Agentic AI
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Artificial Intelligence & Machine Learning
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Next-Generation Technologies & Secure Development
KPMG Survey Finds Organizations Must Transform Ops to Scale AI
Enterprises are spending millions on artificial intelligence projects, but whether those investments will reap rewards comes down to how well they’re integrated into business operations across the enterprise, according to a new report by KPMG.
See Also: AI Security Risks Rise With Agentic Systems
KPMG’s Global AI Pulse: Q1 2026 survey asked 2,110 C-suite leaders and their direct reports in 20 countries across eight sectors and found that while 95% of companies have an AI strategy and 39% said they’re scaling up AI or driving adoption across the enterprise, only 8% say they’ve seen tangible return on investment.
And those investments can be steep. Companies on average expect to spend $186 million in the next 12 months on AI.
What separates the leaders from the laggards? The difference isn’t a willingness to experiment or access to technology or resources. IT infrastructure is a priority expense for 58% of respondents, and 50% are boosting cybersecurity and data protection.
It’s the structure of the enterprise itself. The consulting firm said success aligns with how the business is organized, its governance structures and talent pool. Most organizations just haven’t been built or redesigned to support AI at scale, and most are experimenting broadly without translating AI investments into enterprise-wide gains.
Leaders of the Pack
“What sets ‘Al leaders’ apart is that they have a clear link between their AI activity and the business results, they use consistent performance metrics across functions, and they have visibility into impact as systems operate not just at the end,” said Samantha Gloede, global head of risk services and global trusted AI leader at KPMG International.
“Measuring the value of AI is still a big challenge for most organizations, but the companies that are getting it right are making measurement part of how AI works across their business,” she said.
The 11% of organizations that KPMG identified as “AI leaders” that demonstrate the ability to translate AI into measurable outcomes at scale, have some common traits. They’re scaling AI maturity, delivering measurable business value and operating AI across workflows at scale.
Firstly, they create agent ecosystems in an orchestrated way that genuinely transforms business outcomes, rather than getting stuck in the pilot stage, Gloede said. They also upgrade their systems of governance to manage risks and preserve trust, and they bring their people with them, supporting teams through change and investing in the skills needed as AI becomes part of everyday work.
The difference between leaders and the rest of the pack is evident in the data, and 82% of AI leaders say they have seen meaningful business value from burgeoning tool. For those still piloting, 62% said they gained meaningful value. In fact, leaders are 2.5 times more confident in their ability to manage risk. Leaders also stand out when it comes to developing multi-agent systems and orchestrating AI across workflows.
The Governance Equation
Governance remains a challenge across organizations. While 52% say they’re using AI to automate workflows across functions, only 9% have orchestrated multiple agents across workflows. And fragmented systems and data sources are complicating agent decision-making across workflows, Gloede said.
“When you start coordinating multiple AI agents across business functions, getting the governance right is both difficult and vital,” she said. “CIOs need to be clear about who owns decisions made by agents because once agents operate across teams, decisions don’t sit in one place anymore.”
AI leaders are those who “don’t treat governance as an afterthought. They build it into how agents are designed and run from the beginning. That means ownership, accountability and controls are clear as agents move across teams and functions,” she said.
KPMG’s data shows that the scale of AI ambitions is tied directly to the maturity of governance structures. For example, 81% of AI leaders said they have the capabilities and governance to manage AI risk at scale, compared to 63% of non-leading organizations. Leaders also report higher investments in compliance, cybersecurity and board-level AI expertise.
For CIOs, building governance systems ahead of AI deployments is crucial to building trust across the enterprise, Gloede said. “It means having clear ownership of AI-driven decisions, integrating risk and compliance directly into workflows, and designing governance as part of the system architecture,” she said.
CIOs can avoid slowing things down by treating governance as an enabler, not a barrier, she said. “That means focusing on real-time monitoring and observability, clear accountability and adaptive controls that keep pace as agents scale.”
Humans in the Loop
When it comes to roadblocks, most organizations say they’re hampered by workforce readiness, not by technology, funding or ambition. Only 22% say they’re “very confident” their talent pipeline can meet the needs of an AI-enabled workforce, and 25% identify workforce readiness as a challenge.
For Gloede, hands-on learning is key to readying the workforce, embedding AI skills education into real workflows rather than teaching through abstraction – an approach the firm deploys internally. Leading companies have updated training and introduced sandbox environments that allow team members to experiment with AI tools in real-world immersive simulations, and launched an internal initiatives to award cash prizes to team members who develop AI solutions that meaningfully affect client work or deliver measurable improvements to internal operations, creating real value for the business, she said.
“Approaches like these help us build a workforce that can adapt and thrive as our profession evolves,” she said.