CAIO Fall schedule

• Enhancing customer and employee experiences through intelligent solutions
• Driving sustainable growth by embedding AI into core business strategies
• Fostering a culture of innovation, continuous learning, and AI fluency
• Transforming business outcomes through measurable AI adoption and maturity progression

• How can we accelerate AI innovation without exposing the organization to unacceptable legal, ethical, or operational risks?
• How do we ensure our AI systems remain fair, unbiased, and ethical as they scale across diverse users, markets, and use cases?
• Are we continuously monitoring and adapting to new regulations to avoid penalties and ensure responsible AI deployment across all regions?
• How can we safeguard sensitive data while still unlocking its value for innovation, analytics, and AI-powered decision-making?

• 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?

• 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

• 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 

• 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?

• Enterprise AI rarely fails because of technology alone. What does effective alignment between data, AI, risk, legal, security, and the business actually 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?

Panelists:

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

Moderated by:

Taige Eoff, Go to Market Data Practice Lead, Google Cloud, GOOGLE 

• The gap between generating an insight and acting on it has historically been measured in days. The organizations closing that gap to seconds are building a fundamentally different competitive capability.
• Embedded analytics, edge decisioning, and real-time AI inference are converging to put intelligence directly into operational workflows. Where are the use cases with the clearest measurable impact right now?
• Most analytics platforms were designed to answer questions. The next generation needs to trigger actions. How does that shift change what you need from your technology, your data, and your people?

• 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

Suresh Kumar Gunasekaran, Senior Software Engineer, WALMART GLOBAL TECH

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

• What should CEOs and boards expect from a Chief AI Officer?
• How do you build executive support for AI initiatives during periods of economic uncertainty?
• What's the most important thing leaders must do to turn AI from a technology initiative into a sustainable business advantage?

• Managing the evolution of the CAIO role from technology advocate to business strategist
• Preparing organizations for an AI-first future through leadership, governance, and operating-model transformation
• Evaluating whether today's CAIOs are ready to lead the next phase of enterprise AI transformation

• How did you secure executive sponsorship and business buy-in for large-scale AI initiatives?
• What early decisions had the greatest impact on your ability to scale AI later?
• How important was having a clear AI strategy versus learning through experimentation?

Panelists:

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

• The EU AI Act, evolving US federal guidance, and sector-specific financial services regulation are creating a genuinely complex compliance landscape. How are organizations 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 been 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

• Human in the loop has become a reassuring but often meaningless shorthand for AI oversight. When systems are making thousands of decisions per second, what does meaningful oversight actually look like?
• The cognitive load of overseeing AI systems is a growing problem organizations are only beginning to grapple with. How do you design oversight that humans can actually sustain at scale?
• Different decisions warrant different positions on the spectrum from full human control to full autonomy. How are organizations making those calibration decisions systematically rather than case by case?
• The most dangerous moment in AI deployment is often not the launch, when everyone is watching, but six months later when oversight becomes routine and then perfunctory. How do you design against that?

• The CDAO and CAIO roles were often created independently, sometimes in competition with each other, and occasionally in confusion about where one ends, and the other begins. Now that both functions are maturing, what have you learned about how to make this partnership work in practice?
• The data layer and the AI layer are inseparable in theory but frequently siloed in reality. Where does the friction between these two functions live day to day, and what does it cost the organization when it goes unresolved?
• There is a version of this relationship where the CDAO and CAIO spend most of their time negotiating with each other over ownership, budget, and priorities. And there is a version where they operate as a genuine leadership unit that is stronger than either function alone. What is the difference between those two organizations and how do you deliberately build the second one?
• If you could say one honest thing to the person sitting across the aisle in the other community tonight, something you would not normally say in a formal setting, what would it be?

Panelists:

Jillian Landi, Chief AI Officer, NEEDHAM BANK

Adem Albayrak, Chief Data and AI Officer, ALZHEON

Sanjay Sidhwani, Chief Data & Analytics Officer, VALLEY BANK 

• How has the definition of “accountability in AI” evolved in the past few years?
• What does “success” look like for AI beyond model accuracy?
• What leading indicators should organizations track to ensure AI initiatives are on the right path?
• How are organizations preparing for evolving regulatory requirements around AI?

Panelists:

Ramesh Natarajan, Head - GenAI/ML Solutions, DAIICHI SANKYO US 

• Which data quality challenges pose the greatest risk to AI success?
• Where do data silos limit visibility, insights, or innovation?
• Who owns accountability for AI-related decisions and outcomes?
• How important is explainability for business-critical AI decisions?

• Optimizing cost structures by balancing compute intensity, model complexity, and usage patterns
• Prioritizing performance through rigorous evaluation of accuracy, latency, and scalability
• Designing autonomous systems that can act independently while respecting defined boundaries
• Establishing control mechanisms to govern agent behavior, decision rights, and escalation paths

• How is AI governance formally structured across clinical, research, legal, compliance, privacy, and IT functions?
• How often are AI governance policies reviewed and updated as technology evolves?
• What governance principles determine whether specific datasets can be used for AI development?

• Promoting innovation while ensuring ethical AI development and deployment
• Balancing technological advancement with privacy protection and data security
• Navigating evolving regulatory requirements across global markets
• Mitigating bias and enhancing fairness in AI models and decision-making

• 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?

• Unlocking AI-driven growth while maintaining strategic and operational control
• Establishing governance and risk frameworks that enable innovation at scale
• Managing security, compliance, and regulatory challenges in a rapidly evolving landscape
• Building trust through responsible AI practices and transparent decision-making

 AI is expensive, operationally complex, and increasingly impossible to justify as a blanket investment strategy. As organisations move from experimentation to scale, the real constraint is no longer ambition, it's prioritization, cost discipline, and deciding what not to do.
•    If AI is not cheap to build or run, what use cases should simply be stopped, even if they look innovative?
•    Are organizations being honest about ROI, or are they subsidizing experimentation under the label of transformation?
•    Who should have the authority to shut down AI initiatives that don't deliver measurable impact — the CDAO, CAIO, or the business? 

Panelists:

Yvonne Li, Chief AI and Data Officer, STARR INSURANCE

Amin Assareh, Vice President, Data Science, FIDELITY INVESTMENTS

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

• The pipeline of future data and AI leadership talent is real but incomplete. What are the gaps between how organisations are developing the next generation of data leaders and what those leaders are actually going to need?
• Technical excellence gets people into senior data roles. It is rarely what determines whether they succeed once they get there. What are the non-technical capabilities that make the difference?
• Mentorship, sponsorship, and deliberate career architecture for data talent are all underinvested relative to their impact. What do organisations that do this well look like?
• What is the one thing you wish someone had told you earlier in your career that would have meaningfully changed your trajectory as a data leader?

• AI systems frequently fail not because the model is wrong but because the humans working alongside them do not trust, understand, or engage with the output in the way designers expected. How do you build AI that accounts for real human behavior rather than idealized human behavior?
• The adoption gap between deploying an AI tool and having it genuinely change how people work is wider than most organisations anticipate. What determines how quickly and completely that gap closes?
• Change management for AI is different from change management for other enterprise technologies because the system is making judgments, not just processing transactions. How does that change the human dynamics of adoption?
• What does a genuinely human-centered AI deployment process look like, and how do you know when you have got it right?

• The CDAO and CAIO roles were created to solve different problems but are increasingly inseparable in practice. Where does the real friction between these functions live, and how are organizations resolving it before it becomes a structural problem?
• Data strategy and AI strategy are often developed separately and then expected to align. How do you build genuine joint ownership of the decisions that sit at the boundary?
• The reporting line for CDAO and CAIO functions varies enormously across organizations. How much does structure matter versus the quality of the working relationship between the people in those seats?
• What are the decisions that look like CDAO decisions or CAIO decisions but are actually only good decisions when both functions are genuinely in the room together?

• The gap between AI leaders and AI laggards is widening faster than most people anticipated. What happens to the organizations that are still in early stages of their data and AI journey as the leaders pull further ahead?
• Agentic AI, multimodal models, and real-time decision intelligence are converging into something that looks qualitatively different from the AI landscape of two years ago. How should data and AI leaders be positioning their organizations for what comes next?
• The pace of change in foundational AI capability is forcing organizations to make architectural and strategic bets on platforms and approaches that may look very different in 18 months. How do you make durable decisions in an environment that keeps shifting underneath you?
• If you had to bet on the two or three developments in data and AI that will most significantly change how enterprise organizations operate in the next three years, what would they be?