UK Business Leaders Prioritize AI Investment Despite ROI Uncertainty

UK business leaders are maintaining their commitment to artificial intelligence investment despite growing uncertainty about return on investment, according to new research from KPMG that highlights a fundamental shift in how organizations approach AI spending.

The survey of 2,110 global business leaders found that 70 percent of UK executives plan to keep AI at the top of their spending priorities even if economic conditions worsen. This determination persists even as companies struggle to demonstrate clear financial returns from their AI initiatives.

The ROI Paradox

While organizations report being able to measure AI's impact in specific operational areas, the broader business case remains elusive. The research shows that most companies can track ROI in productivity (76 percent), work quality and performance (71 percent), decision-making speed and accuracy (67 percent), and profitability (64 percent). However, only 14 percent express confidence in measuring business value from AI-enhanced analytics used for executive decision-making.

This disconnect between measurable operational improvements and strategic business value represents a significant challenge for technology departments tasked with justifying AI expenditures to boards increasingly focused on demonstrable returns.

Strategic Shift in Mindset

Leanne Allen, head of AI at KPMG, described the evolving perspective on AI investment: "This shift in mindset from viewing AI as something that must deliver an immediate return to one that sees AI as a long-term investment, recognizing it as a strategic enabler for enterprise-wide transformation, is an important milestone."

This philosophical change comes as global AI spending is projected to reach $2.52 trillion in 2026, according to Gartner forecasts. The investment burden currently falls primarily on software vendors and cloud providers, though enterprise customers and consumers will ultimately bear these costs.

Mixed Experiences with AI Implementation

Despite the commitment to continued investment, organizations report varying levels of success with AI deployment. The survey found that 94 percent of UK business leaders plan to implement AI agents in their operations, but experiences differ significantly across industries and use cases.

Recent studies paint a concerning picture of AI's current impact. A survey of nearly 6,000 corporate executives across the US, UK, Germany, and Australia found that over 80 percent detect no discernible impact from AI on either employment or productivity, despite 69 percent of businesses currently using some form of AI.

The Pressure to Perform

Technology leaders face mounting pressure to demonstrate AI's value. A Harris Poll study commissioned by Dataiku revealed that 98 percent of tech leaders report increasing pressure from boards to show ROI, while 71 percent of CIOs believe their AI budgets could face cuts or freezes if targets aren't met by mid-2026.

Gartner's research adds to these concerns, finding that only 28 percent of AI use cases in technology infrastructure fully succeed and deliver expected returns on investment.

Industry Response

Major consulting firms are adapting their approaches to AI implementation. Accenture has instructed staff that AI usage will be considered in promotion decisions, while PwC has warned employees who aren't convinced about AI's value. These moves reflect the broader industry push to normalize AI adoption despite uncertain returns.

AWS CEO Adam Selipsky recently addressed concerns about AI overhype, acknowledging the skepticism while maintaining that AI's transformative potential remains significant.

The current landscape suggests a period of adjustment as organizations move from initial enthusiasm to more measured expectations about AI's capabilities and timeline for delivering value. As John-David Lovelock, distinguished VP analyst at Gartner, observed: "We're starting to see the end of the investment line. We had a thousand flowers blooming, now it's time to prune the garden."

This pruning process may separate genuinely transformative AI applications from those that fail to deliver meaningful business impact, ultimately shaping how future AI investments are evaluated and justified.