Strategic decision-making framework for evaluating and selecting GenAI use cases

Generative-AI (GenAI) rose from boutique research to mass hype in less than three years, but most firms still fail to monetize proofs-of-concept into production worth. Recent surveys show that 69 % of enterprise GenAI initiatives stall before being operationalized while 46 % of PoC initiatives are abandoned outright and Gartner forecasts a 30% abandonment rate by the end of 2025. We contend that a primary cause of this attrition is continued application of classical product-management heuristics—tuned to deterministic feature work—when GenAI problems are probabilistic, socio-technical, and risk-weighted. Drawing on observations from past twelve enterprise GenAI launches (2023-2025), and 25 semi-structured interviews with product managers at multiple firms, we (i) clarify where classical discovery, sizing, and prioritization practices go wrong; and (ii) distill a three-part decision playbook comprising a Three-Gate Decision Funnel, Six-Point Opportunity Scorecard, and Value-Feasibility Prioritization Matrix. Early adopters report cycle-time compression by up to 40%, with an average of 33% and doubled conversion rates from PoC to production after the playbook's use. The paper positions these artefacts as a practitioner-oriented handbook and research-based contribution to the nascent field of GenAI product strategy.