What KPMG's Latest AI Survey Shows

KPMG's second-quarter Global AI Pulse report, based on responses from 2,145 senior leaders across 20 countries and territories, finds confidence in AI climbing across every measure it tracks. Seventy-six percent of leaders say AI is delivering meaningful business value, up 12 points from the first quarter, and 79% say AI would remain a top investment priority even in a recession. Planned AI spending held steady at $188 million over the next 12 months.

The adoption curve tells a more complicated story. The share of organizations in the "driving-adoption" phase — embedding AI across the organization — nearly doubled, from 13% to 22%, the largest single-quarter movement anywhere on KPMG's six-stage maturity model. But the share reporting "established ROI," the most mature stage, barely moved. Confidence and deployment are accelerating faster than proof of return, and KPMG frames this gap as the report's defining signal.

Key Points

• KPMG's Global AI Pulse Q2 2026 surveyed 2,145 senior leaders across 20 countries between April and May 2026
• Organizations in the "driving-adoption" phase nearly doubled quarter over quarter, from 13% to 22%, while established ROI stayed flat at 7%
• Organizations with clearly defined accountability for AI outcomes report established ROI at more than three times the rate of those without it
• Nearly half of organizations have rephased AI deployments after costs began outweighing expected value
• Only about a third of organizations have full visibility into their AI operating costs, but those that do report ROI five times more often than those that don't

Accountability Separates the Winners From Everyone Else

The report's clearest finding involves who actually owns AI outcomes. Three-quarters of organizations say their CEO actively treats AI as a strategic priority, but only 24% name the CEO or executive committee as ultimately accountable for AI-informed decisions. Accountability, in other words, is far more scattered than sponsorship.

That gap matters because it correlates directly with results. Organizations with clearly defined accountability report established ROI at more than three times the rate of those without it — 14% versus 4%. They're also far more likely to say their AI strategy is future-proofed and that AI is delivering meaningful value. As Samantha Gloede, KPMG's Global Head of Risk Services, put it in the report, governance can no longer sit on the sidelines as AI becomes woven into daily operations — the organizations creating the most value are building accountability directly into how decisions get made.

Cost Visibility Is Becoming a Competitive Capability

The report's third major theme is economics. Access to lower-cost, high-fidelity AI models is now the fastest-rising influence on AI strategy, up 7 points in a single quarter, and nearly half of organizations have rephased AI deployments after costs began outweighing expected value. Yet only about a third report having full, actively monitored visibility into what their AI systems actually cost to run — most are working from partial, delayed, or fragmented cost data.

The pattern here echoes the accountability finding: organizations with full cost visibility report established ROI five times more often than those without it, at 15% versus 3%. KPMG frames visibility as a precondition rather than a guarantee — it doesn't create value on its own, but it gives leaders the information needed to make good decisions about where AI spending is actually working.

What This Means for Marketing and Growth Teams

For teams building out their own AI adoption strategy, the report's practical lesson is that deployment speed isn't the bottleneck most organizations think it is — clear ownership and cost tracking are. A marketing or growth function that can name who's accountable for an AI initiative's outcome, and can show what it costs to run against what it's returning, is already ahead of most of the organizations in this survey.

That same discipline belongs in how teams approach growth strategy more broadly: the data suggests that scaling AI usage without scaling the governance and cost tracking around it is a common way organizations end up with a lot of activity and very little provable return.