The FY26 National Defense Authorization Act sends a clear message: the Department of Defense needs to build an AI-enabled workforce. Congress understands that artificial intelligence will reshape how we gather intelligence, manage logistics, and make battlefield decisions. What remains unclear is how we are supposed to get there.
The government has launched several high-profile initiatives to close the gap. The Army recently established a new AI and machine learning officer career path. The White House introduced the U.S. Tech Force to recruit top engineering talent into federal service. These programs are good headlines, but they share a common blind spot. They assume the AI workforce challenge is primarily a recruiting problem.
It is not. It is a training problem.
The Workforce We Need is Already Here
Right now, thousands of intelligence analysts, logistics officers, contracting specialists, and program managers are being handed AI-powered tools and told to use them. These are not hypothetical future employees. They are the people already doing the work, and they need to understand how to evaluate AI outputs, recognize when a model is producing unreliable results, and integrate AI-assisted recommendations into human decision-making.
Creating a new career track for AI specialists does not help the GS-13 analyst who received an AI-assisted threat assessment tool last quarter. Recruiting elite engineers into a two-year program does not help the acquisition officer who needs to evaluate vendor claims about machine learning next month.
The Defense Department has a workforce in the millions across uniformed and civilian personnel. The idea that we can recruit our way to readiness misunderstands the scale of the challenge.
Training Infrastructure Does Not Exist at Scale
The private sector learned this lesson years ago. When companies deploy AI tools across their organizations, they invest heavily in workforce enablement. They do not just hire data scientists. They train product managers to interpret model outputs, teach teams how to work alongside AI tools, and help leaders understand what questions to ask when AI becomes part of core operations.
The federal government has not made comparable investments in AI literacy at scale. Yes, there are pockets of progress. Individual services and organizations have launched pilots and produced training resources. But the efforts are often fragmented, inconsistent, and far short of what is needed to build practical competency across the workforce.
What is missing is a systematic approach that builds AI capability beyond technical skills and addresses the harder operational questions:
- How do you maintain human judgment when working with AI recommendations?
- How do you identify when a model’s training data does not reflect current operational realities?
- How do you build appropriate trust in AI systems without either over-relying on them or dismissing them entirely?
These are not academic questions. They are the difference between AI becoming a trusted advantage and AI becoming an expensive, inconsistent layer that no one fully relies on.
A Path Forward
Solving this problem requires acknowledging that AI workforce development is not just about computer scientists and machine learning engineers. It is about building baseline AI literacy across every functional area that will interact with these systems.
That means investing in training programs designed for non-technical personnel. It means creating clear competency frameworks that define what readiness looks like for different roles. It means moving beyond one-time workshops toward sustained professional development that keeps pace with rapidly evolving technology.
Most importantly, it means treating training as part of mission readiness, not as an optional add-on.
The FY26 NDAA direction is the right instinct. But mandates do not create capability. If the Defense Department is serious about building an AI-enabled workforce, it needs to start with the workforce it already has, and give that workforce practical training that supports responsible, consistent use of AI in real-world settings.
The author, Angie Lienert, is President and CEO at IntelliGenesis LLC, an AI and cybersecurity company founded 18 years ago with origins in national security AI labs.
This article was originally published on Government Technology Insider on February 10, 2026.