Topic starter
04/06/2026 3:01 pm
TL;DR: Only 44% of AI proofs of concept had reached production in regulated industries by early 2025, according to WitnessAI. Runtime enforcement, audit trails and intent-aware policy now determine whether deployments can survive scrutiny.
Editorial analysis by NHI Mgmt Group, based on content published by WitnessAI: “How can regulated industries use AI?”.
Key questions
Q: Why do AI pilots fail to reach production so often?
A: AI pilots fail when organisations design for experimentation but not for operational control.
Q: What breaks when legacy security controls are used for conversational AI?
A: Controls built for files, web traffic and static application sessions miss the interaction context that drives AI risk.
Q: How can organisations prove AI governance to auditors and boards?
A: Organisations prove AI governance by producing evidence that the control operated, not just that a policy existed.
Practitioner guidance
- Define production readiness as demonstrable control Require evidence that governance, security and compliance controls operate in live AI environments before wider rollout, not after the pilot phase closes.
- Map AI interactions to audit trails Capture prompts, responses, tool calls and identity attribution in a form that can satisfy risk committees and regulatory review.
- Move policy enforcement to runtime Apply intent-aware controls at the point of inference so the system can inspect meaning before an AI action is taken or propagated.
Bottom line: Regulated AI fails most often at the point where organisations must prove governance in live environments rather than merely describe it in policy.
Glossary terms: Runtime Inspection, Intent-Aware Policy, Evidence of Control, Production readiness
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This topic was modified 19 hours ago by NHI Mgmt Group
04/06/2026 3:14 pm
The production problem in regulated AI is an evidence problem, not a model-quality problem. The article shows that organisations can reach a technically workable AI state while still failing the proof burden demanded by compliance, audit and risk review. That means the limiting factor is no longer whether the model can answer the question, but whether the organisation can demonstrate what happened, why it happened and under what policy. Practitioners should treat production readiness as an evidentiary control plane.
A question worth separating out:
Q: How should security teams choose between ISO 42001 and NIST AI RMF 1.0 for AI governance?
A: Choose ISO 42001 when you need a certifiable management system, external audit evidence, and a formal governance signal for customers or regulators. Choose NIST AI RMF 1.0 when you need a flexible, engineer-friendly risk playbook for day-to-day AI controls. Many mature organisations use both, with NIST guiding operations and ISO providing the umbrella for accountability and assurance.
👉 Read our full editorial: AI governance for regulated industries still fails at production