99% model accuracy means nothing if customers are unhappy. Here's what to actually measure for AI ROI.
The Metric Trap
ALM Corp's 2026 advice: "A polished answer is not the same thing as a good outcome."
Teams optimize for what they measure. If you measure model accuracy, you get accurate models. But that might not help the business.
Vanity Metrics vs Value Metrics
| Vanity Metrics (Don't) | Value Metrics (Do) |
|---|---|
| Model accuracy % | Resolution rate |
| Response quality score | Customer satisfaction |
| Prompts submitted | Tasks completed |
| Active users | Value per user |
| Feature adoption | Business outcome change |
| Confidence scores | Decision quality |
Metrics That Correlate With Value
ALM Corp identifies key metrics:
1. Resolution Time
- Time from input to completed outcome
- Measure speed to resolution, not response time
- Include human intervention if needed
2. Cost to Serve
- Total cost per task/resolution
- AI costs + human oversight costs
- Compare to pre-AI baseline
3. Conversion Lift
- For AI in sales/marketing
- Conversion rate before vs after AI
- Control for other variables
4. First-Contact Resolution
- Issues resolved without escalation
- Higher = better AI performance
- Track why escalations happen
5. Customer Satisfaction
- Post-interaction surveys
- Compare AI-handled vs human-handled
- NPS, CSAT, or similar
6. Exception Rate
- How often AI fails or needs help
- Track exception types
- Decrease over time = improving AI
Process vs Output Metrics
| Metric Type | Example | When to Use |
|---|---|---|
| Process (AI performance) | Speed, accuracy, reliability | Debugging, improving AI |
| Output (Business result) | Sales, savings, satisfaction | Justifying investment |
Both matter. Process for optimization. Output for ROI.
Metrics by Use Case
| Use Case | Key Metrics |
|---|---|
| Customer service AI | FCR, CSAT, cost per ticket |
| Sales AI | Conversion rate, deal velocity, pipeline value |
| Marketing AI | Lead quality, campaign performance, CAC |
| Operations AI | Process time, error rate, throughput |
| Content AI | Engagement, time saved, quality rating |
Building a Metrics Framework
- Define success: What does "good" look like?
- Baseline: Measure before AI
- Control variables: What else changed?
- Track continuously: Weekly/monthly reviews
- Adjust AI: Improve based on data
Common Metric Mistakes
- Measuring once: Needs ongoing tracking
- No baseline: Can't prove improvement
- Ignoring context: Other factors affect outcomes
- Over-optimizing: One metric at expense of others
- Not involving business: IT picks metrics, not business owners
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