Change is hard, even when it promises something better. I learned that firsthand when my doctor suggested a new technology to manage my diabetes. I hesitated at first. Not because I doubted the science, but because trust takes time. That same careful consideration is what I see in financial institutions evaluating AI today, and it’s not a weakness. It’s wisdom.
I’ve lived with Type 1 diabetes for most of my life. Managing it requires constant attention, discipline, and trust in the tools and people around me. A few years ago, my endocrinologist recommended I switch to a continuous glucose monitor (CGM), a device that tracks blood sugar in real time and offers far more insight than traditional finger pricks.
At first, I was reluctant. Not because I didn’t believe in technology, but because I wasn’t ready to change routines that had kept me safe for decades. I had questions. I had concerns. I needed to understand how it worked, what risks it carried, and whether it would truly improve my health.
Only after asking every question and feeling confident in the answers did I make the switch.
The result? My glucose control improved dramatically. I understand my condition better, and I manage it more safely. The technology didn’t replace my judgment, it enhanced it. It gave me real-time visibility into trends and outcomes, alerting me before danger struck and helping me steer toward better decisions.
The Same Dynamic Exists with AI in Financial Services
Financial institutions face a similar situation. Leaders see the potential: better insights, faster decisions, more personalized experiences. Yet many proceed carefully, and they’re right to.
Banking is a trust-driven, highly regulated industry. Every decision is scrutinized, and every misstep has consequences. I’ve heard banks say, “Your technology is interesting, but we won’t be first to use it.” That’s not resistance, it’s prudence. And it’s understandable.
Regulation Shapes Culture
Financial institutions operate under strict regulatory frameworks. From UDAAP to AML to Fair Lending, the rules are clear: protect consumers, ensure fairness, and avoid unintended harm. These guardrails are table stakes, and they shape organizational culture. They create a bias toward risk avoidance, which can slow innovation, even when that innovation could improve outcomes for both customers and shareholders.
This is the tension: the desire to innovate is paired with the responsibility to stay compliant and protect trust.
The Path Forward: Expert Guidance and Outcome-Based Adoption
Just like I needed my endocrinologist to guide me through the transition to a CGM, financial institutions need expert partners to help them adopt AI responsibly. That means understanding how the technology works, how it aligns with regulatory expectations, and how it can be implemented without compromising safety or fairness.
It also means asking hard questions:
- What personal data is being used?
- How are decisions being made?
- Can we explain the outcomes to regulators and customers?
These aren’t barriers. They’re the foundation of trust.
And here’s the key: AI isn’t just about automation. It’s about improving outcomes. The CGM didn’t just give me data; it gave me foresight. It helped me avoid danger and make better decisions. Financial institutions can do the same. With the right tools, they can simulate potential outcomes before decisions are made and steer toward better ones.
Final Thought
AI can be used safely and productively in financial services. But it requires time, research, and the right guidance. Institutions that approach adoption deliberately, with curiosity, caution, and expert support, will be the ones that unlock real value.
Because caution isn’t the enemy of progress. It’s part of the process. And when paired with outcome-driven thinking, it becomes a powerful catalyst for change.