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Look past the title and count what the role is expected to deliver. An AI keyword alone does not make a job an AI job: the useful distinction is whether AI is incidental, a routine tool, a substantial responsibility, or the role’s central purpose.
The four-level scale below is a practical way to classify an individual posting, not an official occupational classification or validated scoring system. Read the responsibilities and success measures first, then match the required skills to the work.
The four levels of AI work in a job posting
Level 1: AI is mentioned, but the job does not depend on it
AI may appear in boilerplate, a preferred qualification, or a passing reference. If the listed responsibilities and deliverables would be unchanged without AI, the mention is incidental. Check whether the employer says the skill is actually used in day-to-day work.
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Level 2: AI is a tool for familiar work
The employee uses AI to complete or speed up work in another occupation. The role’s output remains, for example, the usual work of that occupation; AI is one means of producing it. This distinction matters because broad AI exposure does not necessarily mean a worker needs specialist AI skills. The OECD’s 2024 working paper states: “Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.).” Andrew Green’s OECD paper discusses changing skill demand across occupations.
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Level 3: AI is a substantial responsibility
A meaningful part of the job involves selecting, adapting, integrating, evaluating, monitoring, or governing AI systems—or redesigning workflows around them. Look for concrete deliverables and clear ownership, such as responsibility for system integration or model-quality evaluation. A tool name by itself does not establish this level; the responsibility does.
Level 4: AI is the role’s defining purpose
The central output is building, training, researching, deploying, or advancing AI systems. The posting should connect specialist requirements—such as machine learning, natural-language processing, model evaluation, or AI infrastructure—to those duties. Specialist terminology without corresponding responsibilities may be promotional language rather than evidence of specialist work.
What to check before classifying a posting
- Responsibilities and success measures: What work must the employee complete, and how will the employer judge whether it is done well?
- System responsibility: Does the role build or configure AI, integrate it into a workflow, or own deployment and ongoing performance?
- Evaluation and safeguards: Is the employee accountable for model quality, safety, data, compliance, or monitoring?
- Output: Is AI-generated work an input to the job, or is an AI system itself the product or deliverable?
- Skill match: Do the required skills fit the described duties, or are specialist terms listed without a clear connection to the work?
- Specificity: Does the posting identify a current workflow or funded project, or rely on vague, future-facing language?
When duties are unclear, treat the classification as uncertain rather than assuming responsibilities the employer has not described. Online ads can provide detail about requested skills, but they do not cover every job and may not represent the wider labor market accurately. Employers may also describe or assess requirements imperfectly, as the European Commission Joint Research Centre explains.
Questions to ask the recruiter
- Which AI systems or tools would I use, build, or maintain, and what deliverables would I own?
- Roughly what portion of a typical week involves AI-related work, and how is that measured?
- Would I own model quality, deployment, evaluation, data, safety, or compliance—or mainly use output from another team?
- Which skills are required on day one, and which could be learned after hiring?
- Is the AI work part of a funded project and current workflow, or is the language a general expectation for the future?
How to compare two postings that both mention AI
Use these axes to compare the actual work, not to calculate a formal score:
- Core purpose: Is an AI system the product, or is AI a tool used to produce something else?
- Responsibility: Does the employee use AI, or integrate, evaluate, deploy, or govern it?
- Skill specificity: Does the role ask for general AI literacy or specialist skills such as machine learning or natural-language processing?
- Ownership: Is the employee accountable for system operation, quality, safety, or outputs?
- Effect on work: Does AI replace particular tasks, support the employee’s work, or both?
That last distinction is important: AI exposure, automation risk, and complementarity describe different things. The U.S. Bureau of Labor Statistics says its exposure measures concern whether AI could assist with or complete some work in an occupation; measures also draw on observed use. Exposure is relative to other occupations, is not a productivity forecast, and does not distinguish automation from augmentation. The BLS explains its AI exposure categories and limitations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What labor-market studies can—and cannot—tell you
Studies describe patterns across occupations, establishments, or groups of postings. They can explain why AI mentions are becoming more common, but they cannot classify a particular vacancy without its responsibilities and deliverables.
- OECD, 2024: The share of vacancies in highly AI-exposed occupations requiring at least one emotional, cognitive, or digital skill increased by 8 percentage points over time in the study. The paper also reports panel evidence that demand for these skills is beginning to fall, so the trend is not uniform or one-directional. This is an occupation- and establishment-level finding, not a forecast for every posting. Read the OECD paper.
- Federal Reserve Bank of Cleveland, 2026: A preliminary working paper by Kevin Rinz found that an additional standard deviation of occupational exposure was associated with a 3.1-percentage-point increase in the rate of U.S. ads mentioning AI. This is an association, not evidence that exposure caused an individual employer to add AI duties. The paper was published 24 September 2026. Read Working Paper 26-24.
- Employment and Social Development Canada, 2026: In Canadian posting data covering 2019–2024, over 75% of jobs high in both AI exposure and AI complementarity were also green jobs. That describes a group-level overlap in the Canadian data, not the nature of any one vacancy. Read the department’s summary.
For example, Canadian findings showed high automation risk usually appearing in low-exposure areas, while high complementarity mostly appeared in medium-exposure occupations. Those group-level relationships are not a verdict on an individual role. Use the posting itself to determine what the employee will do.
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