AI can do the segmentation math, but a human has to own the pricing call.

The analytical structure—cluster, model elasticity, project revenue—is repeatable. But each pricing cycle involves different market conditions, competitive pressures, and strategic context, so the judgment layer changes each time.

Success criteria are underspecified: 'price elasticity clusters' and 'migration strategy' require assumptions about churn thresholds, acceptable revenue risk, and competitive alternatives that the agent must guess at or invent. There is no ground truth to validate against.

The CSV is provided, which covers the core inputs. However, the agent lacks churn history, NPS data, competitive pricing benchmarks, and sales team context—all of which are material to a credible elasticity estimate.

A misjudged pricing recommendation applied to 850 active customers could trigger significant churn, damage customer trust, and cost far more in lost ARR than the increase would gain. This is not easily reversible once communicated to customers.

Pricing strategy in a vertical SaaS involves competitive positioning, customer relationship capital, sales team capacity to manage migrations, and board-level risk tolerance—none of which an agent can access or weigh appropriately.