Most community financial institutions begin their AI journey with a simple prompt in a chat window. That is the right place to start. But AI is a tool, and like any tool, its value grows as you understand what it can do and how to produce outcomes. Once your team has built confidence with the basics, these are the next capabilities worth understanding — and the banking use cases where each one earns its place.

These topics generated some of the most common questions in Navanta’s recent webinar series. Eric Jones, our president and CEO, covers the broader AI landscape for community banking in his article in Navanta’s newsletter, The Navanta Advantage, Edition 2026:7. This post gives you a focused look at the tools themselves.

1. The Core Principle

A tool is only valuable if it delivers better outcomes

Before evaluating any AI capability, ask the same question you would ask about any technology investment: does this make something better? Not more interesting, not more impressive, not trendier—better. Faster, more accurate, less manual, more consistent, safer. If the answer is yes and you can measure it, the tool earns its place. If not, it does not matter how advanced the technology is.

The institutions seeing real results from AI right now are not chasing the most sophisticated capabilities. They are finding the highest-friction tasks in workflows they already understand and replacing that friction with something that works reliably.

2. Copilot Agents

A focused AI assistant built for one job

An Agent is configured with a specific instruction set and grounded in a specific knowledge source, your documents, your policies, and your data. Unlike a general AI chat window that draws from everything, an Agent reasons only over what you tell it to reason over. That precision is its value.

A frontline employee with a question about an internal policy or a specific bank regulation can ask the Agent and receive an answer sourced directly from your institution’s own documentation, not from the public internet. Agents are private by default and sharing them across a department requires deliberate configuration. This is a feature of good governance, not a limitation.

3. Copilot Notebooks

Persistent, multi-document analysis that does not reset

Uploading files into a chat session is temporary. The session ends and the context disappears. A Notebook is a standing container. You load your reference documents once and they remain available for ongoing analysis across multiple sessions, with the ability to add new documents as they arrive.

For recurring analytical workflows — month-over-month invoice comparisons, multi-vendor SOC 2 reviews, regulatory examination materials — that persistence is a meaningful advantage over starting fresh every time.

4. Power Automate vs. Agents

Rules-based automation vs. reasoning-based automation

Power Automate moves work from one place to another based on defined triggers and conditions. Use it when you know exactly what needs to happen and can specify the rules in advance. A Copilot Agent interprets questions and generates contextualized responses. Use it when the task requires judgment, synthesis, or answering questions that cannot be fully anticipated.

The two are increasingly complementary. Power Automate can trigger an Agent to perform an analytical task, and the Agent’s output can feed back into a Power Automate workflow. For complex banking processes, the most capable solutions will ultimately combine both.

5. The Right Sequence

Start simple. Add capability when it earns its place.

None of these tools requires a technical team to operate. What they require is a clear use case, a team that understands what good output looks like, and the discipline to verify before scaling. Start with the workflow that is costing your team the most time on work that does not require their expertise. Build confidence there. Then ask what comes next.

AI is most valuable not when it replaces people, but when it handles what is grinding and repetitive so that your people can do the work that requires judgment, relationship, and institutional knowledge. That is the standard every AI capability should be measured against.

For a broader look at how community financial institutions are approaching AI right now — including the governance questions, the security considerations, and real stories from institutions already seeing results — read Eric Jones’s featured article from the July Navanta Navigator Brief: The Question Every Banker Is Really Asking About AI.