Flashy models won’t save you. JPMorganChases’s data and AI CPO breaks down what actually delivers enterprise-scale ROI.
There’s no shortage of promises in the fast-paced world of AI. Over the last three years, founders and leaders in the industry have bellowed assurances of faster insights, smarter agents, autonomous workflows and even so-calledBut for Gerard Francis, chief product officer of AI and data at, all of that hype falls flat without a disciplined, enterprise-wide approach to data.
“We had the right governance and controls, ensuring the data is protected,” Francis explained in the interview. “The concept was simple: Deploy AI where it can immediately deliver value and stay safe doing it.”actually look like inside one of the world’s largest financial institutions? Structured data — scattered across countless internal systems — tends to come last, yet often proves the most powerful when integrated. That’s why JPMorganChase built Fusion — an internal data platform for customers that acts as a “data factory” for orchestrating pipelines, normalizing formats and making datasets AI-ready.
Managing those controls manually would be a nightmare. JPMorganChase’s goal is to transition “from a heavy human process aided by technology to an entirely technology-enabled process.” Until then, scalability and compliance depend on how well governance is operationalized into the AI development lifecycle.Imagine an AI system that doesn’t just summarize a document but reconciles data, files reports, books appointments and updates compliance records.
For an organization with JPMorganChase’s scale and complexity, AI makes sense only when it drives value. “We determine the values we want to pursue and that determines how we prioritize AI,” Francis said.
AI Governance AI Readiness Data Discipline Agentic AI Agentic Solutions AI ROI Enterprise AI Genai
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