AI Across the Federal Government: What Agency Leaders Need to Do Now
The administration signaled a clear shift: reduce red tape and push agencies to deploy AI. In April, the White House budget office directed every corner of government to put the technology to work.
As the statement put it, "The Federal Government will no longer impose unnecessary bureaucratic restrictions on the use of innovative American AI in the Executive Branch."
What this means for your agency
You have permission to move. Pilot useful tools, document the impact, and scale what works. Keep humans in the loop and show your math-transparency will protect the program and the mission.
Quick wins to target first
- Document summarization for case files, grants, and briefings
- Inbound request triage for call centers, email, and FOIA
- Fraud, waste, and abuse alerts on transactions and claims
- IT service desk assistance and knowledge retrieval
- Procurement support: market research, draft SOWs, compliance checks
Guardrails you cannot skip
- Privacy: honor the Privacy Act, minimize PII, and log access
- Security: align with FISMA controls and your zero trust roadmap
- Fairness: test for bias, document known limits, and track complaints
- Records: classify outputs, retain per NARA, and enable e-discovery
- Transparency: plain-language notices, clear user instructions, and a feedback channel
- Accessibility: Section 508 compliance for all user interfaces and documents
- Procurement: comply with FAR, define data rights, and require audit logs
A simple 90-day plan
- Weeks 1-2: Appoint an AI lead, set success metrics, and inventory 10-15 high-volume workflows
- Weeks 3-4: Pick two low-risk use cases; run privacy and security reviews; finalize data access
- Weeks 5-8: Launch pilots with human review; track accuracy, cycle time, and error types
- Weeks 9-12: Publish results; decide go/no-go; write a one-page playbook to scale
Metrics that matter
- Cycle time per case or request
- Cost per transaction or decision
- Accuracy vs. human baseline (with confidence ranges)
- User satisfaction and complaint rate
- Escalation rate to human review
- Model drift: performance stability over time
Vendor and procurement checklists
- Security: FedRAMP authorization (where applicable), data isolation, incident response SLAs
- Transparency: model cards, training data sources (at least at a high level), known limits
- Controls: human-in-the-loop features, audit trails, and content filtering
- Data rights: no vendor training on your data without written approval; clear deletion terms
- Testing: provide a sandbox and allow third-party or internal red-team evaluation
Governance that enables speed
- Create a lightweight AI intake form (use case, data, risks, expected gains) and review weekly
- Maintain an inventory of AI use across programs with owners and metrics
- Adopt a risk framework and keep it simple: impact, likelihood, mitigations, owner
- Publish short transparency notes for public-facing systems
Helpful references
Build your team's skills
Upskill program managers, privacy officers, and contracting officers together. Shared language beats siloed expertise.
Bottom line
The mandate is clear: deploy useful AI, cut busywork, and document results. Start small, keep humans accountable, and show measurable gains before you scale.
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