Learning Objectives

After this 120-minute lecture and workshop, participants will construct an enterprise AI portfolio view aligned with OMB M-24-10 rights-impacting and safety-impacting designations, EO 14110 AI use case inventory obligations, and agency capital planning and investment control processes under OMB A-11 and A-130. Learners will apply portfolio-management principles including stage gates, resource allocation, risk balancing, and retirement decisions to government AI initiatives. Participants will tie AI portfolio governance to the NIST AI RMF GOVERN and MANAGE functions, the GAO AI Accountability Framework, and the Chief AI Officer role created by M-24-10. Learners will evaluate vendor concentration, cross-agency dependencies, procurement readiness under the Federal Acquisition Regulation, and workforce and change management across the portfolio. Participants will recognize real cases including IRS ID.me, VA claims modernization, DHS CBP biometric exit, SSA disability triage, and DOD CDAO portfolio decisions. Finally, learners will produce a portfolio scorecard and prioritization plan for their own agency using a balanced scorecard approach covering mission value, risk posture, equity, workforce, financial sustainability, and technical maturity.

Key Topics Covered

The lecture covers the definition of an agency AI portfolio and its boundaries; integration with the AI use case inventory under EO 14110 and M-24-10; stage-gate governance from ideation through pilot, scale, sustain, and retirement; balanced scorecard approaches across mission value, risk posture, equity, workforce, financial, and technical maturity; prioritization methods including weighted scoring, option-value analysis, and dependency mapping; capital planning and budget cycles under OMB A-11 and A-130, including Major IT Business Case requirements; vendor portfolio considerations including FedRAMP authorization, concentration risk, and procurement ready contracts; cross-agency dependencies through Interagency Agreements, Shared Services, and the GSA AI Centers of Excellence; workforce and change management across the portfolio including AFGE and NTEU consultation; risk governance aligned with NIST AI RMF GOVERN and MANAGE, GAO AI Accountability Framework, and OMB M-24-10 pause-and-fix authority; case studies of IRS ID.me, VA claims modernization, DHS CBP biometric exit, SSA disability triage, and DOD CDAO; and portfolio reporting to leadership, OMB, GAO, and Congress.

Why This Matters for Government

An AI portfolio is not a list of projects. It is the set of AI-enabled commitments that an agency is making to its mission, citizens, workforce, and budget. Without portfolio management, agencies accumulate disconnected pilots, vendor dependencies, and unmanaged risks. GSA and OMB assessments suggest that up to eighty percent of federal AI pilots never reach sustained production. One root cause is the absence of portfolio governance. Another is failure to align individual AI initiatives to agency strategic plans, Capital Planning and Investment Control cycles, and statutory obligations.

The regulatory foundation is now explicit. Executive Order 14110 requires each federal agency to maintain an AI use case inventory, identify Chief AI Officers, and integrate AI governance with existing governance bodies. OMB Memorandum M-24-10 establishes minimum practices for rights-impacting and safety-impacting AI, including impact assessments, pre-deployment testing, ongoing monitoring, human oversight, public notice, decommissioning procedures, and workforce consultation. OMB Circular A-130 governs federal information resource management. OMB Circular A-11 governs budget formulation. Agencies must align AI investments with these cycles. The Major IT Business Case process must account for AI components. The Capital Planning and Investment Control cycle must include AI governance milestones.

Portfolio governance starts with the inventory. Every agency must publish an AI use case inventory. That inventory is the source of truth for portfolio management. Each entry should include purpose, stage, rights-impacting and safety-impacting designations, data sources, vendor identification, FedRAMP authorization, performance metrics, incident history, workforce and equity considerations, and dependencies. Stage gates move initiatives from ideation through pilot, scale, sustain, and retirement, with go or no-go criteria at each step. Retirement is often overlooked. Outdated AI systems must be decommissioned with documented reasons, data disposition, and successor planning.

Prioritization requires a balanced scorecard. Mission value asks whether the initiative advances agency mission in measurable ways. Risk posture asks whether the initiative's risks are manageable within current controls. Equity asks whether the initiative serves or harms specific populations, aligned with the Blueprint for an AI Bill of Rights principle on algorithmic discrimination protections. Workforce asks whether the initiative respects worker voice, collective bargaining obligations under the Federal Service Labor-Management Relations Statute, and EEOC guidance on AI workplace tools. Financial sustainability asks whether recurring costs are covered beyond initial funding. Technical maturity asks whether the agency has the infrastructure, skills, and authorization boundaries to deliver. No initiative should scale without acceptable scores across all dimensions.

Vendor portfolio considerations are substantial. FedRAMP authorization is required for federal cloud services and AI APIs. Concentration risk means relying too heavily on a single vendor whose outage, acquisition, or pricing change could disrupt multiple agency AI initiatives. The GSA AI Centers of Excellence and the GSA Multiple Award Schedules provide procurement vehicles. Alliant 3 and other governmentwide contracts provide alternatives. Every vendor relationship should be documented with SLAs, exit rights, and audit provisions. The Federal Acquisition Regulation governs contract modifications. OFCCP obligations apply to federal contractors.

Cross-agency dependencies are real. An agency using a shared service must account for that service's performance and security posture. The GSA USA Spending and USAJobs platforms are examples. DOD's Chief Digital and Artificial Intelligence Office demonstrates an enterprise approach to AI portfolio management. VA's AI Office coordinates AI across VA's Veterans Benefits Administration, Veterans Health Administration, and National Cemetery Administration. DHS's CBP biometric exit program illustrates how cross-component portfolios require explicit governance.

Risk governance across the portfolio ties to NIST AI RMF GOVERN and MANAGE functions. GOVERN establishes roles, responsibilities, and policies. MANAGE tracks ongoing risks and responses. The GAO AI Accountability Framework organizes accountability into Governance, Data, Performance, and Monitoring components. OMB M-24-10 requires pause-and-fix authority for rights-impacting AI when monitoring reveals problems. Agencies that build a portfolio with documented governance, monitoring, and pause authority are in position to lead. Agencies that operate with disconnected pilots are in position to produce the next Michigan MIDAS.

Case studies anchor the practice. IRS ID.me became a portfolio lesson when Treasury reversed the rollout. VA claims modernization is an ongoing multi-year portfolio effort with AFGE coordination. DHS CBP biometric exit illustrates cross-component execution with privacy and civil liberties considerations. SSA disability triage illustrates the balance of caseworker workflow with statutory adjudication responsibilities. DOD CDAO illustrates an enterprise-wide AI portfolio in defense. Each case surfaces lessons about prioritization, governance, and risk that participants map to their own agencies.

Portfolio reporting is essential. Leadership needs concise, comparable views across initiatives. OMB may request portfolio summaries. GAO conducts audits. Congress holds hearings. A portfolio dashboard with clear status, risks, and value metrics supports all of these. Public transparency through the AI use case inventory reinforces accountability. The workshop portion of this lecture asks each participant to draft a balanced scorecard for their current portfolio, identify top three risks, and commit to one portfolio improvement they will implement within 60 days.

Overview

The lecture frames portfolio management as the strategic discipline that turns disconnected AI pilots into a coherent agency commitment tied to mission, budget, workforce, and risk governance.

Inventory, Stage Gates, and Retirement

Participants map the AI use case inventory to a stage-gated portfolio covering ideation, pilot, scale, sustain, and retirement. Retirement and decommissioning are emphasized, aligned with M-24-10 decommissioning obligations.

Balanced Scorecard and Prioritization

Participants apply a balanced scorecard across mission value, risk posture, equity, workforce, financial sustainability, and technical maturity. Weighted scoring and dependency mapping are introduced.

Capital Planning, Procurement, and Vendor Concentration

The session integrates OMB A-11 and A-130 capital planning, Major IT Business Case requirements, FedRAMP, FAR, GSA Schedules, Alliant 3, concentration risk, and exit rights.

Risk Governance, Cross-Agency Dependencies, and Reporting

Participants tie portfolio governance to NIST AI RMF GOVERN and MANAGE, GAO AI Accountability Framework components, cross-agency dependencies, and reporting to leadership, OMB, GAO, and Congress.

Start Your CLUB Certification

This lecture is part of L3: AI Strategist, 80 hours of advanced training aligned with NIST AI RMF, OMB M-24-10, EO 14110, GAO AI Accountability Framework, and ISO 42001.

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L3 3.5.1 AI Metrics and KPIs for Government, 90 minutes. L3 3.5.2 Moving from Pilot to Production, 120 minutes. L3 3.5.5 Continuous Improvement for AI Systems, 90 minutes. L3 3.5.6 Scaling Capstone, 180 minutes. L4 4.1.2 Strategic AI Investment Planning, 120 minutes.