Data Readiness for AI Enablement

Many organizations pursue AI and ML initiatives that rely on enterprise data without first addressing the foundational data requirements. This point was reinforced during the recent fireside chat between Sam Altman (CEO of OpenAI) and Sridhar Ramaswamy (CEO of Snowflake) at Snowflake Summit 2025. One of the most direct statements from that discussion was, “There is no AI strategy without a data strategy.”

I have led programs to unify fragmented enterprise data, establish governance for data pipelines, and enable ML-driven personalization. Without this foundation, enterprise AI and ML efforts are limited in scope and difficult to scale reliably.

Altman described the progression of AI agents, starting with simple tasks and moving toward more complex use cases. Ramaswamy emphasized the importance of trusted data pipelines in supporting this progression. Building these pipelines requires consistent data governance and disciplined coordination between engineering, data, and compliance teams.

We were onboarding ML teams to a newly unified data platform. Real-time signals were already flowing through compliant channels, but historical data had not yet reached the required level of quality and consistency. There was pressure to use legacy pipelines to close this gap, which would have introduced governance risks. We prioritized accelerating data conditioning while maintaining compliance standards. Managing these types of trade-offs is critical to supporting scalable AI.

The ability to move from experimentation to inference depends directly on the readiness of the data layer. Enterprise data must be consistent, governed, and enriched to support both model training and real-time inference. Without this foundation, personalization and other AI-driven use cases either stall or rely on fragmented, untrusted signals. Readiness and experimentation must progress in alignment. Without trusted data, moving fast will create risk and rework downstream.

Once the data foundation is in place, it also lowers the cost and risk of experimentation. Teams can iterate faster and explore new AI-driven capabilities more effectively, with the confidence that underlying data quality will support reliable outcomes. In my experience, this is where organizations that invest in data readiness gain significant advantage over those that address it late or inconsistently.

Organizations pursuing AI initiatives need to assess the maturity of their data foundations. Inconsistent governance, fragmented pipelines, or compliance gaps will limit the value AI can deliver. Establishing a trusted, usable data layer is required to support AI enablement at scale.

I welcome further discussion on building trusted data foundations to support AI enablement. You can reach me on LinkedIn. #DataStrategy #AIEnablement #MLInfrastructure #EngineeringLeadership