NOTE: Demo visuals include blurred data or synthetic placeholders to protect customer privacy.

Across product areas, PMs receive hundreds of RFEs in Jira and are expected to keep them moving while also shipping roadmap work. In practice, the backlog grows quietly until it becomes both a planning problem and an execution disruptor.

Three frictions showed up in the walkthrough:

1. The open backlog is massive and hard to sift through, especially in Jira’s “All Work” style views.

2. Each RFE requires significant effort to evaluate: understanding the customer, interpreting the ticket, aligning stakeholders, and communicating the decision.

3. RFEs age until CSM escalations force urgent tradeoffs, often tied to renewals or customer frustration.

RFE Prioritizer V1 is an AI solution that aggregates the context PMs typically have to manually chase down, then ranks RFEs in an action-oriented report delivered via email. Instead of building one-off spreadsheets or dashboards to track “what matters,” PMs get a consistent starting point with links back to source systems.

Key capabilities in V1:

• Pull open RFEs automatically from Jira.

• Enrich each RFE with Salesforce context, including customer ARR.

• Search Gong transcripts for mentions and escalation signals tied to the request.

• Generate a prioritized list with reasoning, not just a score.

• Present three planning views: top priorities, renewal risks, and grouped work items with combined ARR.

One line from Brandon captured the scale of the problem the tool is built to address: “As of last night, we have 3,200.”

Brandon shared rough math to frame the cost of today’s workflow: if a PM spends ~15 minutes per RFE researching, aligning, and communicating, clearing the current backlog alone can represent hundreds of hours of work and significant labor costs. The bigger issue is what happens when that work gets deferred: RFEs age, then re-enter planning as urgent escalations that interrupt execution.

What V1 changes for different teams:

• PMs: Faster weekly triage and a clearer short list for quarterly planning, with reasoning and context in one place.

• CSMs: Earlier visibility into renewal-linked risk, reducing the need for late escalations to get attention.

• Engineering and product leadership: Fewer reactive roadmap pivots and a more consistent prioritization narrative tied to customer impact.

• Finance leaders: Better linkage between customer value (like ARR context) and what gets prioritized.

Next, Brandon aims to extend V1 from context gathering into V2 workflows that help drive action, for example, suggested next moves, owners, and lightweight status updates back into Jira.

Peers reacted similarly: the painful part is not just choosing what to build; it’s also collecting evidence to make a confident call. Centralizing ticket context and presenting it in a single report replaces the scattered “scrappy” trackers that teams create when the system of record becomes too noisy.

The broader signal is cultural: when prioritization becomes easier to explain and repeat, teams spend less time debating inputs and more time executing. That aligns with Abnormal’s AI Transformation goal of delivering practical wins across internal stakeholders while improving outcomes that customers feel.