IDC Reports 30% Underestimation of AI Infrastructure Costs by Major Companies by 2027
IDC forecasts that the world's 1,000 largest companies will underestimate their AI infrastructure costs by an average of 30% by 2027 due to inadequate budgeting methods for resource-intensive projects. As AI workloads grow, complexities in calculating costs for GPUs, inference, and network usage highlight the need for companies to adopt FinOps practices to manage expenses effectively. IBM's CEO warns that investments in data center capacity may not yield profitability, urging firms to prioritize AI projects with high ROI potential.
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IDC predicts that the world's 1,000 largest companies will underestimate their AI infrastructure costs by an average of 30% by 2027. This discrepancy arises as IT leaders and financial executives realize that traditional budgeting methods are inadequate for resource-intensive AI projects.
The complexities of calculating costs for GPUs, inference, and network usage complicate budget planning, requiring consideration of security and training expenses. IBM's CEO, Arvind Krishna, warns that the investment for building sufficient data center capacity to support AI ambitions may be unprofitable.
As AI workloads expand, the cost of necessary support systems can surpass initial estimates. IDC's Jevin Jensen emphasizes the importance of adopting FinOps practices to effectively manage and adapt to evolving infrastructure costs. Companies should focus on implementing AI projects with the best ROI potential, while carefully evaluating resource utilization to avoid unnecessary expenditure.
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