Enterprises are rapidly investing in Generative Artificial Intelligence (GenAI), yet most initiatives struggle to move beyond pilot stages or deliver measurable returns. Recent studies, including MIT’s findings that 95% of GenAI pilots fail and OpenAI’s methodology for identifying and scaling AI use cases, highlight the urgent need for structured evaluation frameworks. This paper introduces the GenAI Strategic Assessment (GSA) Framework, a four-pillar decision-making model designed to guide organizations in prioritizing, evaluating, and scaling GenAI initiatives. The framework integrates lessons from existing modular and public governance models such as MLOps practices and the NIST AI Risk Management Framework—while addressing the critical strategic gap at the ideation and investment stage. Through weighted scoring across Value Chain Optimization & Innovation, Market and Competitive Reconfiguration, Organizational Readiness & Adaptability, and Ecosystem & Regulatory Landscape, the GSA provides executives with a quantifiable basis for go/no-go decisions. Empirical validation against enterprise AI failure modes demonstrates its ability to mitigate risks such as strategic ambiguity, poor integration, and misalignment between technology and competitive advantage. Case studies of Duolingo and Chegg illustrate the framework’s practical application, revealing how disciplined evaluation determines whether GenAI serves as a driver of growth or a source of disruption. This research contributes a scalable, governance-oriented approach for converting AI hype into sustainable enterprise value.

Generative Artificial Intelligence AI Evaluation Frameworks Strategic Assessment Enterprise AI Investment AI Governance Organizational Readiness Value Chain Optimization Market Reconfiguration Risk Management AI Adoption OpenAI MIT Chegg Duolingo