Skip to main content
VTE
Sign inCreate account

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

Method

How we measure — and where we fall short

This section publishes, in four blocks, the methodological honesty floor of the observatory: what we measure, how, what we DON'T measure, and what could be wrong. It's the section we most want you to read before citing the IILE-IA.

What we measure — and where each dimension comes from

The six IILE-IA dimensions are not original. Each one is anchored to a public source with open methodology. What IS ours is the editorial composition — the weights, the qualitative reading and the cadence.

  • E · Technical exposure

    What share of an occupation's tasks can be done today with generative AI.

    Anchored on

    Felten et al. (AI Occupational Exposure) · ILO–NASK Refined Index · Anthropic Economic Index.

  • A · Real adoption

    What share of firms and professionals use AI in production, not in headlines.

    Anchored on

    Eurostat AI by enterprise · Funcas (Spain) · Microsoft Work Trend Index.

  • T · Task transformation

    What fraction of tasks, not jobs, is changing — the actually observable indicator.

    Anchored on

    Anthropic Economic Index (O*NET mapping) · MIT Project Iceberg.

  • S · Skill velocity

    How fast demanded skills shift and what wage premium they carry.

    Anchored on

    LinkedIn Economic Graph · Lightcast Disruption Matrix · PwC AI Jobs Barometer.

  • M · Labour-market signal

    What is ACTUALLY observed in employment, wages, postings and turnover — the counterweight to hype.

    Anchored on

    Yale Budget Lab · BLS · Eurostat · Brookings · Indeed Hiring Lab (open CSV).

  • B · Adaptation gap

    How far the incoming transformation runs ahead of organisational capacity to absorb it.

    Anchored on

    OECD AI Policy Observatory · McKinsey (1% mature firms) · Microsoft (33% leaders considering headcount cuts).

How we measure

  • Explicit editorial weights: IILE-IA = 0.20·E + 0.15·A + 0.20·T + 0.15·S + 0.15·M + 0.15·B. The weights are an editorial decision under review.
  • 0–100 scale with five qualitative bands (low pressure, emerging change, active transformation, high disruption, critical fracture) — the band rules over the number.
  • v0.2 reading is provisional with confidence: low. The numbers are an editorial reading, not a statistical calibration.
  • Monthly snapshot with a public date. Every snapshot leaves a trace in `evolution`; the comparison across snapshots is the honest reading of change.
  • Every claim feeding a dimension declares its source, metric, geography and confidence level. Untraceable claims don't enter.

What we DON'T measure

  • Informal employment and self-employed without statistical registration — geographies where the bulk of work doesn't appear in official statistics.
  • Sectors with sparse telemetry (construction, hospitality, skilled manual) — under-represented in LinkedIn/Indeed/Anthropic.
  • Second-order effects: how consumption shifts, how education shifts, how fiscal policy shifts. The observatory looks at the labour market, not its surroundings.
  • Geographies outside the EU / US / Spain anchor. Latin America, Asia and Africa are under-represented in our source corpus — we state it so readers don't extrapolate blind.
  • Subjective work quality: the observatory measures volume / velocity / value, not satisfaction or meaning. That dimension lives on a different dashboard.

What could be wrong

  • Confusing correlation with causation: adoption rising while a labour metric falls does NOT prove one causes the other.
  • Unauditable private telemetry: Anthropic, Microsoft, LinkedIn publish data without fully open methodology. We triangulate with ≥2 sources but cannot reproduce them.
  • Media and consultancy bias: Tier 4 (PwC, McKinsey) tends to package for PR. We use them as corporate-adoption signal, not as macro readings.
  • Arbitrary editorial weights: 0.20 vs 0.15 is our call. Real statistical calibration (Phase 6) will revisit weights when ≥3 real snapshots exist.
  • Spanish framing: our primary reader is ES, which biases which sources we prioritise. Funcas + Randstad carry more editorial weight than their counterparts in other geographies.

Next editorial review: 2026-06-15.

To discuss a specific reading or contribute a missing Tier 1 source, write to the editor — the monthly cadence is designed to incorporate it.

From source to decision

How a figure travels from the official source down to the editorial decision. Each step is a filter: if one stage fails, the decision does not stand.

  1. 01S — Skill velocity

    1. Verified source

      —

    2. Traceable claim

      WEF: 39 % of key skills change by 2030.

    3. IILE dimension

      S — Skill velocity

    4. Editorial insight

      The minimum-employability bar moves faster than the traditional training cycle.

    5. Suggested decision

      Prioritise continuous reskilling before the wage premium becomes a barrier to entry.

  2. 02E — Technical exposure

    1. Verified source

      —

    2. Traceable claim

      IMF: ~40 % of global employment is exposed to AI.

    3. IILE dimension

      E — Technical exposure

    4. Editorial insight

      Exposure measures potential impact, not automatic substitution.

    5. Suggested decision

      Do not confuse exposure with job destruction: some work will be augmented.

  3. 03T — Task transformation

    1. Verified source

      —

    2. Traceable claim

      ILO: refined index built on 52,558 task-level datapoints.

    3. IILE dimension

      T — Task transformation

    4. Editorial insight

      The relevant unit for labour impact is the task, not the job.

    5. Suggested decision

      Analyse task portfolios inside each role before any headcount decision.

  4. 04M — Labour-market signal

    1. Verified source

      —

    2. Traceable claim

      Stanford HAI: AI Index 2025 Economy chapter as priority source.

    3. IILE dimension

      M — Labour-market signal

    4. Editorial insight

      Operational signals (vacancies, wages) precede aggregate effects.

    5. Suggested decision

      Track AI-skills demand as a leading indicator instead of waiting for the observed datum.

The flow does not imply automatic decisions: every step still requires editorial reading.

Reading by audience

Four editorial readings of the dashboard, one per audience. These are not automatic recommendations: they are starting points each audience must reconcile with their context.

  • Business and executives

    What to watch
    Real adoption (A·45) and task transformation (T·70): T is already high — tasks inside each role are migrating fast. A·45 says organisational adoption hasn't yet generalised in production.
    What to decide
    Map tasks, not jobs: redesign task portfolios before any headcount decision. The A→T gap is the productivity-capture window before competitors close it.
    What not to overinterpret
    T·70 measures task transformation in exposed occupations, not job destruction. The 22% disruption-by-2030 figure is an employer forecast (WEF), not a layoff projection.

  • Professionals (cognitive roles)

    What to watch
    Skill velocity (S·72) and labour-market signal (M·42): S is the highest score in the observatory — skills shift fast. M·42 says that shift has NOT yet translated into aggregate employment disruption (Yale Budget Lab).
    What to decide
    Update your skills portfolio continuously, prioritising AI-complementary skills. Today's AI-skills wage premium (+28% per Lightcast) is an open window, not a permanent state.
    What not to overinterpret
    Technical exposure (E·65) is not substitution: many tasks are augmented, not eliminated (Anthropic Economic Index: ~49% of jobs already use AI in ≥1/4 of tasks).

  • Education and talent

    What to watch
    Skill velocity (S·72) and adaptation gap (B·52): S sets the market's pace; B·52 says organisations are NOT yet mature enough to absorb it (McKinsey: only 1% consider themselves AI-deployment mature).
    What to decide
    Accelerate reskilling cycles so the curriculum does not lag the skills market. Curricula need shorter cycles than the usual annual review.
    What not to overinterpret
    The 39% key-skills change by 2030 is employer expectation (WEF), not a prescribed curriculum. And B·52 is editorial reading, not a direct measure of organisational capacity.

  • Public policy / institutions

    What to watch
    Adaptation gap (B·52) and technical exposure (E·65): E·65 is the second-highest score — high task exposure, especially in advanced economies (IMF: ~60% of employment exposed in developed economies).
    What to decide
    Watch the gap B before exposure E: the vulnerable population is not the exposed, but those without a safety net to absorb the transformation. Prioritise safety nets and active labour policies.
    What not to overinterpret
    The 60% exposure in advanced economies (IMF) is methodological, not a count of lost jobs. ILO 2025 disaggregates by gender: 9.6% female employment vs 3.5% male in high-exposure occupations.

Each reading anchors the IILE-IA v0.2 scores (confidence: low, provisional editorial reading) to an actionable recommendation. Next review: 2026-06-15. No reading is a statistical prediction.

Other readings

IILE-IA v0.2 in the index ecosystem

Four external readings the observatory tracks, each with its headline, scope, and our editorial position vs IILE-IA. We don't compete with them — we use them as anchors.

  • International Monetary Fund · imf.org

    IMF AI Preparedness Index (AIPI)

    ~60 %

    Scope
    174 economies · global · annual
    Measures
    Employment exposed to AI in advanced economies. Composite index 0-1 across 4 pillars: digital infrastructure, human capital + labour policies, innovation + economic integration, regulatory + ethical framework.
    Vs. IILE-IA
    AIPI is composite and country-comparable; IILE-IA is NOT by design — it is an editorial multi-signal reading with inline caveats. If you need one comparable number for Spain vs Germany, AIPI is the tool. If you want to know what changed this month and why it matters for your role, IILE-IA is the reading.

  • Stanford HAI · hai.stanford.edu

    Stanford AI Index 2026 — Economy

    Dedicated chapter

    Scope
    Global · macro synthesis · annual
    Measures
    AI investment, enterprise adoption, productivity and labour-market signals. The gold standard in narrative + chart-per-claim. Hype-vs-evidence side-by-side by default.
    Vs. IILE-IA
    The AI Index is the broadest evidence corpus of the year, but lands as an annual PDF. IILE-IA picks its most relevant claims (Anthropic Economic Index, enterprise adoption) and re-anchors them monthly alongside the other Tier 1-3 sources.

  • PwC · pwc.com

    PwC AI Jobs Barometer 2025

    +56 %

    Scope
    ~1B postings · 6 continents · annual
    Measures
    AI-skills wage premium in exposed occupations, from a billion-posting analysis. Also 66 % acceleration in skills change (vs non-AI-exposed).
    Vs. IILE-IA
    PwC is pure market signal (M in IILE-IA) and the best reference for the 'AI premium'. The Tier 4 bias (consultancy PR-packaging) is declared in the method; we use the number but triangulate it with the Lightcast Disruption Matrix.

  • ILO · NASK · ilo.org

    ILO 2025-NASK Refined Index

    9.6 % vs 3.5 %

    Scope
    Global · occupational · by gender
    Measures
    Refined occupational GenAI exposure by task, disaggregated by gender and country. 9.6 % female employment vs 3.5 % male in high-exposure occupations.
    Vs. IILE-IA
    ILO 2025 is our canonical anchor for the E (technical exposure · 65) dimension of IILE-IA. Gender disaggregation is a methodology IILE-IA does NOT currently replicate — one of the gaps the Método section declares explicitly.

When a headline claims 'AI will destroy X jobs', cross-check against at least two of these four. If all four disagree with the headline, it's almost always overclaim.

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.