Executive Summit

As analytics and AI adoption accelerates, CDAOs are shifting from reactive data quality to proactive, end-to-end visibility across the data lifecycle. By embedding governance and automation, organizations can scale reliable, decision-ready data products that deliver measurable business value.

Join this session to:

• Build trusted, enterprise-scale data workflows
• Enable proactive visibility for scalable data products
• Automate data reliability for analytics and AI success

Many organizations implement AI as an end goal, often resulting in limited solutions like chatbots. Real value is realized when AI initiatives are directly tied to business objectives and measurable outcomes. What are the successes, failures, and critical lessons learned along the way?

Join this session to discuss:

• Prioritizing business outcomes over AI adoption
• Leveraging AI to solve real challenges
• Focusing on measurable results, not technology hype

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.

The coming year is pivotal for enterprises seeking to unlock the full potential of agentic AI. Gartner reports that 17% of enterprises have already deployed AI agents, and another 42% plan to adopt them by the end of 2026. Yet nearly half of pilot projects are expected to be canceled by 2027 due to cost pressures, unclear value realization or a lack of proper controls.

This session will address:

• The strategic value of agentic AI and how to prioritize high‑impact use cases
• The importance of data, semantic, and architectural readiness
• Effective measurement, monitoring, and ownership of agent performance to accelerate innovation

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 boardroom to discuss:

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

As organizations shift to agentic AI, success hinges on engineering data environments that deliver precise context for autonomous, role-based agents. Leaders face challenges in redefining workflows and proving ROI as machine intelligence drives decisions beyond traditional dashboards, demanding new metrics and governance.

Join this boardroom to explore:

• Building context-rich data for agentic AI
• Designing role-based agents for enterprise workflows
• Quantifying ROI and risk in agent-driven operations