Living dashboard
Talent & Augmented Skills Observatory
Work is not disappearing as fast as the skills it demands are changing. By 2030 two in five core skills will have shifted and nearly six in ten workers will need training; those who pair their craft with AI command a measurable wage premium. The question is no longer whether you will be replaced, but whether you will have real access to relearn.
Work is not disappearing as fast as the skills it demands are changing. By 2030 two in five core skills will have shifted and nearly six in ten workers will need training; those who pair their craft with AI command a measurable wage premium. The question is no longer whether you will be replaced, but whether you will have real access to relearn.
- No editorial index
- Curated monthly
- Weekly source watcher
- Experimental
Evidence × Impact
Evidence × Impact
Editorial reading of each verified claim against two axes: evidence strength and expected labour impact.
Expected labour impact ↑
Market expectations (survey, high impact)
Employer surveys and market forecasts.
No claims here yet.
Theoretical exposure (strong evidence)
Academic / multilateral methodologies with high labour impact.
No claims here yet.
Weak signals
Priority sources without an extracted claim yet.
World Economic Forum, Future of Jobs Report 2025
official_projection
World Economic Forum, Future of Jobs Report 2025
official_projection
World Economic Forum, Future of Jobs Report 2025
employer_survey
World Economic Forum, Future of Jobs Report 2025
employer_survey
PwC, 2025 Global AI Jobs Barometer
labour_market
PwC, 2025 Global AI Jobs Barometer
labour_market
PwC, 2025 Global AI Jobs Barometer
labour_market
Real adoption (strong evidence, observed impact still bounded)
Adoption / deployment data with bounded effects so far.
Pending: no observed real-adoption claim yet.
Survey / expectation
Traced methodology
Evidence strength →
The matrix is not a statistical scatter plot. A cell position is an editorial zone, not a quantitative coordinate.
Evidence vs. hype matrix
Four editorial readings of AI's impact on employment, sorted by evidence strength rather than a numeric score.
Expected labour impact ↑
Evidence strength →
Theoretical exposure
Academic / multilateral methodologies on potential exposure.
No claims here yet.
Real adoption
Production-use data / actual deployment.
Pending: no verified adoption claim yet.
Observed results
Measured changes in employment, wages or occupations.
Pending: no observed market claim yet.
Weak signals
Surveys, expectations and priority sources without an extracted claim.
World Economic Forum, Future of Jobs Report 2025
official_projection
World Economic Forum, Future of Jobs Report 2025
official_projection
World Economic Forum, Future of Jobs Report 2025
employer_survey
World Economic Forum, Future of Jobs Report 2025
employer_survey
PwC, 2025 Global AI Jobs Barometer
labour_market
PwC, 2025 Global AI Jobs Barometer
labour_market
PwC, 2025 Global AI Jobs Barometer
labour_market
The matrix implies no causality. A position is not a score.
Verified claims
Verified evidence
Claims traceable to their primary source, with how to read them and their limits.
Tier 1 = multilateral bodies (WEF). Tier 2 = labour-market analytics (PwC, McKinsey, LinkedIn).
Tier 1official_projection
39
39% of workers' core skills will change or become outdated between 2025 and 2030.
- Verified
- High confidence
- skills_velocity
- Geography: Global
- Timeframe: 2025-2030
- How to read it
- High as it is, this figure is down from the 44% projected in the 2023 edition, a sign of some stabilisation.
- What it does NOT prove
- It is an employer estimate about the future, not a measurement of skills already lost.
- Source
- World Economic Forum, Future of Jobs Report 2025
Tier 1official_projection
59
59% of workers will need training by 2030: 29% could be upskilled in their current roles, 19% reskilled and redeployed, and 11% would not receive the reskilling they need.
- Verified
- High confidence
- reskilling_need
- Geography: Global
- Timeframe: 2030
- How to read it
- The challenge is not only training more people, but closing the 11% that today falls outside any training at all.
- What it does NOT prove
- The 29/19/11 breakdown is an aggregate projection; it does not guarantee how it will split across countries or sectors.
- Source
- World Economic Forum, Future of Jobs Report 2025
Tier 1employer_survey
63
63% of employers identify the skills gap as the biggest barrier to transforming their business in 2025-2030.
- Verified
- High confidence
- skill_gap_barrier
- Geography: Global
- Timeframe: 2025-2030
- How to read it
- Companies see the lack of talent, not the lack of technology, as their main brake.
- What it does NOT prove
- It is the self-reported perception of surveyed employers, not an objective measurement of the gap.
- Source
- World Economic Forum, Future of Jobs Report 2025
Tier 1employer_survey
85
85% of employers plan to prioritise upskilling their workforce, and 50% expect to move staff from declining to growing roles.
- Verified
- High confidence
- reskilling_need
- Geography: Global
- Timeframe: 2025-2030
- How to read it
- The intent to invest in training is nearly universal; the challenge is turning intent into real, accessible programmes.
- What it does NOT prove
- It measures stated plans, not committed budget or training outcomes.
- Source
- World Economic Forum, Future of Jobs Report 2025
Tier 2labour_market
56
Workers with AI skills earn a 56% wage premium, up from 25% the previous year.
- Verified
- High confidence
- ai_skill_premium
- Geography: Global
- Timeframe: 2025
- How to read it
- Knowing how to use AI is no longer a bonus: it translates into salary, and the premium more than doubled in a year.
- What it does NOT prove
- It draws on job postings across a set of countries; it reflects advertised, not necessarily paid, wages and may skew towards highly qualified profiles.
- Source
- PwC, 2025 Global AI Jobs Barometer
Tier 2labour_market
66
The skills employers ask for change 66% faster in the occupations most exposed to AI.
- Verified
- High confidence
- skills_velocity
- Geography: Global
- Timeframe: 2025
- How to read it
- Where AI arrives, the shelf life of each skill shortens: the pressure to relearn is greatest exactly where the technology is most intense.
- What it does NOT prove
- It measures turnover in the skills named in job postings, not the real depth of change on the job.
- Source
- PwC, 2025 Global AI Jobs Barometer
Tier 2labour_market
27
Employment is still growing in virtually every AI-exposed occupation, including the most automatable ones, and productivity in the most exposed industries nearly quadrupled (from 7% to 27%).
- Verified
- Medium confidence
- augmentation
- Geography: Global
- Timeframe: 2018-2024
- How to read it
- In this phase, AI is augmenting human work more than replacing it: the dominant lever is productivity, not layoffs.
- What it does NOT prove
- It is a snapshot of an early phase; it does not project what happens if automation deepens, and aggregate job growth can mask losses in specific tasks.
- Source
- PwC, 2025 Global AI Jobs Barometer
AI figures come from job-posting analysis, not censuses; they reflect stated demand, not necessarily filled jobs.
Reading guide
How to read this dashboard
What it is for
To recognise the pressure of labour transformation by sector, occupation and profile — and the capacity to absorb it. A curated editorial reading, not a prediction.
What it is NOT for
It does not predict job destruction. It is not an official statistical index. It does not replace Tier 1–3 sources; it weights them with explicit editorial weights.
How to use it while reading the book
Come back here whenever a chapter cites a specific vector (exposure, adoption, gap) to situate it in the wider picture and compare the rhythm across sectors.
How it will evolve
Future iterations will add real per-source signals, a declared editorial cadence, and an automated watcher for Tier 1–3. IILE-IA calibration will remain editorial.