KPMG’s first Global AI Pulse report suggests the enterprise AI story is shifting from deployment to orchestration. KPMG surveyed 2,110 C-suite and senior business leaders at organizations with at least $100 million in annual revenue.
The survey ran from Feb. 19 to March 17 across 20 countries, territories and jurisdictions. It found that 95% of organizations now have an AI strategy and plan to invest an average of $186 million over the next 12 months. Yet only 8% said they had established return on investment, even though 64% reported meaningful business value.
The consulting firm says maturity is advancing, but that is not consistently translating into enterprise outcomes. 39% of organizations are now scaling AI or driving adoption across the enterprise, while 54% remain in research, experimentation or strategic planning.
Within that spread, about 11% qualify as “AI leaders,” a group KPMG defines as organizations in the top two maturity stages and the top three categories of agent deployment.
Agents are deploying faster than they’re coordinating
The report adds 22% of organizations are still exploring AI agents, 17% are piloting them, 14% are deploying them and 18% are scaling them across multiple functions.
Another 17% are developing or implementing multi-agent systems, but only 9% have reached orchestration across workflows. Even when KPMG combines multi-agent work with broader coordination capabilities, the share rises only to 26%, leaving most organizations short of enterprise-wide orchestration.
Where agents are already embedded
Agents are already widely used across core functions. KPMG said agentic AI is already embedded in technology or IT at 66% of organizations, in operations at 55% and in marketing and sales at 43%.
More than half of respondents using agents said they are automating workflows that span multiple functions, while 41% said agents provide shared knowledge environments and 40% said they support joint decision-making across teams.
The report says that combination points to broader use of AI across the enterprise, but not yet to consistent coordination across it.
What’s blocking strategy from becoming outcomes
The firm identifies several barriers to meeting AI strategy goals. It lists data privacy and cybersecurity at 42% each, data quality at 34%, regulatory uncertainty at 31% and gaps in risk management and governance at 24% as barriers to meeting AI strategy goals. It also says 75% of executives express broader concern about AI-related risk and security.
Looking ahead 12 months, respondents ranked risk management as their top challenge at 43%, followed by data quality at 36%, measurable ROI at 32%, employee adoption at 31% and integration with other emerging technologies at 27%.
How leaders differ from the rest
The leader and non-leader split is clearer in KPMG’s detailed findings. AI leaders are more likely than non-leaders to prioritize revenue growth through new products, services and AI-enabled experiences, 33% to 28%, and they place more weight on human-AI collaboration, governance and trust and security.
Non-leaders, by contrast, are more likely to prioritize cost reduction, 32% to 25%. AI leaders also report much greater confidence than non-leaders in measuring AI’s effect on revenue, profitability, decision-making and risk: 48% versus 27% on revenue, 50% versus 28% on profitability, 49% versus 32% on decision-making and 45% versus 25% on risk.
That difference runs through governance and oversight as well. KPMG said AI leaders invest more heavily in infrastructure, security and risk and compliance, and report stronger board coverage of AI topics, 89% to 76%, deeper board-level AI expertise, 45% to 20%, and greater governance readiness, 81% to 63%.
The same report says organizations confident in their AI talent pipeline are nearly four times as likely to report meaningful business outcomes, 77% to 20%, suggesting workforce readiness is closely associated with performance rather than adoption alone.
Regional splits and ecosystem gaps
Regional differences add another layer. KPMG said the Americas lead in enterprise-scale deployment at 35%, compared with 22% in EMEA and 23% in ASPAC.
It also found different operating assumptions around human and AI control, with 41% of organizations in the Americas expecting humans to manage and direct AI agents, while 38% in ASPAC expect AI agents to take lead roles in managing projects.
The report says only 12% of organizations are prioritizing external AI ecosystems, a figure KPMG links to limited coordination across partners, platforms and third-party capabilities. The result is a narrower and more concrete enterprise question than whether companies are using AI: whether they can organize data, governance, workforce capability and decision-making well enough to run it as a system.