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Are businesses increasing AI spending faster than they can govern it?
The surveys point to a broad 2026 pattern: organizations are committing resources to AI while reporting gaps in oversight, accountability, and controls. They do not establish that spending itself causes governance weakness, or that every company is moving at the same pace.
| Survey | Investment or access finding | Governance finding | Scope |
|---|---|---|---|
| IBM Institute for Business Value, 2026 | Projected AI spending rises from just under 15% of IT budgets in 2025 to nearly 25% by 2027; these are reported budget projections. | 77% said AI adoption was outpacing current governance capabilities. | 2,000 senior executives responsible for IT, technology, or AI decisions across 33 geographies and 19 industries; surveyed January–April 2026. |
| KPMG Global AI Pulse Q1 2026 | Weighted average planned AI investment of US$186 million over the next 12 months; 74% said AI would remain a top investment priority even in a recession. | The cited findings focus on investment and value, not a comparable governance score. | 2,110 senior business leaders in 20 markets, fielded February 17–March 17, 2026; all represented companies had at least US$100 million revenue, and three-quarters exceeded US$1 billion. |
| EY US, 2026 | 91% of respondents said their organizations used agentic AI. | 98% reported formal AI governance policies; 47% said their organization had previously not applied its process for urgent deployments. Of agentic AI users, 49% said governance had not been updated for agentic-specific risks. | 202 senior AI executives at organizations generating at least US$1 billion annually. |
| McKinsey & Company, 2026 | Not stated in the cited maturity findings. | About one-third of organizations reached level 3 or above for strategy, governance, and agentic AI governance in McKinsey’s maturity model. | Approximately 500 organizations; respondents responsible for or expert in AI governance, risk, or investment. The maturity levels are McKinsey’s model, not a universal standard. |
| Deloitte, 2026 | Worker access to AI rose by 50% in 2025, according to Deloitte. | One in five companies had a mature governance model for autonomous AI agents. | Deloitte’s access and governance observations have distinct denominators; they should not be treated as one matched measure or directly compared with the other surveys. |
KPMG’s US$186 million is a weighted average plan among the surveyed companies, not a typical budget or a realized-spending total. Likewise, IBM’s IT-budget percentages describe projections, not verified expenditure. Both help show investment intent, but neither establishes how much companies ultimately spent or the returns they achieved.
Why formal AI policies can still leave a control gap
A written policy is only one part of governance. It must be usable in day-to-day deployment decisions, applied when timelines are compressed, and updated as systems and their capabilities change. EY’s findings illustrate the difference between having a policy and consistently following it: despite near-universal policy reporting in its sample, nearly half said their organization had previously bypassed its process for urgent deployments.
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That matters because an exception made under time pressure can become a route around review unless someone records why it was needed, who approved it, and what follow-up is required. The EY survey reports respondents’ accounts; it does not measure how frequently exceptions occurred across all deployments.
Agentic AI makes governance coverage more consequential
AI agents can be designed to take actions through connected tools or workflows, rather than only generate content for a person to use. That makes it important to know what an agent can access, what actions it can take, and when it must stop for human approval. The cited surveys do not define agentic AI identically, so their percentages are best read as separate signals rather than a single market-wide adoption rate.
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EY found that 49% of agentic-AI users in its sample said their governance had not been specifically updated for agentic risks. McKinsey found about one-third of organizations at maturity level 3 or above on its agentic-governance dimension, while Deloitte said one in five companies had a mature autonomous-agent governance model. Those findings reinforce the concern, but each uses its own sample and assessment; they are not interchangeable scores.
What boards and operating teams should ask
The survey results describe gaps, not a universal checklist. For a practical review of whether investment is keeping pace with control, boards and operators can ask:
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- Ownership: Who is accountable for approving an AI system before it is deployed, and who owns it after launch?
- Inventory and visibility: Can the organization identify its deployed AI systems, their business owners, data access, connected tools, and intended uses?
- Human approval: Which actions can a system take on its own, and which require a person to approve, confirm, or review the result?
- Urgent exceptions: If a deployment bypasses the normal review path, who authorizes the exception, where is it recorded, and when is it reviewed afterward?
- Agent-specific controls: Have permissions, monitoring, escalation paths, and recovery procedures been assessed for systems that can act through tools or workflows?
- Assurance: How does the organization test that controls work in practice, and what evidence shows that weaknesses are corrected?
These questions do not substitute for a governance framework or prove that a company is mature. They help distinguish documented intent from controls that are assigned, exercised, and checked.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the survey evidence does—and does not—show
Together, the 2026 findings support the view that AI is a high investment priority and that many organizations see governance as lagging adoption. KPMG surveyed large-company leaders about plans and priorities; IBM surveyed executives about budget projections and control capabilities; EY examined policy, urgent processes, and agentic AI; McKinsey assessed maturity through its own model; and Deloitte reported separate observations on access and autonomous-agent governance. Different samples, respondent roles, geographies, and definitions prevent a precise apples-to-apples ranking.
They also do not show that every business is overspending, that governance is absent everywhere, or that greater AI investment will automatically create value. KPMG’s Steve Chase summarized the distinction: “The first Global AI Pulse results reinforce that spending more on AI is not the same as creating value.”
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