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Work · Research & Decision Systems

AI Career Intelligence System

An AI-assisted research and decision framework for evaluating emerging AI roles against defined, weighted criteria.

01

Context

A structured research project examining the emerging AI job landscape and testing it against a defined set of evaluation criteria rather than impressions.

02

Question / Challenge

Which emerging AI roles are genuinely viable for a specific practitioner profile, and on what evidence?

03

My role

Sole researcher and system designer: criteria design, source gathering, verification, comparison and written conclusions.

04

Process

  1. 01Define the evaluation criteria: remote-work potential, accessibility, technical requirements, creative and research fit, portfolio evidence and professional viability.
  2. 02Gather role descriptions and market material across the AI-only role families under investigation.
  3. 03Use AI assistance to summarise, cluster and structure the material.
  4. 04Verify claims against primary sources and discard unsupported or promotional material.
  5. 05Compare roles against the criteria and record the reasoning behind each judgement.

05

AI contribution

AI was used for summarising volume, clustering role families and structuring comparisons — not for the conclusions.

06

Human judgement

Criteria design, weighting, source verification and the final assessments. Where AI summaries flattened distinctions between roles, they were rewritten.

Contribution map

Human

  • Research question
  • Criteria and weighting
  • Source verification
  • Selection
  • Final decision

AI

  • Summarisation
  • Clustering
  • Structuring
  • Drafting

07

Output

A comparison framework covering junior and associate AI engineering, generative-AI workflow design, AI automation, creative AI technology, AI research analysis and responsible-AI support, with documented reasoning.

08

AI disclosure

Why AI was used
To compress a large, repetitive body of role descriptions into comparable structures quickly.
What was verified
Role definitions, stated requirements and working patterns were checked against primary listings and published material.
What AI got wrong
Conflated adjacent role families, and reproduced promotional framing from job-market content as if factual.
Rejected or changed
Any summary presenting market speculation as established fact; all salary or demand claims without a traceable source.
Human contribution
The criteria, the weighting, the verification and every conclusion.
Ethics & authorship
Accessibility appears as one evaluation criterion among several, treated as a design constraint rather than a narrative.

Skills demonstrated

  • Criteria design
  • Source evaluation
  • Comparative analysis
  • Structured decision-making
  • Research synthesis

Links

Reflection

The value sits in the criteria, not the answer: an explicit, weighted framework makes a decision reviewable by someone else.

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