Which parts of your job search should you automate with AI?
Short answer: use automation in discovering appropriate job titles, identifying possible gaps in your resume, conducting research prior to an interview, and any other mechanical task. Avoid automating resume writing, answering interview questions, and sending bulk applications until you make sure that they are personalized.
The main principle: allow AI to do all the mechanical work but not to write in your voice. AI deals well with technical tasks, and the moment it tries to imitate you, it fails every time.
People who achieve success with using AI use automation selectively. Working with clients weekly, I could observe a distinct trend: successful ones use less rather than more automation.
What AI actually handles well in a job search
Discovered job titles. Using AI to find similar but differently called positions based on a description you like is a great use case. My client found positions called “Revenue Operations Manager” and “Growth Analyst” and landed a director-level offer. Such positions weren’t on her radar until she used an automated approach.
Keyword gap analysis is helpful in making sure that your resume matches job descriptions. Copy-paste your resume and the job description next to each other and ask AI what you need to add to be more qualified. Fast, accurate, repeatable process. There is only one thing to remember about using AI this way – you shouldn’t take recommendations blindly since you can end up with a resume that passes ATS filtering but collapses in your recruiter call. For a deeper understanding of how AI-generated resumes fare in real life situations, read why recruiters reject AI-written resumes.
Researching the company before an interview. Product overview, recent news, business model, etc. – this type of information is provided correctly by AI every time. But don’t rely on it when asking questions about specific numbers, recent hires, and quotes because it will create them confidently even if it knows nothing about them.
What should you never automate in job searching?
Automated resume summary. It’s usually the first sentence a recruiter notices in a resume, and once they see that it was automatically generated, they don’t even bother reading the rest of the resume thoroughly. Even two simple sentences that you can formulate yourself easily win against several perfectly written paragraphs in terms of recruiter attention.
Interview answers generated by AI become useless once an interviewer asks follow-up questions about your stories and experience. It happened several times already when my clients learned their interview answers by heart. Those answers were technically correct but had no information about a candidate’s background. Interviewers usually notice it, which is why they ask a follow-up question. When you can’t provide the requested information, you have no chance to proceed.
Application mass emailing. It often yields fewer responses than personalized email writing. Submitting hundreds of emails may yield the same result as sending 20 personally tailored applications. Recruitment teams can’t specify why, but they immediately recognize batch sending. Companies are working on creating filters to eliminate such behavior. Understanding the basics of ATS keyword recognition, you’ll find out that tailoring application letters isn’t as hard as people think.
Nowadays, some recruitment platforms monitor application velocity. If you submit, say, 30 applications within a short period of time, your account will be blocked as a bot, and you won’t be able to apply to the company anymore, even manually.
Can AI write STAR stories for job interviews?
Yes, it can create a STAR structure of any story you give. However, when an interviewer asks follow-up questions, the answer shows that AI didn’t write the original story. In other words, it creates technically accurate versions of anything.
STAR stories work because candidates were actually involved in them. Thus, they can talk about numbers, results, and other details. On the contrary, AI writes a technically accurate copy of anything but leaves you at a loss if you are asked about follow-up questions.
Corrective action: inputting your raw notes about something that actually happened into AI and asking it to put them into STAR format. As a result, your personal story will remain relevant and survive follow-up questions.
When AI use goes wrong even when you’re doing it right
Failing in achieving the goal despite following all the recommendations happens because everybody else uses the same tools and gets the same results. That is something that the majority of tips don’t mention explicitly.
It is already noticed by recruiting teams. The representatives of Greenhouse and Lever recruitment software companies have confirmed that they can distinguish candidates based on similar cover letter structures that don’t look like plagiarism.
What is the difference between using AI as author vs co-writer for resume?
In this case, the solution lies in switching from author mode to co-author mode. Stopping using AI is one of the variants. A much more productive method: write your raw draft first and ask AI to edit it afterward. The difference between success and failure is this order of actions.
Author mode means that you generate your entire resume using AI. In other words, you simply paste a job description into it and receive a ready-made letter. Such an approach results in getting an identical resume that passes the ATS filtering stage and ends at human check without even a reply.
Co-author mode presupposes that you create a draft of your resume in your own words first. Then, you ask AI to refine your resume to make sure that your draft has proper style and is technically accurate.
That’s mostly what I tell my clients when they ask about using AI in their job search. Use what’s useful, ignore the rest.
