A bank deploys a new AI-powered credit decisioning model. The technology works. The model performs well. The business team is ready to move forward. Then the questions begin. Can we explain the recommendation? What data was used? Who approved the model? How do we monitor performance over time? What happens
Most conversations about agentic AI focus on how autonomous these systems can become. Financial services leaders are asking a different question: Where should autonomy stop? For banks and insurers, the challenge is not simply deploying AI agents. It is determining how those systems, governance requirements and human expertise can work
Editor’s note: This post was co-authored by Diana Rothfuss. For the past few years, banks have focused on proving that AI can deliver business value. Across financial services, organizations have launched pilots, explored generative AI use cases and tested how AI can improve everything from customer engagement and fraud detection
