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

Opening Keynote Fireside Chat: Scaling AI-ready data foundations: Lessons from big tech for capital markets execution

This fireside chat examines how financial institutions can adopt modern platform engineering and analytics practices to transition from experimental AI to scalable, production grade business execution. Drawing on his experience across Tier-1 wealth management, big tech cloud and data leadership; Samit explores how unifying streaming data, cloud-first analytics and decision making engines creates the foundation for trustworthy AI agents and predictive business capabilities.

• How can capital markets data leaders bridge legacy data estates with modern, cloud-first architectures to democratize data without compromising control?
• What structural capabilities are required to transform passive data lakes into active analytics frameworks that power real-time, prescriptive decisioning?
• Where do traditional data operating models fail when scaling AI, and how can firms adopt big-tech engineering practices to accelerate speed-to-market?
• How do enterprise data leaders balance rapid predictive/prescriptive innovation with strict data governance, quality, and regulatory boundary enforcement?

Interview with: Samit Korgaonkar, Enterprise Data Platform & Governance, Google
Interviewed by: Peggy Tsai, Data Management and AI Leader

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 Wholesale Client Data 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  – The data office at a crossroads
Presenting top line findings from the CDO Office survey
Andrew Delaney, President and Chief Content Officer, A-Team Group
Mark Davies, Partner, Element 22

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.,
Sri Bhupatiraju, Director | Principal AI Engineer, BlackRock AI Labs
Rohan Kodialam, Co-Founder, Sphinx
Matt Katz, Field CTO, Arcesium
Michael Hom, Head of Financial Services Solutions, 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 achieve 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, Head of Asset Management and Product Data, Manulife John Hancock Investments 
Naveen Chavali, Director – Product Solutions, NeoXam Americas, NeoXam
Mick Hittesdorf, Cloud Product Architect, KX
Lydia McCurdy, Informatica Account Solution Engineer, Salesforce

Keynote: More data, lower costs: The case for AI-enabled specialization

• How is AI accelerating data growth while simultaneously lowering the cost of building custom data infrastructure?
• What strategies can firms use to identify and capture meaningful cost efficiencies when scaling large-volume data workloads?
• How can fit-for-purpose system designs enable capital markets firms to process significantly more data at lower operational costs?
• How can organizations maintain strict governance, control and auditability when deploying specialized AI-enabled data architecture?
  

Rodrigo Coelho, CEO, Edge & Node 

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
Alex Golbin, Technology and Data Executive, Author, Governing AI Risk — The RIVER Charter: An Enterprise Resilience Standard
Samuel Anani, Head of Strategic Account Management – North America, Behavox
Craig Tutterow, Director of Data, Edge & Node

Data Management Insight Awards – Presentation of the Editor’s Recognition Award for USA Data Management Industry Professional of the Year

Keynote: Moving beyond the AI hype to deliver tangible, bottom-line business value

• How is BNY pioneering the transition to a sophisticated multi agent model with the Eliza Platform?
• What strategies were essential to successfully transition the Eliza Platform to production?
• How have you built a trusted data foundation for AI-ready data?
• What governance and guardrails have you implemented to transition from human oversight to an agentic-centric decision model?
• What metrics have you developed to show value?
• How do you keep up with the fast pace of change and when will we see agents executing workflows independently?

Sreelatha Purushothaman, Head of Enterprise AI & Data Governance, BNY

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

• What real-world use cases are firms deploying today using AI-driven data products and automated decision workflows, and what quantifiable business benefits are they delivering?
• How are data vendors evolving their AI data products, and how must licensing, pricing, and product strategies adapt?
• Many capital markets firms are building agentic workflows faster than data vendors are modernizing their feeds. How are data offices bridging the gap between internal AI product innovation and legacy delivery models?
• How are firms moving beyond report building to embrace the shift to agents that uncover unique insights and patterns?
• How can organizations design human-in-the-loop intervention patterns so the data producer maintains clearly defined ownership/accountability and so the data consumer can feel comfortable with the results?
• What lessons have been learned regarding the cultural and change-management efforts required to get traditional business teams to trust and act on AI-driven decisions—and how do we identify when we have hit the “too much AI” limit?

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
Janelle Veasey, CEO, 3d innovations
Yatharth Sejpal, CEO & Co-Founder, KNOWIDEA Technologies
Tom Bradley, Chief Product Officer, Fitch Solutions