Capgemini AI Economics Report: The Rising Cost of Enterprise AI Investments
Murali Teja
AI Expenditure Growth: Nearly three in five executives reported AI spending significantly exceeding their expected or budgeted expenditure over the past 12 months.
AI Cost Overruns: Organizations experiencing AI budget overruns spent an average of 24% more than initially expected, highlighting growing financial management challenges.
Enterprise AI Cost Visibility: Around 60% of organizations lack consistent enterprise-wide visibility into AI costs, making financial forecasting and investment decisions increasingly difficult.
AI Investment Tracking: Organizations can systematically track 61% of their AI expenditure, but only 45% connects directly with verified business value.
AI Budget Controls: Among organizations experiencing cost overruns, 53% responded by tightening expenditure controls, while only 20% reconsidered existing tools or processes.
CFO Involvement in AI Investments: CFO participation in approving major AI investments increased to 52% of organizations, compared with 42% reported one year earlier.
AI FinOps Implementation: AI FinOps helps organizations understand technology expenditure, monitor resource consumption and connect infrastructure costs with measurable operational and financial outcomes.
AI Model Selection and Workflow Design: Smarter model selection and improved workflow design can help enterprises balance AI implementation costs, output quality and operational reliability.
AI Yield and Investment Decisions: Capgemini recommends using AI Yield, spending guardrails and outcome-based metrics to prioritize responsible experimentation and investments delivering measurable business value.