Labour Market Impact Assessment¶

This guide provides frameworks for assessing and mitigating the workforce impact of AI deployments, based on economic research and responsible AI principles.

Context: The AI Employment Transition¶

Dario Amodei, 'The Adolescence of Technology' (January 2026)

"AI could displace half of all entry-level white collar jobs in the next 1-5 years."

This projection, while uncertain, underscores the need for thoughtful planning around AI deployment and workforce transitions.

Impact Assessment Framework¶

When to Conduct Assessment¶

Conduct a labour impact assessment when:

AI system automates tasks currently performed by employees
Deployment affects >5% of workforce in any department
AI changes skill requirements for existing roles
Customer-facing AI replaces human interactions
AI enables significant productivity increases

Assessment Template¶

Section 1: Deployment Overview¶

Field	Description
System Name	Name of AI system being deployed
Deployment Date	Planned rollout date
Affected Departments	List of impacted business units
Primary Function	What the AI system does
Integration Type	Augmentation / Automation / Autonomous

Section 2: Workforce Impact Analysis¶

## 2.1 Direct Employment Impact

| Role | Current FTE | Post-Deployment FTE | Change | Timeline |
|------|-------------|---------------------|--------|----------|
| [Role 1] | | | | |
| [Role 2] | | | | |

## 2.2 Skill Requirement Changes

| Current Skills | New Skills Required | Training Gap |
|----------------|---------------------|--------------|
| | | |

## 2.3 New Roles Created

| New Role | FTE Required | Skills Needed | Source |
|----------|--------------|---------------|--------|
| | | | Internal/External |

Section 3: Mitigation Planning¶

## 3.1 Transition Support

- Redeployment opportunities identified: [Yes/No]
- Retraining programs planned: [Yes/No]
- Severance/transition packages: [Details]
- Timeline for workforce transition: [Dates]

## 3.2 Stakeholder Communication

- Employee notification date: [Date]
- Union/works council consultation: [Date/N/A]
- Public disclosure requirements: [Details]

Section 4: Approval¶

Approver	Role	Date	Signature
	HR Director
	Department Head
	Ethics Committee
	Executive Sponsor

Augmentation vs. Replacement Decision Tree¶

flowchart TD
    A[Proposed AI Deployment] --> B{Can AI fully replace human judgment?}
    B -->|No| C[Augmentation Approach]
    B -->|Yes| D{Is human judgment legally required?}
    D -->|Yes| C
    D -->|No| E{Does replacement create unacceptable risk?}
    E -->|Yes| C
    E -->|No| F{Can affected workers be redeployed?}
    F -->|Yes| G[Automation with Transition Plan]
    F -->|No| H{Is timeline flexible?}
    H -->|Yes| I[Phased Automation + Retraining]
    H -->|No| J[Full Assessment Required]

    C --> K[Deploy as Human-AI Collaboration]
    G --> L[Deploy with Redeployment Program]
    I --> M[Deploy with Training Program]
    J --> N[Executive Review Required]

Best Practices for Workforce Transition¶

1. Early Communication¶

Timing	Action
6+ months before	Initial briefing to leadership
3-6 months before	Department head notification
2-3 months before	Affected employee notification
1 month before	Detailed transition plans shared
Deployment	Support resources activated

2. Retraining Programs¶

Design retraining programs that address:

AI Literacy: Understanding how AI systems work
Collaboration Skills: Working effectively with AI tools
New Technical Skills: Skills needed for evolved roles
Adjacent Role Skills: Skills for potential redeployment

Example Training Tracks:

Current Role	AI Impact	Recommended Training	New Role Path
Data Entry Clerk	High automation	Data quality management, AI oversight	Data Quality Analyst
Customer Service Rep	Partial automation	Complex issue resolution, AI escalation	Customer Success Specialist
Junior Analyst	Task automation	AI tool mastery, strategic analysis	Senior Analyst
Content Writer	Augmentation	AI-assisted writing, editing	Content Strategist

3. Transition Support¶

Provide comprehensive support:

Career Counseling: Individual career path planning
Skills Assessment: Identify transferable skills
Job Placement: Internal mobility programs
External Support: Outplacement services if needed
Financial Bridge: Transition allowances during retraining

4. Gradual Rollout¶

Phase	Duration	Scope	Evaluation
Pilot	1-3 months	5-10% of scope	Feasibility, impact
Limited	3-6 months	25-50% of scope	Workforce adaptation
Expanded	6-12 months	75-100% of scope	Full impact assessment
Optimized	Ongoing	Full deployment	Continuous improvement

Economic Impact Considerations¶

Organizational Level¶

Factor	Consideration
Productivity Gains	Quantify expected efficiency improvements
Quality Changes	Impact on output quality
Cost Structure	Changes to labor vs. capital costs
Flexibility	Ability to scale up/down
Innovation Capacity	Freed capacity for higher-value work

Community Level¶

Factor	Consideration
Local Employment	Impact on regional job market
Tax Base	Changes to local tax revenue
Skills Ecosystem	Impact on local skill development
Economic Multipliers	Downstream economic effects

Mitigation Investments¶

Consider investing AI productivity gains in:

Employee Development: Retraining and upskilling programs
New Roles: Creating higher-value positions
Community Programs: Supporting affected communities
Innovation: R&D for new products and services
Customer Value: Improving products/services

Regulatory Considerations¶

EU Requirements¶

Under the EU AI Act and related regulations:

Works Council Consultation: Required in many EU countries
GDPR Implications: For AI processing employee data
Collective Bargaining: May require union agreement
Redundancy Procedures: Country-specific requirements

US Considerations¶

WARN Act: 60-day notice for mass layoffs (50+ employees)
State Laws: Additional requirements in CA, NY, NJ, etc.
NYC LL144: Bias audits for employment AI
EEOC Guidance: AI and discrimination

Documentation Requirements¶

Maintain records of:

Impact assessments conducted
Stakeholder consultations
Transition plans and execution
Training programs offered
Outcomes for affected employees

OxideShield Policy Configuration¶

Enforce labour impact requirements through policy:

apiVersion: oxideshield.ai/v1
kind: SecurityPolicy
metadata:
  name: workforce-protection-policy
spec:
  useCaseRestrictions:
    requiredSafeguards:
      - human_in_the_loop      # Preserve human involvement
      - audit_trail             # Document all decisions
      - appeal_mechanism        # Allow employees to contest
      - bias_monitoring         # Monitor for discrimination

    customProhibitions:
      - name: mass_termination_automation
        description: Prevent AI from automating mass layoff decisions
        pattern: "(terminate|fire|layoff).*(all|mass|bulk)"
        action: block

      - name: performance_auto_termination
        description: Require human review for performance-based decisions
        pattern: "(auto|automatic).*(termination|dismissal|firing)"
        action: alert

Impact Assessment Checklist¶

Before Deployment¶

During Transition¶

Communicated with affected employees
Activated support resources
Began retraining programs
Monitored employee wellbeing
Tracked redeployment progress
Addressed concerns promptly

Post-Deployment¶

Documented outcomes for all affected employees
Measured productivity changes
Assessed quality impact
Evaluated retraining effectiveness
Reviewed for unintended consequences
Updated processes based on learnings

Case Study Template¶

Document your deployments for organizational learning:

# AI Deployment Case Study: [System Name]

## Overview
- Deployment Date:
- System Purpose:
- Affected Departments:

## Workforce Impact
- Roles Affected:
- Employees Impacted:
- Net Employment Change:

## Transition Approach
- Communication Timeline:
- Retraining Programs:
- Redeployment Rate:

## Outcomes
- Productivity Change:
- Quality Impact:
- Employee Satisfaction:
- Lessons Learned:

## Recommendations
- What worked well:
- What to improve:
- Advice for future deployments:

Resources¶

Research and Data¶

Frameworks and Guidelines¶

OxideShield Documentation¶

This guide provides frameworks for consideration. Organizations should work with HR, legal, and ethics professionals to develop appropriate policies for their specific context and jurisdiction.