Module 7 Lesson 3: KPIs for AI Initiatives
You can't manage what you don't measure. Go beyond 'Accuracy' and learn the business-centric KPIs for tracking the health and impact of your AI systems.
Module 7 Lesson 3: KPIs for AI Initiatives
A "Model Metric" (like Accuracy or F1 Score) helps the engineers. A "Business Metric" (like Time Saved or Customer Satisfaction) helps the CEO. This lesson bridges the gap.
1. Technical Health Metrics
Even as a business leader, you should know these three "vital signs" of your AI system:
- Latency: How long does it take for the AI to answer? (If it's over 3 seconds, users will stop using your chatbot).
- Accuracy (vs. Ground Truth): How often does the AI get the answer right compared to a human expert?
- Throughput: How many requests can the system handle at once? (Important for scaling).
2. Adoption & Engagement Metrics
Just because you built it doesn't mean they'll use it.
- DAU/MAU (Daily/Monthly Active Users): Is the tool becoming a habit, or a one-time novelty?
- Task Completion Rate: Does the user get their answer from the AI, or do they eventually have to "Ask a Human"?
- Retention of "AI Champions": Are your power users still using the tool 3 months later?
3. Impact & Efficiency Metrics (The "Value" KPIs)
This is how you justify your budget for next year.
- Workforce Leverage: "Our 10-person support team now handles 25,000 tickets instead of 10,000."
- Speed-to-Action: "It used to take 3 days to approve a vendor; now it takes 2 minutes."
- Revenue Uplift: "Customers who interact with the AI recommendation bot spend 15% more."
4. Safety & Governance KPIs
Don't ignore the risks in your dashboard.
- Hallucination Rate: What % of audited responses contained factual errors?
- Bias Deviation: Is the AI approving one demographic at a significantly higher rate than another?
- Data Leakage Alerts: Number of times the "Sensitive Data Filter" was triggered by an employee's prompt.
Summary Table: Metric Hierarchy
| Stakeholder | Primary KPI | Why it matters |
|---|---|---|
| Engineer | F1 Score / Loss | Model performance |
| Project Lead | Latency / Throughput | Reliability & Scalability |
| User | User Rating (CSAT) | Utility and "Vibe" |
| Executive | ROI / Headcount Leverage | Profitability & Growth |
Exercise: The Dashboard Design
Scenario: You are launching an "AI Sales Assistant" that helps sales reps draft follow-up emails and summarize discovery calls.
- Pick YOUR North Star: If you could only see one number to decide if this project is successful, what would it be?
- The "Safety" Metric: How will you track if the sales rep is "Over-relying" on the AI and sending weird, robotic messages?
- The Feedback: How will you capture the sales rep's thumbs-up/thumbs-down in a way that the technical team can use to improve the model?
Summary
Metrics transform AI from a "Cool Science Experiment" into a "Predictable Business Asset." By tracking a mix of technical, engagement, and value KPIs, you can proactively manage your AI portfolio and double down on what works.
Next Lesson: We look at the final calculation—Success metrics and ROI.