DJC Insights
Measuring AI Effectiveness (Metrics That Matter)
Vanity metrics kill AI projects. "We had 10,000 conversations!" Great. Did you make any money?
When measuring AI, you must ignore "Usage" and measure "Outcome."
The Metrics That Do Not Matter
- Number of Messages Sent: Irrelevant. A confused bot sends more messages than a smart one.
- Tokens Consumed: This is a cost, not a value.
- "Sentiment Analysis": Often inaccurate and doesn't correlate with revenue.
The Metrics That Drive Business
1. Deflection Rate (Support)
- Definition: Percentage of tickets fully resolved without a human.
- Why: Direct labor saving.
- Goal: >40% is good. >70% is world-class.
2. Time-to-Resolution (TTR)
- Definition: Average clock time from "Issue Reported" to "Issue Solved."
- Why: Customer Happiness correlates with speed.
- Goal: Under 5 minutes.
3. Conversion Rate (Sales)
- Definition: Percentage of AI-engaged leads that become Booked Appointments.
- Why: Revenue.
- Goal: Should match or exceed your human SDR baseline.
4. Error Rate (Reliability)
- Definition: How often does a human have to intervene to fix an AI mistake?
- Why: Trust. If this is >5%, your team will stop using it.
The "Human-in-the-Loop" Ratio
This is my favorite metric. How many AI conversations can one human supervisor manage?
- Starting: 1 Human : 1 AI (No leverage)
- Good: 1 Human : 10 AI Agents
- Great: 1 Human : 50 AI Agents
If this ratio isn't successfully increasing over time, your AI isn't getting smarter—it's just making noise. Measue leverage, not activity.