According to the 2025 Gartner Generative and Agentic AI survey, around 75% of organizations have either deployed or are piloting some form of agentic AI and a similar percentage report the ongoing development of new use case opportunities.
This multi-group discussion session for D&A executive leaders will focus on sharing experiences and addressing the following questions:

-- How are organizations scoping & funding agentic investments? How are these efforts similar or different from other investments?
-- What agentic use cases are getting the most funding right now and what guardrails are they putting in place?
-- How are organizational defining agentic program objectives & outcomes?

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

Data engineering faces increasing pressure to deliver AI-ready data that meets agentic AI requirements. This session explores changes in data engineering functions, practices, architectures and technologies.

In this roundtable, D&A leaders will discuss how they are balancing the centralization and decentralization of data management, empowering LOBs to achieve self-sufficiency while leveraging the benefits of a centralized data management function. They will also address how to maintain control over local initiatives and prevent the spread of risky patterns that are misaligned with governance policies.

No data? No AI! No metadata? No data! Gartner codified AI-ready data concepts and crafted the language in mid-2022. Practices have evolved for the four AI-ready data modes and 30 expected data evaluation and preparation steps. In this session, we recap the approach combined with how organizations are using AI data readiness as a foundation to evolve toward standardizing data for production AI.