The Tool Desk
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Start with the mission task, not a hardware request
Describe the work the AI system is meant to improve before asking for a model, GPU, cloud environment, or data center. Identify the users, the current process, the intended outcome, and how the agency will know whether the system is useful and safe. Name the person or office accountable for the result.
Distinguish an internal productivity aid from decision support or an operational system. Their consequences of failure, human oversight needs, uptime expectations, and approval requirements may differ substantially. A pilot can help answer bounded questions, but it does not by itself establish that a system is ready for mission use.
Characterize the workload and its constraints
Record the expected inputs and outputs, data volume, request frequency, concurrent users, response-time needs, peak demand, and required availability. Determine whether the work involves inference, retrieval, batch processing, fine-tuning, or training; these activities can have different compute and data needs. Also identify geographic constraints, unreliable connectivity, or situations where the system must operate while disconnected.
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These are engineering questions for comparing options, not a checklist mandated verbatim by federal policy. Their purpose is to avoid sizing infrastructure around an assumed platform or a peak that rarely occurs. An agency may find that a managed service suits variable demand, while a predictable, sustained workload or a disconnected environment calls for a different arrangement.
Make data readiness part of the infrastructure plan
Identify authoritative data sources, owners, access rights, restrictions, and the paths data will take into and out of the AI system. Assess quality, representativeness, maintenance, and whether the agency has lawful authority to use the data for the proposed purpose. Decide what can be shared within government, obtained from third parties, or drawn from public information under applicable authority.
OMB Memorandum M-24-10, issued March 28, 2024, calls on agencies to build capacity to share, curate, and govern data for AI training, testing, and operation. It states: “Any data used to help develop, test, or maintain AI applications, regardless of source, should be assessed for quality, representativeness, and bias.” Data stewardship, curation, and documentation therefore belong in the funded work plan, not in an unfunded cleanup phase after infrastructure is selected.
Choose where the workload should run
Compare agency-managed infrastructure, shared government capacity, and commercial cloud or managed services against the actual workload. The options below are patterns to evaluate, not a federal ranking or a finding that one location is generally superior.
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| Placement pattern | When to examine it | Questions to resolve |
|---|---|---|
| Agency-managed infrastructure | Consider when workload control, local operation, predictable sustained use, or disconnected operation is important. | Can the agency staff and secure it, provide facilities and energy, maintain hardware, handle peaks, and manage the full lifecycle? |
| Shared government capacity | Consider when an agency can meet its needs through an available shared capability rather than standing up a separate environment. | Does the service support the data and workload, required authorization, capacity, availability, connectivity, and operational responsibilities? |
| Commercial cloud or managed service | Consider when elastic capacity, managed operations, or access to a service fits the task and agency requirements. | Are security, privacy, performance, service levels, asset visibility, portability, data egress, and exit terms sufficiently specific and verifiable? |
For each candidate, weigh mission fitness, data governance, latency and throughput, baseline and peak utilization, resilience, connectivity, licensing and portability, operational staffing, lifecycle cost, and energy or facilities dependencies. Give the greatest weight to the constraints that matter for the particular task rather than assigning a universal winner. Provider pricing, authorization, availability, and acquisition routes vary by agency, workload, and date; a government-wide comparison cannot settle them for an individual program.
Design security, authorization, and operations at the outset
Plan how the system will be authorized and monitored, how access will be controlled, and how security updates and incidents will be handled. Set procedures for changes to models and data, human review, evaluation, and retirement. For generative AI, define safeguards and oversight appropriate to the way outputs will be used; do not leave accountability implicit in the model or service.
OMB M-24-10 advises agencies to update authorization and continuous-monitoring processes to account for AI, and to establish safeguards and oversight for generative AI. The details must be worked through in the agency’s own security and operating context. Classified workloads, legal restrictions, and specialized operational environments require agency-specific review; a general infrastructure strategy cannot resolve them.
Make cloud and managed-service contracts measurable
Procurement terms should turn operational expectations into requirements that can be monitored and enforced. GAO’s review of Cloud Smart procurement found gaps in agency guidance on cloud procurement requirements and recommended sharing examples of service-level and contract language. For a cloud or managed service, address the following where relevant:
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- Availability targets and how performance will be measured and reported.
- Security monitoring, logging, incident notification, and privacy obligations.
- Continuous visibility into high-value assets and the records needed to support oversight.
- Data access and egress, subcontractor responsibilities, and rights to audit or obtain relevant records.
- Transition assistance, data return or deletion, and exit terms if the service changes or ends.
Define how the agency will verify each commitment, who reviews the evidence, and what happens when a target is missed. A service-level promise that cannot be measured or tied to an accountable process is not an operational control.
Fund the people and lifecycle, not just compute
Infrastructure requires an operating model: systems and data engineering, cybersecurity, product ownership, user support, evaluation, and acquisition expertise. Budget for routine maintenance, security updates, monitoring, capacity changes, and eventual retirement, as well as the initial deployment. If the agency cannot staff or oversee a proposed environment, that burden is part of its cost and risk.
GAO’s 2025 review of generative AI adoption identified policy compliance and limited technical resources and budgets as challenges described by agencies. Officials also raised the difficulty of keeping appropriate-use policies current. These constraints can affect whether a workload should proceed, how quickly it can be deployed, and which operating pattern is sustainable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Read federal adoption figures in scope
GAO-25-107653 reported that AI use cases at 11 selected agencies with inventories rose from 571 in 2023 to 1,110 in 2024; generative AI use cases at those agencies rose from 32 to 282 over the same years. These are figures for GAO’s selected agencies, not a census of all federal AI activity. In the same report, 10 of 12 selected-agency officials told GAO that existing federal policy, such as data privacy policy, could present obstacles to generative AI adoption.
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GAO-25-107933 identified 94 government-wide or government-impacting AI requirements and 10 executive-branch oversight or advisory groups with a role in federal AI. GAO’s September 2025 report said agencies had a requirement to develop and publicly release an AI strategy by September 30, 2025; that date alone does not establish that every agency completed every required deliverable.
Keep large data-center policy separate from agency architecture
The White House’s July 2025 America’s AI Action Plan discusses infrastructure at a national scale, including chips, data centers, energy, and grid capacity. Its recommendations address permitting, the possible use of federal lands for data centers and power generation, infrastructure supply-chain security, and grid capacity. These are policy-plan statements and recommendations, not agency-specific instructions to build a facility.
Executive Order 14318, issued July 23, 2025, defines a “Data Center Project” as a facility requiring greater than 100 megawatts (MW) of new load dedicated to AI inference, training, simulation, or synthetic-data generation. The order’s covered components include energy infrastructure, semiconductors, networking equipment, and data storage. That threshold belongs to the order’s definition and qualifying-project policy; it is not a minimum workload size or a decision rule for whether an ordinary agency AI use case merits investment.
Use a decision record to make the choice reviewable
Before committing to a deployment pattern, document the mission outcome, workload assumptions, data authority and readiness, candidate placements, security and authorization approach, contract measures, staffing plan, and lifecycle costs. State which constraints ruled options in or out and how performance, risk, and continued mission value will be reviewed. This gives agency leadership a traceable basis for revisiting the choice when workload demand, data, policy, or service conditions change.
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