What the New Federal AI Framework Means for Government Contractors

Federal AI policy shifted dramatically in 2025. The December 11, 2025 Executive Order (EO 14365) established a "minimally burdensome" national AI policy framework—and put state AI regulations under scrutiny. Colorado's algorithmic discrimination statute has already been delayed from February to June 2026 as a result.

For government contractors building or deploying AI systems, the landscape has changed. Here's what you need to know.

The Policy Shift

From Innovation-First to Unified Standards

The America's AI Action Plan released in July 2025 identified over 90 federal policy actions across three pillars:

1. Accelerating Innovation - Remove barriers to AI development
2. Building American AI Infrastructure - Compute, data, and talent
3. International Leadership - Compete with China, lead global standards

The December 2025 EO operationalized this by pushing back against state-level AI regulation that the administration views as fragmenting the market.

What the EO Does

Action	Impact on Contractors
Promotes "minimally burdensome" federal framework	Potential relief from compliance patchwork
Directs agencies to evaluate state AI laws	State requirements may be preempted
Emphasizes innovation over precaution	More permissive environment for AI deployment
Maintains agency-specific requirements	OMB memos still apply to federal contracts

What It Doesn't Change

• Existing OMB AI guidance remains in effect
• Agency-specific AI requirements (HHS, VA, DoD) still apply
• Contract-level AI clauses continue to be enforceable
• Federal acquisition regulations governing AI procurement

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Agency AI Strategies to Watch

Federal agencies are operationalizing AI policy differently. Understanding their approaches is essential for contractors.

Department of Health and Human Services

HHS launched its AI Strategy emphasizing:

• Governance structures for AI oversight
• Workforce readiness and training
• Risk management frameworks
• Operations modernization

Contractor implications:

• Expect AI governance requirements in health IT contracts
• Bias testing and fairness documentation likely required
• Human oversight mandates for clinical AI

Department of Veterans Affairs

The VA published a strategy to expand AI adoption across:

• Workflow streamlining
• Healthcare delivery enhancement
• Benefits processing acceleration

Contractor implications:

• Opportunities in AI-assisted claims processing
• Telehealth AI integration requirements
• Veteran-facing AI subject to accessibility standards

Department of Defense

DoD continues its Responsible AI approach with emphasis on:

• Autonomous systems governance
• AI assurance and testing
• Supply chain security for AI components

Contractor implications:

• CMMC requirements extend to AI systems handling CUI
• AI testing and evaluation requirements
• Explainability requirements for decision-support AI

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Acquisition Changes for AI

The federal acquisition process is adapting to AI procurement. Contractors should prepare for new requirements.

Expected Contract Language

MeriTalk reports that agencies are developing:

• Common clause libraries for AI procurement
• Governmentwide "minimum vendor evidence" standards
• Operationalized "responsible AI" criteria for source selection

What "Responsible AI" Means in Practice

Expect evaluation criteria around:

Criterion	Evidence Required
Bias testing	Documentation of testing methodology and results
Explainability	Ability to explain model decisions to non-experts
Human oversight	Defined escalation paths and override capabilities
Data governance	Provenance, quality, and consent documentation
Security	Protection against adversarial attacks, data poisoning
Privacy	Compliance with applicable privacy requirements

Sample Compliance Checklist

// AI System Compliance Documentation
interface AICompliancePackage {
  // System identification
  systemName: string;
  version: string;
  purpose: string;
  deploymentContext: 'internal' | 'public_facing' | 'decision_support';

  // Bias and fairness
  biasAssessment: {
    methodology: string;
    protectedClasses: string[];
    metricsUsed: string[];
    results: BiasTestResult[];
    mitigationActions: string[];
    lastAssessmentDate: string;
  };

  // Explainability
  explainability: {
    techniqueUsed: 'SHAP' | 'LIME' | 'attention_visualization' | 'rule_extraction' | 'other';
    userFacingExplanations: boolean;
    technicalDocumentation: string; // URL to docs
    sampleExplanations: ExplanationExample[];
  };

  // Human oversight
  humanOversight: {
    appealProcess: boolean;
    humanReviewThreshold: number; // Confidence below which human reviews
    overrideCapability: boolean;
    escalationPath: string[];
  };

  // Data governance
  dataGovernance: {
    trainingSources: DataSource[];
    consentDocumentation: string;
    dataRetentionPolicy: string;
    privacyImpactAssessment: string; // URL to PIA
  };

  // Security
  security: {
    adversarialTesting: boolean;
    dataPoisoningProtection: boolean;
    modelIntegrityVerification: boolean;
    accessControls: string;
  };

  // Certifications
  certifications: {
    signedBy: string;
    date: string;
    attestations: string[];
  };
}

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Navigating the State Patchwork

Despite federal preemption efforts, state AI laws aren't going away immediately. Contractors must understand the current landscape.

States with AI Legislation

State	Law	Status	Key Requirements
Colorado	SB 21-169	Delayed to June 2026	Algorithmic discrimination prevention
California	Various	Active	AI transparency, deepfake disclosure
Texas	HB 2060	Active	AI inventory for state agencies
Utah	AI Policy Act	Active	Disclosure requirements
Illinois	BIPA, AI Video Interview Act	Active	Biometric consent, video interview notice

Practical Approach

Until federal preemption is fully established:

1. Track state requirements for each customer location
2. Build to the highest standard when feasible
3. Document compliance even where not strictly required
4. Monitor preemption developments closely

// State compliance tracker
interface StateAIRequirement {
  state: string;
  law: string;
  effectiveDate: string;
  preemptionStatus: 'none' | 'under_review' | 'preempted';
  requirements: {
    category: string;
    description: string;
    applicability: string;
  }[];
}

const stateRequirements: StateAIRequirement[] = [
  {
    state: 'Colorado',
    law: 'SB 21-169',
    effectiveDate: '2026-06-30',
    preemptionStatus: 'under_review',
    requirements: [
      {
        category: 'impact_assessment',
        description: 'Impact assessment for high-risk AI systems',
        applicability: 'Deployers of high-risk AI'
      },
      {
        category: 'disclosure',
        description: 'Disclose AI use to consumers',
        applicability: 'Consumer-facing AI'
      }
    ]
  },
  // ... other states
];

function getApplicableRequirements(
  deploymentStates: string[],
  systemType: string
): StateAIRequirement[] {
  return stateRequirements.filter(req =>
    deploymentStates.includes(req.state) &&
    req.preemptionStatus !== 'preempted' &&
    new Date(req.effectiveDate) <= new Date()
  );
}

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Building AI Systems for Federal Contracts

Architecture Considerations

Design AI systems with federal requirements in mind:

1. Audit Trail by Default

// Every AI decision should be logged
interface AIDecisionLog {
  decisionId: string;
  timestamp: string;
  modelId: string;
  modelVersion: string;
  input: Record<string, unknown>;
  output: Record<string, unknown>;
  confidence: number;
  explanation: string;
  humanReviewRequired: boolean;
  humanReviewer?: string;
  humanDecision?: string;
}

2. Explainability Built In

// Generate explanations at inference time
interface ExplainableOutput {
  prediction: unknown;
  confidence: number;
  explanation: {
    topFactors: { factor: string; influence: number }[];
    narrative: string;
    limitations: string[];
  };
  alternativeOutcomes: { outcome: unknown; probability: number }[];
}

3. Human Override Capability

// Always allow human override
interface HumanOverride {
  originalDecision: AIDecisionLog;
  overrideDecision: string;
  overrideReason: string;
  overriddenBy: string;
  overrideTimestamp: string;
  feedbackToModel: boolean; // Use for model improvement
}

Testing Requirements

Federal AI systems should undergo:

Test Type	Frequency	Documentation
Functional testing	Pre-deployment, updates	Test results, coverage metrics
Bias testing	Quarterly	Protected class analysis, disparity metrics
Adversarial testing	Annually	Attack scenarios, vulnerability assessment
Performance monitoring	Continuous	Drift detection, accuracy tracking
User acceptance	Pre-deployment	Stakeholder sign-off

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Preparing for 2026 and Beyond

Near-Term Actions

1. Inventory existing AI systems - Document all AI in use on federal contracts
2. Gap assessment - Compare current documentation to expected requirements
3. Remediation planning - Prioritize gaps by contract risk
4. Training - Ensure staff understand responsible AI requirements

Longer-Term Strategy

1. Build compliance into development - Shift left on AI governance
2. Establish AI governance function - Dedicated oversight capability
3. Develop reusable compliance artifacts - Templates, testing frameworks
4. Monitor policy evolution - Track OMB memos, agency guidance, state preemption

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Key Takeaways

1. Federal AI policy is consolidating - Expect more unified standards, less state variation

2. Agency requirements still apply - HHS, VA, DoD each have specific AI expectations

3. Acquisition is adapting - Responsible AI criteria entering source selection

4. Document everything - Compliance evidence is as important as technical capability

5. Build for the highest standard - Future-proof by exceeding current requirements

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Navigating AI Policy Complexity

The federal AI landscape is evolving rapidly. Contractors who build governance into their AI systems from the start will have competitive advantages in federal procurement.

PEW Consulting helps government contractors develop AI systems that meet federal requirements while delivering real capability. Our experience spans federal AI acquisition, responsible AI implementation, and compliance documentation.

Schedule an AI compliance consultation to assess your readiness for evolving federal requirements.

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Sources

• Sidley: December 2025 AI Executive Order Analysis
• White House: America's AI Action Plan
• MeriTalk: Federal IT in 2026
• SIG: US AI Legislation Overview
• FedTech: Tech Trends 2026

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