Executive Summit

Data warehousing promised a single version of truth. Self-service BI, cloud, and now AI made similar promises, but none resolved the question underneath: does the organization know what its data means and where it came from? The metadata debt left behind is now the primary blocker to trusted, scalable AI.

Join this session to explore how enterprise data leaders can:

• Judge new data platforms by the metadata debt they leave behind
• Treat AI reliability and ROI as a metadata and governance discipline first
• Establish cross-functional metric ownership to fund metadata as enduring infrastructure

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

 AI has plenty of intelligence. What it lacks is enterprise context. To act with precision, AI must understand how a business operates.

Join this session to discover:

• Transforming fragmented data with a real-time System of Context
• Scaling AI with greater accuracy, accountability and business impact
• Aligning data strategy with enterprise AI initiatives

As CDAOs face rising scrutiny over token consumption, they must connect cost efficiency to business value and enterprise priorities. Depending on the CDAO's level of influence and engagement with fellow C-level executives and the board, it can be difficult to bring these conversations from the tactical to the strategic. How can data and AI leaders strengthen executive relationships to clarify expectations and influence cost/benefit decisions, even without a formal seat at the table?

Join this session to discuss:

• Translating token economics into enterprise value
• Aligning C-level and board expectations around outcomes
• Building influence beyond formal authority and access

Delivering value with AI at pace and scale requires addressing the tension between speed to market and risk management, especially in regulated industries. This session explores how to implement enterprise class risk controls while moving at the speed of an AI native.

This session will focus on:

• Delivering AI solutions with both speed and rigor through agile, multi-disciplinary governance
• The growing importance of agentic solutions and FinOps to deliver ROI at scale
• Critical success factors in the implementation of a comprehensive and real time agentic control plane

Organizations have made significant investments in data and AI, but many continue to struggle with adoption and value realization. The challenge is no longer access to technology. It is ensuring employees have the confidence and skills to incorporate data and AI into everyday decisions. This session explores how organizations are using data literacy programs to accelerate adoption and drive measurable business outcomes.

Join this session to learn:

• How to build workforce capabilities that accelerate AI adoption
• Strategies for embedding data-driven decision making into daily operations
• Methods for demonstrating the business value of data literacy investments

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

The biggest challenge facing many CDAOs is not technology, it's translation. As AI and data investments accelerate, leaders must clearly demonstrate data and analytics results to senior leadership and stakeholders. Yet differences in language, incentives and perspectives often create barriers to alignment with CEOs and CFOs. Organizations that bridge this gap are better positioned to build trust, influence decisions and maximize the value of their data and AI investments.

This Gartner led discussion explores:

• Clearly connecting data and analytics results with business objectives 
• Communication strategies to strengthen trust and influence
• Effectively communicating the impact of data and analytics investments to the enterprise

As AI shifts from experimentation to execution, organizations are entering a new era where intelligent agents augment decisions, automate work and transform operating models. For CDAOs, the challenge is no longer proving AI's potential. It is building the data, engineering, governance, talent foundations and operating models required to scale AI responsibly and deliver measurable business value. Drawing on recent executive discussions and AI engineering insights, this session explores how leading enterprises are evolving towards becoming more agentic in the future. Attendees will gain practical perspectives on prioritizing investments, redesigning teams, and redefining the role of the CDAO in an increasingly autonomous future.

This Gartner led keynote will focus on:

• Building foundations for an agentic enterprise through scalable data and AI engineering
• Balancing innovation, governance, risk and cost
• Evolving the CDAO role from data steward to transformation leader

The biggest challenge facing many CDAOs is not technology, it's translation. As AI and data investments accelerate, leaders must clearly demonstrate data and analytics results to senior leadership and stakeholders. Yet differences in language, incentives and perspectives often create barriers to alignment with CEOs and CFOs. Organizations that bridge this gap are better positioned to build trust, influence decisions and maximize the value of their data and AI investments.

This Gartner led discussion explores:

• Clearly connecting data and analytics results with business objectives 
• Communication strategies to strengthen trust and influence
• Effectively communicating the impact of data and analytics investments to the enterprise