AI-automated feedback collection in 2026 ingests NPS, in-app surveys, support tickets, app-store reviews, and social mentions, then tags topic, sentiment, and urgency — routing each to the owning team.
assisters.dev → LinearFeedback automation turns raw text (survey responses, tickets, tweets) into structured data: topic, sentiment, feature request vs bug vs praise, and routes it to the PM / engineer / support owner.
Productboard's 2026 PM survey: PMs spend 32% of their time triaging feedback. Teams with automated topic tagging reduce that to under 8% and surface 3× more actionable insights.
1. Consolidate sources. Pipe NPS (Delighted), support (Zendesk/Intercom), app stores (AppFollow), social (Mention) into one inbox.
2. AI classify every piece. Each message gets: topic, sentiment, type (bug/feat/praise/question), urgency.
3. Route on classification.
type:bug + urgency:high → Linear with sev label → on-calltype:feat → Productboard bucket by topictype:praise → #wins Slack channel (team morale)4. Respond on auto. Low-urgency factual questions get an AI draft reply; human approves.
5. Weekly digest. AI summarizes top 5 emerging topics and ships to the PM on Monday.
| Tool | Role | Pricing |
|---|---|---|
| Enterpret | Feedback unification + AI | Contact |
| Productboard | PM platform | From $20/user |
| Delighted | NPS | From $99/mo |
| AppFollow | App store reviews | From $139/mo |
| Intercom AI | Support + feedback | From $74/seat |
| Typeform + n8n | DIY | Free tier + compute |
Feedback automation is how small teams listen at big-company scale. Build the pipeline once and insights compound forever.
More at misar.blog for product automation.
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