Topic starter
10/06/2026 11:23 pm
TL;DR: A Harris Poll survey of 313 U.S. technology decision makers found that 93% want companies to disclose AI tools and agents, 90% support federal disclosure requirements for high-risk AI systems, and 91% say human oversight is critical, according to Collibra. The governance gap is no longer about adoption speed alone, but about accountability, verification, and trust boundaries that existing programmes were not built to enforce.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “New Collibra Survey by The Harris Poll Finds 84% of Decision Makers Say Organizations Must Increase AI Spending in 2026 to Remain Competitive with Big Tech, with Almost Half Saying AI is Falling Short”.
By the numbers:
- 93% want companies to disclose AI tools and agents to encourage transparency, monitoring and responsible use.
- 90% support federal requirements for companies to disclose and document high-risk AI systems.
- 91% of decision-makers believe that human oversight for AI systems is critical.
Key questions
Q: What breaks when AI tools and agents are not fully disclosed?
A: Undisclosed AI systems create blind spots in ownership, access review and accountability.
Q: Why do organisations need formal oversight for high-impact AI systems?
A: High-impact AI can affect hiring, finance, healthcare and other decisions where error or bias has real consequences.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk.
Practitioner guidance
- Build a disclosed AI inventory Catalog every AI tool, embedded model and agent in use, with owner, purpose, data access and approval path.
- Define human override points Identify where AI output can be corrected, blocked or escalated before it affects customers, employees or regulated decisions.
- Map AI access to identity controls Treat AI systems as governed subjects in the identity model, with scoped permissions, monitored usage and revocation paths.
Bottom line: AI disclosure is moving from a policy preference to a governance expectation that organisations will need to evidence.
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This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 5:18 am
Disclosure is becoming the new minimum governance signal for AI. The survey results show that executives are no longer satisfied with informal AI use or vague internal assurances. They want visible inventories, documented use cases and named accountability so that AI systems can be governed as part of the control plane, not treated as shadow experimentation. The practitioner conclusion is simple: if an AI system cannot be disclosed, it is not governed.
A few things that frame the scale:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between human IAM and AI workforce governance?
A: Human IAM assumes a person with a predictable session, while AI workforce governance must manage autonomous execution, delegated tool use, and variable context. That means you need tighter lifecycle controls, stronger logging, and faster revocation for agents than for typical user accounts.
👉 Read our full editorial: AI disclosure and governance expectations are rising fast
This post was modified 1 day ago by NHI Mgmt Group