Topic starter
10/07/2026 9:17 pm
TL;DR: Organizations lose AI ROI when governance cannot keep up with rapid AI adoption, with shadow tools, undocumented retraining, and fragmented approvals creating fire drills and eroding executive confidence, according to OneTrust. The practical answer is not slower innovation, but inventory, workflow automation, lifecycle controls, and runtime assurance that make AI governable at scale.
Editorial analysis by NHI Mgmt Group, based on content published by OneTrust: “Why Organizations Lose AI ROI (and How Data Teams Can Drive Visibility)”.
By the numbers:
- Organisations with real-time monitoring are 34% more likely to see improvements in revenue growth and 65% more likely to see improved cost savings, according to a 2025 survey from EY cited by OneTrust.
Key questions
Q: What is the first governance step when AI use is spreading faster than control coverage?
A: Start with a centralized AI inventory.
Q: Why do AI programmes lose ROI when governance is weak?
A: AI programmes lose ROI when teams cannot prove what exists, who owns it, how it is used, or whether it still operates within policy.
Q: What breaks when organisations rely on spreadsheets for AI governance?
A: Spreadsheets only reflect what someone remembered to enter, so they miss shadow AI, stale records, and systems that moved into production without approval.
Practitioner guidance
- Build a central AI inventory Record every model, system, vendor, and use case with ownership, data sources, risk tier, and business purpose before deployment.
- Automate risk-based intake and approvals Route new AI initiatives through a standard intake workflow that triggers the correct reviewers based on sensitivity and risk.
- Embed controls into the AI lifecycle Connect governance checks to the tools used for data pipelines, model development, deployment, and vendor management so controls run in process.
Bottom line: The article frames AI ROI loss as a governance failure rooted in poor visibility, fragmented workflow, and control drift across the AI lifecycle.
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10/07/2026 9:40 pm
Visibility debt is now a governance risk in its own right: organisations that cannot inventory AI systems are accumulating a control gap, not merely an operational inconvenience. Shadow AI, undocumented retraining, and ad hoc ownership mean the enterprise cannot explain what it is governing, which weakens auditability and executive trust. For programmes that already struggle with asset sprawl, the lesson is to treat AI inventory as a control foundation rather than a reporting exercise.
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.
- 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:
A: Treat it as part of governance. AI systems can drift after release through retraining, data changes, or expanded use, so approval without monitoring only captures one moment in time. Runtime assurance closes the gap between what was approved and what is actually running.
👉 Read our full editorial: AI governance visibility gaps are eroding enterprise ROI