How do you decide which AI features are actually worth keeping in a product?
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While building an AI-powered interview platform, I initially found myself adding more and more AI features because they seemed useful individually.
Eventually, i had to think much more carefully about which features genuinely improve the user's experience vs which ones simply make the product look more impressive.
For other makers building AI products, how did you decide which AI features to keep, simplify, or remove?
was there a particular signal from users,usage patterns,development experience, or something else that helped you make that decision?
I'd love to hear how other makers have approached this.
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usage count was the signal that lied to us the most. people click a shiny AI button once out of curiosity and that shows up as "engagement" in every dashboard. what actually separated real features from decoration was turning them off for a week and seeing who complained versus who didn't notice at all. the ones nobody mentioned losing were the ones that looked impressive in a demo but weren't load-bearing in the actual workflow
Great question Pragya. I usually watch whether users return to a feature unpromoted. If nobody misses it after removal, it wasn't essential.
i track this by looking at completion rates. if an AI feature doesn't move users closer to finishing their task faster or with less effort, I quietly remove it and watch feedback.
Honestly,
I learned this the hard way too. I now ask three questions before adding anything. Does it save time, does it reduce decisions, and would users pay extra for it. If not to all three, it gets cut.