Metrics
Measuring what matters for AI features
Listen — audio summary in Nehal Vyas's voice
Transcript
Hey everyone, Nehal Vyas here. Today, I want to talk about how we truly measure what matters when building AI features. It’s a common pitfall to obsess over model accuracy, but in my experience, that number often tells you very little about real-world impact. Instead, I focus on metrics that directly connect the AI to a user's decision and concrete business value. First off, prioritize task success over mere model accuracy. We need to know if the user actually accomplished their goal, not just if a prediction was correct. This captures the entire user journey, from retrieval to action. Secondly, translate your AI's quality into value metrics the whole organization understands – think minutes saved per request or revenue recovered. These are the numbers that earn executive buy-in and truly demonstrate impact. And third, instrument for leading indicators of retention from day one. Things like repeat usage and users consistently choosing the AI path over manual alternatives predict long-term success well before your retention curve even shifts. Ultimately, our goal should be to measure the behavior change, not just the model itself. When your metrics truly describe what users did differently and what the business gained, you're set up to steer your AI feature towards profound impact. You can dive deeper into these ideas and read the full article over at hinehal.com.
Model accuracy is the number everyone reports and the number that matters least on its own. A feature can be 95% accurate and change nobody's behavior. The metrics I actually watch tie the AI to a decision the user makes and a value the business captures.
Task success over model accuracy
The unit that matters is whether the user accomplished their goal, not whether an individual prediction was correct. Task success captures the full path: retrieval, ranking, presentation, and the user's ability to act. It's harder to measure and far more honest.
Value metrics: time saved and revenue recovered
On the products I've led, the metrics that earned executive buy-in were concrete: minutes saved per request, revenue recovered, turnaround reduced from days to minutes. These translate model quality into language the whole organization understands and can prioritize against.
Leading indicators of retention
Lagging metrics tell you what happened; leading indicators tell you what's coming. Repeat usage in the first week, depth of engagement, and whether users return to the AI path over the manual one predict retention long before the retention curve moves. I instrument for those from day one.
Measure the behavior change, not the model. When your metrics describe what the user did differently and what the business gained, you can steer an AI feature toward impact instead of toward a leaderboard.