How to Actually Measure AI ROI
Move beyond vanity metrics. Here's how to measure the business impact of AI investments.
The measurement problem
"Our AI is 95% accurate" means nothing if you don't know what 95% accuracy does for the business.
Most AI metrics are technical, not business. They tell you how the model performs, not whether it was worth building.
What to measure
Direct impact metrics
- Revenue influenced by AI recommendations
- Cost savings from automation
- Time saved per task
- Error reduction vs. manual process
Adoption metrics
- Active users as % of potential users
- Frequency of use
- Tasks completed vs. tasks started
- User-reported satisfaction
Quality metrics
- Error rate in production (not just testing)
- False positive/negative rates
- Human override rate
- Customer complaints related to AI
The baseline problem
You can't measure improvement without knowing where you started. Before launching any AI initiative, document:
- Current process performance
- Current costs
- Current error rates
- Current time-to-completion
When ROI is negative
Sometimes it is. And that's okay—if you learn from it.
What didn't work? Why? What would you do differently? A failed initiative with clear lessons is more valuable than a successful pilot that can't scale.
The long view
AI ROI often takes longer than expected to materialize. Adoption curves are slow. Models need tuning. Processes need adjustment.
Measure consistently, but don't expect immediate results. The organizations winning with AI are the ones that stay the course.
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