How We Calculate AI Automation Risk
Understanding our methodology for predicting which jobs are most vulnerable to AI automation
What is the AI Risk Score?
Our AI automation risk score represents the probability that a specific occupation could be significantly impacted by artificial intelligence and automation technologies within the next 5-10 years.
The score ranges from 0% (minimal risk) to 100% (high risk), providing a comparative framework for understanding job vulnerability across different industries and roles.
Important: This is not a definitive prediction, but rather a data-driven assessment based on current technological capabilities and occupational characteristics.
Score Interpretation
Our Methodology
We combine cutting-edge machine learning with comprehensive occupational data to provide the most accurate AI automation risk assessments available.
Data Foundation
Built on O*NET's comprehensive occupational database, analyzing 1,000+ job characteristics and skills
AI Analysis
We use advanced AI to analyze in-depth aspects of every job in our database to predict an AI automation risk score
Continuous Updates
Regular model retraining with latest AI developments and occupational data refinements
How We Calculate the Score
Job Task Analysis
We break down each occupation into its core components: daily tasks, responsibilities, and activities that define what you actually do in your role. This forms the foundation of our assessment.
Multi-Dimensional Scoring
We analyze 200+ occupational attributes across multiple categories: skills, knowledge, abilities, work activities, and work context. Each category contributes to the overall risk assessment.
Weighted Risk Calculation
Using a sophisticated weighted average system, we prioritize job tasks and activities (heavily weighted) while incorporating skills, knowledge, and other factors. This reflects how AI automation primarily targets what you do, not just what you know.
AI Vulnerability Assessment
Our advanced AI analyzes each job's composition to predict automation risk, considering current technological capabilities and how easily specific tasks can be automated or augmented.
What Makes Your Job AI-Proof or AI-Vulnerable?
Our analysis reveals the fundamental differences between jobs that AI can easily replicate and those that remain uniquely human.
The Human Edge: What AI Struggles With
Unpredictable Problem Solving: Situations requiring on-the-fly adaptation and creative solutions that don't follow established patterns
Emotional Intelligence: Reading subtle cues, building trust, and navigating complex interpersonal dynamics
Physical Mastery: Complex hand-eye coordination and fine motor skills that develop through years of practice
Cultural Nuance: Understanding context, historical significance, and the unwritten rules that shape human behavior
Adaptive Learning: Rapidly acquiring new skills and knowledge in response to changing circumstances
The AI Sweet Spot: Where Automation Thrives
Pattern Recognition: Work that follows predictable sequences and can be broken down into repeatable steps
Data Crunching: Processing large amounts of information according to established rules and algorithms
Standardized Interactions: Communication that follows scripts, templates, or well-defined protocols
Controlled Environments: Work performed in settings with minimal variation and predictable conditions
Rule-Based Decisions: Choices that can be made by following clear guidelines and decision trees
Industry-Specific Insights
Different sectors face unique automation challenges and opportunities based on their specific characteristics.
Technology
Software development roles are evolving, with AI handling routine coding while humans focus on architecture and innovation
Healthcare
AI augments diagnosis and treatment planning, but human empathy and complex decision-making remain irreplaceable
Manufacturing
Robotics and AI are transforming production lines, though skilled technicians remain essential for maintenance and optimization
Education
AI enhances personalized learning, but human teachers provide motivation, emotional support, and complex guidance
Finance
Algorithmic trading and automated analysis are growing, but strategic decision-making and client relationships require human insight
Creative Arts
AI tools enhance creative workflows, but human imagination, cultural understanding, and emotional expression remain unique
Emerging Trends & Future Considerations
The landscape of AI automation is constantly evolving, with new technologies and applications emerging regularly.
Emerging Technologies
Large Language Models
Transforming content creation, customer service, and knowledge work across industries
Computer Vision
Advancing quality control, medical imaging, and autonomous systems
Robotic Process Automation
Streamlining administrative tasks and workflow management
Adaptation Strategies
Skill Development
Focus on uniquely human capabilities like creativity, emotional intelligence, and strategic thinking
AI Collaboration
Learn to work effectively with AI tools to enhance productivity and decision-making
Continuous Learning
Stay current with technological developments and industry trends
Important Limitations & Disclaimers
Model Limitations
- Predictions are based on current technological capabilities and may not account for future breakthroughs
- Individual job performance and company-specific factors can significantly impact automation risk
- Economic, regulatory, and social factors may influence automation adoption rates
- Our model focuses on technical feasibility rather than economic viability
Professional Advice
These assessments should not be considered as career advice or financial guidance. We recommend consulting with career counselors, industry professionals, and financial advisors when making career decisions.
Data Sources
Our analysis relies on publicly available occupational data from O*NET and other authoritative sources. While we strive for accuracy, data limitations and interpretation differences may affect results.
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This website combines information from multiple databases including the O*NET 29.3 Database