Leaderboard

✅ Vijay Yivaturi — Strongly tied to a real FOAA ARM process; clear incentive redesign via rejection reason codes, dashboards, and measurable quality outcomes; good linkage between behavior and outcomes. 🟡 Kush Singh — Relevant process (banking payments) and the “blind trust vs resistance” risk is well stated, but the revised scorecard needs 2–3 concrete measures and examples of encouraged/penalized behaviors. In future, anchor your metrics and behaviors with a concrete example and thresholds. ✅ Himanshu Lohani — Clear specific process (document extraction + exception queue) and a practical split of human evaluation: validating AI success + diagnosing AI failure; good responsibility framing. ✅ iambpawan — Excellent concrete process (AI co-pilot in IT service desk) with crisp revised metrics (validation rate, KB contribution, complex-ticket FCR) + clear behaviors to encourage/prevent; very practical. ✅ Preethi Bijesh — Strong process (motor accident claims) and very thoughtful incentive alignment: separates human vs AI error, emphasizes decision quality and override justification; good unintended-behavior prevention. 🟡 Abhishek Chaudhary — Relevant example (demand forecasting) and right direction (override impact KPIs), but it’s too brief—needs a clearer “new scorecard” and examples of gaming behaviors to prevent. In future, include a clearer metric set and an example of how it changes decisions. ✅ Vijay Gonsalves — Good specificity (payments bot thresholding) with revised weightages, exception handling, and behavior guardrails; practical and directly usable. ✅ Taby Sheikh — Very strong, concrete process (AI-assisted claims adjudication) with a structured scorecard and clear incentive logic; well covers “agreement ≠ good” and escalation behavior. ✅ Jinad Padiyath — Strong concrete healthcare use case; good incentive redesign and behavior controls; slightly heavy on regulatory references but still coherent and practical. ❌ Dhruva Kapur — Not specific enough to one process; mostly general thoughts about cycle time decomposition. 🟡 Bharath CN — Specific high-risk process and lots of detail, but the answer drifts into DMAIC narrative; the “people performance measures” piece needs to be sharper. In future, state the revised KPIs and the exact behaviors they drive (good vs bad). ✅ Manish Gupta — Explains how performance metrics and their movement can be tracked better and regularly using AI. 🟡 Anil Kumar (CAISA) — Relevant domain (fraud) but still broad; needs a specific workflow step (e.g., sanctions alert adjudication) and explicit metrics for overrides, false positives, and accountability. In future, use one concrete process and define 3–5 measurable success indicators. ✅ Aditya Bhavsar — Clear process (AI handling author queries) and sensible metric shift (accuracy + touchless rate + revised targets for complex cases); good practicality. 🏆 Best answer : iambpawan — most crisp, process-specific, and incentive-aligned with clear behaviors and safeguards.

The Process: AI-Augmented IT Service Desk (Tier 1 Support)In this process, an AI "Co-pilot" drafts responses to user tickets and suggests troubleshooting steps based on past data. The human agent reviews the draft, edits it for context, and sends the final solution to the user. Revised Success Measures (KPIs)Traditional metrics like "Tickets Resolved per Hour" are dangerous here because they encourage agents to mindlessly accept AI suggestions to hit their numbers. We should replace them with: Metric 1: The AI-Validation Rate (AVR) Instead of measuring speed, we measure how often an agent identifies and corrects a technical error in the AI’s draft. This rewards critical thinking over "blind clicking" Metric 2: Knowledge Base (KB) Evolution Contribution We measure how many times an agent updates a system article because the AI provided outdated or incorrect advice. This shifts the agent’s role from a "Consumer" to a "Curator" of AI knowledge. Metric 3: High-Complexity First Contact Resolution (HC-FCR) Success is measured only on complex tickets where the AI had "Low Confidence" This highlights the human’s unique value in solving what the machine cannot. Encouraged vs. Prevented Behaviors1. Behavior to Encourage: "The Critical Editor" We must reward agents who treat AI as a junior assistant, not a boss. The Incentive: Agents who flag the most "AI Hallucinations" (errors) should be promoted as "Process SME" This ensures that "questioning the machine" is seen as a sign of high skill, not a waste of time. 2. Behavior to Prevent: "The Rubber Stamp" (Automation Bias) The biggest risk is "The Rubber Stamp"—where an agent copies and replaces AI text without reading it to finish their shift faster. The Prevention: Shift quality audits to include "AI-Attribution" If an agent passes through an AI error that a human should have caught, they receive a "Double Penalty." This ensures accuracy is never sacrificed for the sake of AI-powered speed. The Practical ResultBy changing these metrics, the agent is no longer a "button-pusher" competing with a machine. Instead, they become the Quality Controller. This structure aligns human intuition with AI speed, ensuring the system improves over time rather than just producing faster, low-quality outputs.