Topic starter
11/06/2026 11:14 pm
TL;DR: 91% of U.S. data, privacy, and AI decision-makers say their organisations are developing or rolling out agentic AI, but only 48% are establishing formal AI governance policies and frameworks, according to Collibra. That mismatch shows adoption is outrunning governance, leaving identity, accountability, and control design exposed.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “New Survey from Collibra by The Harris Poll Found 86% of Tech Decision Makers Are Confident Agentic AI Will Drive ROI”.
By the numbers:
- 91% of tech decision-makers said their organisation is developing or rolling out agentic AI.
- 47% of tech decision-makers said their organisation is providing governance and compliance training to employees.
- 60% of tech decision-makers said they are monitoring AI systems for bias, fairness, and transparency.
Key questions
Q: What breaks when agentic AI is scaled before governance is mature?
A: What breaks first is accountability.
Q: Why do agentic AI programmes increase accountability risk?
A: They increase accountability risk because the system can initiate actions across tools and workflows while ownership remains split across data, security, product, and legal teams.
Q: How do organisations know if agentic AI governance is actually working?
A: Look for three signals: access decisions tied to task context, complete audit records linking agents to datasets, and rapid revocation when scope changes.
Practitioner guidance
- Define an agentic AI actor model Classify each system by who can authorise it, what tools it may use, and which actions it can initiate without human approval.
- Establish policy gates before rollout Require use-case approval, control mapping, and documented ownership before an agentic workflow is moved into production.
- Tie training to governance decisions Train data, privacy, security, and product teams on approval paths, exceptions, audit evidence, and escalation responsibilities.
Bottom line: Agentic AI is moving into production faster than many organisations are putting formal governance, training, and policy controls in place.
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This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 7:50 am
Agentic AI governance is now an identity problem, not only an AI policy problem. Once systems can initiate action, governance has to define the actor, the scope, and the approval boundary, not just the model. Collibra’s findings show that deployment is advancing even where formal policy is not, which means control design is being asked to catch up with behaviour already in production. The practitioner implication is that agentic AI belongs in identity governance discussions from the start.
A few things that frame the scale:
- A Harris Poll survey of more than 300 technology decision-makers found that 86% expect agentic AI to deliver positive ROI, yet fewer than half had AI governance policies in place.
A question worth separating out:
Q: What is the difference between agentic AI and normal automation for IAM teams?
A: Normal automation follows a fixed script, while agentic AI can set sub-goals, adapt to context, and choose actions within its authority. For IAM teams, that means the control problem shifts from validating a workflow to constraining an actor. The agent may need lifecycle management, auditability, and revocation logic that scripted jobs do not require.
👉 Read our full editorial: Agentic AI governance gaps persist as adoption outpaces policy
This post was modified 1 day ago by NHI Mgmt Group