Federal AI Pilots Should Start With Workflows, Baselines, and Measurable Outcomes

Federal agencies are evaluating a growing portfolio of artificial intelligence applications, but deployment counts alone do not show whether the technology is improving public-sector operations. The Office of Management and Budget’s 2025 inventory reported more than 3,600 AI use cases across federal agencies. In a November 6, 2026, Washington Technology commentary, Egon Rinderer, a senior vice president at NinjaOne, argues that agencies and contractors should judge AI initiatives by the work they improve—not by the number of tools adopted or users engaged. The article’s central recommendation is to begin with a well-understood task, define the intended role for AI, and compare results with the existing process before considering broader investment.

The approach has direct implications for acquisition planning and solution design. Before proposing an AI platform or capability, contractors can work with agency stakeholders to identify a specific operational problem and establish a baseline. Existing measures may include processing time, accuracy, cost, service-desk mean time to resolution, or ticket backlog. The pilot should test whether AI improves a meaningful measure across the full workflow. For example, generating a draft quickly may not create a net benefit if subject-matter experts must spend hours correcting errors or rewriting it. A narrower use—such as organizing requirements or creating an initial structure while an expert remains accountable for the substance—may produce a more useful result.

The commentary also emphasizes that federal AI opportunities can originate with employees close to day-to-day work. Department of Commerce Acting Chief Technology Officer Matheus Passos described a U.S. Census Bureau employee using AI to build a tool for tracking and deploying AI across the department; the tool was quickly adopted by other bureaus. The example illustrates how operational familiarity can reveal repetitive tasks and practical automation candidates. Software management is another potential test area: IT teams review information on applications, licenses, and usage, and agencies may be able to measure review time, accuracy, and software utilization. A bounded pilot can evaluate whether AI reduces manual review or flags records for attention without transferring control of the broader process to the technology.

For government contractors, the advice is to frame proposals around agency workflows and measurable mission or operational outcomes rather than leading with the platform. Agencies and vendors should agree on evaluation criteria and human-review limits before a pilot begins so results can be interpreted consistently and oversight remains explicit. Documenting performance, limitations, and lessons learned supports a disciplined decision to expand, revise, or discontinue an initiative. The source does not identify a solicitation, contract award, funding amount, procurement deadline, or specific AI acquisition. Its value to the GovCon market is therefore as guidance on how agencies may assess future AI proposals and pilots, not as notice of an active buying opportunity.

• OMB’s 2025 inventory counted more than 3,600 federal AI use cases; agencies should prioritize demonstrated results over deployment volume.
• Start with a specific, well-understood workflow and record its existing performance before introducing AI.
• Use operational measures such as processing time, accuracy, cost, service-desk resolution time, ticket backlog, or software utilization where relevant.
• Evaluate the full workflow, including downstream correction and review effort, rather than judging an AI tool by its speed or output volume alone.
• Define the AI’s role and human-review limits before the pilot to preserve accountability and make evaluation consistent.
• The Commerce Department example describes a Census Bureau employee’s AI tracking and deployment tool, which was quickly adopted by other bureaus.
• Software and license management may offer bounded test cases involving repetitive analysis and measurable review outcomes.
• Contractors can strengthen proposals by connecting capabilities to an agency’s workflow, baseline, and specific intended outcome.
• Documented pilot evidence can support a decision to scale, revise, or stop an initiative.
• The source identifies no specific solicitation, award, funding amount, or deadline; this is procurement-planning guidance rather than an active opportunity notice.