Topic starter
24/06/2026 7:08 pm
TL;DR: Manual governance, spreadsheets and email approvals are creating AI technical debt as models and agents move faster than approval chains, according to Collibra. The real issue is not ambition but operating without traceability, ownership and repeatable controls that can keep pace with production AI scale.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “The hidden technical debt of AI: Why manual governance is slowing down your AI scale”.
By the numbers:
- 95% of generative AI projects never make it into production, a failure rate that points to a bigger governance problem than ambition.
Key questions
Q: What breaks when identity governance relies on spreadsheets and email approvals?
A: Access decisions lose traceability, version control, and reliable ownership.
Q: When should organisations prioritise AI cost governance over more model experimentation?
A: Organisations should prioritise cost governance as soon as AI moves from experimentation into repeatable production use.
Q: How do teams know whether AI governance is actually working?
A: Look for evidence that every AI interaction can be traced end to end, from identity and intent to output and enforcement.
Practitioner guidance
- Inventory AI use cases as governed assets Create a single inventory that ties each use case to its model, dataset, owner, audience, region and current approval state.
- Replace email approvals with repeatable review workflows Define standard paths for low-risk, medium-risk and high-risk AI changes so teams do not reinvent governance every time a model, dataset or agent changes.
- Separate assistance from autonomous action Classify which AI systems only advise and which can take actions in workflows, then apply different approval, escalation and monitoring rules to each group.
Bottom line: AI governance debt appears when approval processes cannot keep up with the speed at which models, agents and data dependencies change.
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25/06/2026 4:18 am
Manual AI governance is becoming an identity problem, not just a process problem: once AI use cases, models and agents move across teams, the central question becomes who owns the decision and which controls still bind it. Spreadsheets and email approvals cannot preserve that chain of accountability when assets change faster than review cycles. The practitioner conclusion is that AI governance must be treated as a governed identity and lifecycle system, not a document trail.
A few things that frame the scale:
- 52% of respondents see AI security decision-making power shifting toward platform and infrastructure teams rather than the executive suite, according to the 2026 Infrastructure Identity Survey.
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should security teams govern agentic AI that can execute IAM tasks?
A: Start by treating the agent as an NHI with bounded authority, explicit ownership, and revocation procedures. Require human approval for high-risk actions, log every decision path, and enforce least privilege at the workflow level. If the agent cannot be audited or rolled back, it is not yet ready for autonomous IAM execution.
👉 Read our full editorial: AI governance technical debt is slowing production scale