Anyone five to ten years from retirement can feel that artificial intelligence (AI) is different from the technology waves that came before it, and the numbers back that instinct up. The federal government’s own use case inventory, published by the Office of Management and Budget (OMB), documented 3,611 AI use cases in 2025, including 445 uses classified as “high-impact.” The inventory includes AI at various stages of development, but more than 1,800 of the reported uses were already deployed or in pilot. This is not a handful of experiments. It represents a rapid government-wide buildout, and it is landing at the same time agencies are already operating with fewer people.
For federal employees weighing when to retire, the real question is not whether AI is a big deal. It clearly is. The question is what it changes about a specific job, a specific office, and a specific retirement timeline, as opposed to what it changes in the abstract.
Where This Change Potentially Hits Your Federal Retirement Plans
AI isn’t currently changing the FERS or CSRS retirement rules. But if you’re five to ten years from retirement, the rapid adoption of AI could change the job and career path on which your retirement assumptions are based. It does not rewrite the pension formula or directly reduce the retirement credit you have already accumulated. What it can affect are some of the things that determine where your final retirement numbers land: whether a planned promotion still happens, whether your future basic pay and High-3 develop as expected, whether your position remains in its current form long enough to reach a target retirement date, and whether the agency processing your retirement paperwork can do so on schedule. Those are the specific things worth watching as AI moves deeper into the federal workplace.
This Isn’t a RIF, But It Could Still Affect Your Retirement Date
A reduction in force (RIF) is a formal, rule-bound process. It comes with a notice and rules governing competitive areas, competitive levels, retention standing and potential assignment rights (see RIF Basics for how that process works). AI adoption in the federal government is not following that pattern. Instead, agencies are folding AI and automation into daily operations at the same time hiring constraints, attrition and workforce restructuring are leaving some positions unfilled. Those gradual changes do not necessarily arrive as a RIF notice in the mail, which is exactly why they can be easy to miss while they are happening.
The Defense Logistics Agency (DLA), a roughly 25,000-person combat support agency, is a useful example of how far automation can go. Its chief information officer told reporters in September 2026 that the agency runs roughly 185 to 190 automated bots, with about 90% to 95% operating unattended. DLA is now moving beyond traditional robotic process automation toward more autonomous “agentic” AI systems working across the organization. DLA’s leadership has started referring to these emerging systems as “digital employees” that will require new ways of managing them alongside the people who still work there. That is one agency’s experience, not a governmentwide mandate, but it shows what AI doing more of the work can look like once it moves beyond a pilot program.
How Exposed Is Your Federal Job?
Broader labor-market research offers a useful starting point, even though it is not federal-specific. Anthropic’s analysis of how its Claude models are used in practice found some of the highest levels of observed AI task coverage among computer programmers, customer service representatives, and data entry workers, while occupations built around unpredictable physical work, such as cooks or mechanics, showed little or no observed exposure in its data. That does not measure whether a particular job can ultimately be replaced by AI, but the general pattern helps explain what is already happening inside federal agencies.
OPM Director Scott Kupor laid out two concrete examples in a May 2026 interview covered by FedScoop. The first involves federal job classification: OPM has been developing a tool called USA Class that uses AI to draft position descriptions across the government’s more than 600 job classifications. The second involves retirement processing itself. Kupor said OPM’s retirement call centers receive “way more calls than people who can actually service them” and that the agency wants AI bots to handle basic requests, like a retiree changing an address, so human staff can focus on complicated cases. He was direct that the intent is not to replace people but to redirect them toward the parts of the job “only the people can do.”
Taken together with the broader labor-market research, those examples suggest a rough framework, not an official government classification, for thinking about exposure across federal occupations:
- Higher near-term exposure: work built around processing, routing, answering standardized questions, or producing standardized documents, such as retirement customer service, benefits status inquiries, records requests, position-description drafting and routine correspondence.
- Moderate exposure: work that AI can assist with substantially but where a human still owns the final judgment, including analytical, financial, procurement and case-support functions.
- Potentially lower near-term exposure to full automation: adjudicative, investigative and enforcement work in which case-specific human judgment remains central, along with roles focused on managing or validating AI output.
None of this is a precise forecast for any one position. But it explains why two federal employees can have completely different experiences of “AI at work” depending on whether their job looks more like processing a form or more like deciding a contested case.
OPM Is Already Living This, And It’s the Agency Processing Your Paperwork
The clearest evidence that workforce reductions and AI-driven modernization are occurring alongside each other comes from OPM’s own budget documents. According to a July 2026 report from the Government Accountability Office (GAO), OPM’s fiscal year 2027 budget justification proposed using AI tools and modernizing information technology systems in offices operating with fewer staff.
Those workforce reductions are already substantial. GAO found that OPM’s total headcount shrank by 35 percent between December 2024 and March 2026, a reduction of 1,052 employees. Of the employees who separated during that period, 57 percent had eleven or more years of service, representing what GAO described as a significant loss of institutional knowledge. GAO also said the workforce changes reduced operational capacity at the agency. OPM’s Retirement Services office, which administers the federal civilian retirement systems and processes retirement applications, saw a 16 percent staffing decrease between fiscal years 2024 and 2026. Of the OPM employees who separated between December 2024 and March 2026, nearly 60 percent did so through a deferred resignation program and 10 percent through a reduction in force.
That’s the agency that will process your retirement application.
The staffing reductions have occurred alongside real consequences for some retirees waiting on paperwork. A former Internal Revenue Service employee who accepted OPM’s deferred resignation offer told Government Executive that she and others in her position had been forced to “drain our savings” and rely on credit cards while waiting for their retirement checks to start. That account does not prove OPM’s staffing reductions caused every retirement-processing delay, but it is a reminder that anyone weighing retirement timing should plan for the possibility that the agency processing that retirement may take longer than expected, even as OPM works to automate parts of the retirement-service process.
OPM Is Pushing AI Deeper Into Federal Personnel Operations
Separately from workforce reductions, OPM issued governmentwide guidance on August 27, 2026, encouraging agencies to expand their use of AI throughout the hiring process. OPM Director Scott Kupor’s memo argued that agencies “may be compromising efficiency and quality” in federal hiring by failing to adopt AI. OPM’s chief information officer, Adam Starr, went further in public remarks, telling Federal News Network that agencies had been “overly cautious” about using AI in hiring despite existing guidance permitting it.
The guidance distinguishes between AI that supports a human decision and AI that becomes the principal basis for a consequential personnel decision. It identifies several uses that may not qualify as “high-impact” under existing federal AI policy when specified safeguards are followed, including drafting job materials and AI-assisted screening or qualification review when an official independently reviews the underlying record. OPM has also made clear that more consequential uses involving hiring, promotion, performance management, discipline, termination or reassignment can trigger stricter high-impact AI requirements. The memo’s overall message, also covered by FedScoop, is that agencies have more room to use AI in federal hiring than many previously assumed.
This memo governs who gets into federal service, not who gets pushed out. But it establishes something important for anyone watching this trend: the administration is encouraging AI to move deeper into federal personnel operations, while retaining tighter safeguards around uses that directly determine consequential personnel decisions.
What Changes Underneath You If You Stay
What this means in practice will vary enormously by agency and occupation. Most white-collar federal roles are not simply being automated wholesale. But developments at DLA, OPM and elsewhere point to several changes employees may increasingly encounter:
- Employees may spend more time overseeing, reviewing or validating AI systems rather than performing every underlying task themselves, potentially creating new training expectations before retirement.
- Positions vacated through retirement, deferred resignation or other attrition may not always be backfilled one-for-one as agencies operate under hiring controls and workforce-restructuring plans while also adopting automation.
- Performance expectations and position duties may evolve as employees are asked to supervise or validate AI-generated work, making it increasingly important that official position descriptions accurately reflect substantial changes in responsibilities.
- Employees in processing-heavy customer service or standardized document-drafting roles may see duties consolidated, automated or reassigned as agencies expand AI use.
Gradual changes in duties or decisions not to fill vacancies do not, by themselves, trigger the formal protections associated with a RIF. An employee whose job evolves while the position itself remains intact generally does not receive a RIF notice or compete on a retention register simply because automation has changed the work. If an agency later abolishes positions or takes other actions requiring formal RIF procedures, a different set of rules and protections applies.
The Retirement Math: Timing, Promotion, and High-3 Average Salary
The potential financial effect described earlier is indirect, but it is worth spelling out in full. If restructuring changes an employee’s promotion prospects, grade progression, future basic pay or decision about how long to remain in federal service, it could affect the High-3 salary or retirement date on which the employee’s projected annuity is based. For someone only a few years from retirement, that is where a workplace technology change can become a retirement-income issue.
That distinction matters. An employee who had planned to spend the final five years of a career moving into a higher grade, competing for a promotion or simply remaining in a particular position until reaching a planned retirement date may need to reassess if that job changes substantially. AI does not reduce an already-earned pension simply because an agency adopts it, but changes to a career path before retirement can change the assumptions on which a retirement plan was built.
What To Do About It
Federal employees in this stretch of their career have a few practical options:
- Assess where a specific position falls on the exposure spectrum above, processing-heavy work, judgment-based work, or oversight of AI itself, and use that as a starting point for thinking about how much change to expect.
- Ask directly whether AI adoption or workforce-restructuring plans exist for a specific office or division, rather than assuming agency-wide messaging applies evenly everywhere.
- Take advantage of any AI-literacy, AI-governance or oversight training an agency offers, particularly if those tools are beginning to become part of the employee’s regular duties.
- Keep records of how job duties evolve, particularly if substantial AI-oversight responsibilities are added without a formal position-description update. Accurate position descriptions can matter for classification and other personnel decisions, while documented performance remains one of the factors used in RIF retention standing.
- Revisit retirement timing if a role is being restructured in a way that could affect promotion potential or a future High-3 salary trajectory, rather than assuming the only change worth planning around would arrive through a formal RIF announcement.
CHECKLIST: Download this federal retirement planning checklist specifically made for concerns of AI adoption in the workforce.
The Bigger Picture
OPM’s own budget documents show workforce restructuring occurring alongside modernization and greater use of AI and automation as the agency seeks to operate with fewer employees. GAO has already documented what the workforce side of that transformation looks like at OPM itself: substantially fewer staff, a loss of experienced employees and reduced operational capacity. The Defense Logistics Agency shows what the technology side can look like at a more advanced stage, with hundreds of automated processes and emerging “digital employees” designed to work alongside human employees. OPM’s own plans for AI-assisted job classification and retirement customer service show that the agency responsible for processing federal retirement applications is moving in the same technological direction.
Neither development is the same thing as a RIF, and the available evidence does not establish that AI itself is responsible for federal workforce reductions. But the two trends are increasingly unfolding at the same time, and the scale of governmentwide AI adoption — 3,611 reported use cases, with more than 1,800 already deployed or in pilot — suggests this is not a passing phase. For federal employees approaching retirement, that creates a different kind of planning question: not simply whether their position will still exist five or ten years from now, but how much the job, the career path, the workforce around it and the agencies administering their benefits may change before they leave.