AI and automation in recruitment: Are we heading in the right direction?
AI and automation are now an integral part of modern recruitment processes. In many companies, ATS (Applicant Tracking Systems) analyze CVs, cover letters, and attachments before a recruiter even opens the application. The systems assess relevance, keywords, connections between experience and requirements, and rank candidates based on how well they match the job description.
This creates both new opportunities and new challenges.
The cover letter has taken on a new function
Traditionally, the cover letter has been a tool for demonstrating motivation, personality, and reflection. Now it has also become a source of data. AI systems use the text to analyze competence, experience, use of terminology, and compliance with the requirements profile.
This means that candidates who submit very short or general applications—and correspondingly short resumes—may actually be rated lower by the system, even if they are in fact well qualified. The system simply has less information to analyze.
This is a significant change. Previously, a short and precise application could be a strength. Now, too little content can mean that the candidate never reaches the human assessment stage.
The advantages of AI analysis
There are important advantages to this development. AI can:
- handle large numbers of applicants efficiently
- ensure more consistent assessment
- identify candidates who might otherwise have been overlooked
- reduce manual errors and subjective deviations
For employers, this provides better structure and documentation. For candidates, it can mean that relevant skills are actually identified, even when recruiters have limited time.
Challenges: Transferable skills and AI that matches AI
One of the biggest challenges with the increased use of AI in recruitment is how the systems handle transferable skills.
A human being will often be able to read between the lines. A candidate who has worked in one industry may have acquired skills that are directly relevant in another. Management, project management, commercial understanding, or technical insight can be highly transferable, even if the terminology used is not identical to that in the job advertisement.
An AI system will look more for explicit matches in wording and structure. If the job ad uses one terminology and the candidate describes their experience using another, the system may rank the candidate lower – even though their skills are highly relevant in practice.
This is further reinforced by a new phenomenon: AI matching AI.
Many candidates today use artificial intelligence to write or optimize applications and resumes. At the same time, employers use AI in their ATS systems to analyze and rank candidates. The result can be two algorithms trying to “speak the same language.” If the candidate’s AI-generated text does not match the terminology and structure that the employer’s AI analysis is configured for, discrepancies may arise that are not related to competence, but to wording.
This means that candidates who uncritically allow AI to write a generic and “polished” application risk losing the most important thing: precise adaptation to the actual requirements and language of the position. A good application in 2026 is therefore not about feeding as much as possible into AI and hoping for an optimal result. It is about doing a thorough job yourself, understanding what the employer actually wants, and using relevant and precise terminology.
Transferable skills must be clearly explained. Connections must be made explicit. The candidate must help both the system and the recruiter to see the connection.
This presents both a risk and an opportunity. The risk is that we end up with a selection process that is more text-based than skills-based. The opportunity is that candidates who are conscious, structured, and concrete in their communication will actually have a stronger advantage than before.
Are we heading in the right direction?
The question is not whether AI should be used—it will be. The question is how.
If AI is used as a support tool for structure and efficiency, and combined with human judgment, we are probably heading in the right direction. If the systems become decisive decision-makers without qualitative control, we risk reducing recruitment to pure text matching.
A good recruitment process in 2026 should therefore:
- use AI to structure and analyze
- ensure that human judgment is always part of the decision
- be transparent to candidates about how information is used
- encourage candidates to submit clear, relevant, and structured information
What does this mean for candidates?
Candidates should understand that the information they submit is actually analyzed by machine before it is read manually. A good, clear, and relevant resume—combined with a personal and specific application—provides both the system and the recruiter with a better basis for assessment.
The solution is not long documents, but precise and clear information that reflects the requirements in the job posting.
What does this mean for employers?
For employers, it’s all about balance. The systems must be configured correctly, the assessment criteria must be deliberate, and it must be possible to identify candidates who do not necessarily fit perfectly into the algorithm’s initial filtering.
Recruitment is still a profession. AI can strengthen the profession, but should not replace it.