As the landscape of AI services evolves, Forrester has issued a warning that companies should prepare for increased software expenses in the coming year. This shift is largely attributed to AI vendors raising prices and introducing usage charges to offset their operational costs.
Survey Insights on Software Budgets
In a survey of over 2,600 business and technology decision-makers, Forrester found that software budgets are expected to rise as vendors adjust their pricing strategies. Notably, companies like Anthropic, OpenAI, and GitHub have transitioned from flat-rate subscriptions to usage-based billing models, raising concerns among users about escalating costs.
Additionally, Microsoft has been included in this trend with the introduction of its premium E7 license, which integrates features like M365 Copilot and security tools into its existing offerings.
Projected Costs of AI Infrastructure
According to a previous estimate by Bain & Company, the construction costs for AI data centers are projected to reach $2 trillion by 2030. Forrester’s findings suggest that the demand for AI will drive a significant increase in both data and software spending, with 80% of decision-makers anticipating budget increases.
Staffing and Operational Challenges
Despite recent layoffs in the tech sector, including major companies like Oracle, Microsoft, and Meta, Forrester noted that personnel costs have remained stable. The report indicates that staffing accounted for 35% of IT budgets in 2025, with 67% of tech decision-makers expecting to increase their staffing budgets for 2027.
Forrester cautioned against the notion that AI could entirely replace human roles, emphasizing that organizations should focus on building a strong foundation for AI effectiveness through trusted data and governance.
Adapting Financial Operations for AI
As AI usage-based pricing models become more prevalent, Forrester recommends that organizations adapt their FinOps practices to manage these unpredictable costs. Traditional financial operations may not suffice for the complexities of token-based, usage-driven AI expenses. The report suggests implementing runtime cost controls to prevent excessive spending.
Furthermore, a separate study by KPMG highlighted that nearly one-third of corporate leaders struggle to understand and manage operating costs associated with large-scale AI implementations. This indicates a pressing need for organizations to develop the capabilities necessary for effective AI spending management.
This article was produced by NeonPulse.today using human and AI-assisted editorial processes, based on publicly available information. Content may be edited for clarity and style.
