As enterprise AI moves from pilots to large-scale deployment, the focus is rapidly shifting from adoption to outcomes. While organizations have invested heavily in AI copilots, automation platforms, and generative AI solutions, leadership teams are increasingly being challenged to demonstrate tangible business value. The key question is no longer whether AI can drive productivity, but how organizations can effectively measure its impact on efficiency, decision-making, innovation, and overall business performance.
The BFSI sector has made significant investments in modern data platforms, cloud infrastructure and advanced analytics. Yet, having more data and better platforms does not automatically translate into successful AI adoption. The bigger challenge lies in ensuring that data is trusted, governed, accessible and genuinely fit for AI-driven decision-making.
Fragmented ownership, inconsistent data quality, legacy systems, regulatory requirements and varying levels of confidence in AI-generated outputs continue to create barriers to moving AI initiatives from experimentation into production.
This closed-door discussion will explore what it truly means for data to be AI-ready in the BFSI environment. The conversation will examine why technology and platform investments alone have not accelerated AI adoption, how organisations are building confidence in AI outputs, and who should ultimately own the definition and governance of “trusted data” across business, risk, compliance and technology functions.
The discussion will focus on data governance, ownership, quality, lineage, trust and accountability, while identifying the foundations required for BFSI organisations to deploy AI reliably, responsibly and at scale.
From Pilots to Production: Building the Autonomous Enterprise with Agentic AI
A closed-door discussion on how GCCs are moving beyond GenAI experimentation to embedding agentic AI into core enterprise workflows for real operational impact. The conversation will explore how AI agents are being deployed across functions to enable end-to-end process execution, decision support, and measurable business outcomes. It will also examine governance frameworks covering trust, risk, compliance, and control in increasingly autonomous environments.
Sr.Executive Director of Product Management
JPMorgan Chase & Co.
Senior Director, Chief Data and Analytics Office
Vanguard India
Managing Director & Head of Technology
Charles Schwab India
Senior Vice President of Engineering
Swiss Re
Executive Director - Data, AI, Cloud & Enterprise Architecture
DBS Tech India
Chief Industry Practitioner - AI/ML and GenAI solutions
Calibo