Executive Summit

The Chief Data &Analytics Officer (CDAO) role stands at a pivotal crossroads. As AI becomes embedded across all business functions, the CDAO’s influence and ability to drive enterprise-wide value are more crucial than ever. This Gartner keynote examines how AI-ready data, cross-functional collaboration, and human-centered leadership are redefining the CDAO’s responsibilities. Senior data leaders must now make strategic choices about the future they wish to shape.

Join this session to discover:

• How CDAOs can evolve from functional leaders to indispensable enterprise collaborators in the AI era
• Strategies for building AI-ready data foundations that deliver measurable business outcomes
• Why culture, influence and human-centered leadership are now as critical as technology and governance

As organizations move beyond experimentation, the challenge shifts from teaching AI to driving real-world use. Hear how Principal built a foundation of AI literacy across its workforce and is leveraging it to accelerate adoption, support responsible use and create value. This session explores what it takes to turn AI awareness into meaningful adoption at scale. Lessons learned, challenges encountered and strategies for embedding AI into everyday work.

This session will focus on:

• Building a foundation of AI literacy across the workforce
• Accelerating adoption through everyday AI use
• Sustaining momentum through continuous learning and capability building

As AI becomes a business priority, the CDAO role is expanding beyond data stewardship and analytics delivery into enterprise leadership. Following the opening keynote, join fellow data leaders for a candid discussion on what this evolution looks like in practice. Organizations continue to define ownership, accountability and success differently, creating new opportunities and challenges for CDAOs navigating an increasingly complex leadership landscape. Explore how peers are adapting their roles, expanding their influence and positioning themselves to drive strategic impact in the age of AI.

Join this boardroom discussion to explore:

• Who should own AI strategy, governance and business outcomes across the enterprise 
• How the responsibilities and expectations of the CDAO are changing
• What it takes to remain a strategic and indispensable executive leader

Agents are becoming the biggest opportunity in the enterprise, and the biggest adversary. They read your data and act under borrowed identities, at a speed no human review process was built to match. The old question in security was how to keep bad actors out. The new one is more complex: how do you create and manage the biggest risk in your own enterprise, the agents you deployed on purpose?
Join this session to discuss:

• Establishing a framework where data, identity, and agent security operate as one system
• Shifting from asking whether you can trust your agents to determining what access they can be trusted with
• Balancing innovation and risk by creating visibility needed to understand agent activity

Data leaders are under pressure to scale agentic AI and prove ROI fast, but most initiatives stall because of fragmented or ungoverned data, not weak models. To operate as a true Agentic Enterprise, you need a clear, intentional contract between your data systems and AI, plus governance that keeps pace as more of the workforce gains direct access to that data.

Join the discussion to explore:

• How to structure data foundations that scale agentic AI without ballooning cost or risk
• Governance models built for agentic workflows with control and auditability at machine speed
• Practical ways to extend safe, self-serve data access across the broader workforce

Connect with like-minded peers in a one-on-one setting through Peer-to-Peer Meetings. You will be matched with peers in your community based on your shared interests and priorities.

As AI accelerates access to information, the challenge is no longer generating insights. It is ensuring those insights drive consistent decisions and measurable business outcomes. CDAOs are rethinking how analytics reaches the business, fits within day-to-day workflows, and enables action at scale. Success increasingly depends on turning intelligence into execution, not simply delivering more information.

This session explores:

• Expanding access to insights that help teams make better decisions faster
• Embedding analytics into the tools and workflows where work already happens
• Measuring success through business outcomes rather than analytics activity

AI models can generate answers. Creating business value requires understanding how work gets done. The organizations seeing the greatest returns from AI are not simply deploying more models. They are building a shared layer of business context that captures operational knowledge, decision frameworks, governance, and enterprise expertise. This context layer allows AI to operate with greater consistency, trust, and business relevance, transforming isolated use cases into scalable enterprise capabilities.

Join this session to explore how to:

• Identify the context gaps limiting AI value
• Build reusable enterprise context across functions
•  Improve AI reliability, explainability, and adoption to accelerate value realization

The instinct is to blame the AI model when business outcomes fall short. But that reaction misdiagnoses the actual problem. Foundation models produce output, not advantage, because they’re built to reason about the world in general—not the continuously evolving context that defines your unique business.

Join this session to discuss:

• Why the real bottleneck to enterprise AI value sits above the model layer
• What it means to architect AI systems upon business context, rather than around it
• How to evaluate contextual AI platforms against the outcomes that actually matter

Open Data Infrastructure is essential for organizations in the AI era, helping leaders scale analytics and AI while maintaining control. CDAOs must balance innovation, flexibility and cost as data platforms expand.

Join this session to learn about:

• Designing interoperable architectures to avoid lock-in
• Applying openness to semantics, metadata and governance
• Enabling AI and operations with flexibility and cost transparency