The AI Budget Squeeze: Why IT Leaders Are Demanding Tangible Returns

According to Computerworld, Gartner’s recent survey of 253 global IT leaders reveals that budget constraints are forcing a pragmatic approach to artificial intelligence investments. The research firm found that 50% of infrastructure and operations leaders struggle to reallocate sufficient funds to AI projects, leading 54% to focus specifically on initiatives with attainable results and foreseeable cost savings. Released this week, the survey highlights that organizations are prioritizing AI implementations in functions where measurable impact can be quickly demonstrated, particularly in IT service management and digital workplace functions. Gartner research director Melanie Freeze noted that automation and generative AI are showing the greatest momentum in areas that directly enhance productivity and reduce costs. This budget-conscious approach signals a significant shift in how enterprises are approaching AI adoption.

The End of AI Hype and Beginning of ROI Reality

What we’re witnessing is the natural maturation of any transformative technology—the transition from speculative investment to measured implementation. For years, companies poured money into AI experiments with vague promises of future benefits, but we’re now entering an era where AI projects must compete for funding against other business priorities and demonstrate clear return on investment. This isn’t necessarily a negative development; rather, it represents the technology moving from the innovation lab to the operational frontline where real business value gets created. The fact that half of IT leaders can’t find budget for AI initiatives suggests that earlier investments may not have delivered the expected returns, creating skepticism that current projects must overcome.

Why ITSM and Digital Workplace Are Leading the Charge

The focus on IT service management and digital workplace functions makes perfect strategic sense when you analyze where AI can deliver immediate, quantifiable benefits. These areas typically involve high-volume, repetitive tasks that are perfect for automation—password resets, ticket routing, basic employee inquiries, and documentation. More importantly, the metrics in these domains are well-established: reduced resolution times, decreased ticket volumes, lower operational costs. Unlike more speculative AI applications in areas like customer experience or product development, where benefits can be difficult to isolate and measure, ITSM improvements translate directly to the bottom line. This represents a fundamental shift from using AI for competitive advantage to employing it for operational efficiency—a much safer bet in constrained budget environments.

The Hidden Risks of Over-Optimizing for Short-Term Gains

While this pragmatic approach has obvious benefits, it carries significant long-term strategic risks. By focusing exclusively on projects with immediate, measurable returns, organizations may be neglecting the transformative AI applications that could create sustainable competitive advantages. The most groundbreaking AI implementations often start as experiments without clear ROI—the kind of projects that budget-constrained organizations are now avoiding. There’s also the danger of creating what I call “AI silos”—islands of automation that deliver local efficiencies but don’t contribute to broader organizational intelligence. As Gartner’s research indicates, when budget becomes the primary driver of AI strategy, companies risk missing the forest for the trees, optimizing individual functions while falling behind in the larger AI transformation race.

Navigating the Budget Reality Without Sacrificing Innovation

The most successful organizations will find ways to balance this budget pressure with continued innovation. We’re likely to see more creative funding models emerge, such as AI-as-a-service arrangements that convert capital expenditures to operational expenses, or shared-risk partnerships with AI vendors. Another approach gaining traction is the “AI center of excellence” model, where a centralized team develops reusable AI components that multiple business units can leverage, spreading costs while maintaining strategic oversight. The key insight from this Gartner survey isn’t that AI investment is slowing, but that it’s becoming more sophisticated—organizations are learning to do more with less, focusing on applications that deliver both immediate value and foundation for future capabilities.

What This Means for the Broader AI Ecosystem

This shift toward pragmatic AI investment will inevitably reshape the vendor landscape and technology priorities. We can expect increased demand for solutions with clear, measurable ROI and shorter implementation timelines, while more speculative AI platforms may struggle to find enterprise buyers. The emphasis on IT infrastructure that supports these cost-saving applications will likely accelerate, with investments in data quality, integration capabilities, and change management becoming prerequisites for AI success. This isn’t the end of ambitious AI projects, but rather the beginning of a more disciplined approach where organizations build credibility through small wins before pursuing larger transformations—a pattern we’ve seen with every major technology shift from cloud computing to mobile adoption.