So your AI product is “working”—or at least that’s what your engineering team insists while pointing at a dashboard full of numbers nobody understands. Congrats! You’ve entered the circus of AI metrics, where precision, recall, and “business impact” fight like toddlers over the last cookie.
The Metrics Trap (Or, Why Everyone’s Wrong)
Sharanya Rao from Intuit nails it: If you don’t define success, your team will invent their own version of it. Engineers love precision/recall. Execs demand “ROI.” Meanwhile, customers just want the damn thing to work. The result? A Frankenstein dashboard where nobody agrees on what “good” looks like. Take AI search, for example. Did the user get any result? (Coverage ✅) Did it take 3 seconds or 3 centuries? (Latency ⏳) And—here’s the kicker—did they like it? (Cue the thumbs-up emoji nobody clicks.)
The Fix: Ask the Right Damn Questions
Rao’s framework is refreshingly sane:
- Did it work? (Coverage)
- Was it fast? (Latency)
- Did anyone care? (Feedback/Adoption)
Simple, right? Yet most teams drown in vanity metrics while ignoring the actual user experience.
The LLM Wildcard 🎲
Now toss generative AI into the mix. Suddenly, “output” isn’t just a prediction—it’s a poem, a product description, or a hallucinated legal brief. How do you measure that? Manual reviews? Automated checks? Prayers to the silicon gods? Rao’s answer: Start manual, then automate. Define “good” before you pretend an algorithm can.
Bottom Line
If your AI metrics don’t tie back to real user value, you’re just polishing a turd. And trust me, the market smells it.