From AI Readiness to AI Enablement

Federal agencies are accelerating their adoption of artificial intelligence (AI) to meet growing demands to increase speed and efficiency to keep pace with AI requirements. 

Yet, AI is only as effective as the trusted data that powers it. While agencies possess vast data amounts of datasets and data products, much of it remains isolated, inconsistent, and untrusted, putting AI program at risk. To unlock the full potential of AI, data must be accessible, transparent and contextualized to ensure it is trustworthy, secure, and aligned with mission objectives.
Techniques such as building a data marketplace – an internal collaboration space for data products, AI trust models, and components such as vectors, APIs, agents, and prompts – can extend the usefulness of AI-ready data. Creating autonomous data products, which are self-managing, self-governing data entities that encapsulate the entire data lifecycle from generation to consumption, including code, semantics, and governance policies, also lighten the workload.

This transformation is central to the Federal Data Strategy and initiatives such as VAULTIS (Visible, Accessible, Understandable, Linked, Trusted, Interoperable, and Secure), which emphasize the importance of data governance, interoperability, and ethical use of data across agencies. By aligning with these frameworks, agencies can build a foundation of AI-ready data—characterized by transparency, traceability, and continuous quality assurance.

Learning Objectives:

• Explore what makes data trustworthy, high-quality, and usable for AI across federal missions
• Understand how governance and strategy frameworks like VAULTIS support AI readiness
• Discuss how agencies can align data practices with mission priorities and long-term AI strategies