AI is only as good as the data it runs on. If your data is siloed, low-quality, or poorly governed, your AI efforts will stall—no matter how advanced your models are. The first step is to treat data as a strategic asset, not an IT byproduct. Map your data sources, identify what’s usable today, and define what needs to be integrated, cleaned, or secured to support AI at scale.

The Six Layers Framework™  helps you align your data strategy with AI outcomes:

• Layer 1 (Infrastructure): Invest in scalable, secure storage and compute environments that support real-time access, model training, and multi-source integration.

• Layer 2 (Models & Platforms): Ensure your AI platforms include robust data pipelines, quality validation, and lineage tracking. Poor data engineering will sabotage even the best models.

• Layer 4 (Services): Establish governance and compliance workflows: who owns what data, how is it used, and how is risk managed? Embed these rules into tooling and team processes—not just policy decks.

By using the Framework, leaders can move from disconnected data practices to a layered system where infrastructure, intelligence, and governance work together. That’s how you unlock AI that’s not just functional—but trusted, repeatable, and ready to scale.