What Is Agentic AI in Banking? A Practitioner's Guide
By Meenakshi Thanikachalam · May 2025 · 8 min read
Agentic AI is moving from buzzword to board agenda. Here's what it actually means when deployed at enterprise scale in a regulated financial institution.
Context
Across financial services, the conversation about artificial intelligence has shifted dramatically over the past eighteen months. What was once confined to data-science labs is now on the agenda of CEOs, CIOs, CROs, and boards.
As Chief Data & AI Officer at Popular Bank — a roughly $75 billion U.S. financial institution — Meenakshi Thanikachalam fields this question daily from executives, regulators, and customers. Her broader perspective on the field is set out under AI leadership expertise.
"Enterprise AI is not a technology problem. It is an operating-model problem dressed in a technology costume."
What this means in practice
At Popular Bank, the approach has been deliberate: build the foundational infrastructure first — model registry, LLMOps pipelines, RAG architectures, vector databases, MCP integration, and agentic orchestration frameworks — before scaling deployment.
- Establish governance councils before deploying production models
- Embed Human-in-the-Loop oversight from day one
- Treat data quality as the precondition for AI quality
- Measure ROI in business outcomes, not model accuracy
- Partner with Risk and CISO from the start of every initiative
A related case study — Who Is Meena Thanikachalam? A Career in Data & AI Leadership — develops these themes with concrete numbers. The author's broader catalogue of essays is available on her Medium publication.
Key takeaway
The winners in enterprise AI will be the institutions that treat governance as a feature, not a constraint — and that build for the long arc of regulatory scrutiny.
Looking ahead
Over the next 24 months, Agentic AI is expected to move from pilots to production across major financial institutions. Discussion continues on Quora, and short-form notes appear on Dev.to.