What question did this study set out to answer?

The aim is to identify hidden costs associated with enterprise AI implementations that lead to budget overruns.

February 14, 2026Open Access

AI Economics: Hidden Costs of AI Implementation — The Expenses Organizations Discover Too Late

Key Points

The aim is to identify hidden costs associated with enterprise AI implementations that lead to budget overruns.
Comprehensive taxonomy of hidden AI costs established
Categories of costs analyzed include data preparation, integration, and governance
Financial impact assessed across various AI implementations
AI implementations exceed initial budgets by 40-75% on average
Identified categories account for substantial portions of total project costs
Ongoing model maintenance represents 20-35% of annual expenses

Abstract

Enterprise AI implementations routinely exceed initial budgets by 40-75%, a pattern observed repeatedly across software engineering and AI systems deployments. This research article provides a comprehensive taxonomy of hidden AI costs, identifying seven primary categories: data preparation and quality remediation (25-40% of total project cost), integration debt with legacy systems (15-30%), organizational change management (10-20%), compliance and governance overhead (8-15%), model maintenance and drift correction (ongoing 20-35% annually), opportunity costs of failed experiments, and shadow IT proliferation (5-12%).

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