Artificial intelligence has become one of the most discussed topics in banking.
Boardrooms are evaluating AI roadmaps, vendors are promising transformational results, and industry headlines highlight the latest breakthroughs in automation, analytics, and generative AI.
Key idea
While community banks are spending significant time evaluating AI technologies, the greater challenge is ensuring the organization is prepared to put those technologies to work.
Conversations around AI often begin with the technology itself. Which platform should we use? Which model is best? How quickly can we implement it? The institutions that will realize the greatest value from AI are asking a different set of questions. What business problems are we trying to solve? Do we have the foundation in place to support the solution? Those organizations won’t necessarily be the first adopters, but they will be the ones that invest in the capabilities required to support AI at scale, including data readiness, process maturity, governance, workforce enablement, and institutional knowledge management. Before banks focus on AI adoption, they need to focus on AI readiness.
Start with the problem, not the technology
Community and regional banks are under pressure from every direction. Customers expect seamless digital experiences, competition continues to intensify, operational costs remain under scrutiny, and regulatory expectations continue to evolve.
Repetitive manual work
where employees are spending the most time on repetitive manual work
Process friction
which processes create the most friction
Customer experiences
where customer experiences are breaking down
Risk at scale
the risks that are becoming more difficult to manage at scale.
That pressure has made AI a priority for many banking leaders. The strongest AI strategies are grounded in a clear understanding of the operational challenges that need to be addressed, including where employees are spending the most time on repetitive manual work, which processes create the most friction, where customer experiences are breaking down, and the risks that are becoming more difficult to manage at scale. For some institutions, the answer may involve streamlining loan operations. For others, it may be reducing deposit exceptions, accelerating back-office workflows, enhancing financial crimes monitoring, or improving customer service. Once those priorities are clear, banks can identify where AI can create meaningful value, which use cases should come first, and what foundational work may be needed before implementation begins.
Data readiness matters more than AI readiness
Artificial intelligence relies on the quality of the data, processes, and controls that support it. If information is fragmented across systems, processes are inconsistent, or data ownership is unclear, then AI can amplify existing inefficiencies rather than resolve them. This is why banks should spend less time evaluating AI vendors and more time evaluating their readiness to support AI initiatives. They should start by asking key questions, including:
Is our data accurate, accessible, and trusted?
Do we know where critical data resides?
Are data ownership and governance clearly defined?
Have we standardized key processes across the organization?
Can we measure outcomes and performance effectively?
Once these foundational questions are answered, banks will be better positioned to adopt AI with greater confidence and lower risk. As organizations continue to mature their data management capabilities, many are recognizing the growing importance of information management as a strategic function in supporting AI initiatives.
Governance Cannot Be an Afterthought
As AI moves beyond experimentation and into day-to-day operations, questions around governance should be top of mind for banks. They must think about who owns the technology, what data is being used, how outputs are being reviewed, and what guardrails are in place to ensure consistency and accountability.
These considerations are especially important when AI is embedded within operational, compliance, and customer-facing activities.
A clear governance model helps institutions establish ownership, define responsibilities, and create a framework for evaluating how AI is used across the organization.
These considerations are especially important when AI is embedded within operational, compliance, and customer-facing activities. A clear governance model helps institutions establish ownership, define responsibilities, and create a framework for evaluating how AI is used across the organization. Banks that address these questions early are often better positioned to scale AI initiatives with confidence while maintaining appropriate controls and oversight, particularly as AI begins influencing decisions and interacting with operational workflows across the organization.
AI readiness includes knowledge readiness
Data readiness and governance are important components of AI readiness, but they aren’t the only ones. Community and regional banks also need to consider how institutional knowledge is captured, shared, and retained as experienced employees leave the organization. Many of these individuals possess operational knowledge that is difficult to capture in policies, procedures, or training manuals. They understand how processes actually work, where risks emerge, where exceptions occur, and where historical issues have surfaced. For banks operating with lean teams, the loss of that expertise can create significant operational and compliance challenges. AI can help preserve and operationalize institutional knowledge by supporting process documentation, surfacing historical context, guiding decision-making, and making expertise more accessible across the organization.
AI can also support succession planning
by helping institutions transfer critical operational knowledge, ensuring that expertise remains accessible even as roles change and employees move on. AI can also support succession planning by helping institutions transfer critical operational knowledge, ensuring that expertise remains accessible even as roles change and employees move on.
Building the foundation for what’s next
Community and regional banks don’t need massive AI budgets to begin creating value. In many cases, the most successful AI initiatives start with focused, practical use cases that address well-defined operational challenges. Instead of implementing AI everywhere at once, banks should focus on building organizational capability by learning how to evaluate use cases, assess data readiness, measure outcomes, establish appropriate governance, and develop internal expertise. Each successful initiative helps institutions build the experience and confidence needed to expand AI adoption over time. Many organizations see the strongest results by starting with targeted use cases, measuring impact and applying those lessons to future efforts.
THE BOTTOM LINE
For community and regional banks, the path forward is less about chasing the latest technology and more about creating the foundation needed to use it effectively.
While many banks focus on their choice of technology, the true AI challenge is preparing for implementation. For community and regional banks, the path forward is less about chasing the latest technology and more about creating the foundation needed to use it effectively. While many banks focus on their choice of technology, the true AI challenge is preparing for implementation.
