Building products is something I’ve always loved. But getting honest, useful feedback on an early prototype? That’s often the hardest part.
The challenges aren’t new:
- Vague or overly polite feedback
- Overwhelming volume, unclear signal
- Not knowing what to act on
- Or finding people who actually reflect your users
This year, though, I’ve started to see AI quietly shift the landscape.
Tools can now tag emotion, cluster feedback themes, and surface friction points, often within minutes. But faster data doesn’t always mean clearer truth. It’s still on us to interpret what matters.
Here are a few things I’ve taken from our latest playbook at OrbiQ, especially relevant for early-stage teams:
- Let AI guide, not decide.
It’s great for surfacing patterns, but real understanding still needs human input. - Prioritize thoughtfully.
• If it’s both frequent and painful, fix it.
• If it’s rare but meaningful, consider it.
• If feedback conflicts, dig deeper. - Pay attention to emotion.
Some of the best insights show up as confusion or hesitation, things AI might flag, but only we can fully understand.
Used well, these tools don’t replace judgment, they make it easier to focus on the right problems.
