Trust Scores Hallucination Rates and New AI KPIs Every PM Should Know

When I meet PMs working on AI products, I often ask how they measure success. Most start with accuracy or precision. That is a good start, but it is not enough. AI products introduce new risks and behaviors that demand new kinds of metrics. If you only measure accuracy, you will miss the signals that matter most.

Here are some of the new KPIs every AI PM should get comfortable with.

Trust Score

Ask users directly if they trust the AI output. This can be a simple in product survey: “Did you trust this result?” Combine it with behavioral data, like how often users accept or reject suggestions. A high accuracy model with a low trust score is still a failing product.

Hallucination Rate

Large language models sometimes produce answers that sound confident but are factually wrong. Measuring how often this happens is critical. A low hallucination rate means fewer moments where your product undermines itself.

Correction Rate

Track how often users edit or override AI outputs. If corrections are frequent, the AI is not saving time. It is creating more work.

Escalation Rate

In support or decision making products, measure how often the AI has to hand off to a human. A low rate shows confidence, but too low may also signal the system is overconfident and risky.

Latency and Cost per Interaction

AI products must also balance user experience and economics. If responses take too long or cost too much per query, the product will struggle to scale.

Why These KPIs Matter

These metrics reveal the truth about AI in practice. Accuracy alone tells you how the model behaves in a test set. New KPIs tell you how the product performs in real life, with real users.

Final Thought

As AI PMs, our job is to define success in a way that reflects reality. Trust scores, hallucination rates, and correction rates may sound technical, but they are really about user value. Learn to track them, and you will understand your product at a much deeper level.