AI can crunch the segmentation and surface the patterns, but a human should own the budget call.

Segmentation, LTV calculation, and channel profitability analysis follow consistent statistical methods that don't change run to run. This is structurally the same task every time the data is refreshed.

The analytical outputs (segments, LTV, channel ROI) have reasonably crisp success criteria. The 'top 5 recommendations' are inherently judgment-laden — there's no objective test for whether a reallocation recommendation is correct, which introduces meaningful ambiguity.

The task assumes 24 months of structured order data exists and is accessible, but the agent would need it explicitly provided in a usable format (CSV, database connection, etc.). Cost-per-acquisition data by channel is also implied but not confirmed available, and without it, 'profitability' calculations are incomplete.

Misattributing channel profitability or miscalculating LTV could lead to reallocating $120K in the wrong direction — a costly, partially irreversible mistake if acted on without scrutiny. The dollar stakes make human review non-optional.

The analytical layer is well within AI capability. But translating findings into actionable budget recommendations requires understanding brand strategy, competitive context, and risk appetite that aren't in the order data — a human strategist adds real value at the recommendation stage.