Before Federal Agencies Buy More AI: Is the Workforce Ready to Use It?

Federal agencies are already using artificial intelligence across some of government’s most complex mission environments: emergency management, cybersecurity, immigration, investigations, acquisition and internal operations.

And the list is growing.

Agency AI inventories now include use cases that go well beyond experimentation. At FEMA, for example, generative AI tools have been deployed to help employees develop preliminary responses from authoritative internal and public documents and assist with code and query generation. Importantly, those use cases are designed to support — not replace — human analysts and review.

So the question is no longer whether federal agencies will use AI. They already are. The more important question is whether their workforce, governance and operating environment are ready for what comes next. Because there is a significant difference between buying an AI capability and building an organization capable of using AI well.

AI Readiness Is an Organizational Issue

Federal guidance increasingly reflects that distinction.

The Department of Homeland Security’s Playbook for Public Sector Generative Artificial Intelligence Deployment, developed from lessons learned through DHS GenAI pilots, does not treat implementation as a software installation exercise. Its framework addresses mission alignment, governance, tools and infrastructure, responsible use, measurement and monitoring, training and talent, and usability and feedback.

Similarly, current OMB policy assigns agency Chief AI Officers responsibility not only for managing AI use cases and risk, but also for advising on the transformation of the agency workforce into an AI-ready workforce.

That distinction matters because agencies are trying to modernize at the same time many are navigating significant workforce changes.

In June 2026, the Government Accountability Office reported that eight major DHS acquisition programs experienced staff reductions of at least 20% during fiscal year 2025. GAO specifically identified the resulting loss of subject-matter expertise and technical skills as a risk to future program milestones.

That creates an important tension. AI is often positioned as a way to increase capacity when organizations are resource-constrained. And it can. But AI can only amplify institutional knowledge, processes and judgment if those things still exist somewhere within the organization.

If agencies are losing experienced personnel while simultaneously introducing new AI capabilities, they cannot assume the technology will automatically compensate for that loss.

In some cases, adding technology without first addressing the knowledge and workflow problem may simply create another system for an already stretched workforce to manage.

AI Literacy Is Not the Same as AI Enablement

Many organizations begin their AI adoption efforts with training. Employees learn what generative AI is, how large language models work, how to write prompts and a handful of tasks they might perform with an AI assistant. That’s a perfectly reasonable starting point.

But for a mission-driven federal organization, it is nowhere near the finish line.

An acquisition professional does not need exactly the same AI competency as a cybersecurity analyst. Someone responsible for emergency communications does not operate within the same workflow as an executive evaluating program investments. And an employee regularly working with sensitive or protected information should not be learning AI through exactly the same scenarios as someone drafting routine administrative content.

That is why agencies should distinguish between AI literacy and AI enablement. AI literacy helps employees understand the technology. AI enablement changes how they perform their jobs.

The second requires a much more practical question:

What should this person be able to do differently because AI is available?

For an acquisition professional, that might mean using an approved AI capability to organize market research, identify patterns across requirements or accelerate preliminary analysis, while understanding which conclusions still require human verification.

For a cybersecurity analyst, AI may accelerate documentation or support analysis without removing analyst judgment from the process.

For emergency-management personnel, it could help synthesize incoming information or prepare draft communications while preserving authoritative sourcing and established approval processes.

An executive may need something entirely different. Sophisticated prompting skills may be far less important than knowing how to assess an AI use case, identify risk, interpret adoption data and determine whether an investment is actually producing a mission outcome.

Same technology. Very different readiness requirements.

Institutional Knowledge May Be One of AI’s Most Important Opportunities

Workforce readiness also raises another question that receives less attention: What happens to institutional knowledge?

Consider an employee who has supported the same federal program for 15 years. That person knows the history. They understand why certain decisions were made. They know which documents actually matter, which policies interact, which terminology has changed and which questions new employees inevitably ask. Then that employee retires, accepts another position or leaves government.

Organizations traditionally address that problem through documentation, knowledge repositories and transition materials. Those approaches remain important. But documentation has an inherent limitation:

Someone has to know it exists before they can use it.

This is an area where AI can potentially change knowledge management significantly.

In our work, we have been developing what we sometimes refer to as a digital brain. The term does not mean an autonomous AI system replacing subject-matter experts. It describes a structured environment built around an organization’s approved knowledge.

Authoritative documents, institutional knowledge, terminology, procedures, strategic goals, historical context and other validated information can be organized so employees interact with AI from a common foundation. Instead of every employee beginning with a blank chatbot, they begin with shared organizational context.

That distinction matters. One of the biggest risks of decentralized AI adoption is not simply that employees are using AI. It is that 100 employees can use AI in 100 different ways, based on 100 different source sets, and produce 100 different interpretations of the same organization.

The AI tool may be shared.

The context is not.

And without common context, achieving consistency becomes extremely difficult.

Governance Must Reach the Workflow

This is also where AI governance needs to move from policy into operations.

Governance discussions understandably focus on questions such as:

Those are not simply AI questions. They are workflow-design questions. And this is where responsible AI becomes tangible.

For federal agencies especially, “human in the loop” cannot simply be a sentence in a governance document.

Someone has to design the loop!

The human role has to be clear. The decision point has to be clear. The source of authoritative information has to be clear. Accountability has to remain clear. Otherwise, agencies may technically have human oversight without creating a process in which that oversight is meaningful or repeatable.

Readiness Is What Turns Technology Into Capability

There is a useful analogy here. Buying emergency equipment does not make an organization prepared for an emergency.

An agency could have every required supply sitting in a cabinet. But if employees do not know where the equipment is, when to use it, how to use it or what to do when something goes wrong, simply owning the equipment provides very little operational advantage.

Readiness is what turns equipment into capability. AI is not that different.

The models will continue improving. Vendors will continue entering the market. Capabilities that seem extraordinary today will likely become standard features surprisingly quickly.

That means access to the technology itself will become less differentiating.

The harder capability to build is organizational:

• A workforce that understands where AI fits into its mission.
• Institutional knowledge that survives personnel changes.
• Shared context employees can trust.
• Governed workflows people can actually follow.
• Clear points of human accountability. And enough measurement to distinguish a useful AI capability from an expensive experiment.
• The agencies that get this right will not necessarily be the ones that bought AI first. They will be the ones that learned how to use it deliberately.

Before an agency buys its next AI capability, perhaps the first question should not be:

What can this technology do?

A better question may be:

Are our people, knowledge, governance and workflows ready to turn it into a mission capability?

Raitchele Arnell is the CMO of ArtForm Business Solutions, a women-owned digital agency supporting government contractors, enterprise organizations, and public-sector initiatives. She specializes in AI adoption, market intelligence, strategic communications, and modernization strategies for highly regulated and mission-driven industries. Her work spans cybersecurity, healthcare modernization, emergency communications, critical infrastructure, and federal technology initiatives. Known for helping organizations rethink how marketing supports mission outcomes, Raitchele focuses on the intersection of AI, operational efficiency, audience intelligence, and trust-driven communications.

Photo Credit: Mikael Blomkvist, Pexels