Government AI adoption should start with a clearly defined service or operational problem, not with a tool. A practical plan connects a bounded use case to accountable owners, data and security requirements, workforce readiness, procurement, human review, and measures for deciding whether to continue.
The NIST AI Risk Management Framework is a voluntary resource for organizations seeking to manage AI risks. NIST's Generative AI Profile offers additional risk-management guidance for generative AI. These resources can inform planning, but they do not replace an agency's own legal, security, records, procurement, or policy review.
Why a plan matters
AI systems can affect how information is collected, summarized, routed, or used to support decisions. Before selecting a tool, establish what the system may access and do, which staff will check its output, how errors will be handled, and what evidence will be kept.
An adoption plan also makes it easier to compare a proposed use with the actual workflow. A tool may not be an appropriate fit if it does not address a defined need or cannot meet applicable data and oversight requirements.
A practical six-step plan for agencies
- Identify a specific, measurable pain point.
- Set a baseline and define what a useful result would mean.
- Map the current process, including data and handoffs.
- Establish governance, access, and human-review rules.
- Redesign the workflow around the permitted role of AI.
- Test in stages and decide whether to revise, stop, or expand.
Keep an initial evaluation narrow enough that staff can observe how the system behaves and assess its effect on the work. Potential areas to assess include document organization, records search, intake routing, and drafting support.
Hypothetical example
An agency considering AI-assisted intake could first map where submissions arrive, what information staff need to review, and which decisions must remain with authorized employees. In a limited test, staff might compare AI-generated document summaries with the source records, record errors and omissions, and stop the test if data-handling or quality requirements are not met. This is an illustration of a planning approach, not a report of an agency project or result.
1. Identify measurable pain points
Choose a workflow where the problem can be described and observed, such as a queue that is difficult to monitor or repeated manual handling of the same document. Use existing operational information where appropriate, and agree on a baseline before a pilot begins.
Talk with the staff who perform the work. They can identify exceptions, informal workarounds, and service requirements that a tool-selection process might otherwise miss.
2. Set goals and decision criteria
Define what the evaluation is intended to learn. Possible measures include staff review time, completeness of extracted information, correction rates, or whether a handoff reaches the right team. Select measures that fit the workflow and do not treat speed alone as proof of better service.
Set decision criteria before testing: what findings would lead to continuing, changing, or ending the evaluation? Document limitations and any effects on clients or staff that the measures may not capture.
3. Map the current process
Document the steps from intake through completion, the systems and records involved, the people accountable at each stage, and the points where judgment is required. Identify what information an AI system would receive, produce, or pass to another system.
Use a workflow diagram or data-flow map to make proposed changes visible. Confirm that integrations, data access, retention, and vendor arrangements can be reviewed before implementation.
4. Establish governance
Define who owns the use case, who approves access, what tools and data are allowed, how outputs are checked, and how staff report problems. Identify applicable privacy, security, accessibility, records-management, procurement, and program requirements with the responsible agency officials.
A governance checklist can include:
- Data classification and permitted processing environments
- Access controls, retention, and logging requirements
- Human-review responsibilities and escalation routes
- Procedures for correcting errors and documenting limitations
- A named owner and a process to review changes to the tool or workflow
5. Redesign the process around human accountability
Specify whether AI may summarize, suggest, classify, or take another limited action. Identify decisions and communications that require review by authorized personnel. Make it possible for staff to correct or disregard an output, and provide a route for unusual cases.
Explain the system's role to affected employees and, where appropriate, the people using the service. Do not present generated text as a verified fact unless it has been checked against an authoritative source.
6. Test in stages
Begin with a controlled evaluation that uses approved data and includes the staff who will work with the system. Review output quality, exceptions, access logs, user feedback, and the measures selected in advance. Keep a documented route to pause or end the evaluation.
Only consider expansion after the agency has reviewed the evidence, outstanding risks, support needs, and any required approvals. A pilot is an opportunity to learn; it is not, by itself, evidence that a system is suitable for broader use.
How Gallant Business Solutions helps
Gallant Business Solutions describes services in AI implementation and process automation, including use-case and risk assessment, workflow mapping, implementation planning, and adoption support. See the Gallant services page for its current service descriptions.
Frequently Asked Questions
What belongs in a government AI adoption plan?
Include the use case and accountable owner, data and security requirements, human-review points, procurement and policy considerations, staff preparation, evaluation measures, and a process for monitoring or ending the use.
How should an agency choose an AI pilot?
Start with a bounded workflow that staff can observe, data and approvals that can be handled appropriately, clear review responsibilities, and criteria for revising or stopping the test.
What is the role of staff in AI adoption?
Staff can help describe the real workflow, identify exceptions, review outputs, and report whether a proposed change fits operational and service needs.