Enterprise AI Business Value Realization and ROI Measurement Framework

Enterprise AI programs often demonstrate technical success without proving durable business value. Organizations may track model accuracy, usage, or pilot completion while failing to connect AI investments to revenue, cost, productivity, customer outcomes, risk reduction, or strategic capability improvement. This paper proposes an Enterprise AI Business Value Realization and ROI Measurement Framework that links AI use cases to value hypotheses, baselines, benefits, costs, adoption, risk adjustments, realization periods, and accountable owners. The framework covers direct financial benefits, productivity gains, quality improvement, decision speed, risk avoidance, customer experience, innovation capacity, and strategic options. It introduces benefit attribution, counterfactual baselines, total-cost-of-ownership measurement, risk-adjusted ROI, payback period, value leakage analysis, and executive scorecards. The proposed approach enables organizations to distinguish experimentation from realized value and to govern AI portfolios according to measurable enterprise outcomes.