What Career Intelligence is

Not career advice.
Career intelligence.

Career advice tells you what to do. Career Intelligence gives you the capacity to figure out what to do — in any role, in any field, across a working life that will pass through multiple AI disruption cycles.

The distinction matters because the labour market AI is creating does not have stable answers. Jobs are not being replaced on a fixed schedule. The AI tools arriving in any given field change faster than any advice could track. What survives is the ability to read the situation and navigate it — which is exactly what the six-pillar framework is designed to develop.

Career Intelligence is a capability, not a credential. It is not something you finish. The framework describes six dimensions of that capability, grounds each in research, and maps them to measurable development — which is what the Career Scan, the Academy, and the Judgment Lab each do from a different angle.

"Career Intelligence is the organised, evidence-based capacity to read how artificial intelligence is reshaping the world of work, develop the human capabilities that AI raises the stakes for, and navigate a working life of continuous change with agency and direction."

— PositionMeAI Career Intelligence Framework v1.0, 2026

Not assessment. The framework develops capability. It does not score individuals against a fixed standard or certify career readiness.
Not generic. Every application of the framework is field-specific — the six pillars mean different things in different professional contexts.
Not static. The framework is designed for a labour market that keeps changing — it builds ongoing navigational capacity, not a one-time qualification.

The six pillars

Six dimensions of career capability
in an AI-shaped economy.

Each pillar describes a distinct dimension of Career Intelligence. Together they form a complete model for understanding — and developing — career capability under AI disruption. Every PositionMeAI product maps to this structure.

01
Context
Situational reading

The ability to read how AI is actually reshaping your specific sector, field, and role — not in general terms, but in the specific context where your work happens. Context literacy allows you to distinguish signal from noise in labour market AI coverage.

What AI tools are already active in my field, and what are they displacing?
What does the labour market data say about my specific discipline over a 5-year horizon?
What is the difference between automation risk in my sector and in adjacent sectors?
02
Awareness
Self-in-context

The ability to accurately map your own position within the AI-reshaped landscape of your field — your exposure profile, your current strengths relative to AI capability, and the specific gaps that matter most for your career trajectory.

Which of my current tasks have high AI exposure, and which are low?
Where am I currently strong relative to what AI is raising the stakes for?
What is my Career Intelligence profile across all six dimensions?
03
Capability
Active development

The practical AI working skills that are most relevant to your role and field — not generic digital literacy, but the specific capabilities that allow you to work alongside AI tools effectively, critically, and with professional judgment.

Which AI tools are most relevant to my daily work, and how do I use them competently?
What is the gap between my current AI capability and what my field will require in three years?
How do I build AI capability in a way that transfers as the tools change?
04
Strategy
Deliberate positioning

The capacity to make deliberate, informed decisions about where to invest career development energy over a 3–5 year horizon — identifying the positioning moves that will matter most in an AI-transformed version of your field.

Where should I be positioned in my field in five years, and what does AI do to that trajectory?
Which roles in my discipline are likely to expand, contract, or transform?
What adjacent capabilities should I be building to stay positioned in the talent market?
05
Ethics
Responsible practice

The ability to navigate the ethical dimensions of AI in your professional context — including questions of bias, accountability, transparency, and the limits of professional judgment that AI tools cannot substitute for.

What ethical constraints apply to AI use in my professional practice?
Where does professional accountability sit when AI is involved in consequential decisions?
How do I maintain professional ethics in a context where AI tools are creating new grey areas?
06
Navigation
Long-arc resilience

The meta-capacity that enables continuous repositioning across a working life — the combination of self-directedness, adaptability, and career-constructing agency that allows sustained navigation through multiple disruption cycles.

How do I build a career that can absorb multiple AI disruption cycles over 30+ years of work?
What is my theory of my own career construction, and how does AI change it?
How do I sustain direction and motivation across a working life of continuous change?

Research foundations

Five published
research streams.
One integrated model.

The six-pillar framework is not invented — it is integrated from five distinct bodies of published scholarship in career development, employability, labour economics, and AI capability research. Each stream contributes a specific dimension of the overall model.

The citations below are the sources of the theoretical architecture. Academic use of the framework should cite both the originating scholars and the PositionMeAI integration.

Stream 01 — Career construction theory
Career adaptability and self-directedness across the life span
Savickas, M.L. (2013). Career construction theory and practice. In S.D. Brown & R.W. Lent (Eds.), Career development and counseling: Putting theory and research to work (2nd ed., pp. 147–183). Wiley.
The theoretical foundation for Pillar 06 (Navigation) — the concept of career as an ongoing construction project requiring adaptability, concern, curiosity, confidence, and commitment. Informs the long-arc resilience dimension of CI.
Stream 02 — Graduate employability attributes
The Graduate Skills and Attributes Scale — GSAS
Coetzee, M. (2014). Measuring student graduateness: Reliability and construct validity of the Graduate Skills and Attributes Scale. Higher Education Research & Development, 33(5), 887–902.
The primary measurement instrument informing the Career Scan diagnostic. The six CI pillars are derived from the GSAS construct structure, adapted for the AI labour market context. Informs Pillars 01–04.
Stream 03 — South African graduate employability
Employability attributes framework for the South African graduate
Bezuidenhout, M.L. (2010). The development of a measure of graduate employability in the context of the new world of work. University of South Africa.
Grounds the framework in the South African professional context. Informs the field-specific adaptation of employability attributes and the contextualisation of CI pillars for the .co.za deployment.
Stream 04 — AI and the labour market
Tasks, automation, and the rise in US wage inequality
Acemoglu, D., & Restrepo, P. (2022). Tasks, automation, and the rise in US wage inequality. Econometrica, 90(5), 1973–2016.
The economic foundation for AI exposure mapping in Pillars 01 and 02. The task-based model of automation — distinguishing routine cognitive tasks from judgment tasks — underlies the Career Scan's AI Opportunity Layer.
Stream 05 — AI capability and occupational exposure
The Anthropic Economic Index — occupational AI exposure mapping
Anthropic. (2025). The Anthropic Economic Index: AI's impact on the economy and labor markets. Anthropic research publication.
The empirical foundation for field-specific AI capability mapping in Pillar 03. Informs the Academy's course structure and the AI Opportunity Layer's demand signal architecture.
How the streams map to pillars
Coetzee (2014) GSASContext · Awareness
Bezuidenhout (2010)Context · Strategy
Acemoglu & Restrepo (2022)Awareness · Capability
Anthropic EI (2025)Capability · Strategy
Savickas (2013)Ethics · Navigation
The integration of these five streams is the original contribution of the Career Intelligence Framework. The framework does not claim to replace the originating scholarship — it applies and extends it in the context of AI labour market disruption.
Full research overview →
PhD foundations
The Career Intelligence Framework is grounded in doctoral-level research in Industrial and Organisational Psychology. The full theoretical lineage, construct development process, and measurement decisions are documented in the methodology paper and available to researchers, institutional partners, and peer reviewers on request.
Request the methodology paper →

Framework in practice

One framework.
Every product.

The six pillars are not just a reference model — they are the operating architecture behind every PositionMeAI product. The Career Scan measures them. The Academy develops them. The Judgment Lab builds the reasoning capability they each demand.

Career Scan & Report
Measures all six pillars
The 15-minute diagnostic gives you a scored profile across all six CI pillars, grounded in GSAS construct validity and the Acemoglu-Restrepo task model.
Take the Career Scan →
Academy · 140 courses
Develops all six pillars by field
Each of 23 series contains 6 courses, one per pillar. The curriculum is the framework made field-specific and actionable across every professional discipline.
Browse the Academy →
Judgment Lab · Pilot
Builds the reasoning Pillars 03–05 demand
AI-coached simulation builds the working memory, analytical reasoning, and decision-making under uncertainty that the capability, strategy, and ethics pillars require — grounded in the Diamond executive function model and WEF durable skills research.
Judgment Lab →
Enterprise Platform
Deploys the framework at cohort scale
Universities, employers, and governments can deploy the six-pillar framework across entire student cohorts and workforces — with aggregate analytics and the CI Framework Licence for institutional use.
Enterprise →

Methodology

How the framework
was built.

The Career Intelligence Framework was developed through a four-stage process grounded in existing validated instruments, the South African graduate employability literature, and the emerging AI labour market research corpus.

1
Literature synthesis and construct identification
Five research streams were mapped for construct overlap and complementarity. The six CI pillars were identified as the integration points across career construction theory, employability attributes, and AI labour market economics.
2
Construct operationalisation
Each pillar was operationalised into measurable dimensions using the GSAS item structure as the primary scaffold, adapted for the AI context. The Career Scan diagnostic instrument was built against these operationalised dimensions.
3
Field-specific adaptation
The Acemoglu-Restrepo task model and Anthropic Economic Index data were used to weight and contextualise the pillars for 23 specific professional fields — producing the field-specific Academy curriculum structure.
4
Version lock and peer review preparation
The framework was locked at v1.0 on 2026-06-22. The methodology paper documents all construct decisions, scoring logic, and field adaptations. Peer review is invited — contact us to request access.

Governance principles

No outcome promises. The framework develops Career Intelligence capability. It does not promise employment outcomes, job offers, or salary increases. This is a governance decision, not a marketing constraint.
HPCSA registered. All diagnostic instruments are developed and administered within the ethical framework of a registered Industrial Psychology practice. HPCSA registration governs conduct and methodology standards.
Open methodology. The full construct development process, scoring decisions, and theoretical lineage are documented and available to institutional partners and peer reviewers. We do not claim black-box validity.
Academic citations in evidence. Scholarly references are cited precisely and linked to the constructs they inform. We do not cite research we have not read, and we do not over-claim from the sources we have cited.
Versioned framework. The framework will evolve as the AI labour market evolves. Major revisions will be versioned and documented. v1.0 is the locked baseline against which all current products are built.

Cite the framework

Using the framework
in your work.

The Career Intelligence Framework is available for use by researchers, institutions, and policy teams under the CI Framework Licence. Academic use in publications follows the citation format below. Institutional deployment requires a licence agreement.

APA 7th edition citation
De Vlamingh, L. (2026). The Career Intelligence Framework: A six-pillar model for career capability in an AI-shaped economy (v1.0). PositionMeAI / De Vlamingh & Associates Consulting CC. https://positionmeai.com/career-intelligence/
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Institutional licence enquiries
Universities, employers, government bodies, and research institutions may apply to use the framework in assessment design, programme development, and policy work. The CI Framework Licence covers all institutional use.
Institutional licence enquiry
Request the methodology paper
The full methodology paper documents all construct decisions, scoring logic, field adaptation processes, and theoretical lineage. It is available to researchers, peer reviewers, and institutional partners. Contact us to request access and state your use case.
Full construct development documentation
Career Scan scoring and item documentation
Field adaptation methodology for 23 disciplines
Ethics and governance documentation
Email to request the methodology paper →