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Planning for an agentic AI-driven business creates a modeling problem most frameworks were not designed for. Two things are simultaneously true: unit costs are falling (token prices down ~10x/year since 2021), but total AI spend keeps rising as workloads scale. A single forecast line cannot hold both.

Q1 - Cost curve modeling: Is scenario banding - deflationary floor, stabilization ceiling - the right construct for this, or are there ensemble approaches that handle it more precisely?

Q2 - Where does variance live? Value flows through a causal chain: adoption → usage intensity → operational outcome → financial return. Which link carries the most forecast variance in practice - and does that change where you apply uncertainty discounting?

Bounded ROI range - standard estimate at the upper end, risk-adjusted at the lower using a discount factor for technical, adoption, and data quality uncertainty. Recalibrates as actuals arrive.

Two-layer planning model - driver-based base layer for auditability; ensemble forecasting layer that updates cost relationships as new data comes in.

The causal chain problem feels structurally similar to marketing attribution modeling. Curious whether that methodology has transferred in anyone's experience.

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