Despite sustained enterprise investment in AI, many companies remain unable to show measurable business returns, according to new research from KPMG. The report suggests the next phase of AI adoption will be determined largely by financial oversight and executive accountability.
The firm’s Global AI Pulse: Q2 2026 report, based on responses from more than 2,000 senior business leaders across 20 countries at organizations generating more than $50 million in annual revenue, shows AI becoming embedded in day-to-day operations even as returns remain difficult to quantify.
Twenty-two percent of respondents now classify AI as part of everyday work, up from 13% in the previous quarter, representing the largest quarter-over-quarter increase across KPMG’s AI maturity model. AI remains a strategic investment priority for 79% of organizations, up from 74% in Q1, with average AI spending holding steady at $188 million.
Yet investment has not translated into widespread financial success. Only 7% of respondents said they have established a measurable return on AI investments, even as nearly one quarter reported growing pressure from investors to demonstrate business value.
Furthermore, reported productivity gains declined from 42% to 35%, while improvements in decision-making speed slipped from 41% to 36%. Cost reductions attributed to AI also edged lower, dipping from 31% to 29%.
Struggling to Forecast AI Costs
Executive accountability is central for success, the report finds. Only 24% of organizations said the CEO is ultimately responsible for AI-driven business outcomes, while 29% assigned responsibility broadly across the executive team.
Yet organizations that placed accountability directly with the CEO consistently reported stronger results. They were substantially more confident in their AI strategies, significantly more likely to report meaningful business value, and nearly four times as likely to report established ROI than organizations without clearly defined executive ownership.
Financial transparency is a growing competitive advantage, KPMG finds. As AI vendors adopt usage-based pricing models built around token consumption and AI agents, many organizations are struggling to forecast operating costs.
Only 35% of respondents reported full visibility into AI operating expenses. Forty-two percent said they have only partial visibility into AI spending, while roughly one-third acknowledged limited understanding of AI cost structures and pricing models. Nearly one quarter also reported difficulty managing usage-based AI costs.
Those gaps are already influencing, or perhaps complicating, deployment strategies. Forty-nine percent of organizations said they have delayed, reduced AI initiatives after costs exceeded expected business value. Also, 22% said lower-cost AI models are becoming a greater factor in tech decisions, demonstrating an enterprise focus on maximizing returns rather than pursuing the most advanced models available.
Companies with advanced financial oversight appear to be separating themselves from the rest of the market. More than half have implemented AI cost monitoring dashboards, and a similar percentage have integrated formal cost reviews into AI approval processes. KPMG finds that organizations with strong cost visibility were five times more likely to gain ROI than those lacking comprehensive financial oversight.
The findings also suggest companies are recognizing that AI success depends on workforce readiness as much as technology deployment. Human-AI collaboration increased from 60% to 71% over the previous quarter, while 48% of organizations said they are actively investing in employee upskilling to improve AI adoption.
