AI can synthesize 800 feedback items well, but the ROI ranking needs a human in the loop.

The ingestion and categorization steps are structurally repeatable across runs, but the final ranking requires judgment calls that shift with business context, competitive dynamics, and stakeholder priorities — making each instance meaningfully different at the decision layer.

Success criteria are underspecified: 'high-ROI' and 'customer impact' are not formally defined, and the agent has no ground truth for what the business actually values. Without a scoring rubric locked in advance, the agent cannot know when the ranking is correct.

Support tickets, surveys, and Slack threads are rarely in a single clean feed — the agent would need integrations or exports from Zendesk, Typeform/in-app tools, and Slack, plus ARR/company-size data from a CRM. This is achievable but requires significant setup and data wrangling before the agent can start.

A miscategorized or poorly ranked feature list could misdirect months of engineering investment across 12 products, with real revenue and retention consequences. The output feeds high-stakes roadmap decisions, so errors are costly and not easily reversible once sprint planning begins.

Deciding which features are truly high-ROI requires understanding strategic bets, customer relationships, competitive positioning, and internal capacity — none of which the agent can access or weigh reliably. The clustering and theming is automatable; the final prioritization is not.