What this platform is — and isn't
The platform quantifies two things for every combination of profession and country: how likely a given profession is to be disrupted by automation and AI (the Automation Score), and how capable a country's workforce is of adapting to that disruption (the Reskilling Score).
For a given profession in a given country: how hard is automation hitting it, and how prepared is the workforce to respond?
This is not a prediction engine. The scores are relative risk indicators — a structured, comparable, evidence-based way to prioritise attention across a large matrix of professions and geographies. They do not claim to forecast exactly which jobs will disappear or when.
The automation probability figures come from peer-reviewed academic research (Frey & Osborne), the most widely cited quantitative study in this field. Country-level indices come from publicly available institutional datasets. Neither the underlying research nor this platform should be read as deterministic.
Four open, institutional datasets
All four underlying datasets are open, institutional, and freely available. No proprietary or paid data feeds are used — ensuring reproducibility and allowing the methodology to be independently verified.
Five deterministic steps
The pipeline is fully deterministic and reproducible. Given the same input datasets, the same scores will always be produced.
The scoring formulas
Automation Score — a composite measure of effective automation pressure, combining the inherent replaceability of an occupation with the country's actual capacity to deploy automation technology.
| Variable | Source | Role |
|---|---|---|
| automation_probability | Frey & Osborne | Occupational baseline — does not vary by country |
| ai_adoption_index | Oxford Insights 2022 | Country's structural propensity to deploy AI — same occupation in a low-adoption country scores lower |
| (1 − employment_growth) | World Bank 2024 | Labour market correction — shrinking employment amplifies risk; growing employment moderates it |
Reskilling Score — measures potential and urgency for workforce reskilling. A high score indicates both strong need (rapid skill change) and meaningful structural capacity to respond.
| Variable | Source | Role |
|---|---|---|
| skill_change_rate | LLM-estimated (calibrated prompt) | Rate of skill transformation per profession — distinct from replacement risk |
| tech_adoption | ISCO group weight | Professionals (Group 2) carry higher weight than Elementary Occupations (Group 9) |
| education_index | UNDP HDR 2023 | Structural capacity to absorb reskilling — workforce's baseline ability to learn and pivot |
Reading the two scores together:
Strong pressure but real capacity to respond. Priority for structured reskilling programmes.
Highest risk zone. Strong displacement pressure with limited structural capacity to adapt. Requires policy-level intervention.
Stable profession in a country with high adaptive capacity. Proactive upskilling opportunity.
Limited immediate risk. May warrant monitoring as automation expands to lower-probability occupations over time.
0.00–0.15 Almost no change · janitor, agricultural laborer
0.16–0.30 Slow change · cook, security guard, construction worker
0.31–0.45 Moderate change · accountant, cashier, paralegal
0.46–0.60 Fast change · financial analyst, marketing specialist, nurse
0.61–0.80 Very fast change · software developer, radiologist, journalist
0.81–1.00 Near-complete transformation · AI/ML engineer, data scientist, prompt engineer
What we're transparent about
We believe in being direct about what the data can and cannot tell you. The following limitations are inherent to the methodology and should inform how you use the scores.
Data provenance summary
Every input variable, its source, and its validation status at a glance.
The dataset refreshes quarterly. Each refresh attempts live downloads from all four source APIs; failures fall back to the most recently cached values. LLM-estimated skill change rates are versioned separately — when the underlying model is updated, the skill cache is cleared and all professions are re-estimated. Scores from different pipeline versions should not be directly compared without reviewing the changelog.