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This analysis aims to address the unique challenges of calculating ROI for enterprise AI projects.

February 14, 2026Open Access

AI Economics: ROI Calculation Methodologies for Enterprise AI — From Traditional Metrics to AI-Specific Frameworks

Key Points

This analysis aims to address the unique challenges of calculating ROI for enterprise AI projects.
Comprehensive review of traditional ROI methodologies
Introduction of the AI-Adjusted ROI framework
Incorporation of learning curve effects and capability spillovers
Assessment of strategic optionality and risk-adjusted discount rates
Traditional metrics inadequately address AI ROI calculation challenges
The AI-Adjusted ROI framework offers a more tailored approach
Incorporating unique AI factors improves the accuracy of ROI assessments

Abstract

Return on Investment (ROI) calculation for artificial intelligence projects presents unique methodological challenges that traditional IT investment frameworks fail to adequately address. This article presents a comprehensive analysis of ROI calculation methodologies specifically designed for enterprise AI initiatives, introducing the AI-Adjusted ROI (AAROI) framework which incorporates learning curve effects, capability spillovers, strategic optionality, and risk-adjusted discount rates.

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Authors

Oleh Ivchenko

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Odessa National Polytechnic University

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AI Economics: ROI Calculation Methodologies for Enterprise AI — From Traditional Metrics to AI-Specific Frameworks

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Abstract

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Authors

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Institutions

References and Citations

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Connected Papers

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