Score Features by Revenue Impact

Data-Driven Feature Prioritization Engine Multi-Source Signal Aggregation AI pulls data across systems: Sales (Open pipeline ARR blocked by missing features), Support (Ticket volumes related to UX friction), Marketing (Competitive gap analysis), and Usage Analytics (Drop-off rates in current flows). This creates a unified 'Prioritization Data Lake'. Automated RICE Scoring AI auto-populates RICE frameworks. Reach: calculated from active user metrics. Impact: calculated from ARR at risk/opportunity. Confidence: derived from number of customer requests.

Effort: estimated using historical engineering velocity for similar epics. Strategic Alignment Check AI maps each proposed feature against stated company OKRs. A high-RICE feature that doesn't align with the current quarter's objective ('Expand Enterprise Footprint') gets flagged for deferral, ensuring strategy dictates execution. Dependency & Risk Mapping AI analyzes the codebase and architectural documentation to identify hidden dependencies: 'Feature A requires an upgrade to the caching layer, which blocks Feature B.' It calculates the true critical

path duration. Scenario Modeling AI generates alternative roadmap scenarios: 'Revenue-Maximized' (focus on sales blockers), 'Retention-Focused' (focus on support tickets), and 'Technical Debt Reduction' (focus on infrastructure). Provides the expected business outcome for each scenario. Weight 'ARR at Risk' heavier than 'Potential ARR' in calculations—saving a known $100k customer is cheaper than acquiring a new one. Enforce a 'Confidence Score Penalty' for features with zero customer interviews or prototype validation data. Include 'Kill Criteria'

alongside success criteria—what specific metric failure means we roll back this feature? Don't let the 'Sales Deal of the Week' hijack the roadmap—AI should show the opportunity cost (what gets delayed) of inserting a custom feature. Don't estimate 'Effort' without engineering input—AI provides a baseline, but the tech lead must validate the final T-shirt size. Don't treat all users equally in 'Reach' calculations—a feature used by 10% of Enterprise users is often more valuable than one used by 80% of Free users. The 'Opportunity Cost' Metric Whenever a

stakeholder requests an emergency roadmap insertion, have the AI generate an 'Opportunity Cost Statement': "Adding Feature X will delay Feature Y by 3 weeks, risking $250k in renewal ARR tied to Feature Y." This forces stakeholders to debate trade-offs rather than just adding demands.