TL;DR
You do not need to boil the ocean to get started with agentic AI. You need three focused pilots that prove value, de-risk the concept, and build organizational muscle. This post walks through the three I recommend every credit union and community bank run this year. Strategic alert agents for the C-suite. Collections triage agents that lead with empathy. Member service augmentation agents that actually know your knowledge base. Each one is low risk, measurable, and gets you something useful in weeks, not quarters.
One of the most common questions I get from credit union executives right now is some version of, “Where do I even start?” Usually followed by, “And how do I start without blowing up our compliance posture, spooking our core provider, or committing to a vendor I’ll regret in six months?”
Fair questions. Here is the answer I give.
You do not need an enterprise AI strategy before you start. You need three pilots. Run them in parallel. Measure them honestly. Kill the ones that do not work and scale the ones that do. That is it.
Pilot one: the strategic alert agent
This is the pilot I recommend running first, because it is the one that will earn you the most internal credibility the fastest. The job is simple. Every Monday morning, your CEO gets a one-page brief in their inbox that summarizes what happened in the last week across five dimensions.
Your call report trends relative to your peer set. Your AI visibility score changes, including any new mentions in AI answers. Your competitors’ rate moves, branch openings, product launches, and executive announcements. Your own member growth, attrition signals, and early indicators of product momentum or drift. Anything in the regulatory environment that materially affects your institution.
The agent is not doing analysis. It is doing synthesis. It is reading what your team would read if they had eight uninterrupted hours a week, and it is writing the briefing your strategy officer wishes they had time to write.
The reason this pilot is the right first move is not because it is the most valuable. It is because it is the most visible. The CEO reads it. The leadership team sees the CEO reading it. Suddenly AI is not an abstract IT project. It is on the Monday agenda.
Pilot two: the collections triage agent
This one is going to make some people uncomfortable, so let me explain why I recommend it anyway.
Collections is one of the hardest conversations your institution has with its members. It is also the conversation that is most consistently handled poorly across the industry, not because the people are not good, but because the volume, complexity, and emotional weight make it nearly impossible to be consistent at scale.
An agentic pilot here does three things. It triages accounts based on behavior patterns, identifying who needs a gentle nudge, who needs a structured payment conversation, and who genuinely needs help. It drafts member-specific outreach that leads with empathy, acknowledges context, and offers options. It tracks outcomes at a granularity no team can match manually, so you actually learn which approaches work for which member segments.
Every credit union I have worked with that has piloted this kind of capability has seen two results. Recovery rates improve. Member satisfaction with the collections experience improves. The second one is the one nobody expects. It turns out that members do not hate being contacted about missed payments. They hate being contacted in ways that feel tone-deaf. Agents, when built well, are astonishingly good at not being tone-deaf.
Pilot three: the member service augmentation agent
The third pilot is the one your contact center team is going to thank you for, and it is also the one most institutions get wrong.
The mistake is building a customer-facing chatbot as the first agent. Do not do that. Build an internal-facing agent that sits next to your member service representatives and knows your knowledge base better than they do.
When a member calls with a question about a specific product feature, the agent surfaces the answer from your actual policies, not from a general LLM guess. When a rep is not sure how to handle an edge case, the agent pulls the procedure from your internal documentation. When a call involves a complex situation spanning multiple products, the agent drafts the next three steps for the rep to review.
You get three wins from this pattern. Your reps are faster and more confident. Your knowledge base gets better, because the agent will expose every gap and inconsistency in your documentation. And you learn how agents actually behave on your specific data before you let them talk to a member directly.
Only after you have run this pilot for three to six months and are confident in the quality, should you even consider exposing a similar capability directly to members. Most institutions skip this step. They regret it.
What to measure
The temptation with any AI pilot is to measure the thing that is easy to measure. Do not fall for it.
For the strategic alert agent, measure whether the CEO and leadership team are actually using it. If the Monday brief is sitting unread for three weeks in a row, it is not working. If it is being forwarded to board members and referenced in meetings, it is working.
For the collections triage agent, measure recovery rates, member complaints, and the percentage of delinquent accounts that self-cure after contact. All three matter. The third one is the sleeper indicator.
For the member service augmentation agent, measure handle time, first-call resolution, rep confidence scores, and the number of knowledge base gaps identified per week. That last one is the one that tells you the pilot is actually teaching you something.
What comes next
In the next twelve months, expect two patterns to emerge across institutions running these pilots.
The first is that the strategic alert agent will expand into a full executive intelligence layer. It will not just report. It will start to propose. “Here is what happened last week. Here is what I recommend we discuss in Tuesday’s leadership meeting.” The gap between reporting and proposing is smaller than it sounds. Most institutions will cross it in the second half of 2026.
The second is that collections and member service will become the two highest-performing agentic use cases in community financial services. Not because they are the most interesting. Because they are the most scoped, the most measurable, and the most immediately valuable. When your board asks where the AI ROI is, these are the two places it will show up first.
The bottom line
You do not have to wait for a finished AI strategy to start moving. You need three pilots, a measurement framework that is honest about what is working, and a willingness to kill the pilots that do not pay off. The institutions that run these three pilots in 2026 will spend 2027 scaling what works and deprecating what did not. The institutions that wait for certainty will spend 2027 explaining to their boards why they are behind.
The first three pilots are not the hard part. The hard part is starting. Once you start, the rest gets easier.
Kevin Farley writes about AI readiness, agentic systems, and growth strategy for financial services. Read more on the blog.