Opening & Welcome
Andrew Delaney, President & Chief Content Officer, A-Team Group 

Opening Keynote: 

Data Leaders Panel: The evolving data and AI operating model – Synergy, governance and accountability

As artificial intelligence scales across capital markets, the Office of the CDO has become the vital engine for safe, reliable business innovation. This session explores how data leaders are evolving their operating models to integrate separate AI projects built across different business lines into a unified, corporate architecture. We examine how CDOs are leveraging their existing, high quality data foundations to de-risk agentic workflows, establish clear accountability, and maximize the business value of enterprise data assets.

• How are CDOs successfully structuring their relationship with business-line AI leaders to ensure revenue-driven innovation is built upon a foundation of data integrity?
• As AI initiatives attract significant investment, what strategies can data leaders use to ensure that the underlying data quality and governance work remains a funded priority?
• How can the CDO’s mandate for data risk management be integrated into the business-line validation process before a model or agent goes live?
• With AI tools rapidly embedding within siloed business functions, what architectural frameworks can CDOs deploy to maintain a unified, enterprise-wide data strategy?
• How is the Office of the CDO leading the charge on enterprise-wide data and AI literacy programs to build a cohesive, data-driven organizational culture?
• In the event of an unintended model outcome, how should responsibility be shared between those governing the data inputs and those managing the model outputs?

Moderator: Julia Bardmesser, Adjunct Professor; NYU Stern School of Business; Founder and CEO, Data4Real
Yinghua Michelle Zhou, Head of Finance Data and AI, State Street
Andrew Foster, Chief Data Officer, M&T Bank
Amy Kabia, Head of Client Data & Document Strategy, Wells Fargo
Richard Chudley, Chief Data Officer for SG Americas, Societe Generale
David Zimmerman, Chief Data & Analytics Officer, Americas Division, SMBC Corporation

Keynote: The Enterprise Knowledge Layer: The missing architecture for scalable, governed AI

This session explores the evolution from system integration to controlled, real-time access, demonstrating how an Enterprise Knowledge Layer unifies data, content, policy, and analytics to deliver consistent, verifiable, and context-aware decisions by design.

• Why do powerful AI systems struggle with consistency across tools and teams, and how does the underlying architecture fail to deliver data, context, and signals together?
• How does the Enterprise Knowledge Layer successfully bring together structured and unstructured data, policy and real-time analytics at decision time?
• What are the core operational and business benefits of implementing an Enterprise Knowledge Layer when scaling autonomous decision-making across complex capital markets environments?

Michael Curry, President, Data Modernization, Rocket Software

A-Team Group Research Report Update
Andrew Delaney, President and Chief Content Officer, A-Team Group

Panel: Re-defining governance and accountability for the AI era

This session explores how firms must evolve from manual, static governance to a model- ready framework that can govern structured and unstructured data and ensure AI data readiness.

• How should firms define and identify CDEs in the age of AI?
• How can firms move away from ‘everything is critical’? Is there a minimum threshold data must pass to be considered mesh ready or Agent ready?
• Does the definition of critical content change when dealing with structured vs. unstructured data?
• What does a data governance framework look like that can bridge the structured and unstructured divide and establishes clear accountability for data inputs for AI enabled workflows?
• What are the benefits of getting it right and penalties of getting it wrong?

Moderator: Andrew Delaney, President & Chief Content Officer, A-Team Group
Brian Greenberg, Senior Director – Data Operating Model Lead & Data Management Practice Product Manager, BNY
Nirav Shah, Enterprise Data & AI Governance Director, MetLife
Manal Alimari, Executive Director, CDAO Business Partnership and Product Management, SMBC Corporation
Michael Curry, President, Data Modernization, Rocket Software
John Joseph, Director, Sales Engineering, Xceptor
Gille Halle,  Financial Services Data Integration Leader, IBM

Keynote: Your data is already ready for AI: Exploring the missing knowledge layer between raw information and trustworthy agents

This session explores why data alone is insufficient for enterprise AI, demonstrating how capturing institutional knowledge in a flexible semantic layer turns raw information into a competitive advantage.

• How can organizations shift from simply storing data to systematically capturing the institutional tribal knowledge required to make AI agents accurate and repeatable?
• Why do enterprise AI agents require a more flexible, dynamic semantic layer than the rigid data ontologies and frameworks of the past?
• What are the practical steps to operationalize an enterprise data knowledge layer so that autonomous agents can reason about data like an expert data scientist?
• How can data leaders democratize data driven decision making across business functions while keeping the underlying data knowledge strictly governed?

Rohan Kodialam, Co-Founder, Sphinx 

Panel: Building the intelligence layer — semantic architecture for AI-ready data products 

Practitioners will learn how to implement a semantic architecture that enables seamless interoperability and provides the business context essential for trusted AI.

• What does a production-grade semantic layer actually look like, what has been built, what failed, and what was learned?
• When should firms virtualize vs. physically consolidate data — and how does that decision change when AI agents are the primary consumer rather than human analysts?
• How do you design an Enterprise Conceptual Data Model that spans both structured instrument data and unstructured content without becoming a multi-year modelling exercise?
• What is the right interface between a semantic layer and an agentic system: ontologies, context and knowledge graphs, vector stores, all of these?
• How do you govern the semantic layer itself — who owns the business glossary, who can extend the ontology, and how do you prevent definition drift across federated domains?
• How should firms sequence their build: data catalogue first, or domain-by-domain data products first — and how does the answer differ for sell-side vs buy-side?

Moderator: Dessa Glasser, Independent Board Member, Oppenheimer & Co.,
Raguvaran Reddy Kalluri, Lead Developer & Solution Architect, Royal Bank of Canada (RBC)
Arijit Bhattacharya, Head, Data & AI Program Delivery, Ares Management
Rohan Kodialam, Co-Founder, Sphinx
Matt Katz, Field CTO, Arcesium
Nicholai Mitchko, Director, AI Enablement, InterSystems 

Keynote: From data to decisions: Building trusted systems of action for the AI era

This session explores how trusted data, real-time event streams, and active governance form the essential foundation for AI-driven action. It offers financial services leaders a practical framework for evolving from data-driven organizations to decision-driven organizations in the AI era.

• How can organizations move from AI-generated insights to AI-driven business outcomes in real time?
• What role does real-time data streaming play in enabling AI agents and multi-agent workflows at enterprise scale?
• How can enterprises balance autonomous decision-making with governance, transparency, and regulatory compliance?
• What data foundations are required to build trusted AI systems that reduce risk, improve operations, and deliver measurable business value?
  

Gille Halle, Financial Services Data Integration Leader, IBM

Panel: Scaling with trust – data quality engineering as a driver of business performance

Learn how to transform data quality from a back-office cost center into a high-performance engine that ensures AI reliability while creating the operational savings needed to fund innovation.

• How are firms transitioning from reactive data cleansing to proactive shift-left data quality engineering?
• What unique data quality challenges do RAG (Retrieval-Augmented Generation) architectures introduce?
• How can machine learning be deployed to autonomously detect anomalies and heal broken data pipelines?
• What is the role of data observability in moving from static dashboards to continuous AI validation?
• How do you manage data drift in real-time to ensure AI models remain accurate and deterministic?
• How can firms reframe data quality metrics in terms of operational efficiencies or savings to help fund AI, or drive business performance?

Moderator: Brian Buzzelli, Director, Head of Data & Digital Transformation, Meradia
Sumanda Basu, Head of Data Quality, Data Risks, Controls and Data Domain Lead, Societe Generale
Michael McCarthy, Vice President – Enterprise Data Management, MFS Investment Management
Naveen Chavali, Director – Product Solutions, NeoXam Americas, NeoXam
Mick Hittesdorf, Cloud Product Architect, KX
Lydia McCurdy, Informatica Account Solution Engineer, Salesforce

Keynote:

Panel: Proving your AI — building a defensible audit trail for agentic workflows in a regulated environment 

This session provides the regulatory and technical blueprint for meeting US AI scrutiny while maintaining compliance with established frameworks like BCBS 239

• How must data lineage and aggregation frameworks evolve to support the dynamic, often non-linear data consumption of AI agents?
• What new governance frameworks are required to meet specific US AI regulatory requirements that traditional data management models miss?
• How are firms using AI within their data governance and lineage tools to automatically capture AI data consumption and manage control effectiveness?
• How do you demonstrate to a regulator that your human oversight is meaningful and backed by an auditable process rather than just a rubber stamp?
• How are organizations scaling AI training programs to ensure data stewards and business owners have the literacy required to govern AI models?

Moderator: Pete Harris, Editor, A-Team Group
Vanessa Jones-Nyoni, Chief Data Officer North America, Commerzbank AG
Balasubramaniam Iyer, Head of AI Governance – International, Nomura
Representative from Behavox

Keynote: Sarthak Pattanaik, Chief Data & AI Officer, BNY

Panel: Decisions as a product – from static assets to intelligent agents

This session explores the frontier of data management: the transition from passive data delivery to active decision-support systems. As AI agents become more prevalent, customers and regulators are seeking clear mitigation strategies to new and evolving risks. This panel discusses how agentic workflows can optimize business processes and deliver unique insights, while maintaining the customer confidence and rigorous accountability required in capital markets.

• How are firms moving beyond report building to embrace the shift to agents that uncover unique insights and patterns?
• Why is the industry measuring decision velocity and consistency as the new gold standard for ROI, rather than traditional data download metrics?
• How can organizations maintain ownership/accountability of outcomes as AI takes on larger portions of the value chain?
• What can be done to reassure consumers/customers that proper controls remain in place in an AI-enabled workflow?
• Is there a threshold where automation becomes a liability? How do we identify when we have hit the ‘too much AI’ limit?
• How is the role of the data office changing to manage a digital workforce, and what are the strategies for transitioning safely from human oversight to agent-centric decision models?

Moderator: Brian Greenberg, Senior Director – Data Operating Model Lead & Data Management Practice Product Manager, BNY
Peggy Tsai, AI & Data Management Leader
Tyler Frieling, Director of Applied Technology, Blackrock
Suemee Shin, Head of Enterprise Data Office Standards, Controls & Governance, US Bank