When I work with community banks and credit unions across North America, one theme consistently emerges: competition with the big national banks is more intense than ever. Large institutions have the advantage of scale, advanced digital infrastructure and, increasingly, the ability to deploy AI and automation across their operations at marginal cost.
Consider branch expansion as an example. The incremental cost for a national player to open a new branch is now far less than it was even five years ago, because advanced automation — from digital onboarding to AI-assisted customer service — reduces the human resources required to run that branch efficiently for regional players, which raises the competitive bar.
At the same time, macro-economic headwinds — such as tighter net interest margins, evolving regulatory requirements and rising customer expectations shaped by digital-first fintechs — are forcing community institutions to do more with less. However, an EY survey of more than 2,000 American consumers shows that they would prefer regional banks and credit unions if they are provided a full set of products through the digital marketplace infrastructure model. AI has made this digitization more accessible than ever before.
The advancement of AI specific to regional banks and credit unions
The challenge I often hear from CIOs of community banks is not about whether AI is important — it is about how to separate the signal from the noise. With every core banking provider, loan origination system, CRM platform and even contact center vendor now offering “AI add-ons,” the market feels like an endless parade of copilots and chatbots, each promising transformational value at an additional cost.
This proliferation makes fiscal planning difficult. Should CIOs approve every AI line item on vendor roadmaps? Or should they adopt a more selective approach? From my perspective, the answer is to focus on opportunities where AI directly addresses pain points that are unique to community institutions.
For example:
Loan origination: Automating routine credit decisioning can reduce turnaround times without compromising risk controls. Tools like Fannie Mae’s DU systems already use predictive models that community banks can leverage to remain competitive.
Customer service: AI-assisted contact centers can help small teams handle call spikes by deflecting routine inquiries to virtual agents, while still escalating complex cases to human staff.
CRM and marketing automation: AI-driven segmentation can help banks identify cross-sell opportunities in their existing member base, reducing acquisition costs.
The key is to avoid subscribing to every shiny add-on and instead ask: Does this use case align with our strategic goals? Will it deliver measurable ROI in the next 12 to 24 months?
An operating model is needed to implement rapid ROI
Deploying AI is not just a technology choice — it’s an operating model shift. I’ve seen projects fail not because the algorithms didn’t work, but because the institution wasn’t culturally or operationally ready to absorb them.
Three areas are critical:
Training and skills: Staff need more than technical instruction; they need to understand how AI augments their daily workflows. For instance, loan officers must be trained to interpret AI-driven recommendations rather than unthinkingly follow them.
Governance and risk controls: AI introduces new categories of risk — bias, explainability and regulatory scrutiny. CIOs must establish governance frameworks early, including model validation, monitoring and clear accountability. The Federal Reserve’s 2023 guidance on AI in banking emphasizes this.
Cultural shift: The move from manual processes to AI-driven workflows can create anxiety among employees. I’ve found that positioning AI as a copilot rather than a replacement helps build trust. Leadership needs to emphasize that AI is here to enhance human decision-making, not eliminate it.
Rapid ROI comes when technology is paired with a thoughtful operating model that mitigates risk and fosters adoption.
Converging into a compelling business case
As fiscal planning season approaches, community banks and credit unions must weave these threads into a compelling business case for management and the board. In my experience, the strongest cases include:
Clear alignment with macro conditions: Show how AI/automation investments help mitigate pressures like shrinking margins or rising compliance costs.
Selective prioritization: Identify two to three high-impact use cases rather than trying to “do AI everywhere.” This focus accelerates ROI.
Governance framework: Articulate how the institution will manage risks, including regulatory alignment and oversight.
Capability-building roadmap: Lay out how staff will be trained, how the operating model will evolve and how cultural buy-in will be secured.
Boards of community institutions are understandably cautious. But when presented with a disciplined, ROI-focused plan that acknowledges both opportunity and risk, I’ve seen many shift from skepticism to strong sponsorship.
A few tips for bank CIOs for upcoming 2026 fiscal planning
Executives should prioritize two to three high-impact use cases — such as loan origination automation, AI-assisted customer service, or AI-driven CRM and marketing automation — that deliver measurable ROI within 12 to 24 months. Chasing every AI feature on vendor roadmaps dilutes value; focus and selectivity accelerate impact.
Equally important is readiness. Successful adoption requires an operating model that integrates staff training, cultural alignment and robust governance frameworks for risk, bias and regulatory oversight. By establishing clear accountability and phased adoption roadmaps, institutions can reduce dependency on vendors and build long-term internal capability.
Ultimately, boards and management should evaluate projects against a disciplined framework: alignment with macro conditions, strategic prioritization, governance safeguards and a clear capability-building roadmap. When framed this way, AI and automation become not just cost items but compelling business cases for growth and resilience, helping community institutions level the playing field with larger competitors.
Selective AI investments can bring AI within reach
AI and automation are often painted as the domain of big banks with billion-dollar tech budgets. From my vantage point as someone working with regional institutions every day, I see a different picture. Community banks and credit unions have an advantage: they are nimble, closer to their customers and able to pivot faster than sprawling national institutions.
By being selective in AI investments, embedding strong governance and building the right operating model, these institutions can level the playing field. In upcoming fiscal planning cycles, the question should not be if AI fits into the strategy, but how to make it deliver rapid, sustainable ROI.
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