While skepticism remains — particularly among board members and compliance leaders — financial institutions that embrace AI strategically can gain advantages in efficiency and customer experience. With proper implementation focusing on clear goals, ethics, risk management, and data quality, even smaller institutions can compete not just with traditional banks but also with tech-savvy consumer brands entering the financial space.
The first time AI was the talk of the banking industry — roughly a decade ago — most institutions saw it as out of reach. It was costly, complex, and carried too much uncertainty. Today, the conversation has changed. AI is no longer inaccessible; it’s widely available and rapidly becoming a competitive necessity. Yet for many banks and credit unions, it still feels risky.
AI is now the elephant in every business strategist’s room — including those at small and medium-sized financial institutions. The technology is no longer confined to large banks with deep pockets. It’s now embedded in tools that enhance risk modeling, streamline compliance, personalize accountholder interactions, and optimize fraud detection. In short, it has moved from theory to practical implementation.
And yet, skepticism lingers. Some of the most influential voices in your organization, such as board members, senior executives, and compliance leaders may still be wary. But the institutions that embrace AI now will gain a strategic edge in efficiency, accountholder experience, and profitability. Those that wait risk falling behind competitors with superior data capabilities.
So, how do you build consensus and move forward with AI? Here are three ways to get started:
Address the Fear Factor with a Compelling Narrative
Many financial institutions hesitate to adopt AI because they’ve only seen it applied narrowly to chatbots, simple automation, or conversational Q&A tools that deliver only marginal utility. But AI’s true potential lies in rewiring the enterprise, not just adding efficiency at the margin.
A recent McKinsey & Co. paper argues that AI should be used to remake entire business domains, processes, and customer journeys. Instead of just assisting with routine tasks, AI can drive core banking functions, such as evaluating commercial loan applications. Multiagent AI systems, where multiple AI models work together, can assess risk more comprehensively by analyzing financial statements, market trends, and borrower behavior in ways traditional underwriting cannot.
AI can also strengthen compliance by detecting anomalies in real time, identifying fraud risks, and ensuring transactions meet regulatory standards. And beyond risk management, it can create hyper-personalized banking experiences, anticipating accountholder needs and proactively offering solutions.
To make AI a true enterprise-wide advantage, institutions should adopt a coordinated approach. McKinsey recommends establishing a central AI control function or committee to integrate AI into decision-making across business functions, enforce risk guardrails, and drive adoption. Without this level of strategic alignment, AI remains a collection of disconnected tools rather than a driver of change.
Start Small, But Start Now
If McKinsey urges financial institutions to “rewire the enterprise” with AI, the reality for many financial institutions is more grounded: they’re still figuring out how to use data effectively in core areas. According to Bank Director’s 2024 Technology Survey — which canvassed 111 independent directors and executives at U.S. banks under $100 billion in assets — only 37% of financial institutions believe they’re using data effectively in operations. And just 36% say the same about marketing. Before AI can supercharge business operations, it has to prove its value in day-to-day routines and tasks.
AI doesn’t have to begin with sweeping, enterprise-wide reinvention. A smarter approach is to pick a specific, high-impact use case, test it, and scale from there. It’s not about tinkering with a chatbot for the sake of saying you’ve adopted AI; it’s about selecting a function where AI can deliver immediate business value. That might mean using machine learning to enhance fraud detection, improve loan underwriting models, or automate back-office compliance tasks.
Starting small reduces risk, builds internal confidence, and creates tangible wins that make it easier to get leadership buy-in for larger AI initiatives. It also allows institutions to identify and address challenges — whether in data quality, regulatory compliance, or employee training — before AI is embedded across critical operations.
The key is to start. Many institutions are stuck in analysis paralysis, worrying about AI’s long-term implications while competitors are already reaping the benefits of early adoption. A narrow but well-executed AI project today lays the foundation for broader implementation tomorrow. The banks and credit unions that wait for the perfect moment to embark on this important work risk falling years behind and having to play catch-up.
Make AI a Compliance Ally, Not a Threat
For many financial institutions, the biggest challenge with AI is not adoption — it’s managing risk. Compliance teams worry about bias, data security, and regulatory uncertainty. But AI can actually enhance compliance when implemented with the appropriate safeguards. A new eBook from financial technology company Jack Henry™, titled Getting Started in AI, outlines four steps that banks and credit unions can take to integrate AI responsibly and effectively.
First, define clear goals and assess readiness. AI should serve a strategic purpose, whether improving productivity, growing deposits, or managing risk. But institutions must also consider the broader implications: How will employees be impacted when AI automates tasks? Are there plans to retrain staff? Can existing technology infrastructure support AI’s significant data-processing demands? If you’re struggling with how to proceed, you’re not alone: 57% of companies are struggling with AI skill gaps, according to Gartner.
Second, prioritize ethics and bias mitigation. AI models can inadvertently reinforce discrimination if bias creeps in during problem framing, data collection, or algorithm training. MIT Technology Review warns that standard AI development practices don’t reliably detect bias, making transparency essential. Jack Henry advises financial institutions to keep a “human in the loop” (HITL), ensuring AI-driven decisions are reviewed by people.
Third, understand and mitigate risks. AI systems must be continuously monitored for algorithmic bias, data privacy vulnerabilities, and compliance with local regulations. Jack Henry stresses the importance of formal risk management frameworks that track AI-driven decisions and ensure regulatory alignment.
Finally, develop a strong data strategy. AI is only as reliable as the data it processes, yet many financial institutions struggle with data quality. The Bank Director tech survey found that just 11% of financial institutions believe their data strategy exceeds industry standards, while 16% have no strategy at all.
Jack Henry recommends identifying any gaps that could harm performance and regularly evaluate data for consistency, reliability and accuracy by conducting data hygiene exercises to correct any problems. Getting this right can bring huge benefits. According to EY, AI-driven credit models can reduce defaults, lower risk provisions, and improve profitability through improved fraud detection and creditworthiness assessments.
AI isn’t just about automation, it’s about trust. Institutions that integrate AI with clear goals, ethical safeguards, and strong data practices will not only comply with regulations but gain a competitive advantage.
The urgency to act is acute. And it’s not just about competition from big nationwide banks and digital-first providers. Consumer brands including behemoths like Apple, Starbucks, McDonald’s, Walmart, and Uber now market financial products from digital wallets to credit cards, savings accounts and more.
These offerings are built on data and technology, and increasingly incorporate AI in interesting and innovative ways. For banks and credit unions that want to stay hunting with the pack — protecting and expanding their home turf — now may be time to make your mark.
Source: The Financial Brand
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