An unparalleled level of interest in generative AI and agentic AI is driving organizations to rethink their data strategy. While there is a need for data foundation constructs such as data pipelines, data architectures, data stores and data governance to evolve, there are business elements that need to stay constant like cost-efficiency and effectively collaborating across data estates. In this session we will cover how building your data foundation on AWS provides the tools and the building blocks to balance both needs, and empower organizations to grow their data strategy for building AI-ready applications.
What this session is about
Playbook
Editorial commentary · what to actually do about this on Monday
The concept
Foundation = pipelines + storage + governance + lineage. Without all four, AI initiatives are theatre.
Why it matters
Every "AI strategy" deck has a token slide labeled "data foundation." That slide is actually 80% of the work.
The hard parts
Data mesh vs. data lake vs. lakehouse debates miss the point. The point is *contracts between domains* — who owns what, what they guarantee, how breaking changes get communicated.
Playbook moves
(1) Codify data contracts in writing, with SLAs. (2) Treat downstream AI as just another consumer subject to those contracts. (3) Invest in lineage tooling early; retrofitting later is brutal.
The surprise
Cost-efficiency in data foundations comes from eliminating duplicate ingestion (the same data landing in three lakes), not from cheaper storage. Storage is rounding error in 2026; egress and re-processing are not.
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Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.
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