Strategy Memoimpactmetricsenterprise
Measurable impact beats impressive demos
How to define success for applied AI: outcomes, adoption, and reliability over novelty.
The thesis
A demo can be beautiful and still be useless. Mature AI programs define success in terms of outcomes, adoption, and reliability.
What to measure
- Burden reduction: time saved, fewer manual steps, lower cognitive load
- Consistency: reduced variability across users or sites
- Throughput: more work completed safely in the same time
- Decision quality: clearer reasoning, fewer missed issues
- Operational health: latency, failure rate, drift signals
What to avoid
- Metrics that don’t map to decisions
- “Accuracy” without a realistic test set
- Success defined by the model instead of the workflow
A simple rule
If you can’t explain how a metric connects to real-world improvement, it’s probably not a success metric.