KPMG says AI becomes an everyday work tool for 22%

KPMG has found that more organisations are entering a stage where AI is part of everyday work, with its latest global survey putting that share at 22%.

The findings point to a widening gap between companies that treat AI as an operational and financial management issue and those that still approach it mainly as a technology project.

The survey of 2,145 C-suite and senior business leaders across 20 markets found the biggest shift on the AI maturity curve was in what KPMG described as the driving-adoption phase, which rose from 13% in the first quarter to 22% in the second. Even so, only 7% of leaders said their organisations had established a return on investment from AI.

That disconnect is emerging as investor scrutiny rises. Nearly one in four respondents (24%) said they faced pressure to prove the value of AI to investors, while 79% identified AI as a key investment area, up from 74% in the previous quarter. Average spending was broadly steady at USD $188 million, compared with USD $186 million in the first quarter.

Cost pressure

KPMG's research suggests a weak grasp of spending is becoming a major obstacle to broader deployment, particularly as more companies use consumption-based AI services. One-third of leaders (33%) said a limited understanding of usage costs was a key challenge for AI agents.

Another 23% said they struggled with usage-based costs, while 42% reported only partial visibility into AI spending. The issue is becoming more important as AI pricing increasingly depends on tokens, the units used to measure the text, data, reasoning and interactions processed during a task.

In response, many companies are tightening financial oversight. More than half of respondents said they had AI cost-monitoring dashboards in place, and a similar share said cost reviews were now part of AI approval processes.

The survey found a marked difference in results for businesses with stronger cost controls. Leaders with strong cost visibility were five times more likely to report established ROI, at 15% compared with 3% for those without the same level of oversight.

Accountability gap

The research also points to a governance problem inside many organisations. Only 24% of respondents said the Chief Executive Officer was accountable for AI-driven business outcomes, while 29% said responsibility lay with the broader C-suite.

That split often means accountability does not extend beyond sponsorship, making it harder to measure impact and assign responsibility for decisions based on AI outputs. Where accountability was clearly defined at the top, the survey found stronger confidence and better commercial outcomes.

Organisations where the Chief Executive Officer was accountable for decisions based on AI outputs reported 60% confidence in their AI strategy, compared with 22% elsewhere. They were also more likely to report meaningful business value (57% versus 21%) and established ROI (14% versus 4%).

People focus

As deployment broadens, companies are also shifting their focus from experimentation alone to workforce use. The survey found progress in human-AI collaboration, with the figure rising to 71% from 60% in the first quarter.

Almost half of the leaders (48%) said their organisations were choosing to upskill the workforce. The findings suggest that many businesses now see employee adoption as more important to value creation than the technology's underlying novelty.

That reflects a broader shift in how executives assess AI programmes. Rather than treating AI as a stand-alone innovation effort, many are now under pressure to show whether spending improves margins, operating models or measurable business performance.

Steve Chase, Global Head of AI and Digital Innovation at KPMG International, said the figures showed a growing divide between companies with clear senior ownership and those without.

"We're seeing a clear divide between organizations with leadership accountability at the top and those without. These companies are seeing materially better results across the board such as greater confidence, higher value realization and established ROI," Chase said.

Rob Fisher, Global Head of Advisory, KPMG International, said cost discipline was becoming central to AI decision-making.

"AI is now as much a financial management priority as it is a technology one. The real risk isn't investing in AI, but doing so without cost visibility and an understanding of AI's economics. Organisations that have visibility into their costs and maintain strong oversight are the ones translating AI investment into real, measurable value," Fisher said.