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Companies can keep deploying existing AI tools even as some leaders argue for slowing the development of more powerful models. Those are different decisions. For organizations, the immediate challenge is making adoption safer: OneTrust’s 2026 survey found that agent use is encouraged far more often than respondents say clear governance controls are in place.
Why AI adoption can continue during calls to slow AI development
Debate about slowing frontier AI development concerns the pace and risks of building increasingly capable models. Enterprise adoption is a separate question: whether and how an organization uses tools already available to it. A company can support stronger safeguards around future model development while still deploying existing AI for internal work.
In a September 21, 2026 interview with TechTarget’s AI Business, Blake Brannon, OneTrust’s chief innovation officer, described board-level pressure to transform and avoid disruption as a force behind enterprise adoption. That is his account of the pressure organizations face, not evidence that every company should accelerate deployment. His stated principle is that usefulness alone is not enough: “You can create all this great AI, but if you do not trust it, you cannot turn it loose.” (TechTarget/AI Business interview)
What the 2026 survey says about adoption and governance
OneTrust’s 2026 AI-Ready Governance survey, published September 14, reports responses from 1,200 senior business decision-makers in Australia, Canada, France, Germany, Singapore, Spain, the United Kingdom, and the United States. Sapio Research conducted the fieldwork in June and July 2026. Participating organizations had at least $100 million in annual revenue, and the sample had equal representation from CPO, CDO, CISO, and CMO audiences. These are self-reported findings from a vendor-sponsored survey, not a census of all enterprises.
| Survey finding | What respondents reported |
|---|---|
| Agent use and safeguards | 87% said their organizations encourage AI agent use; 47% said clear governance, oversight, and controls were in place. |
| Incidents and deployment decisions | 86% said their organization had experienced at least one measured AI-related incident in the preceding year; 28% reported two or more incidents in which AI systems or agents took unapproved actions. 27% said their organization slowed or paused AI deployment in response to incidents. |
| Unapproved AI use | 33% said their organization had seen employees use unapproved AI because approved tools or processes were not available quickly enough. |
| Adoption and accountability | 74% reported departmental or scaled AI adoption; 52% reported use across multiple business functions or embedding in business processes; 5% reported clear coordination and accountability across the AI lifecycle. |
| Visibility | 48% reported clear visibility into sanctioned and unsanctioned AI use; 46% had visibility into approved AI but limited visibility elsewhere. |
| Planned governance budgets | 98% planned to increase AI governance technology budgets in the next financial year, with an average planned increase of 25%. These figures describe intended spending, not actual increases. |
All figures in the table are from OneTrust’s 2026 report. They suggest a gap between organizational encouragement and reported controls, but they do not establish why every organization has that gap. The survey also reports that 45% of incident-affected organizations implemented formal AI review and approval processes; that figure applies to respondents reporting incidents, not all surveyed organizations.
Why incidents do not always lead to a pause
In this survey, reported incidents were much more common than reported pauses: 86% said their organization experienced at least one measured AI-related incident in the preceding year, while 27% said incidents led it to slow or pause deployment. That contrast does not mean incidents are harmless or that companies ignored them. Organizations may respond with targeted controls, reviews, or changes to particular use cases rather than stopping all AI work. The survey figures do not identify the response taken by every organization.
OneTrust’s finding that 33% of surveyed organizations had seen employees turn to unapproved AI when approved tools or processes were not available quickly enough also points to a governance trade-off. A process that is too slow or leaves needs unmet can make activity harder to see, not simply prevent it. Organizations need usable approved routes as well as rules against risky use.
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How companies can govern agents without treating every action alike
Brannon recommends focusing controls where an AI system connects to enterprise resources or takes consequential action, rather than relying only on rules attached to a model or provider. He describes the practical stakes as an agent trying “to read data from an enterprise system,” send an email, or delete a record. This is his proposed governance lens, not a universal standard or a NIST requirement.
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Map which systems, data, and tools each agent can reach, then apply policy at those boundaries. Reading a low-risk document, sending an external message, and deleting a business record carry different consequences; they should not automatically receive identical permissions. Brannon argues that potentially destructive actions should involve a person. Organizations can make that concrete by requiring approval for specified actions, limiting an agent’s permissions, and recording what it attempted and what happened.
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Review use cases before deployment
Before enabling an agent, identify its purpose, data access, connected tools, potential impact, and accountable owner. Define what it may do independently and which actions require approval. OneTrust recommends approval workflows; its survey reports formal review and approval processes among 45% of incident-affected organizations. That is a reported response pattern, not proof that a particular workflow prevents incidents.
Monitor after launch and assign ownership
Governance cannot end at approval if models, tools, workflows, or business needs change. Organizations need ongoing monitoring and clear responsibility for reviewing use, responding to incidents, and keeping evidence of decisions. OneTrust’s report emphasizes lifecycle monitoring and connected accountability; its finding that 5% reported clear coordination and accountability across the AI lifecycle indicates how uncommon respondents said that arrangement was in this sample.
Make approved use practical
Publish clear guidance and provide approved tools or processes that workers can access in time to do their jobs. The reported 33% use of unapproved AI due to unavailable approved options makes availability a governance issue as well as an IT procurement or policy concern. Teams should be able to ask for an exception or a new use case without relying on informal workarounds.
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Whether an organization builds controls into existing systems or evaluates governance software, these questions follow from the risks described in the interview and survey:
- Visibility: Can the organization identify approved and unapproved AI use, including activity outside centrally managed tools?
- Pre-deployment review: Is there a workable process to assess and approve use cases before they go live?
- Action control: Can policy limit access to data and tools, and require human approval for consequential actions such as sending messages or deleting records?
- Monitoring and accountability: Are deployed systems observed over time, and are owners, decisions, and evidence connected across the lifecycle?
- Framework alignment: Does the approach map to relevant frameworks and obligations, and can the organization substantiate any compliance claim?
NIST describes its AI Risk Management Framework as a voluntary resource, and its official page notes that AI RMF 1.0 is undergoing revision activity in 2026. The framework is not a mandate for the action-point design discussed by Brannon. Organizations should consult the NIST AI Risk Management Framework page for its current status and related generative AI resources.
OneTrust’s product page describes a commercial AI governance offering with system assessment, risk tiering, monitoring, and evidence and reporting features, and represents the product as aligned with NIST AI RMF, the EU AI Act, and ISO/IEC 42001. Those are vendor descriptions, not independent proof of performance or a guarantee of compliance. Any buyer should verify the specific capabilities, coverage, and obligations that matter to its own environment directly with the vendor. (OneTrust AI Governance)
The practical balance: keep useful adoption accountable
The choice is not necessarily between racing ahead and freezing all deployment. Organizations can continue carefully scoped uses of available AI while tightening review, access controls, monitoring, and accountability—especially when agents can affect enterprise data or take actions on a person’s behalf. The appropriate pace depends on the use case and its consequences; the survey does not establish a single safe speed for every organization.
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