The Candidates Your Job Description Turns Away

A supply chain manager with six years of experience is reading one of your job postings. It lists thirteen requirements. She meets most of them comfortably, but the posting asks for eight years, and there's an ERP system she has only used a little. She pastes the posting into an AI assistant and asks whether she's a fit. It compares the two, notices the gap in years, and tells her she's probably underqualified. She closes the tab.

You'll never know she was there, and that's what makes this failure so hard to see. When recruiting teams worry about their pipeline, they look at the parts they can measure: how fast screening goes, how good the interviews are, how quickly offers go out. All of that assumes the right people applied. If they didn't, nothing downstream can fix it, and nothing downstream will tell you.

Long requirement lists have always put people off. The effect is widely reported to be stronger among women, who tend to hold back unless they meet more of the listed criteria than men would. I'd treat that less as a separate diversity issue and more as the most visible symptom of a posting that turns readers away.

And postings keep getting longer. One 2025 estimate put the average corporate job posting at more than 700 words and 13 listed qualifications. I think the reason is that the posting's purpose has quietly changed. It was supposed to attract people who could do the work. Over time it became a way for the hiring manager to feel covered: if the hire doesn't work out, at least every requirement was written down. Each round of edits from another stakeholder adds a line and removes nothing. What you end up with protects the hiring manager and does a poor job of turning a qualified reader into an applicant.

So a lot of your screening happens before any resume reaches you. The people you want read the posting, count the requirements they don't fully meet, and move on. The filtering starts in the candidate's browser, well before your ATS.

AI has made this faster and quieter. Candidates now use AI tools to check job descriptions the same way they use them to write resumes. The tool has no idea which of your thirteen requirements actually matter and which were added to keep someone comfortable. It just matches. A posting that used to put off some share of strong candidates now gets marked as a mismatch by software, instantly, for everyone who asks.

There's a subtler cost too. Some people still apply after reading a cluttered, jargon-heavy posting, but they do it with less conviction. They're hedging. You don't get the person who read it and thought this was exactly the job they'd been looking for. You get the one who thought they'd throw an application in and see. That's a weak start for a relationship you need to turn into a hire, and it tends to surface later as declined offers and no-shows, which then get blamed on something else.

You can fix this without lowering your standards, by describing the work instead of the resume. Compare two ways of writing the same requirement. One says: "7+ years of supply chain management with exposure to ERP systems and cross-functional stakeholder alignment." The other says: "You'll own vendor relationships across three regions and drive a 15% reduction in logistics cost over the next 18 months." Reading the first, a candidate counts boxes. Reading the second, she pictures herself doing the job.

The second version is still demanding, arguably more so, because it says what success looks like. And I'd expect it to bring in people who understood the job rather than a checklist someone assembled in a meeting, which should make first interviews better too.

Even a well-written posting leaves some people unsure, though. A candidate reads it, thinks she's probably a fit, but can't tell whether her background maps onto what's written. Today she either applies and waits days to hear anything, or doesn't apply at all. I suspect that uncertain candidate is often one of your best. She has relevant experience, she's careful enough to question her own fit, and a real conversation would likely settle the question.

That conversation is what we built Asendia to have, and it's easiest to picture from her side. Not long after she applies, her phone rings. There's no form to fill in and no link to book a slot. She can ask concrete questions about the role, explain how her background maps onto it, and answer follow-up questions about what she's just said. Asendia judges her fit against the criteria the hiring team really cares about, rather than the checklist that built up over three rounds of edits, and the result goes into the ATS as a qualification summary with excerpts from the conversation.

The speed matters for another reason. Someone who talked about their background and heard real detail about the role within hours of applying has a different relationship with your process than someone who got a confirmation email and waited. That early exchange makes the rest of the pipeline sturdier against competing offers, against drift, and against the slow loss of people that happens when a process feels impersonal from day one.

Almost nobody tracks how many qualified readers of a posting turn into applicants, which is part of why this goes unnoticed. If you're rethinking what to measure, the post on recruitment KPIs in a post-AI world goes through which numbers are worth tracking and which ones give false confidence.

Candidate shortages are real in some markets and for some skills. But I suspect a good share of what gets called a shortage is a conversion problem that starts in the job description. The pool may be big enough. The posting is turning much of it away before anyone clicks apply.

If you want to try something this week, take your longest open posting, cut the requirements down to the ones the hiring manager would actually reject someone for, and rewrite the rest as work to be done.

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