Topic starter
11/06/2026 11:22 pm
TL;DR: Enterprises are adding AI to fragmented data, vague permissions, and manual workflows, which magnifies existing governance gaps rather than fixing them, according to Gathid. The real control point is identity governance, because AI scale depends on clear access, provenance, and expiry discipline, not just better models.
Editorial analysis by NHI Mgmt Group, based on content published by Gathid: “Why Companies Must Finish Their Digital Foundation Before Scaling AI”.
By the numbers:
- 78% of organizations are using AI in at least one business function, according to Gathid.
- 88% of companies are boosting their AI budgets for agentic AI adoption, according to Gathid.
Key questions
Q: How should teams govern agentic access when AI systems move faster than access reviews?
A: Teams should move governance earlier in the lifecycle and treat issuance, delegation and device trust as the main control points.
Q: Why do agentic workflows increase privacy and security risk in weak environments?
A: Because the AI does not fix broken lineage, unclear permissions, or undocumented approvals.
Q: What breaks when provenance is missing from automated outputs?
A: Teams lose the ability to show which inputs, approvals, and claims produced the result.
Practitioner guidance
- Define agent identities and owners Inventory every non-human actor that can read, transform, or publish customer data, then assign a named business owner, technical owner, and expiry date to each one.
- Replace vague permissions with contextual access rules Map the data sources, actions, and conditions each agent needs, then narrow access so the agent can only act within explicit context and purpose boundaries.
- Attach provenance to every externally visible output Require source data, approvals, and claims to follow the asset so any published decision can be traced back without manual reconstruction.
Bottom line: Agentic AI does not resolve weak data quality, unclear permissions, or manual workflows. It exposes those weaknesses faster and at larger scale.
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This topic was modified 1 day ago by NHI Mgmt Group
12/06/2026 7:58 am
Identity governance, not model capability, is the real control plane for agentic AI scale: the article is correct that adding AI to broken data and access foundations only accelerates failure. Agentic systems increase the speed and frequency of identity decisions, so the enterprise must govern who or what can act, what it can touch, and how long that authority lasts. Practitioners should treat AI scale as an identity governance maturity test, not a model selection exercise.
A few things that frame the scale:
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: What is the difference between contextual access and role-based access for AI agents?
A: Role-based access grants permission based on job title or group membership, while contextual access evaluates the request in real time using factors like task, time, device posture, and location. For AI agents, contextual access is usually the safer model because the software can act beyond human discretion and needs tighter task scoping.
👉 Read our full editorial: Identity governance is the real foundation for agentic AI scale
This post was modified 1 day ago by NHI Mgmt Group