iambpawan

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We measure how many times an agent updates a system article because the AI provided outdated or incorrect advice.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 Behaviors
1. 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 Result
By 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.