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Think about what happens when finance discovers that a loan product has been classified incorrectly during preparation of the NCUA 5300 call report. An analyst fixes the mapping in a reporting spreadsheet, the controller reconciles the figure, and the report goes out with a number the institution can support.
But the correction may never reach the marketing or lending platform receiving a nightly extract from the core. If that vendor introduces an AI feature, its recommendations could rely on the same classification finance has already determined needs correcting.
The credit union does the work to make its regulatory reporting reliable. It just hasn't made that work available to every system that depends on the data.
For CIOs facing growing pressure to approve AI initiatives, this creates a problem. Every AI proposal raises questions about which information can be trusted, who is responsible for it, and what happens when something is wrong.
The good news: credit unions already have a standard for information they’re willing to defend. Extending those expectations beyond financial reporting gives CIOs a practical foundation for deciding where AI can move forward.
The Standard Already Exists. But How to Apply It More Broadly?
Every credit union has financial information it must be able to explain and reproduce. The CFO and controller are responsible for figures appearing in the 5300, general ledger reconciliations, and board financials. Risk, compliance, and supervisory committees have their own responsibilities for reviewing and validating institutional information.
When an examiner asks how a figure was calculated, finance needs more than an answer. It needs supporting records, consistent definitions, and a clear explanation of how the number was produced.
But the processes that make those figures defensible don't always extend to the rest of the institution's technology environment.
A data team may receive a ticket to correct a report or reconcile an inconsistency without being responsible for updating every application that uses the underlying information. Meanwhile, lending, marketing, collections, and fraud platforms may operate on separate extracts, each with its own definitions and refresh schedules.
This creates a disconnect between the information finance has validated and the information other systems continue to use.
For a CIO, the opportunity is to work with the CFO to turn financial reporting expectations into shared requirements for critical data. When a definition or classification is corrected, that change should be documented, owned, and made available to the systems that need it.
That doesn't mean every application must use the same reporting calculation. A collections tool may need more current payment information than the 5300, and an MSR assisting a member needs to know whether a payment posted that morning. It is important that those differences are understood, appropriate for the use, and supported by reliable records.
The standard should be that the credit union can explain which number is being used, why it is appropriate, and where it came from.
A Better Way for CIOs to Approve AI
Without clear expectations for the underlying data, each AI proposal can become a separate negotiation between technology, the business, and the vendor.
A department may want to launch an AI-powered outreach tool using an existing data feed. The vendor may demonstrate impressive capabilities, but that alone doesn't establish whether the information behind its recommendations is accurate, current, or consistent with what the credit union knows. A shared standard changes that conversation.
Before approving a use case, the CIO can ask whether the source records are understood, whether important definitions have been agreed upon, and whether corrections will reach the system. Finance and risk can help establish the requirements, giving the technology team institutional backing rather than leaving it to evaluate every proposal independently.
This also creates a clearer path for business teams. Instead of receiving an indefinite response that the institution's data isn't ready, they can identify the specific information a proposed use needs and the gaps that must be addressed.
Reliable source data alone isn't sufficient to make an AI system safe or effective. As the NCUA's existing supervisory approach to AI makes clear, institutions must also consider model controls, ongoing monitoring, and vendor due diligence.
But these responsibilities are easier to manage when the credit union understands and trusts the information its tools consume.
The CIO can then evaluate AI initiatives against both the reliability of their underlying data and the risks associated with their intended use.
Making the Business Case for Work That Already Needs to Happen
The same approach can help solve another persistent challenge for CIOs: securing funding for foundational data improvements.
Data cleanup is often difficult to justify because its benefits appear removed from the outcomes executives measure. Yet credit unions already spend considerable effort reconciling information, correcting reports, and answering questions about inconsistent figures.
If finance fixes the same loan classification every quarter, the institution is paying to maintain that correction repeatedly. Making the fix available through a shared, maintained data source could reduce the reporting effort while improving the information used by other applications.
The CFO can help quantify that value. Hours spent reconciling the 5300, recurring reporting adjustments, and errors discovered after publication provide measurable starting points. The CIO can connect those improvements to specific business capabilities that reliable data would support.
For example, a credit union might begin with one loan portfolio and an AI-assisted member service workflow. Ensuring that the application receives accurate, current loan information could reduce outreach based on outdated balances or payment statuses.
That creates two measurable benefits from the same investment: less recurring reconciliation work and more reliable member interactions.
Importantly, the institution doesn't need to modernize its entire data environment before moving forward. It can prioritize the information required for a defined use case, establish the necessary controls, and expand as additional needs emerge.
Over time, each approved use can build on definitions, corrections, and integrations that have already been established rather than starting from scratch.
Examiner-Readiness as a Foundation for AI
For credit union CIOs, AI readiness shouldn't require abandoning the standards that already govern important institutional information.
The financial reporting process provides a useful starting point because it establishes what the institution expects from numbers it must defend. Extending that discipline to the systems supporting AI can make approvals more consistent, investments easier to justify, and new capabilities more reliable.
The goal is to make clear which information is ready to support a particular decision, what still needs work, and who is accountable for maintaining it.
We built DataOS as a data context and activation layer for AI and analytics that helps regulated institutions apply shared definitions and access rules across existing systems. By making the same trusted information available to reports, applications, and approved AI tools, DataOS helps institutions build on the data standards they already maintain.