By Alexandra McLeod and Jay Long
Are banks ready for AI? Banks across the United States are grappling with artificial intelligence’s potential impact on operations, customers and competition. While some banks are excited about unlocking new levels of growth through AI, many are rightfully cautious about rushing to embrace technologies they may not fully understand.
The challenge of knowing where to begin is universal. All too often, banks start their AI journey by “buying AI,” confusing large investments in digital platforms with effectively harnessing this transformational technology. As a result, return on investment from AI activities has been limited at banks.
When banks struggle with AI initiatives, it’s tempting to blame the technology vendor or assume they have yet to find the right solution. But here’s what most bank CEOs miss: Successful AI adoption isn’t just about technology. It is about creating a three-way reinforcing loop among culture, AI capabilities and business performance that creates enterprise-wide value, improves topline and bottom line, and establishes an enduring competitive moat.
This insight explains why simply purchasing AI technology, whether an AI-powered credit decisioning system or a chatbot for customer service, rarely delivers the expected results. Instead, the most successful banks recognize that AI readiness begins with their workforce and internal processes.
The hidden engine of AI success
Research from the Massachusetts Institute of Technology’s Sloan School of Management reveals a surprising dynamic: Business culture affects AI deployments, and AI deployments affect business culture. When managed well, these relationships create positive feedback loops where improved performance, enabled by AI, leads to an increasingly data-driven culture, which in turn accelerates future technology adoption.
This virtuous cycle has three key stages in banking:
- Cultural readiness: Teams understand and embrace data-driven decision-making;
- AI implementation: Technology solutions enhance existing banking processes; and
- Performance improvement: Measurable gains in efficiency and effectiveness reinforce the value of the data-driven approach.
This cycle explains why some banks make gains with each AI initiative while others struggle to gain traction. The key difference isn’t their choice of technology. Instead, it is the complementary investment in the cultural and organizational foundations that make AI successful. For many banks, this requires a fundamental shift in how they think about digital transformation.
Starting in the right place: A banking perspective
Before investing in AI, banks must focus on three critical areas relevant to banking operations.
First, they must invest in broad-based data literacy across banking functions. Lending teams need to understand how data shapes credit decisions. Compliance teams must grasp how data patterns can flag potential issues. Branch managers should understand how to interpret customer behavior data. Success in this area can be measured through concrete indicators: percentage of decisions supported by data analytics, number of employees actively using data tools, and improvements in risk assessment accuracy.
Second, bank leaders should focus on aligning their organizations around process improvement, starting with core banking operations. This includes forming cross-functional teams that include credit analysts, branch operators, compliance officers and technologists to map and optimize current workflows. This might begin with something as fundamental as a loan origination process or a customer onboarding journey. Progress should be tracked through metrics like reduced manual processing time and increased straight-through processing rates. The critical requirement is ensuring that team expertise is brought together that can generate solutions that solve the right business problems, nest within the organization’s technology architecture, and create the most financial value.
Finally, it is important to foster a culture that embraces data-driven decision-making, particularly in areas where banking traditionally relies on “gut feel” and relationship-based judgments. For banks, this means creating an environment where relationship managers complement their client knowledge with data insights, where credit committees incorporate both traditional and alternative data sources, and where branch managers blend local market expertise with customer behavior analytics.
Successful banks approach this transformation systematically — identifying early adopters in each department, celebrating quick wins and creating formal channels for employees to suggest process improvements based on their data insights. According to research from Deloitte, companies with strong data-driven cultures are twice as likely to exceed their organizational goals.
“The implementation of AI in banking is not a ‘set it and forget it’ endeavor instead it’s a continuous journey of learning, refinement, and adaptation as the technology evolves and customer needs shift. Crucially, this journey also requires ongoing investment in employee understanding and adoption; without their engagement and expertise, the full potential of AI cannot be realized,” said Ryan Jackson, VP of Innovation Strategy at American Bankers Association.
Unlocking the virtuous cycle in banking
When these foundations are in place, something remarkable happens in banking operations. Each successful AI initiative — whether it’s in credit decisioning, fraud detection or customer service — strengthens the bank’s culture of innovation, which in turn improves the ability to execute future AI projects. Banks that successfully create this virtuous cycle typically see 20-30% reduction in loan processing time, improved risk assessment accuracy, higher customer satisfaction scores, and increased employee retention.
This positive feedback loop manifests in ways that also improve core financial metrics. When experienced underwriters participate in developing AI credit models, their expertise gets encoded into systems that help train new credit analysts. When branch managers help design customer intelligence systems, their frontline insights improve both the AI models and the broader team’s understanding of customer behavior. These collaborations create a virtuous cycle where banking expertise enhances AI capabilities, which in turn empowers bankers to serve customers better, all the while improving the bottom line and the bank’s future competitiveness in the market. MIT researchers also found that 75% of firms report improved team morale, collaboration and collective learning after successful AI adoption.
However, banks must remain vigilant about common missteps, such as rushing to implement AI without sufficient data infrastructure, failing to invest in ongoing training or treating AI as a separate initiative rather than an integral part of banking operations.
The path forward for bank leaders
The message for bank CEOs is clear: The fastest path to AI readiness isn’t through technology investments but through creating a culture where the entire organization feels empowered to solve problems with data-driven strategies.
Before evaluating any AI vendor or solution, start by investing in people and processes. Build cross-functional teams that can identify the right problems to solve in your specific banking context. Develop organization-wide data literacy that respects and enhances traditional banking expertise. When banks eventually invest in AI, they will find that having these foundations dramatically improves results. More importantly, they will have built something far more valuable than any single AI implementation: a banking organization capable of turning each new technology investment into a lasting competitive advantage.
Alexandra McLeod is CEO and Jay Long is COO of Parlay Finance.
