Beyond Proof of Concept: A Structured Economic Framework for Governing AI Value Realization in Enterprise Cloud Architectures - Kiran Kumar Jaghni (Malikussaleh University, Aceh, Indonesia, 2026)
Despite sustained growth in enterprise artificial intelligence (AI) investment, relatively few organizations achieve measurable business value from production-scale deployments. Industry evidence indicates that most enterprises have yet to demonstrate significant financial returns from AI initiatives, while a substantial proportion of projects fail to progress beyond pilot or proof-of-concept stages. This persistent value gap is primarily architectural rather than algorithmic, reflecting the absence of governance-first decision frameworks capable of evaluating AI suitability before investment, accounting for the full lifecycle cost of AI systems, and continuously monitoring realized business value after deployment. This paper introduces the AI Value Realization Framework (AVRF), a five-gate governance model designed to support evidence-based AI investment decisions within cloud-native enterprise environments. The framework integrates three complementary analytical instruments. The AI Suitability Assessment Model (AISAM) evaluates eleven pre-investment criteria using a calibrated 0–35 scoring mechanism to determine whether a proposed business problem genuinely requires AI-based solutions. The AI Value Efficiency Ratio (AVER) measures the relationship between realized business value and a comprehensive five-layer AI cost structure, including infrastructure, data, model operations, governance, and organizational adoption costs. Finally, a continuous value realization cycle aligns governance activities with enterprise AI maturity to support ongoing performance evaluation and strategic optimization. The applicability of AVRF is examined in healthcare platform modernization and government digital transformation, where regulatory compliance, multi-tenant data isolation, transparency, and accountability significantly increase governance complexity. The study argues that organizations deploying AI without structured governance and value measurement mechanisms are likely to produce AVER values below 1.0, indicating that implementation costs exceed measurable business benefits regardless of model sophistication. The proposed framework provides a practical governance architecture for improving AI investment decisions, operational accountability, and sustainable enterprise value realization