Methodology
Our assessment combines task-based analysis with industry adoption patterns to estimate automation disruption exposure over a 3-year timeframe. This approach is grounded in current research on AI capabilities and enterprise adoption patterns.
Task-Level Exposure Model
We analyze your role across nine dimensions that correlate with automation exposure, following research showing that most occupations have meaningful shares of tasks exposed to large language models, rather than being entirely automatable or safe.
Research basis: Our task-weighted approach aligns with findings from Eloundou et al. (2023) that AI exposure varies significantly within occupations based on specific task composition, rather than broad job categories.
Key Task Dimensions:
- Repetitive vs. creative work: Routine tasks score higher for automation risk
- Predictability: Standardized workflows are more automatable
- Human interaction requirements: Face-to-face work provides protection
- Specialized knowledge application: Domain expertise creates barriers
- Emotional intelligence needs: Interpersonal skills remain human-dominant
- Physical dexterity requirements: Manual manipulation limits current AI
Scoring Formula:
Exposure (E) = Σ(normalized_answer × weight) across 9 weighted questions
Questions measuring human advantages (creativity, emotional intelligence, face-to-face interaction) are reverse-scored.
Industry Adoption Factors
Different industries adopt automation at varying speeds based on regulatory environment, competitive pressure, and technical readiness. Enterprise AI adoption rates show significant sectoral variation, with technology and finance leading adoption.
Data source: OECD (2025) reports enterprise AI adoption at ~13.9% overall, but much higher in ICT and knowledge-intensive services. Our industry multipliers (0.85-1.25×) reflect these documented adoption differentials.
| Industry | Adoption Factor | Rationale |
|---|---|---|
| Technology | 1.25× | Early adopter, high technical capacity |
| Finance | 1.10× | High data availability, efficiency pressure |
| Healthcare | 0.95× | Regulatory constraints, safety requirements |
| Construction | 0.85× | Physical work, traditional practices |
Risk Probability Calculation
We transform task exposure and industry factors into a realistic probability range using a logistic function. This prevents extreme predictions (0% or 100%) while maintaining sensitivity to meaningful differences between roles.
Timeframe rationale: The World Economic Forum's Future of Jobs 2025 identifies AI as the most transformative technology through 2030, with significant but uneven adoption expected. Our 3-year window captures near-term disruption while acknowledging uncertainty.
Final Formula:
sigmoid(x) = 1 / (1 + e^(-x))
probability = sigmoid(-2.1 + 2.6×E + 0.9×(A-1))
risk_band = [probability - 0.06, probability + 0.06]
Coefficient rationale: Calibrated for interpretability and bounded probabilities, informed by current research on task exposure and sectoral adoption rather than fit to any single study.
Validation & Limitations
Methodological Strengths
- Task-level analysis vs. broad occupational categories
- Industry-specific adoption timing
- Reverse scoring for human-advantage skills
- Realistic probability bounds (no extreme predictions)
- Transparent, documented approach
Known Limitations
- Estimate of task automation potential, not job displacement certainty
- Individual employer adoption varies significantly
- New human-AI collaboration models may emerge
- Regulatory and social factors may slow adoption
- 3-year predictions inherently uncertain
References & Research Base
Task-Level Exposure Analysis
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023)."GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models."arXiv preprint arXiv:2303.10130.
Foundational research showing most occupations have meaningful task exposure to LLMs without making specific timing predictions.
Sectoral Adoption Patterns
OECD (2025)."OECD Employment Outlook 2025: AI and the Labour Market."OECD Publishing.
Documents enterprise AI adoption at ~13.9% overall with significant variation by sector and firm size, supporting industry-specific multipliers.
Near-Term Transformation Timeline
World Economic Forum (2025)."Future of Jobs Report 2025."WEF, Geneva.
Survey of 1,000+ employers identifying AI as the most transformative technology through 2030, with emphasis on uneven, sector-specific adoption.
How to Interpret Your Results
Your risk assessment represents an informational estimate of task automation exposure, not a prediction of job loss. Use it as a starting point for:
- Identifying skills to develop or strengthen
- Understanding industry-specific automation trends
- Planning proactive career development strategies
- Building human-AI collaboration capabilities
Remember: Successful adaptation to AI involves working alongside these technologies rather than being replaced by them. Focus on developing complementary human skills and strategic positioning.