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.