Ankita Chatrath explains why enterprise AI depends on trusted, discoverable, contextual data before agents and copilots can reliably act, and how leaders move AI from experimentation to adoption.
About Ankita Chatrath
Ankita Chatrath is an experienced product leader with more than 10 years of experience building data- and AI-powered enterprise products that transform financial planning, accounting, and revenue management. She leads cross-functional teams of engineers, product analysts, and data scientists to deliver scalable, secure, and high-impact solutions across FP&A, billing, and sales operations. She currently leads the Corporate Finance AI Hub at State Street Bank and Trust Company, where she builds intelligent automation tools used by enterprise operators, saving millions annually through workflow optimization and AI-assisted decision-making.
What motivated you to join the AI Builders Global Conference?
Absolutely! I'm excited to speak at the AI Builders Global Conference because it brings together people who are actually building with AI and preparing for the next generation of workforce. I'm especially looking forward to the conversations around practical implementation, responsible AI, and what it takes to turn an AI idea into something useful for real users. I joined the AI Builders community because I lead enterprise AI enablement, where the real challenge is often less about the technology itself and more about helping people across the organization understand, trust, and effectively use AI in their work.
What shaped your journey through technology and AI?
I come from a computer science background, and I moved into product management over the course of my career, building data- and AI-powered enterprise platforms in finance. Today, I lead Corporate Finance AI Enablement at State Street, where I focus on responsible AI solutions that help finance teams automate complex workflows and make better decisions. What drew me to AI was the challenge of making it useful in high-stakes environments.
Why is "Building a Trusted Data Marketplace for AI" particularly important to you?
This topic matters to me a lot because it gets at the part of enterprise AI that often gets overlooked: the data foundation. Agents, copilots, and AI workflows can only be as useful as the data they can reliably find, understand, and act on. In large organizations, that means solving for discovery and context before we move to agentification of our workflows.
Who should attend the AI Builders Global Conference?
I'd recommend the AI Builders Global Conference for anyone who needs to move AI from experimentation to real-world adoption, especially enterprise leaders, builders, and process owners who are looking to build the next generation of workflows.