Finance Leaders Cut Spending Except on AI Investments
Amid rising cost pressures, shifting policies, and geopolitical uncertainties, finance leaders are scaling back capital expenditures in almost all areas—except artificial intelligence (AI). A recent Gartner survey of 197 finance leaders reveals that over a third have paused or reduced capital spending. Specifically, 17% cut spending by 1-10%, another 17% reduced it by 10-25%, and 3% have cut by more than 25%. Meanwhile, only 8% plan to increase capital expenditures.
Finance executives are playing it safe until they get clearer signals on inflation, federal tax policies, and geopolitical risks. New tax laws affecting depreciation schedules and interest deductibility have made them cautious about asset-heavy investments. This cautious stance helps preserve flexibility and tighten control over capital allocations.
Cost-Cutting Efforts Continue
Cost reduction remains a priority. About 67% of finance leaders are actively cutting costs or expect to do so soon. In contrast, 33% are trimming expenses in some areas but increasing them in others, resulting in a net reduction overall. Interestingly, 15% report a net increase in spending despite cost controls.
AI: The Exception to Spending Cuts
The area seeing increased investment is AI. Although this trend wasn’t part of the survey’s data, Gartner’s ongoing discussions with finance leaders highlight a clear focus on AI initiatives. Companies are prioritizing AI projects that promise measurable business value and faster returns.
- Customer-facing AI: Generative AI tools for sales enablement, service automation, and marketing personalization remain funded. CFOs expect these technologies to boost revenue efficiency and improve customer retention.
- Product and engineering: AI-driven product features are prioritized but depend on quick time-to-value and market differentiation.
- HR and talent: Automation in workforce planning and recruiting is advancing to align skills with strategic goals cost-effectively.
- Risk and compliance: AI is increasingly used for fraud detection, regulatory monitoring, and vendor risk assessment, especially in finance and healthcare sectors.
- Data and infrastructure: Investments in cloud infrastructure, AI-ready data architecture, and large language model tools proceed only when directly linked to business use cases, rather than broad platform ambitions.
Even as CFOs tighten controls on spending, they remain committed to AI investments that deliver clear, targeted outcomes instead of broad experimentation. This focus reflects a shift toward practical AI applications with measurable impact.
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