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Each installment of this series brings a real question from a recruiter or HR practitioner straight to the JobTarget product team. This one came in as one of the most asked questions from a recent programmatic webinar audience, and it is not going away anytime soon.
- Ask a Product Manager: How Does My Programmatic Budget Actually Get Used?
- Ask a Product Manager: Do I Need to Take Requisitions Down and Repost?
- Ask a Product Manager: How Do I Reach a Niche Role Through Programmatic Without Pulling in Generic Applicants?
- Ask a Product Manager: What Can I Actually See About My Competitors in JobTarget Intelligence?
Q: How do you identify applicants who are using AI for their resumes, cover letters, and portfolios?
A: The honest answer is that JobTarget doesn't have a tool that flags an AI-written resume the way a spam filter flags a phishing email.
Our publisher partners watch for bots and auto-submit tools that flood a job with bulk applications, and we address them directly with the partner when they show up. That is a different problem from a real candidate using AI to help write a resume or cover letter, and the two are often treated as one more often than they should be. We do not have a tool that reviews resume content for AI involvement.
None of that replaces your own judgment on a specific candidate. Your own screening process is where this is actually resolved, since that's what verifies that a candidate is who their resume says they are before you ever get to the interview.
Does this apply the same way to cover letters and portfolios?
Cover letters tend to be the easiest to spot on a human level, since generic AI output often ignores the specifics of the role and the company. Portfolios are harder, especially in creative and technical fields where AI-assisted work is now common and not automatically a red flag. Penalizing every candidate who used AI to polish their resume costs you good people. The real problem is someone using AI to invent experience they don't actually have, and that gap usually shows up fast once you're in a real conversation with them.
What can I actually do on my end to reduce generic, low-effort applications?
This is where you have more control than any detection tool would give you. A generic job posting invites a generic, AI-assisted response. Specific, well-written postings with clear must-have qualifications tend to filter for effort before an application ever reaches your desk. If you are running a Video Screener alongside your postings, that is also worth a second look here. Candidates responding on video or audio to a specific question have to show up as themselves in real time, which is much harder to fake than a written document.
Should I be worried about screening out strong candidates who used AI responsibly?
Yes, and this is worth sitting with for a second. A candidate who used AI to tighten up formatting or catch a typo is not the same as a candidate who submitted something they never actually wrote or reviewed. Treating every AI-assisted application as suspect risks losing good people over a tool that most of the workforce now uses in some form. Watch for a mismatch between the application and the person once you actually talk to them. AI use on its own tells you nothing.
Where to start
Start with your own screening process, since that is what actually gives you a read on a candidate. A specific job posting paired with a live response step, like a quick video or audio question, tells you more about who someone really is than a resume alone ever will.
Have a question for the JobTarget product team? Submit it through this link, and it may appear in a future installment.
Want to see how Video Screener fits into your current hiring process? Get a demo and we will walk through your specific roles and workflow.
