Businesses are making AI a larger investment priority, but surveys in 2026 indicate that many have not matched that expansion with governance controls—especially for autonomous and agentic systems. KPMG respondents planned substantial investment, while IBM found that organizations often see AI adoption outpacing their governance capabilities. These surveys measure different populations and do not establish that planned spending has already occurred.
AI investment is rising, but the figures describe plans as well as budgets
KPMG’s Q1 2026 Global AI Pulse found that senior leaders planned a weighted-average US$186 million in AI investment over the next 12 months. It also found that 74% expected AI to remain a top investment priority even if a recession occurred. The survey covered 2,110 senior business leaders in 20 markets, fielded February 17–March 17, 2026. Each represented company had at least US$100 million in revenue, and three-quarters had more than US$1 billion; the weighted average is not a typical or guaranteed company budget. KPMG’s survey release reports the results.
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IBM’s Institute for Business Value, working with Oxford Economics, found that surveyed organizations expected AI spending to rise from just under 15% of IT budgets in 2025 to nearly 25% by 2027. These are projected budget shares, not audited expenditure. The same survey found that 77% said AI adoption was already outpacing current governance capabilities. IBM surveyed 2,000 senior executives responsible for IT, technology, or AI decisions across 33 geographies and 19 industries from January to April 2026. IBM’s report gives the survey scope and findings.
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The figures point to a real tension, but they should not be combined into one estimate: KPMG measured planned investment among large-company leaders, while IBM asked executives about budget shares and governance. Neither survey alone shows how much organizations ultimately spent or proves that higher spending caused weaker controls.
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Formal AI policies do not guarantee that teams follow them
EY’s 2026 survey illustrates the gap between having a policy and applying it under pressure. Although 98% of surveyed organizations said they had formal AI governance policies, 47% said their organization had previously failed to apply its process for urgent deployments. The respondents were 202 senior AI executives, including board members and senior leaders, at organizations with annual revenue of at least US$1 billion. The finding therefore describes a large-organization sample, not all businesses. EY’s survey release details the results.
An urgent launch can reveal whether governance is operational or merely documented. If a team can bypass review without a named approver, a recorded reason, and a later check, the written policy may not provide effective oversight in practice.
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Agentic AI adds controls that general AI policies may miss
AI agents can take actions across tools or workflows, so oversight has to address not only what a model says but also what it can do, when it may act, and how people can intervene. EY found that 91% of surveyed organizations were using agentic AI; among those users, 49% said governance had not been specifically updated for agentic risks. That finding is limited to the agentic-AI users in EY’s large-organization survey. Richard Jackson, EY’s Americas Assurance Chief Technology Officer and Global and Americas Assurance AI Leader, said: “Organizations are applying yesterday’s governance rules to today’s interactions with AI.”
Other surveys suggest that dedicated controls remain uneven. McKinsey’s 2026 AI Trust Maturity Survey, covering approximately 500 organizations across regions and industries, found that only about one-third reached level three or above in its firm-specific maturity model for strategy, governance, and agentic AI governance. Its levels are not a universal industry standard. McKinsey’s survey describes its assessment.
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Deloitte separately reported that only one in five companies had a mature governance model for autonomous AI agents, while worker access to AI rose by 50% in 2025. These observations come from Deloitte’s report and should not be treated as directly comparable percentages to the IBM, EY, KPMG, or McKinsey findings. Deloitte’s State of AI in the Enterprise provides its findings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Questions boards and operators can use to test governance
The surveys describe reported gaps, not a single proven cause or a universal maturity benchmark. Organizations can use the following questions to check whether oversight reaches actual deployments and agent actions:
- Who is accountable? Name the business owner for each deployment and the person authorized to approve material changes or exceptions.
- What is deployed? Keep an inventory of AI systems, including agents, the workflows they touch, the data and tools they can access, and the teams responsible for them.
- Which actions need human approval? Define actions an AI system may perform on its own, actions that require confirmation, and actions it must never take.
- How are urgent exceptions reviewed? Record who approved an exception, why it was necessary, what temporary limits apply, and when normal review must occur.
- Are controls tested? Check that access limits, approval gates, monitoring, and escalation routes work in practice—not only that they appear in policy documents.
- Can leaders see whether controls are working? Use evidence such as exception records, incidents, access changes, and review outcomes to assess whether governance keeps pace with deployment.
These are practical governance checks, not findings that the surveys tested as a package. They help distinguish investment activity from the ability to manage systems safely and accountably.
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