CAIO Fall schedule

• The CDAO role has shifted from infrastructure custodian to strategic architect almost overnight. How are data leaders redefining their mandate as AI moves the goalposts on what governance actually means?
• Organizations have invested heavily in data quality and lineage, yet AI is exposing gaps that traditional frameworks weren't built to handle. Where are the most dangerous blind spots right now?
• As real-time decisioning becomes the expectation, how do you maintain meaningful oversight without becoming the bottleneck that slows the business down?
• The board relationship is changing fast. What does it take to translate data strategy into language that drives genuine executive commitment rather than polite nodding?
• If you could redesign your data organization from scratch today, knowing what you know about where AI is heading, what would you do fundamentally differently?

Panelists:

Kamal Distell, Vice President Enterprise Data – TRAVELERS

Trang Nguyen, Vice President, Data Science – PRUDENTIAL FINANCIAL

Elena Alikhachkina, Chief Data and AI Officer, TE CONNECTIVITY

Achuth Rao, Chief Product Officer & Head of Data & Analytics, AI Products – NEW YORK LIFE INSURANCE COMPANY

• Every CDAO is making a foundational bet right now on how they structure their data and AI platform. Whether deliberate or by default, those decisions will constrain or enable everything that follows.
• The unified data platform has been promised many times before. What has genuinely changed in the last 18 months that makes platform coherence a real competitive differentiator?
• Where are the build versus buy versus partner decisions that look straightforward today but create serious lock-in problems down the road?
• What does the practical journey from fragmented tools to an integrated, AI-ready architecture look like for organisations that have already made it?

• Everyone wants to talk about models. Almost nobody wants to talk about the data preparation and quality engineering that determines whether those models are useful or dangerous. Why does the industry keep skipping this conversation?
• Data contracts, mesh architectures, and domain ownership models are all being adopted to solve fundamentally the same problem from different angles. Which approaches are proving durable in real enterprise environments?
• The organisations furthest ahead on AI often made boring, unglamorous infrastructure investments three to five years ago. How do you make that case to leadership when everyone wants to fund the exciting thing?
• If your data foundation were genuinely AI-ready, what would be measurably different about how your organization operates day to day?

Panelists:

Erin Fidyk, Director Oncology Enterprise Data Science – JOHNSON & JOHNSON

Moshmi Sanagavarapu, SVP Group Director, Data Analytics, OMNICOM-IPG MEDIABRANDS

Yogesh Bhardwaj, Sr. Director GSO Business Partner, Data & IT Shared Services, SENSATA

Capegemini/WNS Moderator

Up to 95% of all AI deployments fail to deliver measurable ROI. Why? And what can enterprise leaders do about it? New global quantitative research from Actian shows a clear pattern: organizations lacking data products and enforceable data contracts are far more likely to fail. Based on survey data from enterprise data leaders, this session reveals what separates scalable AI from perpetual pilots and failed experiments. Attendees will leave with concrete design, governance, and operating practices they can apply immediately to their next AI initiative.

• We spent three years building AI that helps humans decide. We are now building AI that decides and acts on its own. That demands a fundamentally different kind of thinking. Are organizations ready for it?
• Over 70% of enterprises are using agentic AI in some form, but fewer than 20% have a governance model designed for autonomous agents. What does closing that gap require?
• Agentic systems fail differently from traditional software. They can fail silently, gracefully, and in cascading chains. How are organizations building the observability and intervention capabilities they need?
• At what point does an agentic AI system warrant the same level of scrutiny and validation as a human being given significant business authority?

Panelists:

Ravinath Kausik, Vice President, Data Science, AI and Analytics – THE HARTFORD

Tasneem Nipplewala, Head of Data, Cyber and AI Enterprise Architecture – MASSMUTUAL

Sakat Sankhla, Business Transformation & Product Leader, Modular Data Center for NAM – SCHNEIDER ELECTRIC

Celio Oliveira, Chief Data and AI Officer, GOVERNMENT OF CANADA

Ewa Kozlowska, Senior Vice President, Treasury and Trade Solutions - Global In-Business Product Risk – CITI BANK

Mohan Krishna Rayapuvari, Sr Director AI & Machine Learning, MANULIFE

Daisy A. Moreno, Senior Information Technology Project Manager, PUBLIC SERVICES AND PROCUREMENT CANADA

Moderator: Anjali Arora, Chief Technology Officer – PERFORCE

• Enterprise AI rarely fails because of technology alone. What does effective alignment between data, AI, risk, legal, security, and the business look like in practice?
• Governance is often seen as the function that slows innovation down. How can leaders design governance models that enable responsible speed rather than create bottlenecks?
• As AI moves from experimentation into production, ownership becomes more complex. Who should be accountable for decisions that sit across data, models, workflows, and customer outcomes?
• The organizations scaling AI successfully are building repeatable operating models, not one-off pilots. What structures, decision rights, and cross-functional ways of working are needed to move quickly without creating unnecessary risk?

Aiswarya (Ammu) Menon, Chief Data & AI Officer – NYC HEALTH + HOSPITALS

Amritha ArunBabu Mysore, Head of Product – Marketing and AI – STAPLES

David Krauza, Vice President, Enterprise Data Strategy, Products, and Governance – COMCAST

Nirav Parikh, Head of Enterprise Data and AI – MASS GENERAL BRIGHAM HEALTHCARE

• Data and AI transformation require cultural change at least as much as technical change, yet most CDAOs are hired for their technical credibility. How do you develop the change of leadership capabilities the role now demands?
• Resistance to data-driven decision-making rarely looks like outright opposition. It looks like slow adoption, metric disputes, and business units building their own shadow data capabilities. How do you diagnose and address the real blockers?
• The data leader who tries to centralize everything creates bureaucracy. The one who decentralizes too aggressively creates chaos. How are practitioners finding the right balance between control and enablement?

Duyum Ryan, Finance Chief Operating Officer & SVP (Finance Data Strategy), THE CIGNA GROUP

Frederique De Letter, Head of Data Analytics & AI – PLANTE MORAN

Fariha Chaudry, Head of Engineering M&A – GE HEALTHCARE

Moderator: Greg Freeman, CEO & Founder – DATA AND AI LITERACY ACADEMY

• Data integration used to be a plumbing problem. In an AI-first environment where models need fresh, clean, and contextual data continuously, it has become a strategic capability. How should CDAOs be thinking about this differently?
• The volume, velocity, and variety of data sources has grown dramatically with AI adoption, including new sources like model outputs, vector embeddings, and agent action logs. How are organisations managing the integration complexity this creates?
• Many enterprises are still running batch pipelines in a world that increasingly demands real-time. What is the realistic migration path, and where should organisations prioritize the move to streaming first?

Moderator: Andrew Gallagher, Regional Director of Enterprise Sales – FIVETRAN

Richard Inserro, Director, Data Governance, Standards, and Assurance – MERCK

Aaron Chang, Director, Head of Data Science, Disease Area X – NOVARTIS

Aidar Kadess, Sr Director, Data and AI Governance – CLEAN HARBORS

Anu Sundaram, Vice President Business Analytics – RUE GILT GROUPE

• For twenty years, BI has been a spectator sport. We built dashboards, admired the charts, and then walked to another system to actually do something about them. Insight and action lived in different worlds.
• That gap is closing. This session shows what happens when analytics stops observing and starts acting when agentic workflows and self-service analytics fuse with operational systems to turn a finding into a fulfilled outcome without leaving the flow.
• Using Autonomous Data Analytics in ServiceNow, we'll demonstrate live: an AI agent that surfaces an anomaly, reasons about its cause, and triggers the workflow to resolve it governed, auditable, and in the hands of the business user, not just the data team.
• We'll tackle the harder questions, too. What does trust look like when analytics can act on its own? Where should the human stay in the loop? And what happens to the traditional dashboard when the answer to "so what?" is already in motion?
• Come see the end of the read-only era and what a decision that executes itself really means for your organization.

• Between new models shipping every month and regulations being rewritten just as fast, most enterprise AI architecture decisions are built on an assumption that today's constraints will hold. That assumption leads to pilots stalling out or landing in pilot purgatory.
• This session looks at why AI infrastructure needs to be built for change from the start, not retrofitted once the ground shifts. We'll walk through the architectural pattern that lets organizations adapt to new models, new regulations, and new data environments without rebuilding. Flexibility and control, delivered without the runaway costs typically attached to both.
• · We'll share the architectural requirements: sovereignty, governance, and context, that separate AI systems built to stand the test of change. We'll show you how to design for adaptability so that new models, new regulations, and new data sources are things your AI absorbs rather than breaks under, and how to get there with an approach that makes your data sovereign by default for AI.

• Moving beyond pilots: what separates scalable AI execution from disconnected experimentation
• Building AI-ready foundations: data quality, governance, access, lineage, and trust
• Designing the operating model: ownership, accountability, and decision rights for enterprise AI
• Preparing for agentic AI: what changes when AI systems begin to act, not just advise
• Scaling responsibly: how to balance speed, innovation, risk, and measurable business value

This panel explores how financial services organizations can move beyond reporting and dashboards toward analytics that actively shape business decisions, improve performance, and drive measurable outcomes.

• Where analytics teams still get stuck between insight generation and action
• How leaders can improve adoption of analytics across risk, finance, marketing, customer, and frontline teams
• What it takes to measure the real business impact of analytics beyond activity metrics

Moderator: Kate Butcher, GM, AI & Finance Transformation – ALTERYX

Dr. Tarun Sood, Chief Data and AI Officer, AMERICAN CENTURY INVESTMENTS

Mitch Holt, AVP – Head of Data & Analytics, Pet Insurance – METLIFE

Bill Carey, Managing Director, Client Analytics, Commercial Insurance Risk Control Services – LIBERTY MUTUAL INSURANCE

Isa Terzi, Vice President, Data, ERGO NEXT INSURANCE

Every time a human or an agent uses AI in your organization it creates a treasure trove of valuable signal. Be it risk, innovation, use cases, business objectives, spend, ROI — visibility into your AI consumption data creates an evidence-based approach to successfully guide you to a secure and well-governed AI program that delivers on productivity and ROI. In this session you will hear first hand how one company, lead by an AI-skeptic, leveraged full visibility into their AI consumption to create a winning program.

Neil Cohen, Vice President, Marketing,
PORTAL26

Christopher Hippensteel, Director of Information Technology/CISO,

NEW RESOURCES CONSULTING

• The EU AI Act, evolving US federal guidance, and sector-specific regulation are creating a genuinely complex compliance landscape. How are organisations building frameworks that work across multiple regimes?
• Legal and compliance functions want to slow deployment until the picture is clearer. Business units want to deploy and deal with it later. How are data and AI leaders mediating that tension?
• Responsible AI has been a priority conversation for years. How much of it has translated into operational processes that actually change how systems are built, versus principles that sit in a document?
• Where do you genuinely believe the regulatory environment is heading in the next 24 months and how is that shaping your governance investments today?

Panelists:

Colleen Tartow, Senior Director, Enterprise Data Engineering, CAPITAL ONE

Theodora Skeadas, Trust and Safety Policy Manager – DOORDASH

• What does a successful transition from AI vision to measurable business value look like?
• Which business objectives should be prioritized when defining an AI strategy?
• How can organizations ensure alignment between business strategies, technology investments, and data capabilities?
• What are the key enablers of successful end-to-end AI transformation?

• How to establish clear AI governance frameworks to define accountability, oversight, and decision rights?
• How to ensure data integrity and quality through standardized data management and validation processes?
• How to implement ethical AI principles covering fairness, transparency, and accountability?
• How to design explainable AI systems that build user trust and support decision-making?
• How to mitigate bias and unintended outcomes through continuous testing and monitoring?

• Identifying high-impact AI use cases that address real business challenges
• Linking AI initiatives to key performance indicators such as revenue, cost reduction, and productivity
• Assessing data readiness by ensuring quality, accessibility, and governance
• Evaluating technical and organizational capabilities to support AI delivery

Panelists:

Lauren (Liutong) Li, Executive Director, Head of AI & Innovation Product Strategy, NOVARTIS

Sreedhar Sistu, Vice President, AI Offers, SCHNEIDER ELECTRIC

Lily Li, Head of AI Adoption and Solutions, FRANKLIN TEMPLETON

Daisy A. Moreno, Senior Information Technology Project Manager, PUBLIC SERVICES AND PROCUREMENT CANADA

Moderator: Nanonet  

• Defining a clear AI vision aligned with business strategy and measurable outcomes
• Building cross-functional collaboration between business, IT, data, and risk teams
• Embedding responsible AI principles into every stage of the lifecycle
• Prioritizing high-impact use cases that deliver tangible value quickly
• Establishing strong governance frameworks without slowing innovation
• Investing in talent development and fostering an AI-driven culture

Panelists:

Biswa Sengupta PhD, Chief AI Technologist: LLM Suite (CDAO), JPMORGAN CHASE & CO

Haroon Abbu, Senior Vice President, Digital Technology, AI & Data Analytics & CDAIO, BELL AND HOWELL

• What are the biggest barriers to operationalizing AI and analytics at scale?
• How do you ensure AI-generated recommendations are acted upon rather than ignored?
• What organizational changes are required to embed AI into day-to-day execution?
• How do you break down silos between strategy, operations, technology, and business teams?