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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsTo tell whether a job posting is really for an AI job, look at the work the person will own—not the title or the number of times the ad says “AI.” The key questions are whether AI is incidental, a tool for routine tasks, a substantial responsibility, or the role’s central purpose. Use the four levels below as a practical reading guide, not an official occupational classification.
The four levels of AI work in a job posting
Classify the role by its expected responsibilities, deliverables, and required skills. An AI keyword alone does not establish what the job involves.
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Level 1: AI is mentioned, but it is not part of the job
The posting mentions AI in boilerplate, a preferred qualification, or a passing reference, but its duties and expected outputs do not depend on using or building AI. Ask whether the stated requirement is actually used in the day-to-day work. If the employer cannot connect it to a responsibility or deliverable, the mention may be incidental.
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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. AI is a means of doing the job, not the defining output. That distinction matters: the OECD notes that most workers exposed to AI will not need specialized AI skills such as machine learning or natural-language processing.
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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 outputs and clear ownership. Using a named tool does not by itself put a role at this level; responsibility for the system or its results is the more useful signal.
Level 4: AI is the role’s central purpose
The job exists primarily to build, train, research, deploy, or advance AI systems. Its duties should match specialist requirements such as machine learning, natural-language processing, model evaluation, or AI infrastructure. Technical terminology without corresponding responsibilities is not enough to establish that this is the role’s purpose.
What to inspect before deciding
Read the responsibilities and success measures before counting keywords. Match what the employer says the person will do with the expertise it requires. A vague posting leaves the actual level uncertain; do not fill in missing duties based on the title.
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- Purpose: Is an AI system the product or service being built, or a tool used to produce other work?
- Responsibility: Does the role merely use AI outputs, or does it select, integrate, evaluate, deploy, monitor, or govern a system?
- Ownership: Who is accountable for output quality, safety, data, or ongoing operation?
- Deliverables: Does the ad name specific work products, systems, or outcomes—or only say the candidate should be familiar with AI?
- Skills: Are requirements general AI literacy, or specialist skills that match the stated duties?
- Workflow: Is AI-generated work an input to the employee’s work, or the final product the employee is expected to deliver?
These questions help distinguish routine tool use from a job with substantive AI ownership. They are a practical reading framework, not a formal score.
Questions to ask the recruiter
If the posting does not say enough to classify the role, ask for examples tied to the actual work:
- Which AI systems or tools would I use, build, or maintain, and what deliverables would I be responsible for?
- 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 outputs produced by another team?
- Which skills are required on day one, and which can be learned after hiring?
- Is the AI language tied to a funded project and current workflow, or is it a general future-facing requirement?
How to interpret the broader AI-job evidence
Labor-market studies can help explain why AI appears in job ads, but they cannot classify a particular vacancy without its duties. Keep the scope of each finding in view:
- The U.S. Bureau of Labor Statistics describes AI exposure in terms of whether AI could assist with or complete some work performed in an occupation, alongside measures based on observed use. Exposure is relative to other occupations; it is not a productivity forecast and does not distinguish automation from augmentation. BLS: AI exposure categories.
- A 2024 OECD paper reports that the share of vacancies in highly AI-exposed occupations demanding at least one emotional, cognitive, or digital skill rose by 8 percentage points over time. Its abstract also reports panel evidence that demand for these skills is beginning to fall, so the trend is not uniform or one-directional. The paper’s author, Andrew Green, writes: “Most workers who will be exposed to artificial intelligence (AI) will not require specialised AI skills (e.g. machine learning, natural language processing, etc.).” OECD Artificial Intelligence Papers No. 14, published 10 April 2024.
- A preliminary 2026 Federal Reserve Bank of Cleveland working paper found that an additional standard deviation of occupational AI exposure was associated with a 3.1-percentage-point increase in the rate of U.S. job ads mentioning AI. This is an association across occupations, not evidence that AI caused a particular employer to add a requirement to a particular ad. Kevin Rinz, Working Paper 26-24, published 24 September 2026.
- Employment and Social Development Canada reports that, in Canadian job-posting data covering 2019–2024, over 75% of jobs high in both AI exposure and AI complementarity were also green jobs. That group-level result is not a verdict on an individual opening. Employment and Social Development Canada, 8 September 2026.
AI exposure, automation risk, and complementarity describe different things. In Canadian postings, high automation risk usually appeared in low-exposure areas, while high complementarity mostly appeared in medium-exposure occupations. Those are occupational-group findings, not a way to infer the duties of one job from its title. Employment and Social Development Canada’s summary.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallWhy a job ad may not tell the whole story
Online postings can reveal which skills employers request, but not every job is advertised online. Online ads may also distort the labor-market picture, and employers may not describe or assess requirements accurately. The European Commission’s Joint Research Centre explains these limitations in its overview of online job-ad data. European Commission JRC: Online job advertisements. Treat unclear language as uncertainty to resolve with the employer, rather than proof that AI is either central or irrelevant.
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