Federal AI readiness starts with the workforce

Federal agencies have moved quickly from AI exploration to implementation, but the bigger challenge is ensuring the workforce can use the technology at scale. 

Though AI pilots often show promise, those projects are usually staffed by volunteers or hand-picked employees who already understand the technology’s value. When capabilities are rolled out to the larger workforce, agencies can see little return if the rest of their employees struggle to understand where or how those tools fit into their workflows.

“This gap isn't a technology problem and it’s not a budget problem, which is typically the first place agencies start,” said Tony Holmes, practice lead for solutions consulting at Pluralsight. “It’s a workforce problem. Nothing in the deployment plan bridges the people that are already engaged and the rest of the organization.” 

The traditional top-down approach, where technical leaders hold primary responsibility for identifying technology needs, is only “half the puzzle,” Holmes explained. Workforce leaders and employees themselves also need a voice early in the decision-making process because they have a deeper knowledge of the organization’s skillsets and day-to-day needs.

Many of the highest-value use cases come from those closest to mission work, including program staff, contracting specialists, analysts, benefits professionals and other frontline teams with first-hand experience with operational pain points.

“That’s where you’re going to get the real return on investment. If you can take half an hour or an hour of manual work off of the GS-7s desk every week, all of a sudden they can use that time to learn, train or be more strategic with their time,” said Holmes, noting that once teams understand what AI is capable of, they can make better decisions about where it should be applied in their own job functions.

The Department of Labor’s AI literacy framework offers a useful model for how agencies should think about workforce readiness before deployment. Part of the framework calls for a basic understanding of how the technology works, but much of it centers on judgment, including when to apply AI, how to direct it effectively, evaluate outputs and use it responsibly.

“If you look at it from that perspective, what you’re seeing is that four-fifths of the problem we need to solve is an understanding problem or a literacy problem, not a technology problem,” said Holmes. “Without that shared vocabulary and that shared judgment about where the tool can help and where it doesn’t help, the usage data is always going to look like a failure, and agencies are going to keep buying capability and shelving it because they're not thinking about the execution layer.”

Operationalizing AI literacy through Pluralsight AI Academy

That’s where Pluralsight AI Academy comes in, a program designed to help agencies build the execution layer through a structured, end-to-end learning journey. Coincidentally, AI Academy’s architecture was finalized at the same time the Department of Labor (DOL) published its literacy framework, and the two approaches closely mirror each other (“Great minds think alike,” said Holmes). Both focus on experience over theory and emphasize practical, judgment-centered learning that can evolve as AI tools and use cases continue to change.

“AI Academy is a maturity journey,” Holmes said. The program is tailored to each organization and individual, beginning with assessments to establish a readiness baseline, then moving through three levels: 

• AI literacy. All employees are provided a shared language around AI’s capabilities, limits, risks and responsible use. The goal is not to make every employee an AI expert, but to create a common floor of understanding across the organization so teams can discuss AI-enabled opportunities with the same vocabulary. 

“That floor is how pilot success translates, because now non-technical teams or people not part of the pilot cohort are able to identify the places that artificial intelligence can help them,” said Holmes.

• AI productivity. At this stage, agencies move from awareness to application, using hands-on labs, role-aware learning and contextualized seminars tied to the tools and workflows the agency actually runs.

“This is where the sorting happens,” Holmes said, “because it helps people understand how to automate the toil, and how to augment the friction where you still need a human in the loop.”

• Agentic AI. Smaller cohorts learn to prototype agents against real agency contexts, paired with safety, testing, readiness and governance considerations.

“The ordering matters because agents before literacy means outputs that nobody can tell are correct or useful,” Holmes said, noting that more autonomous tools will require more human expertise and sound judgment.

The program also offers a variety of learning pathways that serve different purposes.

• On-demand learning builds vocabulary and mental models.
• Labs build instincts through practice, trial and error.
• Seminars connect lessons to the tools and workflows agencies actually use.
• Workshops help produce working artifacts, not just completion certificates or hours watched.

“AI can obsolete standard courses faster than you can certify people through them,” Holmes said. “The curriculum has to be built around the one thing that doesn’t move, which is workers’ judgment about their own work.”

Literacy is the constant in an evolving landscape 

AI is evolving faster than traditional training cycles and static governance documents can keep pace. For federal agencies, that makes literacy a foundational requirement. Ultimately, a governance framework can define what responsible use should look like, but employees apply those expectations in real time across thousands of daily interactions with AI systems.

“It can be two or three years between iterations of a particular governance document,” said Holmes. “A framework document can’t catch a confidently hallucinated citation in a draft decision memo, or notice an agent that’s gone subtly wrong, it can’t apply context-specific caution in a high-stakes case, but your workforce can if you give them that literacy to evaluate that output. Literacy is the governance, and it makes it operationalized.”

Pluralsight AI Academy helps agencies turn that idea into practice, giving agencies a structured way to build judgment, confidence and applied skills in the context of the work employees already do.

“Government doesn’t need a different product, it needs the program pointed at the actual work that's there,” said Holmes. “AI Academy is the operational version of that approach. The DOL framework independently confirmed our direction and design. For a federal leader, it’s essentially the same de-risk decision validated twice.”

Learn more about how Pluralsight AI Academy can help your workforce prepare for AI.