Proof of performance is an approach to measuring the impact of artificial intelligence (AI) in which results are validated against a control group. A portion of interactions runs without the AI logic, creating a direct comparison. The measured gain is the difference between the two groups, so it reflects what the AI actually changed rather than what would have happened anyway.
How It Works
In control group testing, incoming interactions are divided. Most run with the AI logic applied; a defined share runs without it, under standard operations. Everything else stays the same for both groups.
Outcomes such as resolution, retention, or revenue are then compared between the groups over the same period. Because both groups face the same seasonality, customer mix, and market conditions, the difference between them can be attributed to the AI.
This runs continuously in production, not as a one-time pilot, so performance claims stay verifiable over time.
Why It Matters
- Before-and-after comparisons are unreliable: results shift with seasonality, campaigns, and customer mix whether or not the AI did anything.
- A control group isolates the AI’s contribution, which makes the business case auditable rather than estimated.
- It changes the vendor conversation from claimed results to demonstrated ones.