The conversation around artificial intelligence (AI) in community banking has moved from speculation to serious evaluation. Institutions that once approached AI with either great enthusiasm or skepticism are arriving at a more nuanced position. The technology has genuine potential, but realizing that potential requires careful attention to governance, infrastructure and organizational readiness.
For community and regional bankers, how AI is incorporated matters more than it might for larger institutions. The margin for error is narrower, and the trust customers place in their local bank is harder won and more easily lost. An EY 2025 report cites trust as banking’s most valuable currency. Even as 70% of people express comfort with AI in customer service, community bankers must be careful not to sacrifice relationship trust for the short-term gains AI promises. The decision is no longer about “if” — it is about “when” and “how”.
Navigating a Rapidly Shifting Landscape
Financial services technology spending is outpacing the broader market, according to a 2026 Forrester report. Fintechs are deploying capital aggressively, and the downstream effects are real: fluctuating platform pricing, shifting cybersecurity exposure and growing variability in service continuity.
For community institutions, two findings stand out. First, roughly 36% of financial institutions lack the tools to measure return on investment on AI investments. Second, 84% face shareholder pressure to demonstrate that very ROI. That gap between expectation and visibility defines much of the current discomfort.
A similar readiness gap is appearing among community financial institutions as AI moves from interest to evaluation. In a recent review of Navanta clients and prospects, 47% were exploring or evaluating AI, 61% identified operational efficiency as a potential use case and only 16% had a defined AI road map. Institutions are being asked to assess AI’s value while still building the governance and measurement discipline needed to manage it. That makes the underlying technology architecture especially important; determining how well an institution can control data, scale capacity and demonstrate oversight as AI use expands.
Start With Data Foundations
It is tempting to frame AI primarily as a capability question — what can the technology do? The more useful question is foundational: What is the technology being asked to act on? Survey data suggests many U.S. bankers are still organizing their data foundations to capture AI value, and that data quality tends to be where pilots succeed or stall.
The issue is not data volume but data coherence. When customer context, business rules and prior decisions live in systems that do not share a common view of truth, AI produces results that are inconsistent or unverifiable. In regulated workflows where accuracy, explainability and auditability are nonnegotiable, that inconsistency is why many AI initiatives never move beyond proof of concept. Three foundational conditions matter most: reliable customer context, a shared source of truth across systems and clearly governed authority for AI to act.
AI Risk as a Strategic Decision
AI vendor selection and deployment are best approached as strategic risk decisions, not purely technical ones. Effective risk management means documenting the rationale, controls and intended results of AI initiatives with clear board-level visibility:
- 1. Aligning AI use with existing model risk management and data governance.
- 2. Maintaining human oversight with clear accountability.
- 3. Using technical controls, not training alone, to limit unapproved AI use.
Institutions are well-served to treat AI risk as part of their broader information security framework, aligned with their established IT risk appetite. There is no easy button. It is the multilayered approach of training, policy, technical controls and active governance that makes a responsible AI journey possible.
Questions Boards Should Ask
Before approving AI investments, boards should press prospective providers on the following:
- Will the provider contractually guarantee that institution data is not used to train public models, and where is that data stored and processed?
- What access controls, audit trails and protections against shadow AI are in place?
- What does five-year total cost of ownership look like, including AI-related price changes and consumption structures?
- If terms change or features are discontinued, how can models and data be ported to another provider?
- How does the AI operate across full workflows, and what prevents conflicting decisions between platforms?
Discipline Over Speed
For community institutions willing to engage thoughtfully, AI presents a genuine opportunity to strengthen operational performance, improve decision-making and support long-term growth. Success will not come from moving fast, but from moving with discipline, sound governance and clarity on where AI creates the most value. AI does not change the fundamentals of community banking. It reinforces the importance of getting them right.
