When we began planning the rollout of our agentic artificial intelligence agent, I was energized by what it could do for our clients and our teams. Our procedures were well documented and our knowledge base was strong, so I expected that connecting the AI to that foundation would let it support a broad range of call types quickly, with minimal customization.
That optimism was well placed in one important way. The technology was ready.
What I underestimated was what it would take to get the rest of our operations ready alongside it.
What Changed My Mind
Testing gave us valuable insight into how our highest-performing agents navigate real customer interactions. Documentation captures the standard process flow, but actual conversations require judgment and experience. A cardholder disputing a transaction, a client requesting a program exception or a caller whose situation spans multiple procedures all call for nuanced decision-making built over years.
We found that much of this expertise lived as institutional knowledge rather than documented process. That gave us an opportunity to codify it, strengthen our decision frameworks and define more clearly where automation delivers the greatest value and where human expertise remains essential.
Just as important, testing surfaced these nuances early, well before they could affect a client. In financial services, trust is built on accuracy, consistency and compliance, so validation belongs in development from the start.
This reshaped my view of AI readiness. Connecting AI to a knowledge base is only the starting point. True readiness comes from reviewing, auditing and validating each call type so the agentic agent responds accurately and in line with regulatory and client requirements.
What I Am Doing Differently Now
We shifted from a broad deployment to a disciplined, call-type-by-call-type approach. Each call type is programmed, tested, audited and validated before it is approved for production. That process confirms accuracy and compliance and shows whether each call type belongs with the agentic agent or with a live agent whose judgment, empathy or specialized expertise adds more value.
The rigor has paid off beyond AI readiness. Validating each call type has helped us strengthen procedures, clarify decision paths and capture knowledge that once existed only in our agents’ experience. We are not simply training AI; we are strengthening the entire customer experience.
This approach requires more investment up front, but each validated call type becomes a trusted building block that reduces risk and accelerates the next deployment. As our validation framework matures, every new implementation becomes faster and easier to scale.
For executives planning their own agentic AI initiatives, my advice is simple. Invest as much in validation and operational readiness as you do in integration. Treat every call type as its own deployment, involve the people who handle those interactions every day and let accuracy, compliance and customer experience set the pace.
In financial services, trust is earned one interaction at a time, and when implemented thoughtfully, agentic AI can help scale that trust without compromising quality.
