A retail operations lead proposes an agent that triages 2,000 weekly support tickets, auto-resolves password resets, and escalates refunds over $50 for human review. Finance asks for hard evidence before funding expansion to three regions. Which impact and business value assessment justifies the agentic workflow investment?

The correct answer succeeds because it establishes causality and monetization for a high-volume workflow: capturing pre-pilot handling time and error rate creates the counterfactual for 2,000 weekly tickets, measuring pilot deflection quantifies auto-resolution of password resets, cycle time shows throughput gain, escalation accuracy proves the $50 refund guardrail works, and translating those deltas into labor and error-cost value gives Finance hard ROI to fund three-region scale. The savings projection built from baselines, deflection and cycle time alone is standard for capacity planning but fails on risk because without escalation accuracy there is no evidence refunds over $50 were correctly routed, so apparent savings could hide compliance losses. The pilot-only calculation of deflection, cycle time and escalation accuracy into savings is common for rapid pilots but fails on attribution because without pre-pilot baselines improvement cannot be distinguished from existing performance or volume variation. The baseline-to-pilot comparison of operational quality for readiness is a sound service review but fails on the decision criterion because Finance requested financial evidence, and quality lift without dollars, hours-to-FTE and error-cost modeling cannot justify expansion. On exams distinguish operational success from business-value success and select the choice pairing baseline comparison with monetization when funding is at stake. Practically validate by pulling baseline averages from ticketing logs, auditing a sample of over-$50 decisions, then multiplying verified minutes saved and errors avoided by fully loaded cost and projected tri-region volume.