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Labour Market Intelligence

The signal in the noise
about AI and work.

There is no shortage of claims about what AI will do to employment. There is a significant shortage of grounded, evidence-based analysis. This page is our attempt at the latter — what the research sources we actually cite tell us, what they don't tell us, and what the CI Framework implies for how professionals should respond.

What the data shows

Three things we can say
with confidence.

From the five research streams underlying the Career Intelligence Framework. Cited precisely — not paraphrased into false certainty.

Finding 01 · Acemoglu & Restrepo
Automation displaces tasks, not jobs — but the task structure of most jobs is changing
The task-based model shows that automation targets specific tasks within roles, not whole occupations. But as more tasks are automated, roles reorganise around the remaining tasks — often at higher cognitive and relational complexity.
Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in US wage inequality. Econometrica, 90(5), 1973–2016.
Finding 02 · Anthropic Economic Index
AI is being used for augmentation more than replacement — but exposure varies sharply by field
The Anthropic Economic Index (2025) finds that the dominant pattern of AI use is augmentation of professional tasks rather than wholesale replacement. However, field-level exposure varies enormously — from very high in data-intensive roles to much lower in hands-on professional fields.
Anthropic. (2025). The Anthropic Economic Index: AI's impact on the economy and labor markets. Anthropic.
Finding 03 · WEF Future of Jobs
The skills with the longest horizon are cognitive, relational, and self-regulatory — not technical
The World Economic Forum's Future of Jobs research identifies analytical thinking, creative thinking, resilience, and self-efficacy as the most durable skills across AI disruption scenarios — precisely the capabilities the Judgment Lab is designed to develop.
World Economic Forum. (2025). Future of Jobs Report 2025. WEF.

What we cannot say with confidence: Which specific jobs will be eliminated, over what timeframe, in which geographies. Anyone claiming precision on these questions is speculating. The Career Intelligence Framework is designed precisely for the condition of genuine uncertainty — it develops the capacity to navigate regardless of which specific scenarios unfold.

AI exposure by field

Where AI is changing
work fastest.

The following exposure ratings are derived from the Acemoglu-Restrepo task model applied to the 23 professional fields covered by the Career Intelligence Academy, cross-referenced against the Anthropic Economic Index occupational exposure data.

Higher exposure means more tasks within the field are susceptible to AI augmentation or substitution. It does not predict job loss — it predicts the urgency of developing Career Intelligence in that field.

These are illustrative ratings, not precise quantitative scores. The CI Framework's AI Opportunity Layer develops field-specific demand signals for the Academy based on this methodology — available to Research Subscribers.

Field AI exposure Level
Data & Analytics
High
Information Technology
High
Marketing
High
Legal
High
Accounting
High
Human Resources
Medium
Project Management
Medium
Sales
Medium
Education
Medium
Healthcare
Medium
Engineering
Medium
Social Work
Lower
Construction
Lower
Agriculture
Lower
Key themes

What this means for
professional development.

01
Complementarity is growing faster than substitution — for now
The current AI cycle is producing more complementarity (AI makes professionals more productive at complex tasks) than substitution (AI replaces professional tasks entirely). This is consistent with early automation cycles. It will not hold indefinitely.
CI implication: The window to develop Career Intelligence is open. Professionals who develop Context and Capability now build a compounding advantage. Those who wait for clarity will wait past the inflection point.
02
The half-life of specific AI skills is measured in months, not years
Tool-specific AI skills (proficiency in particular AI systems, prompt engineering for specific models) depreciate very fast. Organisations investing heavily in tool-specific training are on a treadmill that accelerates with each model generation.
CI implication: Pillar 03 (Capability) in the CI Framework is deliberately tool-agnostic. The Academy develops the cognitive infrastructure for AI capability — not proficiency in any specific tool.
03
Judgment is becoming the scarce input in knowledge work
As AI takes on more legible cognitive tasks — drafting, analysis, summarisation, code generation — the tasks that remain for professionals are disproportionately those requiring judgment under uncertainty. This is not a transitional state. It is the direction of travel.
CI implication: The Judgment Lab exists because judgment is a developable capability, not a fixed trait. The simulations are designed to build the eight dimensions of professional judgment that AI exposure makes more valuable.
04
The South African labour market faces compounded disruption
SA graduates face both the global AI transition and structural domestic labour market challenges — high graduate unemployment, misalignment between qualifications and employer needs, and limited institutional capacity to update curricula at the speed AI requires.
CI implication: PositionMeAI's SA-first posture (positionmeai.co.za) is a direct response to this. The free Career Scan removes the price barrier for the professionals who most need labour market intelligence and cannot afford bespoke career support.
Numbers from the sources
39%
of skills expected to change
WEF Future of Jobs Report 2025
~57%
of AI use is task augmentation
Anthropic Economic Index 2025
170M
new jobs projected by 2030
WEF Future of Jobs Report 2025

All figures cited directly from the source reports. We do not editorially adjust or aggregate statistics to support a narrative.

The Career Intelligence response

What the framework
is built to address.

The six CI pillars map directly onto the Future of Work challenges the research identifies. The framework is not a response to a generic skills gap — it is a response to the specific structure of the AI disruption and what it demands of professionals.

Challenge
Rapid field-level AI exposure — professionals don't know what's changing in their specific field
CI Pillar 01 · Context — situational reading of AI's actual impact in your specific professional field, updated as the field evolves
Challenge
Judgment becoming scarce — AI handles legible work, professionals must handle the rest
CI Pillar 03 · Capability — deliberate development of the cognitive capabilities AI raises the stakes for, including through the Judgment Lab simulations
Challenge
Multiple disruption cycles across a career — a single reskilling event is not enough
CI Pillar 06 · Navigation — long-arc career resilience grounded in Savickas career construction theory; the capacity to navigate, not just survive, repeated disruption
Start with the Career Scan

Understand your own AI exposure.
Across six pillars.

The Career Scan takes 15 minutes and measures your Career Intelligence profile across the six pillars. Free. No account required. Available on positionmeai.co.za.

Take the free Career Scan → Read the research →