By Edward Vincent
McKinsey’s projection that financial institutions could lose up to $170 billion in profit as consumers deploy agentic AI tools to shift deposits automatically has sparked understandable concern. Much of the industry’s reaction has centered on speed: how fast institutions can adopt AI, how quickly member behavior may shift, and how rapidly the competitive landscape could change.
However, this framing misses the more fundamental issue. AI cannot function until the data beneath it is complete, accurate and organized. Without that foundation, a credit union can find itself exposed, with its governance gaps and operational weaknesses magnified at a moment when trust, liquidity, stability, and long-term member relationships are most at risk.
AI Ambition Outpaces Readiness
Credit union teams are exploring AI for good reason – improved operational efficiency, better service, more personalized member touchpoints, and precise outreach to prospective members. Those goals are valid, and the industry is right to pursue them. The mistake starts with jumping straight into models and use cases rather than starting with the condition of the underlying data environment.
Many credit unions still operate fragmented systems that make it difficult to maintain a unified, enterprise-level picture of member activity. Data definitions and governance standards vary by department. Key performance indicators are calculated differently depending on who builds the report. When information is inconsistent or delayed, a credit union cannot accurately anticipate member needs, monitor deposit sensitivity, or evaluate risk with confidence. AI cannot overcome this, instead it will reflect, and often magnify, the underlying inconsistency.
Poor Data Governance Introduces Risk
Governance failures rarely surface as dramatic breakdowns. In credit unions, they emerge as slow, persistent friction that leadership grows accustomed to managing. Reports take longer to reconcile because figures from the core do not match data pulled from the lending platform. Operational teams rely on spreadsheets to compensate for unclear data ownership. Risk, finance, and lending teams struggle to align their understanding of performance because each group is working with slightly different information. Over time, this creates delays in decision-making, unnecessary operating expense, and an erosion of confidence in the institution’s own numbers – an issue that becomes particularly consequential during exam cycles, liquidity stress, or sudden shifts in member behavior.
The Risks Multiply
When AI enters this environment, the risks multiply. Models can generate outputs that look sophisticated, but if they are built on incomplete, stale, or inconsistent data, they create false confidence not insight. Credit unions that treat governance as a technical afterthought discover new tech magnifies the structural weaknesses already present in their data estate and organizational processes.
Add to this the stakes are higher: member relationships are personal, liquidity cushions are thinner, and concentration risk manifests quickly. Without strong, enterprise-level governance, credit unions may not unlock the power of AI.
Strong Data Governance Turns AI Into a Strategic Advantage
Credit unions need a data governance structure that defines how decisions are made, which data is authoritative, and how information moves across the organization. Assigning ownership, establishing quality thresholds, and creating reporting rhythms allows leadership, risk, finance, and operations to work from the same baseline. Pairing a complete, accurate and organized data foundation with robust governance requires enterprise discipline and enables strategic action, supported by reliable evidence.
With this structure in place, AI becomes easier to validate and far safer to deploy, rather than introducing uncertainty or unintended risk. Leadership can understand how a model arrives at an output. Risk teams can verify that activities align with existing controls. Regulators can see that decisions are explainable, repeatable, and tied to a well-managed data environment.
Strong governance positions AI to enable front-line colleagues to target precisely the right (prospective or current) member, with the right message and value proposition, at the right moment.
Credit unions cannot slow consumer use of AI, but they can shape how prepared they are to meet it. The institutions that lead the next decade will not necessarily be the ones that adopt AI the fastest, but rather the ones that sequence their work correctly: governance first, technology second. Complete, accurate and organized data, along with a strong governance foundation enables credit unions to adopt AI safely, strategically, and with clarity. Without it, AI becomes a source of noise rather than insight.
The most important question any credit union can ask at the beginning of its AI journey is simple: Do we trust our data enough to build on it? Credit unions that can answer yes will move into the future with confidence and discipline.
Edward Vincent is CEO of Lumio Solutions.
