The Margin-Cap Trap: Using Value-Based AI Arbitrage to Turn AI Productivity into Enterprise Value

Abstract

Organizations may experience productivity improvements from artificial intelligence without corresponding increases in enterprise value, as AI-driven productivity does not inherently lead to additional economic value. The Margin-Cap Trap occurs when AI enhances productive capacity without a strategic decision regarding its allocation. This article examines three strategies for utilizing released capacity: Labor Cost Optimization, Operational Workforce Augmentation, and Strategic Capacity Investment through Value-Based AI Arbitrage. An Enterprise AI Value Simulation assesses these strategies by modeling variations in workforce, workload demand, operating costs, operating profit, and enterprise value under different business scenarios. Under the modeled conditions, Strategic Capacity Investment through Value-Based AI Arbitrage yields the highest enterprise value by converting released capacity into additional value-generating demand in areas such as innovation, market expansion, and customer value. This framework offers a systematic approach for assessing AI-enabled capacity-allocation decisions and their financial implications.