Feature Request Triage: Edison AI for B2B SaaS

Most product orgs ship the wrong things 40% of the time. Edison reads every support ticket, NPS comment, and sales-call transcript — then prioritizes the 3 features that will move retention the most. For Feature Request Triage specifically, that means turning a chaotic backlog of 200+ requests into a clear, data-backed shortlist — without a single manual vote or gut feel.

The Feature Request Triage problem most teams have

Handling Feature Request Triage manually costs B2B SaaS teams an average of $120,000 per year in wasted engineering hours — that's 15% of a 10-person dev team building features that never see 10% adoption. Your product managers spend 8 hours per week reading and categorizing tickets, yet 62% of feature requests are duplicates or misclassified. Worse, the average enterprise SaaS org loses $2.3M annually in churn from ignoring the top 3 retention-driving requests — the ones buried under noise. When you rely on the loudest customer or the most recent sales call, you miss the silent majority.

How Edison owns Feature Request Triage end-to-end

Edison doesn't just tag tickets. Edison ingests every signal: support tickets, NPS comments, sales-call transcripts, and customer interview recordings. Then Edison synthesizes user feedback across channels, removing duplicates and weighting by customer segment and revenue impact. Edison uses feature impact scoring to rank each request by its projected effect on retention and expansion revenue — not just popularity. Finally, Edison generates a ranked roadmap prioritization list, complete with A/B test orchestration plans to validate the top 3 picks before engineering commits a sprint. No dashboard to configure. No manual scoring matrix. Edison owns the workflow from raw signal to prioritized backlog.

A concrete Edison workflow

BEFORE: Acme Analytics (300 customers, $5M ARR) had 247 open feature requests. Their VP Product spent 6 hours per week reading tickets, then held a monthly voting meeting where the sales team pushed for a custom API endpoint that 2 enterprise clients wanted. Engineering built it in 8 weeks. Usage: 0.3%. Meanwhile, a request for automated data rollups — surfaced in 14 support tickets and 8 NPS comments — sat untouched for 6 months.

EDISON'S ACTIONS:

1. Scanned 247 requests, 1,200 support tickets, and 340 NPS comments in 3 minutes.
2. Identified 19 duplicates, 37 misclassifications, and 4 clusters: automated rollups (42 signals), SSO with Okta (31 signals), dashboard export to CSV (28 signals), custom API (2 signals).
3. Scored each cluster using feature impact scoring — automated rollups projected to reduce churn by 4.2% based on customer segment data.
4. Generated a ranked roadmap with automated rollups as #1, and an A/B test plan to validate the rollout in 2 weeks.

AFTER: Engineering built automated rollups in 3 weeks. Adoption: 68% within 30 days. Churn dropped from 1.8% to 1.2% monthly — saving $360,000 in annualized revenue. Acme's VP Product now spends that 6 hours per week on strategy, not triage.

Why Edison wins vs. hiring

Hiring a human VP Product costs $180,000–$250,000 per year, plus 3–6 months to ramp. They take vacations, get sick, and eventually leave — the average VP Product tenure is 18 months. Edison costs a fraction, is fully operational in 24 hours, and never misses a day. But Edison is not a replacement — Edison is an augmentation layer. Your human VP Product keeps the strategic vision, customer relationships, and organizational alignment. Edison handles the 40 hours per month of manual triage, synthesis, and scoring that no human should do. Together, a human VP + Edison triages 5x more signals per week with 90% fewer prioritization errors.

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