The Department of the Air Force is moving to deploy AI tools across 15 mission domains, according to a November 12 Air & Space Forces Magazine report.

The initiative, outlined by Chief Data and Artificial Intelligence Officer (CDAO) Susan Davenport at a November 6 GovCIO AI Summit, is designed to move the services beyond experimental projects and put AI capabilities into operational use.

The domains cover mission areas including intelligence, nuclear operations and human resources. Each is led by a general officer or Senior Executive Service civilian, known as a domain principal, who is responsible for advancing deployment and ensuring that reliable, authoritative data is available to support the work. 

The strategy applies to both the U.S. Air Force and Space Force. Davenport said the department is now shifting its attention from establishing AI policy to redesigning processes and preparing the workforce to use the technology effectively.

Decentralized Data Management Replaces Centralized Bottlenecks

A central part of the plan is a change in how the department manages data. Davenport said the previous approach relied on a centralized team to tag, clean and copy information before making it available to other units, according to the report. This created bottlenecks and made it difficult to identify authoritative data sources.

The new strategy emphasizes decentralized data management and treating data as a product. Information will remain with the organizations that produce it, while those teams take greater responsibility for making it accessible and trustworthy across the department.

The approach is intended to improve data access at scale and help mission teams use information without waiting for a central group to process and distribute it. Domain principals will oversee progress within their respective areas and ensure that data supports the outcomes each mission is expected to achieve.

Quarterly Reviews Track Deployment and Identify Gaps

According to the report, the department is using quarterly showcases to measure progress across the 15 domains. Domain principals present their roadmaps and report on progress toward specific mission outcomes. The meetings also help identify gaps, overlapping efforts, risks and opportunities for collaboration.

Davenport said the department has held two such showcases, with a third planned for December or January. The next meeting is expected to bring in research and development organizations, including the Air Force Research Laboratory, so that existing prototypes can be matched with operational requirements.

This process is intended to connect technology development more directly with the needs of individual mission areas and help move promising capabilities into use.

AI Training Will Extend Across the Workforce

The report states that the deployment strategy includes a three-level AI literacy program for airmen and guardians. The 100-level course will cover foundational AI concepts and everyday use, while the 200-level will focus on building AI-enabled organizational workflows. The 300-level is intended for developers.

The department plans to introduce the training by the end of November, according to Federal News Network. Training will also cover the technology’s limitations and the legal considerations involved in developing AI-powered workflows. Participation is expected to be recorded in service members’ training records.

Data Accountability Will Shape AI Deployment

The Air Force’s approach puts data ownership, access and reliability at the center of its AI rollout. For chief data officers (CDOs), the initiative offers a practical example of how enterprise AI deployment can be tied to domain-level accountability rather than handled solely through a centralized team.

The decentralized model also raises important governance requirements. Organizations need clear responsibility for the data they produce, consistent ways to establish authoritative sources, and controls that allow information to be shared without compromising its reliability or security.

As AI tools spread across business and mission functions, these responsibilities become closely connected to the broader work of defining AI governance roles and ownership.

The department’s quarterly reviews provide another important element: a mechanism for tracking whether AI investments are delivering measurable mission outcomes and identifying where data or governance gaps are slowing progress.