Prerequisite: None

Douglas Laney

Data, Analytics, and AI Strategy Advisor

Author, Infonomics

This session will include a moderated Q&A featuring questions from the live audience.

As organizations move from isolated AI use cases to increasingly autonomous, agent-driven systems, data becomes the primary constraint, not algorithms. Each level of agentic AI places fundamentally different demands on data, from basic availability and quality at lower levels to real-time integration, feedback loops, governance, and economic accountability at higher levels.

In this session, Doug Laney will outline the distinct data requirements across the seven levels of agentic AI and explain why many organizations stall, not because of model limitations, but because their data foundations were never designed for autonomy.

Key takeaways:

• How data requirements evolve across the seven levels, from descriptive, human-in-the-loop systems to self-directing, self-optimizing agents
• Why higher levels of agentic AI depend on data integration, provenance, trust, and continuous learning, not just larger data sets
• What leaders must change in data architecture, governance, and operating models to enable safe, scalable AI autonomy