A few months ago, I asked a room full of professionals a question:
“If AI gave you a recommendation right now, how would you know whether it was wrong?” and… the room got quiet.
Not because they were unfamiliar with AI. Many were already using tools like ChatGPT, Copilot, Gemini, or other approved platforms to summarize documents, draft content, organize ideas, and speed up routine work.
The silence came from something deeper: They realized they had spent far more time learning how to get answers from AI than learning how to evaluate those answers.
That distinction matters! Because as AI becomes part of daily work, the most valuable skill is no longer simply generating information. It is judging information.
Prompting Is the Starting Point, Not the Finish Line
Learning how to use AI tools matters. Employees need to understand how to ask better questions, give clearer instructions, protect sensitive information, and use approved platforms responsibly. Prompting is an important part of AI literacy.
Prompt engineering is important, and having good prompts moves the needle, but it is no longer enough.
A well-written prompt can produce a polished answer, but it can also produce a polished answer that is incomplete, misleading, outdated, biased, or wrong. That is where human judgment comes in.
The next phase of AI adoption is not just teaching people how to use the technology. It is teaching them how to think with it.
The New Skills Gap
For the past two years, much of the conversation around AI has focused on access and adoption.
Do employees have the tools? Do they know how to use them? Are they saving time?
Those are important questions, but they are no longer the only questions leaders should be asking. The more mature questions are:
- Can employees recognize when an AI-generated answer is incomplete?
- Can they identify flawed assumptions?
- Can they verify claims before using them?
- Can they apply mission context, policy constraints, and institutional knowledge?
- Can they explain why they accepted, edited, or rejected an AI-generated recommendation?
These are not just technical skills; they are judgment skills. And in government, judgment is not optional.
Why This Matters for Government Teams
Government professionals make decisions that affect programs, budgets, services, policies, operations, and people. AI can already help teams accelerate research, summarize complex information, generate options, and reduce administrative burden. That alone creates meaningful efficiency.
But the next phase of AI is bigger than task support!
With the rise of agentic AI, we are moving toward systems that can help manage multi-step work. Instead of simply responding to a prompt, these tools can be designed to pursue a goal, organize the steps required, interact with approved systems, flag missing information, route work for review, and keep processes moving.
That shift has major implications for government teams. Imagine:
A grants team using AI to compare applications against eligibility criteria, identify inconsistencies, summarize reviewer notes, and surface risk areas before a final decision is made.
AI helping a program office monitor incoming requests, identify which ones need action, gather relevant background, draft a response, and prepare it for human review.
A communications team using AI to monitor public feedback, detect emerging questions, prepare message options, check language against approved guidance, and route drafts through the appropriate approval path.
A procurement team using AI to organize requirements, compare vendor responses, identify gaps, and highlight where human review is needed most.
In this environment, AI is no longer just helping someone write faster or summarize faster. It is beginning to reshape how work moves. That is exactly why judgment becomes more important.
When AI supports one task, the risk is limited to that task. When AI begins supporting workflows, decisions, handoffs, and recommendations, the stakes get higher. Teams need to understand not only what AI produced, but how it got there, what it assumed, what information it used, and where human oversight belongs.
Agentic AI can help government teams become more responsive, coordinated, and efficient. But it also requires a higher standard of review.
The question is no longer simply, “Can AI help us do this faster?”
The better question is, “Where should AI act, where should it assist, and where must a human decide?”
The Hidden Risk: Faster Average Work
Much of the public conversation around AI focuses on security, privacy, compliance, and governance, and those concerns are important. But there is another risk leaders should be paying attention to:
What happens when organizations become exceptionally efficient at producing average work?
AI can help teams create reports faster. Emails faster. Briefings faster. Recommendations faster.
But if employees are not trained to question, validate, and refine what AI produces, organizations may simply become faster at producing mediocre outcomes. That is not transformation…that is acceleration without direction.
The goal should not be to produce just more output. The goal should be to produce better decisions, clearer communication, stronger analysis, and more effective public service.
A Simple Method: VET Every AI Output
One practical way to build better AI judgment is to teach employees to VET AI-generated work before they use it. VET stands for:
Verify the facts.
Examine the assumptions.
Test the recommendation.
It is simple enough to remember, but strong enough to change how teams use AI.
1. Verify the Facts
AI can produce information that sounds confident even when it is wrong.
Before using an AI-generated answer, employees should ask:
- What factual claims does this output make?
- Are the claims accurate?
- Are they current?
- Can they be verified through trusted sources?
- Is anything missing that would change the conclusion?
- Is the output relying on general knowledge when the situation requires specific expertise?
This step is especially important when AI is used for research, policy summaries, technical content, legal or compliance-adjacent topics, public communication, procurement support, or executive briefings.
The more consequential the work, the higher the standard for verification should be.
2. Examine the Assumptions
Every AI output contains assumptions. Some are obvious. Others are hidden.
AI may assume the audience is general when the audience is technical. It may assume the goal is speed when the real goal is accuracy. It may assume a private-sector best practice applies neatly to a government environment. It may assume a policy, program, or process is more flexible than it actually is.
That is why employees should ask:
- What did AI assume about the audience?
- What did it assume about the goal?
- What did it assume about the constraints?
- What did it assume about available resources?
- What context did it not have?
- Would this answer change if AI had more agency-specific information?
This is where institutional knowledge matters!
AI can generate options. But unless the environment has been intentionally configured with the right context, data, guardrails, and review processes, it cannot automatically understand the internal history, stakeholder dynamics, operational realities, or mission context behind a decision.
A general-purpose AI tool may produce a reasonable answer based on broad patterns. A well-designed AI environment can be much more useful because it can reflect the organization’s language, priorities, workflows, policies, and mission.
But even then, context is not the same as judgment.
The more tailored the AI environment becomes, the more important it is for leaders to ask: What information is it using? What sources does it trust? What assumptions is it carrying forward? Where does human review belong?
This is a deeper conversation for another article, because setting up AI tools to understand your organization, your mission, and your standards is quickly becoming one of the most important parts of AI readiness.
3. Test the Recommendation
The final step is to test whether the AI-generated output actually holds up in the real world. Employees should ask:
- Does this recommendation align with the mission?
- Does it fit the audience?
- Does it account for risk?
- Is it realistic given the timeline, budget, policy or staffing constraints?
- What could go wrong if we acted on this?
- Who else should review it before it moves forward?
- What would a subject matter expert challenge?
This step moves AI use from passive acceptance to active evaluation. That is the difference between using AI as a shortcut and using AI as a strategic tool.
A Practical Exercise for Teams
Here is a simple exercise leaders can use with their teams.
Choose one AI-generated output. It could be a draft email, report summary, project plan, policy overview, communication strategy, or recommendation.
Then ask the team to review it using the VET Method.
VET Checklist
Verify the facts
- Are all factual claims accurate?
- Are the sources credible?
- Is the information current?
- Are there missing facts that could change the recommendation?
- Does this need legal, policy, technical, or subject matter expert review?
Examine the assumptions
- What audience did AI assume?
- What goal did AI optimize for?
- What constraints did AI ignore?
- What context was missing?
- What would change if this were for a different stakeholder, agency or mission need?
Test the recommendation
- Does the output align with the mission?
- Is the recommendation realistic?
- What are the risks?
- What could be misunderstood?
- What would happen if we acted on this without further review?
- What needs to be refined before this is usable?
The goal of the exercise is not to prove AI is unreliable. The goal is to build the habit of thoughtful use.
AI should make employees faster, but it should also make them more deliberate.
What Leaders Should Measure
Many organizations are measuring AI adoption by activity.
How many employees are using it? How often are they using it? How much time are they saving?
Those metrics have value, but they do not tell the full story. Leaders should also be asking:
- Are employees improving the quality of their work?
- Are they documenting how AI was used?
- Are they verifying AI-generated claims?
- Are they escalating high-risk outputs for review?
- Are they applying human expertise before making decisions?
- Are teams becoming more thoughtful or simply faster?
AI maturity is not measured by how many prompts an organization runs! It is measured by how well people evaluate what comes back.
From AI Literacy to AI Maturity
AI literacy is knowing how to use the tool.
AI maturity is knowing when to trust it, when to challenge it, and when to walk away from the answer entirely. That is the shift leaders need to make!
The first phase of AI adoption is about access, experimentation, and basic skills. The next phase is about quality, accountability, and judgment.
That does not mean organizations should stop teaching people how to use AI. They should absolutely continue building AI literacy, but the real value comes when employees can combine machine speed with human discernment.
One Question Leaders Should Start Asking
The next time someone on your team uses AI, do not just ask: “Did it save time?”
Ask: “What did you do to verify the answer?”
That one question can reveal a lot. It shows whether employees are using AI passively or thoughtfully. It shows whether they understand the risks. It shows whether they are applying expertise, context, and accountability.
In a world where more people have access to the same tools, the advantage will not come from who can generate the most answers.
It will come from who can make the best decisions.
AI readiness in government is an organization’s ability to use AI tools responsibly, effectively, and securely. It goes beyond access to technology. True readiness includes workforce training, governance, human oversight, data quality, verification practices, and the ability to apply AI in ways that support the agency’s mission.
Human judgment matters because AI can produce outputs that sound confident but may be incomplete, outdated, biased, or incorrect. Government professionals must be able to verify information, examine assumptions, apply mission context, and decide whether an AI-generated recommendation is appropriate to use.
Yes. Prompt training is an important part of AI literacy because employees need to know how to ask better questions, provide useful context, and use approved tools responsibly. But prompt training should not be the final goal. Government teams also need to learn how to evaluate, challenge, and refine AI-generated outputs.
AI literacy means knowing how to use AI tools. AI maturity means knowing when to trust AI, when to question it, how to verify its outputs, and how to apply human expertise before acting on a recommendation. AI maturity is what turns tool use into responsible decision-making.
Agentic AI refers to systems that can pursue goals, complete multi-step tasks, interact with approved tools, and help move work across a process. For government teams, this creates opportunities to improve responsiveness and efficiency, but it also raises the need for stronger oversight, clear guardrails, and human review.
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.
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