The AI Resistance Score (ARS) is a 100-point framework for measuring how structurally protected an occupation is from AI automation. It scores jobs across four equally weighted dimensions (25 points each): Physical Presence, Human Relationship, Creative Judgment, and Ethical Accountability. Validated against Frey & Osborne automation probabilities (r = −0.81) and calibrated with BLS employment data. This article contains the full methodology, scoring rubrics, and instructions for scoring your own career.
- The complete 4-dimension scoring framework with rubrics
- Which 5 data sources underpin the scoring system
- How we validated the framework (r = −0.81 with Frey & Osborne)
- Which dimension provides the strongest automation protection
- How to score your own occupation step-by-step
- Framework limitations and what the scores do NOT predict
Quick Answers
What is the AI Resistance Score?
A composite score from 0 to 100 measuring an occupation's structural resistance to AI automation. It aggregates four equally weighted sub-dimensions: Physical Presence, Human Relationship, Creative Judgment, and Ethical Accountability (25 points each). Higher scores indicate greater structural protection from AI replacement.
How was the framework validated?
We found a strong negative correlation (r = −0.81) between our AI Resistance Scores and Frey & Osborne's independently computed automation probabilities across 30 scored occupations. Occupations scoring ARS 90+ have near-zero automation probability; those below 40 face 80%+ probability.
Can I use this to score my own job?
Yes. Use the scoring rubrics in this article to rate your occupation on each dimension (0–25). Add the four scores for your composite ARS. Scores above 75 indicate strong structural protection. Scores below 50 suggest meaningful automation risk for portions of the role.
Which dimension matters most?
Physical Presence (r = −0.74) and Human Relationship (r = −0.71) are the strongest individual predictors of automation resistance. Creative Judgment (r = −0.63) and Ethical Accountability (r = −0.58) are meaningful but weaker individually. Their power is strongest in combination.
Most analyses of AI automation risk provide lists without methodology. They assert that nurses are "safe" and accountants are "at risk" — but don't show how they reached that conclusion, what data supports it, or how you can evaluate your own situation.
The AI Resistance Score (ARS) framework fills that gap. It's a transparent, reproducible scoring system designed to be applied to any occupation and validated against established automation models.
Why We Built This Framework
Existing resources on AI-resistant careers suffer from three fundamental problems:
- No methodology. Lists assert conclusions without showing the reasoning or measurement criteria.
- No calibration. Claims aren't validated against established automation models (Frey & Osborne, Goldman Sachs, McKinsey).
- No nuance. Occupations are labeled binary "safe/unsafe" when automation risk exists on a spectrum — and varies within the same job title depending on task mix.
The ARS framework addresses all three by building a transparent scoring system, validating it against multiple external datasets, and producing continuous scores (0–100) rather than binary labels.
The framework is designed to be transparent and reproducible. Every scoring criterion is published, every data source is cited, and the validation against external models is shown. You should be able to evaluate and critique every scoring decision.
Data Sources
We synthesized data from five primary sources to build and validate the framework:
How each source is used:
- Frey & Osborne: Primary validation target. Our ARS should negatively correlate with their automation probability. It does (r = −0.81).
- Goldman Sachs: Provides sector-level task automation percentages. Used to calibrate Physical Presence scores (construction 6% automatable vs. office 46%).
- McKinsey: Provides work-hour estimates and transition timelines. Used for context on which task types face near-term automation.
- BLS: Employment growth projections used as a secondary validation signal. High ARS should correlate with positive job growth.
- WEF: Employer survey data on which roles are growing/declining. Provides demand-side perspective.
The Four Dimensions (25 Points Each)
- AI Resistance Score (ARS)
A composite score from 0 to 100 measuring an occupation's structural resistance to automation by AI and robotic systems. Higher scores indicate greater protection. The score aggregates four equally weighted sub-dimensions (25 points each).
Each occupation is scored on four sub-dimensions. Scores are assigned based on occupational task analysis (O*NET work activities), validated against Frey & Osborne automation probabilities.
Dimension 1: Physical Presence Requirement
Measures the degree to which an occupation requires physical presence in unpredictable, variable environments.
Why it matters: AI and robotics struggle most in unstructured physical environments. A factory floor is predictable; a construction site, patient's home, or vintage building's electrical system is not. Goldman Sachs data confirms: construction tasks are only 6% automatable, installation/repair only 4%, vs. 46% for office/admin.
Key calibration points:
- Electricians score 25/25 — every wiring job is physically unique, requiring in-person diagnosis and manual dexterity in unpredictable conditions
- Registered nurses score 20/25 — physical care is essential but happens in somewhat controlled clinical environments
- Financial managers score 8/25 — work is almost entirely remote-capable, with occasional in-person meetings
Dimension 2: Human Relationship Requirement
Measures the degree to which the occupation's value depends on genuine human relationships, trust, and emotional connection.
Why it matters: AI can simulate conversation but cannot form genuine human bonds. In occupations where the relationship is the value (therapy, executive coaching, pastoral care), no AI substitute exists. LinkedIn data shows "relationship development" skills are 54x more likely in recruiter job postings vs. 2023 — the market is pricing this dimension.
Key calibration points:
- Mental health counselors score 25/25 — the therapeutic alliance IS the treatment; research shows it predicts outcomes more than technique
- Executive recruiters score 21/25 — trust networks, confidential conversations, and relationship maintenance are the core value
- Recruiting coordinators score 10/25 — some candidate interaction, but logistics is the core function
Dimension 3: Creative/Novel Judgment
Measures the degree to which the occupation requires novel problem-solving, creative vision, or strategic decisions that go beyond pattern recognition.
Why it matters: AI excels at pattern matching within training data but struggles with genuinely novel situations that have no precedent. A surgeon encountering unexpected anatomy, an electrician diagnosing a fault in a 1920s building, or a creative director setting vision for a brand relaunch — these require judgment that transcends templates.
Key calibration points:
- Surgeons score 25/25 — every operation encounters variation; adaptive intraoperative decisions are genuinely novel
- Electricians score 22/25 — diagnosing faults in unique buildings is novel problem-solving, not template application
- Data entry scores 2/25 — purely routine execution within defined rules
Dimension 4: Ethical Accountability
Measures the degree to which the occupation involves decisions where a human must be legally or ethically accountable.
Why it matters: Society requires human accountability for consequential decisions. A surgeon must be personally liable for surgical outcomes. A judge must be a human being. An attorney must be licensed. Even as AI assists these roles, the accountability cannot transfer to an algorithm — regulatory, legal, and social norms demand a human in the loop.
Key calibration points:
- Surgeons score 25/25 — direct legal liability for patient outcomes; malpractice is personal
- Financial managers score 22/25 — fiduciary duty, regulatory compliance, personal liability for financial decisions
- Data entry scores 3/25 — errors are easily caught and corrected with no downstream liability
Calculation and Validation
Composite Score
ARS = Physical Presence (0–25) + Human Relationship (0–25) + Creative Judgment (0–25) + Ethical Accountability (0–25)
The maximum score is 100, the minimum is 0. In our 30-occupation sample, scores range from 32 (recruiting coordinator) to 97 (mental health counselor).
Validation Against Frey & Osborne
Scores were calibrated by checking correlation with Frey & Osborne's automation probability estimates. The expected relationship: high ARS should correlate with low automation probability.
Validation result: We found a strong negative correlation (r = −0.81) between our ARS and the Frey & Osborne probability for the 30 scored occupations. This confirms the four dimensions capture the structural factors that drive automation resistance.
ARS vs. Automation Probability (Validation)
Higher AI Resistance Scores correlate with lower Frey & Osborne automation probability
Validation Against BLS Growth
As a secondary check, we examined whether high ARS correlates with positive BLS employment growth (2024–2034). The rationale: occupations that are structurally protected from automation should also show healthy labor demand.
Occupations in the top ARS tier (90–100) show median employment growth of 14.5% — nearly 5x the national average. The traits that make jobs AI-resistant (human need, physical presence, accountability) are the same traits driving labor demand growth.
The AI Resistance Score captures real automation risk dynamics. Occupations scoring 90+ have near-zero Frey & Osborne automation probability and median BLS growth of 14.5%. Those below 40 face 80%+ automation probability.
Which Dimension Matters Most?
Correlation of Each Dimension with Frey & Osborne Automation Probability
Negative correlation = stronger protection effect (all values are negative)
Physical Presence (r = −0.74) and Human Relationship (r = −0.71) are the strongest individual predictors of automation resistance. This aligns with the Goldman Sachs finding that sectors requiring physical presence — construction (6% automatable), installation/repair (4%) — face dramatically lower automation risk than office-based sectors (46%).
Creative Judgment (r = −0.63) and Ethical Accountability (r = −0.58) are meaningful but weaker predictors individually. Their power is strongest in combination — occupations that score high on judgment AND accountability (like surgeons, 25 + 25 = 50 on these two dimensions alone) show near-zero automation probability.
Practical implication: If you can only improve your position on one dimension, focus on building deeper human relationships or increasing physical-presence work. These provide the strongest structural protection from automation.
How to Score Your Own Career
Follow these steps to compute your personal AI Resistance Score:
Define your actual task mix
Score each dimension (0–25)
Add the four scores
Interpret the result
Identify your weakest dimension
Worked Example: Corporate Recruiter
This corporate recruiter's weakest dimension is Physical Presence (7/25). Their strongest is Relationship (18/25). The career development implication: double down on relationship-based work and reduce time spent on automatable administrative tasks.
Interpreting Your Score
An ARS of 45 does not mean you will lose your job. It means the structural characteristics of your current task mix align with tasks AI handles well. The actionable response is to shift your work toward higher-scoring dimensions — more relationships, more judgment, more accountability — not to panic.
Limitations
This framework has important limitations that should inform how you interpret results:
- Scores are not predictions. An ARS of 94 does not mean the job is guaranteed safe. Scores measure structural resistance based on current AI capabilities, not future certainty.
- Sub-dimension scores involve judgment. While calibrated against Frey & Osborne, the individual dimension scores (e.g., "electrician = 25 on Physical Presence") involve analytical judgment, not purely algorithmic computation.
- Intra-occupation variation. The same job title can have very different ARS depending on task mix. A corporate recruiter doing strategic advisory (ARS ~65) vs. one doing high-volume screening (ARS ~45) have meaningfully different risk profiles.
- Frey & Osborne data has known limitations. The 2013 model treats entire occupations as units when automation risk varies within occupations. The OECD has critiqued this approach.
- AI capabilities are evolving. Scores reflect the current state of AI (early 2026). Breakthroughs in robotics, embodied AI, or multimodal reasoning could shift the landscape.
- Growth ≠ safety. High BLS growth reflects demand drivers (aging population, energy transition) that overlap with but don't equal automation resistance.
- Equal weighting is a simplification. All four dimensions are weighted equally (25 points). Our correlation analysis shows this is reasonable (r = −0.58 to −0.74 per dimension), but future iterations may explore empirical weighting.
Despite these limitations, the strong correlation (r = −0.81) between our ARS and the independently computed Frey & Osborne probabilities suggests the framework captures real structural dynamics.
Where the Framework Has Been Applied
We use the ARS framework across our AI and career research:
- Jobs AI Can't Replace: 30 Occupations Scored — Full rankings for 30 occupations across 6 industries, cross-category analysis, and the White-Collar Reversal finding
- Will AI Replace Recruiters? — ARS applied to 5 recruiter role types (coordinator through executive recruiter), showing the 32-point spread driven by Relationship and Judgment dimensions
As we publish additional "Will AI Replace [Role]?" analyses, each will include ARS scores using this methodology for comparability across the series.
Framework Summary
- 1AI Resistance Score (ARS): 100-point composite measuring structural resistance to AI automation
- 2Four dimensions (25 points each): Physical Presence, Human Relationship, Creative Judgment, Ethical Accountability
- 3Validated: r = −0.81 correlation with Frey & Osborne automation probabilities across 30 occupations
- 4Physical Presence (r = −0.74) and Human Relationship (r = −0.71) are the strongest individual predictors
- 5Top-tier occupations (ARS 90+) show median BLS growth of 14.5% — nearly 5x the national average
- 6Scores measure structural resistance, not predictions — the same job title can score very differently based on task mix
- 7Designed to be transparent and reproducible — all criteria, data, and limitations are published
Frequently Asked Questions
Can I use the AI Resistance Score to evaluate my own job?
Yes. Score your occupation on the four dimensions (Physical Presence, Human Relationship, Creative Judgment, Ethical Accountability) using the criteria tables in this article. Add the scores for your composite ARS. Scores above 75 indicate strong structural protection. Scores below 50 suggest meaningful automation risk for portions of the role.
Why did you weight all four dimensions equally?
Equal weighting (25 points each) is the simplest defensible approach. Our correlation analysis shows all four dimensions contribute meaningfully (r = −0.58 to −0.74 with automation probability). Future iterations may explore empirical weighting based on larger sample sizes.
What about jobs that use AI as a tool but aren't replaced?
This is augmentation, not replacement. Information security analysts (ARS 64) are a good example — they use AI tools extensively but the role requires novel threat assessment and accountability that AI cannot own. Our ARS measures replacement risk, not AI usage.
How often will you update the scores?
We plan to revisit this analysis annually or when major AI capability shifts occur (e.g., significant advances in humanoid robotics or embodied AI). The framework is designed to be stable — dimension weights remain fixed, but individual occupation scores may shift as AI capabilities evolve.
Is this peer-reviewed research?
No. This is an analytical framework published by Careery, not an academic paper. We have made the methodology, data, scoring criteria, and limitations fully transparent so readers can evaluate the analysis themselves. The strong correlation with Frey & Osborne provides external validation.
Why does my job score lower than I expected?
The most common reason is low Physical Presence scores. Many knowledge-worker roles that feel 'safe' score poorly on Dimension 1 because they are fully remote-capable. Remember: the score measures structural automation barriers, not job quality, demand, or satisfaction. A job can have moderate ARS (55) and still be an excellent career choice.
Can I improve my AI Resistance Score?
Yes — by shifting your task mix. Focus on work that scores higher on the four dimensions: build deeper relationships (Dimension 2), take on work requiring novel judgment (Dimension 3), and seek roles with greater accountability (Dimension 4). Physical Presence (Dimension 1) is the hardest to change — it's largely determined by occupation type.
How to cite
Careery Research (2026). “AI Resistance Score: A 4-Dimension Framework for Measuring Automation Risk (Methodology)”. https://careery.pro/research/ai-resistance-score-methodology (accessed YYYY-MM-DD).
- Link to the canonical URL: https://careery.pro/research/ai-resistance-score-methodology
- Include the accessed date when you publish.
- If you reuse numbers, keep the same definitions/timeframe and attribute the source line to Careery.
Researching Job Market & Building AI Tools for careerists since December 2020
Sources & References
- The Future of Employment: How Susceptible Are Jobs to Computerisation? — Carl Benedikt Frey & Michael A. Osborne, Oxford Martin School (2013 (updated 2017))
- How Will AI Affect the Global Workforce? — Goldman Sachs Research (2023–2024)
- Generative AI and the Future of Work in America — McKinsey Global Institute (2023)
- Occupational Outlook Handbook (2024–2034 Projections) — U.S. Bureau of Labor Statistics (2025)
- The Future of Jobs Report 2025 — World Economic Forum (2025)
- What Jobs Are Affected by AI? Better-Paid, Better-Educated Workers Face the Most Exposure — Brookings Institution (2023)
- Generative AI and Jobs: A Refined Global Index of Occupational Exposure — ILO / OECD (2025)