Our risk scores are derived from a multi-factor model combining automation research, occupational data, and AI capability assessments. Here's exactly how it works.
Each career is evaluated across nine dimensions, each scored 0–10. The first seven feed the AI-only score; all nine feed the AI + Robotics score:
| Factor | 0 = … | 10 = … | Effect on Risk | Used In |
|---|---|---|---|---|
| Routine Tasks | Fluid, unpredictable | Highly scripted & repetitive | ↑ Increases risk | Both |
| Data Processing | No data work | Primary role is data analysis | ↑ Increases risk | Both |
| Creativity | No original thinking | Constantly generates novel ideas | ↓ Reduces risk | Both |
| Social Skills | No human interaction | Deep empathy & relationship-building | ↓ Reduces risk | Both |
| Physical Dexterity | Purely mental work | Complex unstructured physical tasks | ↓ Reduces risk | Both |
| Decision Making | Follows strict rules | High-stakes judgment under uncertainty | ↓ Reduces risk | Both |
| Adaptability | Domain is static | Requires constant learning & flexibility | ↓ Reduces risk | Both |
| Physical Routineness | Movements are varied & unpredictable | Movements are repetitive & structured | ↑ Increases robotics risk | AI + Robotics only |
| Dexterity Required | No fine motor skill needed | High precision hand/body coordination | ↓ Reduces robotics risk | AI + Robotics only |
Every career receives two independent scores. The AI score captures software automation risk today. The AI + Robotics score adds physical automation risk — relevant for roles involving predictable physical labour, manufacturing, logistics, or structured manual work.
The robotics formula adds Physical Routineness as a risk amplifier (repetitive physical movements are exactly what robots do well) and uses Dexterity Required as a partial shield — high dexterity roles like surgery or fine craftsmanship remain harder to automate physically. As robotic capability improves post-2027, both weights will increase.
| Tier | Score Range | Meaning |
|---|---|---|
| 🟢 Very Low | 0–19% | Strong human-centric elements. AI will augment, not replace. |
| 🔵 Low | 20–39% | Some tasks will be automated. Role evolves but survives. |
| 🟡 Moderate | 40–59% | Significant disruption ahead. Reskilling recommended. |
| 🔴 High | 60–74% | Core tasks are automatable. Role demand will decline. |
| 🟣 Critical | 75–100% | Most tasks replaceable today. Transition planning urgent. |
• Geography matters: AI adoption varies widely by country, company, and sector.
• Specialization matters: A general accountant faces higher risk than a forensic accountant or CFO.
• Regulatory lag: Even technically automatable roles (e.g. radiologist) may be protected by regulation for years.
• Unknown careers: For careers not in our database, we infer factors from keywords — accuracy is lower for niche roles.
• AI capabilities evolve: Physical robotics is improving rapidly. Physical tasks will face more risk after 2027.
Our model is inspired by:
• Frey & Osborne (2013) — The Future of Employment (Oxford)
• McKinsey Global Institute (2017, 2023) — Jobs Lost, Jobs Gained
• World Economic Forum — Future of Jobs Report 2023
• OpenAI / Anthropic capability benchmarks (2024–2025)