Community financial institutions (CFIs) can’t match big-bank AI budgets, but gradual, low-risk adoption, clear governance, and ROI metrics let community institutions use affordable AI tools to boost efficiency, fraud control, and customer experience over time.
Outlet stores initially came onto the scene in the US in the 1930s, with companies selling surplus inventory or damaged merchandise at reduced prices. In 1974, Vanity Fair opened a multi-store outlet center in Reading, Pennsylvania, offering consumers the ability to acquire a wide variety of goods at discounted pricing. As the popularity of outlet malls grew, retailers began manufacturing goods specifically for outlets, often using lower-quality materials to provide lower pricing. Today, there are more than 340 outlet centers in the US, with annual revenue totaling roughly $65B as of 2024.
The outlet industry built an empire based on the ability to provide consumers with access to luxury goods at affordable prices. The banking industry knows something about bargain hunting. As community financial institutions (CFIs) prepare to delve into artificial intelligence (AI) or upgrade existing AI models, they are in search of high-value offerings at affordable pricing.
AI has moved from being a luxury to a necessity. While CFIs can’t compete with the AI initiatives of their bulge-bracket peers, they also can’t afford to stay on the sidelines. Many CFIs have been cautious about AI because of cost, regulatory uncertainty, and data‑security concerns, but the industry’s mindset is changing. In a recent ABA survey, bankers identified “doing nothing” with AI as the greatest risk, citing fears of losing competitive edge, becoming overly dependent on vendors, and falling behind on talent and expertise. Customers — retail and business alike — are getting used to faster service, intelligent fraud detection, and more personalized experiences. Overcoming Barriers
It isn’t just a lack of funding keeping CFIs from embracing AI. A major barrier keeping many organizations from utilizing AI tools and platforms is uncertainty regarding where to begin — particularly how to handle regulatory risks and governance — as well as concerns about securing data. Yet, the realization that inactivity can be even more dangerous is also setting in. According to the findings of a recent survey from the American Bankers Association, most bankers realize that doing nothing will harm any competitive edge they have, will force them to be increasingly dependent on vendors and third-party service providers, and can erode the level of expertise within their organizations.
Dipping a Toe
Absent the deep pockets of larger competitors, the initial use cases of most CFIs are far more modest, yet can still help with productivity and enhanced customer service. Massachusetts-based Country Bank began dipping its toes into AI a few years ago by creating AI policy and risk guidelines. Before diving in completely, however, the organization was thoughtful and intentional regarding its plans for AI and started by creating a data warehouse. The bank has been collecting data of its own, as well as data from third-party vendors for analytics, and has spent time figuring out how AI can be most beneficial within its operations and where the responsibilities for oversight will lie within the organization — all of which is leading towards fully automated mortgage loans.
Meanwhile, Monson Savings Bank, also based in Massachusetts, is using AI to eliminate repetitive, time-consuming tasks to free up employees to spend more time on valuable customer-facing interactions. The organization uses Positive Pay, an AI-backed fraud mitigation service, to handle tasks such as the detection of check fraud.
Like the above organizations, many CFIs are starting small with AI and taking the time to get a handle on specific uses before broader adoption.
A Starting Point
AI can span a broad range of applications today, so it is important for CFIs that are just starting out on this front to take the time to narrow down what they hope to achieve by implementing AI — both immediate, initial uses and what they are building towards. The following are foundational steps to consider at a high level as your leadership team works toward starting or progressing further into AI implementation:
- Exploration of tools. Start with a short list of clearly defined, narrow use cases. The first AI technology you use should be low-risk, high-impact. These might be productivity uses such as summarizing meetings and drafting internal emails, or helping frontline staff with customer service inquiries.
- Analysis of internal infrastructure. Once you have some options narrowed down, leadership teams should assess whether your current tech stack can integrate with the new AI and if you’ll need additional infrastructure to implement any of the potential AI solutions. In addition, you’ll need to determine if you have internal talent that can run a vendor solution or if you’ll be relying on the vendor for any tech needs.
- Governance and risk management. You’ll also need to assess the third-party risk of using a particular vendor, identify who owns the risk, and what policies and protections you’ll need in place both for internal staff usage and for vendors.
Measuring Success for Managers and Boards
For AI to survive annual budget cycles, CFIs must measure impact in terms that resonate with the C‑suite. Here are a few of the major metrics that leadership teams will want to see as they assess AI usage and effectiveness within your CFI:
- Hard financial metrics. Tie AI to specific workflows and show concrete cost reductions.
- Time savings linked to labor costs. Quantify how many hours of repetitive or low-value manual work the tool has saved staff. If AI frees up staff for higher‑value work, describe the value of that work and how it has impacted your organization’s efficiency as well as the customer experience.
- Risk reduction in monetary terms. Estimate the potential cost of a compliance breach, fraud event, or processing error, and show how AI meaningfully reduces that exposure (e.g., earlier anomaly detection or more consistent checks).
- Proof of scale. Benefits can’t remain locked in a single pilot. Demonstrate how the same model or tool can be reused in other departments or regions and how performance improves as adoption grows.
When you can answer these questions in numbers, not just narratives, it’s much easier for executive teams and boards to support continued AI investment.As CFIs begin experimenting with the benefits of AI, it is important to keep in mind that usage can start small and gradually expand. Unlike bulge bracket banks that have built extensive custom AI applications, CFIs should start small by identifying one or two tasks that AI can assist with and ramping up applications as benefits are recognized. Slowly expanding AI capabilities not only allows for more thoughtful deployment, but it also provides time for employees who may be apprehensive of AI to experience the benefits and efficiencies it can provide in their jobs. One thing is for sure: any small movement toward AI adoption in banking right now is far more valuable than sitting out.
