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.

As business leaders seek the adoption of AI-enabled capabilities across all aspects of operations, less than a third of D&A leaders express confidence that their organization is ready to meet the challenges associated with meeting AI-driven demand’. This multi-group discussion will cover:
Which components of the D&A value delivery chain are most in need of evolution?
What are the best next-generation D&A organizational & operating models suited for the AI-era?
What are the best KPIs for measuring ‘AI-readiness’ among systems, teams, and leaders?

Data lakehouses continue to be hyped but do they replace or complement data lakes and data warehouses? Where do we stand from an architectural perspective? What is hype and what is real? What should be expected in the coming years?

With growing focus on AI in organisations, deliveringAI-ready data has become the number one investment priority of data management leaders. This session will define AI-ready data, how it differs from traditional data management and discuss AI-ready data practices and technologies.

Data management resources face increasing challenges due to growing demand, including the need for AI-ready data. It is essential for data management leaders to make sure that data management is not misdirected and aligns with the business value. Proper alignment will maximize the value of data management initiative and support business growth.

Data management continues to evolve and is increasingly becoming a dedicated function led by heads of data management. Gartner has been investigating what makes heads of data management successful. This session will discuss the key characteristics around organizational structures, operating models, architecture and technology to establish characteristics of successful data management.