How AI Skills Assessments Help Government Agencies Map Workforce Skills Gaps

The federal workforce is in the middle of a policy shift that changed how agencies hire, how they promote, and how they justify the workforce spending they take to Congress. The framework arrived first. The internal capability to execute against it did not. Agencies now have to demonstrate that new hires meet defined skill benchmarks, but most agencies cannot say with equivalent precision what skills the existing workforce already holds. That measurement gap sits behind every AI adoption stall, every reskilling plan that never got funded, and every workforce inventory a program office had to reconstruct manually before the next budget cycle.

This piece walks through how AI-powered skills assessments close that gap, what a defensible skills inventory produces for a CHCO or workforce planner, and how the assessment layer connects to the competency frameworks agencies are already required to use.

The Statutory Push and the Measurement Gap It Exposed

Three regulatory and operational shifts changed federal hiring in 18 months. The same shifts made the absence of an internal skills measurement layer visible across almost every agency.

The 18-Month Federal Skills-Based Hiring Timeline

The Chance to Compete Act and OPM's 2210 Deadline

The Chance to Compete Act of 2024 (Public Law 118-188), signed December 23, 2024, directs federal agencies to embed skills- and competency-based recruitment and technical assessments into their hiring practices. OPM's Merit Hiring Plan, released May 29, 2025, translates that statute into an integrated framework agencies use for classification, qualification, and assessment.

The 2210 IT Management series was the first proof point. OPM's Merit Hiring Plan runs the Skills-Based Hiring for Information Technology Management Positions pilot to move the series to fully skills-based hiring by the end of 2025, covering nearly 100,000 federal positions. In parallel, OPM directed agencies to stop ranking candidates on self-assessments by September 30, 2025 (with narrow exceptions for GS-4 and below, seasonal, and Federal Wage System roles). The number of skills-based assessments federal agencies use has increased by more than 2.5 times over the past 4 years per OPM leadership. What agencies cannot do at the same pace is measure the workforce that already sits inside the agency.

What the Federal HR Data Layer Cannot Currently Answer

Federal HR data systems were built to track position, grade, series, hire date, and tenure. They were not built to track measured skill proficiency at the individual employee level. When a CHCO gets asked what share of the analyst workforce is current on the AI literacy expectations under OMB Memorandum M-25-21, the answer cannot come out of the HR system because the HR system does not carry that data. The absence of an internal skills measurement layer means the agency is executing a skills-based hiring plan on top of a workforce it has not measured on skills.

AI Use Cases Are Now Outrunning Documented Capability

Federal agencies submitted more than 3,600 AI use cases across 41 agencies in the 2025 Federal AI Use Case Inventory, up from 1,757 across 37 agencies in 2024. Each use case creates workforce implications. Someone has to oversee it, audit it, and interpret its outputs.

• A Google Public Sector survey found 37% of federal respondents citing skills gaps as the biggest barrier to accelerating AI adoption.
• GAO's March 2026 review of IRS AI workforce planning (GAO-26-107522) found staffing reductions had left the agency without enough skilled employees to support or develop new AI tools, and the IRS lacked a workforce plan to identify and address the skills its AI workforce needs.
• The National Security Commission on Artificial Intelligence, in its 2021 Final Report, concluded the talent deficit in the Department of Defense and Intelligence Community represents the greatest impediment to the U.S. being AI-ready.

The pattern is the same across agencies. AI adoption is scaling faster than the workforce measurement layer that would tell an agency who can support the scaling and who cannot.

What an AI Skills Assessment Produces for a Federal Agency

AI-powered skills assessments are the diagnostic instrument between the workforce that exists and the workforce a strategic plan or hiring authority describes. The output is not a single test score. It is a structured, role-level, proficiency-mapped inventory that answers 3 specific questions no HR system carries by default.

Individual Proficiency Scores Against a Defined Threshold

A skills assessment produces a per-employee proficiency score against a defined skill and a defined threshold. The output separates the workforce into employees who meet the standard, employees approaching it, and employees who require targeted development. For a CHCO responding to an M-25-21 AI literacy inquiry, that separation replaces the estimate with a measurement.

Aggregated Gaps by Role, Series, or Component

Assessment data aggregates to the role, series, or component level. A workforce planner can see whether AI oversight skills are missing across a specific 2210 subseries, concentrated in one component, or distributed evenly across the workforce. That aggregation is what turns a general AI skills gap into a specific budget request, a specific training assignment, or a specific hiring authority request.

The Internal Reskilling Candidate Pool

OPM guidance encourages agencies to invest in reskilling current employees for AI-adjacent roles rather than defaulting to external hiring. Executing on that guidance requires identifying which internal employees hold the foundational skills that make reskilling to an AI-adjacent role feasible. Skills assessments produce that internal candidate pool by ranking existing employees against the entry proficiency for a target role. The agency stops choosing between hire and train and starts identifying who can move.

How Assessment Output Becomes a Workforce Plan

An assessment by itself is a snapshot. The value multiplies when the output attaches to a competency framework that structures how the agency thinks about role progression, reskilling paths, and hiring authorities.

The Framework Defines the Target State

A competency framework describes the specific proficiencies a role requires at each level. It is the definition of readiness the assessment measures against. Federal agencies working from O*NET, SFIA, or an agency-defined framework already have this layer. OPM's AI competency model (issued April 29, 2024) is a recent federal example, with 44 general and 14 technical competencies for AI-adjacent roles. What agencies typically lack is a mechanism to score current employees against those defined competencies at scale.

The Assessment Locates the Current State

Skills assessments applied against the framework's definitions produce the current-state map:

• Which employees hold the required proficiency at the required level
• Which are one level below
• Which are two or more levels below

That current-state map is the input every subsequent workforce decision requires, and it is the layer federal HR systems have historically lacked.

The Gap Becomes a Named Reskilling Assignment

When the current-state map and the target-state framework live in the same environment, the gap between them becomes a concrete assignment. An employee two proficiency levels below the target on data governance for AI systems is assigned the specific reskilling path that closes those two levels. The CHCO no longer needs to ask what training the workforce needs. The system produces the answer as an artifact of connecting the assessment to the framework.

Where the Assessment Layer Changes What Agencies Can Do

The applications sit inside the mandates federal agencies are already under.

Reskill Versus External Hire Decisions

The U.S. Tech Force, launched December 15, 2025, aims to place approximately 1,000 early-career engineers, data scientists, project managers, and AI specialists directly inside federal agencies on 2-year commitments, with private-sector partners including AWS, Apple, Google Public Sector, Microsoft, Nvidia, OpenAI, Oracle, Palantir, and Salesforce. Those placements produce the most value when the receiving agency has already mapped where its internal AI skills concentrate, where they are missing, and which internal employees the Tech Force placements should pair with for maximum knowledge transfer.

The reskill-versus-hire decision has a concrete pay-cap backdrop. GS-15 Step 10 base pay is capped at $197,200 in 2026 under the Executive Level IV limit regardless of locality, while Tech Force recruits earn $150,000 to $200,000 per CNBC's reporting on the program. Agencies that identify which existing employees can grow into AI-adjacent roles reduce the number of external hires the ceiling has to cover.

Reskill vs. External Hire Decision Signals (Illustrative Framework)

The framework below is illustrative — a synthesis of OPM's "reskill first" guidance and standard workforce-planning practice, not a published matrix. Agencies should calibrate the proficiency-gap thresholds and time-to-readiness ranges against their own competency framework, GS-level pay bands, and hiring authorities.

AI Literacy Compliance Under OMB M-25-21

Agencies subject to OMB Memorandum M-25-21 (issued April 3, 2025) must include AI training and literacy plans in the agency AI strategies due to OMB by December 26, 2025, with workforce compliance implementation windows extending into the second half of 2026. AI skills assessments provide the pre-training baseline, the post-training measurement of proficiency change, and the ongoing certification tracking that regulators, IGs, and congressional oversight will ask about at the end of the fiscal year. Without the baseline, an agency reports training completion but cannot demonstrate the proficiency shift the memo asks for.

Retirement Wave Workforce Planning

42% of federal workers are older than 50 (versus 33% in the broader US labor market), and public sector workforce planning has intensified around near-term retirement risk. The specialized public sector training programs many agencies are now building need a skills-inventory starting point to target correctly. Assessment output feeds the retirement-successor mapping directly, so the agency identifies which departing employees hold institutional knowledge that needs to transfer and which internal employees are positioned to receive it.

How KnowledgeCity's Workforce Development Platform Supports Federal Skills Assessment at Scale

The value of an AI skills assessment layer is not the assessment itself. It is that the assessment output connects to the training, the competency framework, and the workforce record on the same platform, so the agency does not maintain three parallel data systems for the same workforce. KnowledgeCity's workforce development platform runs the assessment-to-reskilling loop across 3 solutions.

What KC Skills Delivers for Federal Assessment

KC Skills sits in the Grow suite and produces the per-employee proficiency data federal agencies need for the M-25-21 baseline and the reskilling decision:

• AI-Generated Assessments: Quizzes built automatically from the competencies the role requires.
• Skill Tree Builder: A hierarchical taxonomy that mirrors OPM, O*NET, or an agency-defined framework.
• Gap-to-Training Loop: Identified gaps auto-assign learning paths; completion reads back into the profile.
• Skill Matrix and My Skills: An agency-wide proficiency grid plus a personal profile per employee.
• Skills-Drift Tracking: Proficiency change measured over time, so the M-25-21 pre-post training signal is captured.
• Campaign Engine: Roll out an assess-train-reassess cycle across a cohort, series, or component.

What KC Map Delivers for Competency Framework Mapping

KC Map sits in the Grow suite and holds the target-state framework:

• O*NET / SFIA Frameworks: Start from standards or your own model (including OPM's AI competency model).
• AI Competency Suggestions: Proposes competencies given a role, series, or program.
• Visual Map Editor: Build and maintain the map across components.
• Three-Tier Proficiency Mapping: Introductory, intermediate, and advanced proficiency per competency.
• One-Click LMS Sync: Push the mapping into the training delivery layer.
• Excel Import / Export: Bring existing agency-defined frameworks in and share the map out.

What KC LMS Delivers for Reskilling Delivery and Completion Records

KC LMS sits in the Learn suite and delivers the reskilling assignments KC Map routes:

• Compliance and Assignment Engine: Rule-based, recurring assignments with an audit-ready trail.
• Learning Paths and Curricula: Sequenced reskilling modules aligned to target-role proficiency.
• Certification and Recertification: Automated issuance with expiry-based recertification for M-25-21 literacy cycles.
• Analytics and Integrations: Completion dashboards, SSO, SCIM, HRIS, and webhooks into the agency workforce stack.

What This Looks Like End-to-End for a CHCO

The competency framework in KC Map defines the target state. KC Skills produces the per-employee proficiency measurement. The gap between them auto-assigns a reskilling path in KC LMS. Every completion attaches to the same employee record. A CHCO can produce the same AI literacy proficiency report for the audit committee, the IG, and the House Oversight staff without three reconciliations.

Frequently Asked Questions

1. What is the Chance to Compete Act and how does it affect federal agencies?

The Chance to Compete Act of 2024 (Public Law 118-188), signed December 23, 2024, directs federal agencies to embed skills- and competency-based recruitment and technical assessments into their hiring practices. It moved skills-based hiring from a discretionary reform to a statutory requirement. OPM's Merit Hiring Plan, released May 29, 2025, provides the implementation framework. Agencies now have to demonstrate measured proficiency at the hiring stage rather than relying on candidate self-assessments, which OPM directed agencies to stop using for candidate ranking by September 30, 2025.

2. Why is the 2210 IT Management series being converted to skills-based assessments?

The 2210 IT Management job series covers nearly 100,000 federal positions and represents the majority of technical hires across the government. OPM's Merit Hiring Plan runs a pilot to move the series to fully skills-based hiring by the end of 2025. The conversion signals that skills-based measurement is the operating model federal technical hiring is being restructured around, and it establishes the assessment infrastructure agencies need in place for downstream conversions across other occupational series.

3. What does an AI skills assessment measure that a traditional evaluation does not?

AI-powered skills assessments measure proficiency at the individual employee level against a defined skill and a defined proficiency threshold. Traditional performance reviews measure role performance against a supervisor's judgment. Traditional training completion tracks whether an employee finished a course. Neither produces a defensible current-state map of workforce capability against the specific competencies a workforce plan or hiring authority describes. AI skills assessments generate that map from measured responses to task-based items scored against the target proficiency.

4. How do AI skills assessments connect to federal competency frameworks like O*NET?

A skills assessment produces per-employee proficiency scores against defined skills. A competency framework (O*NET, SFIA, OPM's AI competency model, or an agency-defined library) defines the specific proficiencies a role requires at each level. When the assessment output maps to the framework directly, the gap between an employee's current proficiency and the target-role proficiency becomes a specific reskilling assignment rather than a generalized development recommendation. Interoperability with the framework is what keeps the assessment usable for classification, qualification, and career progression decisions.

5. How do federal agencies use skills assessment output for reskilling versus external hiring decisions?

OPM guidance encourages agencies to invest in reskilling current employees for AI-adjacent roles rather than defaulting to external hiring. Skills assessment output identifies the internal candidate pool by ranking existing employees against the entry proficiency for a target role. Employees close to the entry threshold become viable reskilling candidates. Employees far below the entry threshold and outside the reskilling window become the roles the agency prioritizes for external hire through hiring authorities such as the U.S. Tech Force. The assessment output is what makes the reskill-versus-hire decision defensible.

References

1. U.S. Congress. Chance to Compete Act of 2024, Public Law 118-188.
2. U.S. Office of Personnel Management. Merit Hiring Plan, May 29, 2025.
3. U.S. Office of Personnel Management. Skills-Based Hiring Guidance and Competency Model for Artificial Intelligence Work, April 29, 2024.
4. Office of Management and Budget. Memorandum M-25-21, Accelerating Federal Use of AI through Innovation, Governance, and Public Trust.
5. Office of Management and Budget. 2025 Federal Agency AI Use Case Inventory.
6. U.S. Government Accountability Office. Artificial Intelligence: IRS Actions Needed to Address Skills Gaps, Information Quality, and Strategic Management, GAO-26-107522.
7. U.S. Office of Personnel Management. U.S. Tech Force.