The Intersection of Financial Institutions and Technology Leaders

Not All AI Is Created Equal: Strategic Insights for Banking Leaders

February 4, 2025

By Ben Nichols

Artificial intelligence (AI) is everywhere in the financial industry, touted as the solution to everything from fraud detection to customer engagement. Yet, as AI grows more integral to banking, one truth emerges: Not all AI is created equal. For community banks especially, understanding this distinction is critical to making informed technology investments that align with their mission and serve their customers effectively.

The term “AI” is often surrounded by confusion, thanks to marketing buzzwords that promise idealism. Vendors highlight their AI-driven platforms or intelligent solutions without always clarifying what’s under the hood. As a result, executives might wonder, “Are we buying genuinely intelligent solutions or elaborate vaporware?”

AI can refer to a wide range of technologies, from simple task automation to advanced content-generating models (generative AI). Bank leaders need to cut through the noise by asking the right questions:

1. Does the system adapt when introduced to new data?
2. Does it perform tasks autonomously or augment bank operators?
3. Does it use natural language interfaces for employees or customers?

If the answers to these questions are yes, then you’re likely looking at an AI system. But beyond its classification, the real question is whether it aligns with your institution’s needs. Community banks should evaluate AI solutions based on three key dimensions:

1. Intelligence. AI systems are only as good as the data they learn from and their algorithms. A platform built with domain expertise in banking will outperform generic AI tools. For example, AI trained on community banking-specific data can better understand customer needs, whether it’s resolving account issues or identifying fraud patterns.
2. Reasoning. The type of reasoning embedded in the AI matters greatly. Predictive AI will highlight trends in deposit growth, while prescriptive AI will suggest actionable steps to capture market opportunities. Decision-makers must evaluate whether these insights align with their strategic goals.
3. Interface. Seamless experiences, whether customer-facing or internal, are essential. AI that supports natural language and integrates with existing banking systems enhances operational efficiency without overburdening staff or customers.

Community banks distinguish themselves from larger institutions by prioritizing personalized, relationship-focused services. AI should play an augmentative, not autonomous, role. For instance, conversational AI platforms can empower banks to enhance self-service options while keeping the human touch intact. Leadership teams should prioritize AI systems that:

1. Align with their mission of community-oriented service.
2. Offer transparency in decision-making processes.
3. Include operator oversight to mitigate risks.

Questions for Your AI Vendor
Selecting the right AI partner is as important as choosing the right technology. These questions can help executives vet potential vendors, but there is no right or wrong answer.

1. How is your AI trained and on what data?
2. What mechanisms ensure the system remains unbiased and accurate?
3. How does it integrate with our existing banking systems?
4. What’s the roadmap for updates and maintenance?
5. How is operator oversight incorporated into the system?

The answers to these questions will help executives identify tools designed to drive value from those that capitalize on AI’s popularity. As AI capabilities expand, community banks must approach adoption thoughtfully. Success depends on leveraging AI to amplify strengths rather than replace them. The right AI tool doesn’t just automate — it supports smarter decision-making, empowers staff and ultimately enhances the customer experience.

By asking the hard questions and focusing on intelligence, reasoning and interface, community banks can leverage AI’s opportunities. The road to AI success isn’t about adopting the flashiest tools; it’s about choosing the ones that align with your institution’s values and strategic goals.

Ben Nichols is Co-founder & COO at Directlink. In his career has worked with Large, Medium, and Small organizations all within the product/analytics discipline in the domains of AI, communications, and financial services. In addition to his focus on technology, he also serves as a professor of Data Science at Syracuse University’s School of Information Studies.