Repeatability
Medium
The analytical structure is repeatable — compute utilization rates, correlate with profitability, flag outliers — but the recommendation layer requires fresh judgment each cycle based on business context that changes. Monthly reruns are feasible; the interpretation layer is not fully templatable.
Ambiguity Tolerance
Medium
Dashboard outputs have crisp success criteria (charts, stats, outlier flags), but 'recommend staffing or pricing adjustments' is open-ended and depends on thresholds and priorities the user hasn't defined. The agent must make assumptions about what counts as over/under-utilized and what a meaningful outlier is.
Data & Tool Availability
Medium
The spreadsheet is the primary input, but the agent needs it actually attached and parseable — not just referenced. Profitability data must also be present in the file; if it's in a separate system or requires manual reconciliation, the agent hits a wall immediately.
Error Cost
Medium
A miscalculated utilization rate or a spurious correlation could lead to a bad staffing or pricing decision, but the output is a recommendation, not an action — a human reviews before anything is implemented. The reversibility buffer keeps error cost from being catastrophic.
Human Judgment Required
High
Staffing and pricing recommendations depend on factors the agent can't see: team morale, client relationship health, competitive market rates, strategic growth bets, and individual circumstances. The data analysis is automatable; the business judgment layered on top is not.