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By Erin Hennessy, Chief Member Experience Officer at PSECU
Most people understand the basics of living a healthier life: eat nutritious food, exercise regularly, get enough sleep, and make time for mental well-being. The challenge is that these habits require consistency. That is why the market is filled with cleanses, shortcuts, and miracle products promising fast results without the daily effort.
Financial institutions often take a similar approach to digital transformation. Leaders understand the fundamentals, yet many still invest in the newest platform or artificial intelligence tool before addressing the underlying problems holding their digital strategy back.
In many cases, the issue is not the technology. It is the data beneath it.
When information is trapped in organizational silos, defined differently by separate departments, or difficult to access, every digital investment becomes less effective. Poor data quality can weaken personalization, slow innovation, and create inconsistent member experiences. There is no quick fix or GLP-1-style solution for unhealthy data.
Credit Unions Face Growing Digital Pressure
Credit unions continue to perform strongly in overall member satisfaction. According to J.D. Power’s 2026 U.S. Credit Union Satisfaction Study, credit unions scored 725 out of 1,000, compared with 657 for retail banks.
However, that advantage is narrowing. Member satisfaction declined by four points compared with the previous year, while 59% of credit union members reported having a checking account with another financial institution.
This trend is often described as “soft switching.” Rather than closing an account immediately, members open additional accounts elsewhere and gradually move more of their money to competing providers.
Key insight: Digital channels are often where the gap becomes most visible. Banks currently outperform credit unions in areas such as mobile application performance and website satisfaction. As digital expectations rise, a strong reputation alone is no longer enough to retain members.
It is easy to explain the difference by saying that credit unions are cautious, large banks are slow, and fintech companies are naturally faster. These descriptions have been repeated so often that they can sound like fixed truths. They are not.
Speed is shaped by operating decisions. Those decisions include how an organization prioritizes work, assigns accountability, manages approvals, and gives teams access to reliable data. When data transformation and digital transformation are addressed together, they create an experience transformation that members notice each time they use an app, website, or other digital channel.
Digital Speed Is an Operating Decision
Fintech companies do not move quickly because of a secret formula. Their pace usually comes from a collection of practical choices that are repeated consistently.
- Prioritizing a small number of measurable outcomes
- Giving each initiative a clearly accountable owner
- Building cross-functional teams that remain involved after launch
- Providing rapid access to accurate, usable data
- Reducing unnecessary approval layers
- Using flexible platforms that can evolve over time
- Testing, learning, and improving continuously
This approach removes ambiguity and reduces the number of handoffs required to make progress. It does not necessarily demand longer hours or excessive risk. Instead, it requires organizations to eliminate processes that exist only because no one has challenged them.
Many institutions create invisible barriers by adding extra reviews, committees, and approval steps. These structures can slow decisions, dilute accountability, and make it difficult to determine who is responsible for delivering an outcome.
A more effective model is to hire capable people, equip them with reliable information and the right resources, and give them room to act. Mistakes will happen, but empowered teams can learn and adjust more quickly than groups that must wait for every decision to move through multiple layers.
Key insight: Experience across mortgage companies, credit unions, and startup banking organizations shows that company size does not determine speed. The operating model matters more than the number of employees or the age of the institution.
Building a Culture That Values Delivery
Some organizations make execution a central part of their culture. At Rocket Companies, for example, company philosophies known as ISMs include the idea that innovation should be rewarded while execution should be treated as essential.
Another guiding principle, “Launch and learn,” reflects the belief that taking more time to make a decision does not automatically produce a better result. Organizations can often learn more by releasing a well-considered product, listening to customers, and improving it based on real-world feedback.
Credit union values often emphasize being trusted. Trust is important, but it is best understood as an outcome rather than a complete operating strategy. Members trust financial institutions that consistently deliver, respond to changing needs, experiment responsibly, provide employees with useful information, and maintain dependable systems.
These behaviors create trust. They are also available to institutions of any size. The main obstacle is not a lack of opportunity, but the choices leaders make about how their organizations operate.
PSECU has a history of adopting new approaches early. In 1995, the credit union mailed floppy disks to members so they could access banking services from home. More recently, PSECU selected Candescent’s digital banking platform after a competitive review. The decision began a multiyear effort to create more connected and personalized member experiences.
The operating philosophy is straightforward: launch, listen to members, and make changes when the evidence shows that a different approach is needed.
Artificial Intelligence Cannot Repair Bad Data
Artificial intelligence is the latest focus of attention in financial services, although institutions have used forms of AI for years. Most existing applications are modular, built into individual products, or deployed through highly programmed assistants and chatbots.
Wipfli’s 2026 State of the Credit Union Industry report found that 79% of credit union executives were researching or implementing AI. Much of that activity involves tools embedded within existing vendor platforms. The report also noted that some institutions are adopting AI because it is available, rather than because they have developed a clear business strategy for using it.
AI systems are only as effective as the information they receive. If data is incomplete, inconsistent, outdated, or poorly governed, even an advanced vendor solution can produce disappointing results.
The most effective way for institutions to prepare for AI is to strengthen the foundation first. That means improving data quality, establishing clear ownership, creating consistent definitions, and making information easier for authorized teams to access and use.
Early results from digital assistants show why this work matters. J.D. Power’s 2026 digital banking studies found that only 28% of national bank and credit card application customers use virtual assistants. Satisfaction drops significantly when customers attempt to resolve complex issues, dispute transactions, or report fraud.
These are precisely the moments when customers evaluate whether they can trust a financial institution. In sensitive situations, many people still want the reassurance of a human being who can understand context and take responsibility for the outcome.
Agentic AI Raises the Stakes for Data Governance
The need for reliable data becomes even more urgent as financial institutions explore agentic AI. Traditional data problems might result in an inaccurate report or a flawed recommendation. Agentic systems can carry one output into the next action, allowing an initial error to influence an entire sequence of decisions.
Financial institutions often collect information from numerous systems. Each source may store data differently, apply different definitions, or be interpreted differently by separate teams. Without strong governance, these inconsistencies can become more difficult to identify as automated systems make more decisions.
As Cornerstone Advisors’ Ron Shevlin has noted, an effective AI strategy requires an effective data strategy. The two cannot be separated.
Key insight: Institutions do not need to wait for a perfect, future-proof architecture before improving data governance. They can begin by making information more accessible, dependable, and usable across the organization.
At PSECU, the goal is to build data architecture that supports better digital interactions every day. Treating data and digital banking as separate initiatives misses the central issue. Together, they form the foundation of the member experience.
Digital Transformation Starts With the Fundamentals
Data structure, governance, security, and resilience may not attract the same attention as a new AI product or digital feature. However, they are the equivalent of healthy habits in a long-term wellness plan. The work is often unglamorous, and the results may take time to appear.
Once the foundation is strong, the benefits become more visible. New tools can perform closer to their full potential. Teams can make decisions faster. Members can receive more relevant and consistent service. Leaders can measure results with greater confidence.
Credit unions are not destined to move more slowly than banks or fintech companies. Institutional speed is not determined by size or tradition alone. It depends on leadership choices, operating discipline, reliable data, and the willingness to launch, learn, and improve.
The next step in digital transformation is not necessarily another tool. For many financial institutions, it is the decision to make their data healthier first.
About the author: Erin Hennessy is Chief Member Experience Officer at PSECU. She has more than two decades of experience in financial services and previously served as Senior Vice President of Marketing at Rocket Companies, where she oversaw marketing, digital technology, omnichannel customer experience, data analytics, and branding.
Source: TheFinancialBrand.com