isn't to produce the largest defensible number — it's to produce the number a finance leader could put in front of a skeptical auditor without flinching.