Topic starter
09/07/2026 3:17 pm
TL;DR: Banks are spending nearly 10% of revenue on IT while 70% to 90% of enterprise data is now unstructured and 84% of financial organizations report overexposure, according to Collibra citing Gartner, McKinsey, and Ponemon Institute. The governance problem is not just data volume but whether the information AI systems and agents need is discoverable, classified, and access-controlled before automation scales.
Editorial analysis by NHI Mgmt Group, based on content published by Collibra: “Clock is ticking: Why banks can't ignore unstructured data”.
By the numbers:
- US financial institutions collectively spend nearly 10% of their revenue on IT, according to Collibra.
- Gartner says unstructured data is growing at 40% to 60% per year, according to Collibra.
Key questions
Q: What breaks when unstructured data is turned into AI-ready inputs without governance?
A: Without governance, unstructured data can carry sensitive content, inconsistent labels, and unclear usage rights into AI workflows.
Q: Why do unstructured documents create risk for AI and automation programmes?
A: Because the most useful material for banking AI often sits outside structured systems, the controls around it are weaker and harder to enforce.
Q: How can banks tell whether their AI data foundation is actually working?
A: The clearest signal is whether high-value unstructured content is discoverable by approved workflows while remaining constrained by business need.
Practitioner guidance
- Map unstructured content to business-critical workflows Inventory where credit memos, loan files, transcripts, filings, emails, and underwriting artefacts live, then tie each repository to the workflow that consumes it.
- Pair classification with enforced entitlements Apply sensitivity labels and data owners to unstructured content, then bind those labels to access rules so discoverability never exceeds business need.
- Scope agent permissions to content class and task Limit AI agents and service accounts to the specific document sets, repositories, and actions required for each workflow, with separate controls for read, extract, and act.
Bottom line: Unstructured banking data is a governance blocker because AI systems need context from documents and messages that legacy controls do not manage well.
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09/07/2026 3:35 pm
Unstructured data governance is now AI governance. Banks that treat document sprawl as a back-office inconvenience miss the fact that AI agents depend on the very content humans have failed to organise. When the underlying corpus is duplicated, unlabeled, and broadly accessible, AI readiness becomes impossible to separate from data access governance. Practitioners should therefore treat unstructured data control as a prerequisite for safe automation, not a downstream cleanup exercise.
A question worth separating out:
Q: What is the difference between access control and data governance in AI environments?
A: Access control decides which identities may reach a system or dataset. Data governance decides how data is classified, monitored, and approved for use. In AI environments, those two disciplines must work together because model input, output, and action paths can expose sensitive data even when the source dataset appears well governed.
👉 Read our full editorial: Unstructured banking data is blocking AI readiness and governance