Let’s be honest.
AI is already inside your agency.
Maybe not officially. Maybe not approved. But it’s there. Someone is pasting text into a generative tool to “speed things up.” Someone is summarizing PDFs. Someone is drafting policy memos with a chatbot open in another tab.
The question is not whether AI is being used.
The real question is: Is it governed?
If you work in federal digital services, this is where things get serious. AI without governance is risk. AI with governance becomes capability.
And yes, that difference matters.
Government does not get to experiment the way startups do.
Federal agencies operate inside:
AI does not get a hall pass.
The guidance from Digital.gov on digital governance makes something very clear: governance is about decision rights, roles, standards, and accountability. It is not just policy documents sitting on a shelf.
AI must fit inside that structure.
Not around it.
Not ahead of it.
Inside it.
AI governance is not a single memo.
It is a structured system that answers five questions:
If your agency cannot answer those clearly, you do not have governance. You have experimentation.
And experimentation in public service has consequences.
Start simple.
Create categories of AI use:
Most agencies should prohibit high-risk use cases until formal review processes exist.
This is not anti-AI.
It is pro-accountability.
Generative AI systems are probabilistic engines trained on large datasets. They are not inherently aware of federal privacy obligations.
Your policy must explicitly state:
If this feels strict, good.
Public trust is fragile.
As outlined in Digital.gov’s guidance on trust, transparency and clarity are foundational to maintaining credibility in digital services.
You cannot build trust if you leak data through convenience.
AI-generated content must never be published without human review.
Period.
Every AI governance policy should include:
AI assists.
Humans remain responsible.
If no one owns the output, the system will eventually fail.
Governance is not a one-time decision. It is an operating model.
Agencies should establish:
AI decisions should flow through existing governance bodies where possible. Do not build a shadow governance layer.
Fold AI into your current digital governance structure.
That keeps it sustainable.
If you cannot explain how AI was used, you are exposed.
Your policy should require:
FOIA does not disappear because a machine helped write the document.
AI does not remove transparency requirements.
It increases them.
Start with contained pilots.
Good federal AI pilot examples include:
Avoid starting with citizen-facing decision systems.
Build governance muscle before scaling risk.
You would be surprised how often this goes wrong.
AI governance fails when it is reactive instead of intentional.
This is the part that matters most.
Government digital services operate on legitimacy.
When a citizen reads your website, they assume:
If AI undermines any of those, trust erodes.
If governance reinforces them, trust strengthens.
The real risk is not AI itself.
The real risk is unmanaged AI.
If you are building your first AI safe-use policy, start here:
That is not flashy.
But it is operational.
And in federal environments, operational wins.
AI will continue to evolve. Models will improve. Tools will multiply.
Governance is what keeps agencies steady.
Innovation inside governance builds capability.
Innovation outside governance builds headlines.
Choose carefully.