By Jeremy Shroeder, Pinion Chief Technology Officer, and Kaylie Miller, Pinion Financial Institutions Advisor

Artificial intelligence (AI) is already shaping how businesses operate, and while it represents a major opportunity in many industries, in banking, it can also feel risky, complex, and unclear given the highly regulated work environment and sensitive data.

Lean internal IT resources can make AI adoption feel out of reach, but you don’t need a large technology team or a complete transformation to get started. Below are practical steps to introduce AI to your financial institution in a controlled, valuable way.

Tips to Get Started

1. Start With a Clear AI Policy

Before introducing new tools, it’s important to establish clear guidelines and expectations throughout your institution.

A strong AI policy should:

  • Outline what is allowed and not allowed
  • Set expectations for handling client and sensitive information
  • Define appropriate business use cases

Even a simple policy is a good start and better than none. Without guidance, employees may adopt AI tools independently, potentially exposing your organization to risk.

2. Guide AI Use — Don’t Leave It Ungoverned

Many institutions attempt to manage risk by limiting access to AI tools. That approach may seem safe, but it rarely works in practice. In a global survey by Salesforce, 28% of workers said they were already using generative AI at work, and more than half of those users were doing so without formal employer approval.

“If employees see AI as a faster way to get work done, they’re going to look for ways to use it. The better approach is to create structure around that use instead of assuming it can be avoided entirely,” says Donny Reichart, president, Pinion Technology Core.

Instead of blocking access entirely, a more effective approach is to:

  • Provide approved tools
  • Set clear boundaries
  • Encourage appropriate use

Shifting your approach from reactive to proactive helps you shape how AI is used instead of responding to risks as they emerge.

3. Use Trusted Enterprise-Grade Tools

Not all AI platforms offer the same level of security or transparency.

When evaluating tools, aim to prioritize solutions that:

  • Offer enterprise agreements or business-grade protections
  • Clearly define how data is used and protected
  • Provide administrative controls for your organization

This is especially important when handling financial or client-related information where compliance requirements and expectations are high.

4. Define Acceptable Use — Not Just Prohibited Use

Many policies focus only on what employees should not do, but it is just as important to help them understand what they should do.

Clear guidance around acceptable use helps employees recognize where AI can support routine work, where human review is still needed, and which activities should remain off-limits. That clarity reduces confusion, builds confidence, and encourages more consistent, appropriate use across the institution.

5. Start With Low-Risk Use Cases

You don’t need to start with high-impact, high-risk scenarios. Starting with low-risk use cases allows your team to build familiarity and confidence in their ability to use and manage AI appropriately and safely.

Good starting points which deliver immediate value include:

  • Drafting internal emails or reports
  • Summarizing internal documents or meeting notes
  • Creating training or onboarding materials
  • Building outlines for presentations or internal communications

These use cases are relatively easy to implement and typically carry lower regulatory risk.

6. Make AI Part of the Conversation

If employees are not talking openly about AI, that’s usually a sign it’s being used informally and without oversight.

Create a space for conversation and encourage:

  • Open discussion about how AI is being used
  • Sharing of good use cases across teams
  • Awareness of risks and limitations

The goal is not to control every action, but to ensure there is visibility and shared understanding.

7. Connect With Industry Peers

Because AI adoption is evolving quickly, many community banks are working through the same questions and challenges.

Consider:

  • Talking with peer institutions
  • Sharing lessons learned (both successes and setbacks)
  • Participating in industry groups or forums

Learning from others can help accelerate your approach, avoid common pitfalls, and move forward with greater confidence.

Putting AI Into Practice

AI does not have to feel overwhelming or high-risk. With a thoughtful approach, community banks can introduce AI safely, improve efficiency across everyday tasks, and build confidence over time. The key is to begin with practical use cases, stay intentional, and provide clear guidance along the way.

If your institution is evaluating how to approach AI, connect with a Pinion Technology Core advisor to discuss opportunities, establish practical guardrails, and define the next step with greater clarity.