AI basics in plain English. What OMB M-25-21 actually requires — and by when. What "AI-ready workforce" means for your specific role. You'll finish this lesson knowing exactly where AI fits into your work.
Let's start with what AI is not. It is not a sentient being. It is not magic. It does not "understand" your work the way a colleague does. And it will not replace your judgment — at least not the good kind.
What AI actually is: software that finds patterns in large amounts of data and generates outputs based on those patterns. That's it. The reason it feels remarkable is that the patterns it has learned come from nearly every text document ever put on the internet. So when you ask it a question, it draws on a vast — though imperfect — base of human knowledge to respond.
For practical purposes, most AI tools you'll encounter in a federal context do one of four things:
Notice what is not on that list: making final decisions, operating without oversight, or handling classified information without specific authorization. That is not a limitation of AI — it is the current federal policy framework. And understanding that framework is the most important thing you can do right now.
In January 2025, the White House Office of Management and Budget issued Memorandum M-25-21, titled "Accelerating Federal Use of Artificial Intelligence through Innovation, Governance, and Public Trust." This is the governing document for federal AI policy in 2025 and beyond.
Here is what it actually requires — translated from policy language into plain English:
| Requirement | What It Means | Who's Responsible |
|---|---|---|
| AI Use Case Inventory | Every agency must maintain a public inventory of all AI systems in use. Entries must describe the use case, the data used, the risk level, and the human oversight mechanism. | Chief AI Officer (CAIO) with input from all program offices |
| Chief AI Officer (CAIO) | Each agency must designate a CAIO responsible for AI governance, strategy, and coordination with OMB. | Agency head designates; often the CIO or a senior IT official |
| AI Governance Board | Agencies must establish internal governance structures to review AI use cases before deployment. | CAIO chairs; membership should include legal, privacy, and program leads |
| Workforce Training | Agencies must develop and implement AI literacy training for their workforce. This course counts. | CHCO and CAIO jointly responsible |
| Procurement Standards | Agencies must apply specific requirements when acquiring AI systems — including transparency, explainability, and testing standards. | Contracting officers with CAIO guidance |
| High-Impact AI Review | AI systems that affect significant rights or safety must undergo enhanced review with mandatory human oversight. | Program leads with legal and privacy review |
The memo uses the phrase "AI-ready workforce" 11 times. But what does that actually mean for someone who is not a data scientist?
It means three things:
Not every task is a good candidate for AI assistance. A memorandum that requires your institutional knowledge and policy judgment is not. A meeting summary that needs to be drafted quickly from raw notes is. The skill is knowing the difference — and making that call quickly.
AI tools make mistakes. They hallucinate facts. They can be confidently wrong. An AI-ready workforce knows to verify outputs before acting on them, especially in high-stakes situations. This is not paranoia — it is professional judgment applied to a new tool.
You don't need to memorize M-25-21. But you do need to know: does this use case need to go in the inventory? Does this data classification allow this tool? Is there a human in the loop where required? These are operational questions that affect your day-to-day work.
As of April 2026, here is the honest state of federal AI adoption:
What's working well: Document summarization, meeting note drafting, policy research, internal knowledge management, and procurement document preparation. These are low-risk, high-value applications that agencies are deploying today with strong results.
What's being piloted carefully: Benefits determination assistance, fraud detection, case prioritization. These are higher-stakes applications that require robust human oversight and are subject to enhanced review under M-25-21.
What's still off the table: Autonomous decision-making on rights-affecting determinations, use of non-FedRAMP tools with sensitive data, AI systems that can't be explained or audited. These are not just policy restrictions — they are reasonable professional standards.
Understanding this landscape tells you where your agency is likely focused and what types of use cases will get governance approval versus what will face scrutiny.
This exercise produces a real output you can use when you get to Day 3 (writing use cases). Take 10 minutes with this framework:
Keep this list. You will use it on Day 3 to draft formal use case entries.
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