Majority of Businesses Face Higher-than-Expected AI Deployment Costs, Prompting Strategic Shifts
A September 2026 Capgemini Research Institute survey of 500 senior executives reveals 59% of organizations experienced significantly higher AI-related costs than anticipated, averaging 24% over budget. Indirect costs such as human oversight, rework, and governance contribute to cost overruns, with half of the executives reporting insufficient visibility into full AI expenses prior to spending approval. Financial consequences impacted two-thirds of those exceeding budgets, increasing CFO involvement in AI investment approvals. In response, many companies tightened budget controls and are preparing to implement multi-model AI strategies to improve cost efficiency and effectiveness. Despite confidence in verifying AI-driven business value, only 20% of organizations calculate AI Yield—a metric that assesses value relative to full delivery cost—highlighting challenges in linking AI spending directly to outcomes. Metrics focus is shifting towards outcome-centered measures such as first-pass success rate and cost per completed task, aiming to align AI economics with actual business results.
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Majority of Businesses Face Higher-than-Expected AI Deployment Costs, Prompting Strategic Shifts
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A September 2026 Capgemini Research Institute survey of 500 senior executives reveals 59% of organizations experienced significantly higher AI-related costs than anticipated, averaging 24% over budget. Indirect costs such as human oversight, rework, and governance contribute to cost overruns, with half of the executives reporting insufficient visibility into full AI expenses prior to spending approval. Financial consequences impacted two-thirds of those exceeding budgets, increasing CFO involvement in AI investment approvals. In response, many companies tightened budget controls and are preparing to implement multi-model AI strategies to improve cost efficiency and effectiveness. Despite confidence in verifying AI-driven business value, only 20% of organizations calculate AI Yield—a metric that assesses value relative to full delivery cost—highlighting challenges in linking AI spending directly to outcomes. Metrics focus is shifting towards outcome-centered measures such as first-pass success rate and cost per completed task, aiming to align AI economics with actual business results.