The online job search relies on a precise circuit: a candidate submits a structured document, software reads it before any human recruiter, and then a restricted selection reaches a decision-maker. Understanding each link in this circuit allows for action on the right levers instead of multiplying applications without feedback.
Filtering by ATS: the first obstacle to overcome before any recruiter
An ATS (Applicant Tracking System) is a recruitment software that analyzes, classifies, and filters applications before a human reviews them. Most medium-sized companies and large groups use one.
The process is simple: the software compares the terms in your CV to the criteria of the position. If the expected keywords (job title, technical skills, certifications) are absent or poorly formulated, the application drops in ranking or even disappears.
For a CV to pass this filter, it is necessary to use the exact terms from the job listing in the skills and experience sections. A rephrased job title (“digital coordinator” instead of “digital marketing project manager”) is enough to fail the automatic sorting.
Some candidates have attempted to bypass these filters by inserting invisible text or instructions intended for AI in their CVs. These manipulation practices have significantly increased between 2024 and 2025. ATS providers are now strengthening their anti-manipulation controls, making a readable, coherent, and honest CV more reliable than a document stuffed with artificial keywords.
To explore available job listings and test the compatibility of your profile with different positions, you can access the Job and Co website, which aggregates listings across various sectors.
Using generative AI to prepare your online application
Generative AI (ChatGPT, Mistral, Gemini, Claude) has become a common tool in job searching. According to Apec, the share of executives looking for a job who have used AI rose from 15% at the end of 2024 to 31% in 2025. The tool is no longer marginal; it is part of the process for a significant portion of candidates.
AI can intervene at several concrete stages:
- Rephrasing the tasks in a CV to align with the vocabulary of a specific job listing, without altering the facts
- Generating a first draft of a cover letter from a detailed prompt including the targeted position, sector, and your key achievements
- Simulating interview questions tailored to the position and hierarchical level, then evaluating the structure of your responses
- Summarizing a lengthy job description to quickly identify the key skills to highlight
A common pitfall is to copy-paste the raw output. AI produces a draft, not a final document. An experienced recruiter can spot generic text: catch-all formulations, absence of specific numerical examples from your background, uniform tone regardless of the targeted company.
The added value comes from what you inject into the prompt. The more context you provide (company name, sector challenges, concrete results from your previous roles), the more usable the result will be after review and adjustments.
European AI Act and recruitment: what changes for candidates
Automated recruitment is part of the high-risk AI systems classified by the European AI Act. Software that posts targeted job listings, sorts applications, or evaluates candidates is directly affected.
For companies, obligations include risk management, technical documentation, decision traceability, and human oversight. In practice, this means that an ATS can no longer permanently reject an application without a human control process being in place.
For a candidate, this regulation opens a concrete lever: if you believe that an automated pre-selection has dismissed your profile without human review, the European legal framework now provides for transparency obligations. Employers will have to document why and how an automated system filtered profiles.
This evolution reinforces the importance of keeping a record of each application sent (date, position, platform, version of the CV used). In case of dispute or request for explanation, having these elements facilitates the process.
Targeting applications: why applying less yields more
Sending the same CV to fifty job listings in a week results in a very low response rate. The time spent on mass applications would be better invested in tailoring each application to the specific expectations of the position.
Targeting relies on three selection criteria before sending:
- The listing mentions at least three skills that you genuinely master and can illustrate with achievements
- The sector or size of the company corresponds to an environment where your experience has already produced results
- The level of responsibility described is consistent with your background (neither too junior nor too senior compared to your past roles)
Adapting the CV to each job takes time but increases the perceived relevance by both the ATS and the recruiter. Modifying the job title, rearranging the skills according to the priorities of the listing, and adjusting the profile summary are often enough to make a difference.
A simple spreadsheet with the columns “company, position, date sent, CV version, follow-up planned” allows you to track everything without losing the thread. This tracking also prevents you from applying twice to the same position or missing the follow-up window, usually about ten days after sending.
The online job market is evolving rapidly, with the rise of AI on both the candidate and recruiter sides, and European regulatory frameworks redefining the rules of automated sorting. Adapting your tools and methods to these changes remains the most direct way to receive responses rather than silence.
