How Community Banks and Credit Unions Are Building Their Agentic AI Strategy

By Nicole Volpe

Community banks and credit unions are moving beyond the basic question of whether artificial intelligence is safe, affordable and reliable. Their focus is now shifting toward a more practical agenda: identifying where AI can deliver measurable value, deciding how much authority to give automated agents and establishing the controls needed to keep people accountable.

This change is becoming increasingly visible in conversations between financial institutions and technology providers. Narmi reports that concerns capable of stopping an AI project altogether now represent a smaller share of objections, while questions related to implementation, governance and organizational readiness are becoming more prominent.

Data privacy, historically one of the largest barriers to AI adoption, declined by nearly 22 percentage points between 2025 and 2026, according to Narmi. Concerns about implementation timelines and resource demands also fell.

At the same time, compliance and legal issues increased by 13 percentage points, while concerns about control and transparency rose by 12 points. Questions surrounding institutional maturity and readiness gained nine points.

The shift comes as AI adoption accelerates across industries. Autonomous and agent-based applications are gaining particular momentum. A 2026 McKinsey survey found that 40% of large companies and 22% of small businesses were expanding AI agents in production environments.

For smaller financial institutions, the next stage of AI adoption will be shaped by greater confidence in the technology, more defined governance expectations and a growing list of practical use cases. Fiaz Sindhu, head of strategy and operations at Narmi, discussed how this changing environment is influencing community banks and credit unions.

Regulatory Signals Are Creating More Room to Experiment

Recent statements from banking and credit union regulators have helped clarify the path forward. Policy guidance issued in spring 2026 signaled that financial institutions can use AI while reinforcing that existing compliance responsibilities still apply.

At a financial stability roundtable in May, Federal Reserve Vice Chair for Supervision Michelle Bowman said supervisory guidance should not prevent banks from using new and evolving technologies. She also emphasized that smaller banks should be able to innovate and provide modern services, even when they do not have the same resources as the largest institutions.

The National Credit Union Administration delivered a similar message, stating that credit unions may use AI tools and technologies as long as they comply with existing rules. The agency indicated that its supervisory focus would remain on risk management rather than on AI as a technology category.

For community financial institutions, this guidance provides a clearer basis for moving AI projects from experimentation into live operations. It does not eliminate risk, but it helps establish that responsible AI adoption is permissible.

Financial Institutions Now Have More Real-World Examples

Bank and credit union leaders also have a growing number of live deployments to study. Large institutions such as JPMorgan Chase have made extensive investments in AI, while regional banks are exploring how the technology can reshape lending, deposits and payments.

Customers Bank, which has approximately $27 billion in assets, is working with OpenAI on applications across its commercial banking operations. Smaller institutions are also demonstrating how targeted AI projects can deliver value without requiring massive technology budgets.

Grasshopper Bank, a $1.6 billion institution, became the first United States bank to deploy a Model Context Protocol server through its work with Narmi. The system allows small-business customers to securely query live account information using AI assistants such as Claude and ChatGPT.

First Citizens Federal Credit Union, with approximately $890 million in assets, is using AI in secure digital messaging. The technology helps translate member inquiries, research responses and prepare drafts for employees to review. This approach also supports communication with Portuguese- and Spanish-speaking members.

AI Adoption Requires More Than Privacy Protection

Clearer regulation and successful industry examples may make AI adoption easier, but they also raise the standard for institutional planning. Privacy can no longer be treated as the only major question.

Financial institutions must also determine whether they have selected the right use cases, redesigned the related processes and prepared their data environments for deployment. Many AI applications need information from several systems that may use different structures, definitions or taxonomies.

• Which use cases offer the greatest potential for efficiency or growth?
• Can the institution connect data across multiple siloed platforms?
• Who remains accountable when an AI system recommends an action?
• Are policies, testing procedures and escalation processes documented?
• Can the institution explain and audit an AI-supported decision?

Some smaller institutions have not yet formalized their AI policies, Sindhu said. That gap can become more important as banks and credit unions move from low-risk assistance tools toward systems that influence operational decisions.

Explainability and Auditability Are Becoming Essential

Financial regulators have distinguished between understanding how an AI system operates in general and being able to explain why it produced a specific result. Both types of understanding are important for financial institutions.

Recent regulatory guidance has identified explainability as a significant AI risk. Institutions are also expected to maintain effective testing, human oversight and clear governance standards.

These expectations are especially important when AI is used in account opening, lending, fraud prevention or customer service. A financial institution must be able to demonstrate how a recommendation was produced, what information influenced it and which employee or department had final responsibility.

Account Opening Shows the Value of Agentic AI

Community banks and credit unions are increasingly evaluating use cases that combine operational efficiency with business growth. Account opening is one example.

Applications that cannot be approved automatically often enter a manual review process. Employees may need to complete identity checks, locate missing documents, verify information and determine whether the application meets institutional guidelines. Delays can cause prospective customers to abandon the process before an account is opened.

Agentic AI can accelerate this work by gathering missing information, applying established rules and preparing a recommendation supported by relevant evidence. A human employee remains responsible for reviewing the recommendation and making the final decision.

Narmi reports that the applications requiring the most manual attention saw the greatest improvement. Among the slowest 10% of cases, AI-assisted reviews were approximately six times faster.

The Emerging Model Is Collaboration, Not Replacement

For smaller financial institutions, productivity improvements are more likely to support growth than reduce headcount. Community banks and credit unions often operate with lean teams in which employees manage several responsibilities.

AI can help these employees spend less time on repetitive research and administrative tasks while giving them more capacity for customer service, relationship management and other higher-value activities.

Faster account reviews can also increase completed applications and support deposit growth. In this model, agentic AI acts as a leverage tool rather than a replacement for employees.

The secure messaging program at First Citizens Federal Credit Union illustrates this division of labor. AI agents research member questions and prepare draft responses within the digital banking platform. Employees review, edit and approve the messages before sending them. The technology can also translate communications, helping staff serve a broader member base.

Most Institutions Will Operate in the Middle Ground

Agentic AI exists on a broad spectrum. At one end are basic copilot tools that retrieve information and answer questions. At the other are fully autonomous systems capable of taking action with limited human involvement.

For the foreseeable future, community banks and credit unions are likely to operate between these two extremes. In this middle ground, AI agents can manage defined workflows while employees continue to oversee decisions, exceptions and customer-facing outcomes.

This approach requires institutions to redesign roles as well as deploy technology. The most effective implementations will identify which steps can be automated, which decisions require human judgment and how employees should intervene when an agent encounters an unusual situation.

The emerging agentic AI playbook for smaller financial institutions is therefore less about handing over complete control and more about creating a carefully governed connection between information and action. Banks and credit unions that combine targeted use cases, reliable data, clear accountability and human oversight will be better positioned to turn AI investment into measurable operational and member value.

Source: TheFinancialBrand.com