Who? Who at the bank is responsible for the bank’s approach to AI? Without a person or group responsible for AI use within the bank, it is impossible to develop a coherent and consistent approach. Is it the board? A board committee? The CEO? While there is no single right answer, the board retains ultimate responsibility for the bank’s direction. Formally assigning responsibility to one or more people within the organization is foundational to ensuring that the bank’s approach to AI is guided by what is best for the institution and its stakeholders — not by whatever AI-related noise is loudest on any given day. What? What is the bank’s policy on AI use? Even if a bank does not have a formal policy in place, it is almost certain that employees are using AI at home, on their phones and likely at work. While AI offers numerous benefits, it also presents significant risks, including the possibility of entering sensitive or confidential information into a public model. A clear AI use policy helps ensure employees use AI in ways that do not expose the bank to unnecessary risk. How? How does the bank plan to use AI? More specifically, what is the bank’s strategic plan for AI? Does the bank intend to be proactive or reactive? Will AI implementation focus on evolutionary improvements to operations or a more revolutionary redesign of the enterprise? There are obvious use cases, such as BSA/AML compliance and fraud detection, but what about opportunities to scale the bank without meaningfully increasing overhead? Four Questions Every Community Bank Should Ask About AI By Kurt Leeper, Partner, Otteson Shapiro LLP The headlines are constant and ubiquitous. Depending on the publication or post, one can walk away believing AI will either revolutionize banking or create an existential threat to the industry. Will AI-enabled efficiencies make 30% efficiency ratios possible? Or will agentic AI trigger massive deposit outflows as autonomous agents race to the institution offering the highest rate? With so much noise surrounding AI — much of it generated by AI itself — it can be difficult for community bankers to know where to begin. This article does not recommend a particular AI model or explain how to implement AI to grow a bank. Instead, it offers a practical framework for evaluating AI in a way that is useful and actionable for your institution. It begins with four simple questions: Who? What? How? When?
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