Talent Aisle Logo Talent Aisle
← Back to blog home

Beyond Keywords: Why Your ATS Is Failing to Find Your Best Candidates

📅
⏱️ 3 min read

A visual representation of how traditional keyword filtering can miss qualified candidates, with a CV being filtered out despite having relevant experience

Your Applicant Tracking System (ATS) is the gatekeeper of your talent pipeline. You bought it to bring order to the chaos, to filter the signal from the noise. So why does it feel like it's actively working against you?

Here's the uncomfortable truth: your ATS is likely rejecting your best candidates before you ever see them. And it's letting mediocre ones slip through.

The culprit? A reliance on a technology that hasn't changed in decades: keyword matching.

The Flaw of the Keyword

Keyword-based systems operate on a simple, flawed premise: if a candidate's CV contains the right buzzwords, they must be a good fit. This leads to two critical failures:

  1. False Negatives (Missing the Stars): A brilliant software engineer who describes their work as "building scalable data pipelines" might be rejected for a role seeking a "Big Data Architect," even though they're a perfect fit. Great candidates often describe their impact and outcomes, not just the job titles they've held. Your ATS, in its rigid simplicity, discards them.

  2. False Positives (Letting in the Noise): Conversely, it's easy to game the system. Candidates are now coached to "keyword-stuff" their CVs, packing them with terms they know the algorithms are looking for. This creates a flood of seemingly qualified applicants who, upon closer inspection, lack the actual substance and experience you need. Your team wastes precious time sifting through this manufactured noise.

Your ATS isn't finding the best people; it's finding the best writers of CVs for robots.

A Better Signal: From Keywords to Capabilities

At Talent Aisle, we've moved beyond this outdated model. Our AI engine is designed to understand capability, not just keywords. We analyse a constellation of data points to build a true picture of a candidate's potential:

  • Project Contributions: We look at their actual work on platforms like GitHub or Behance. What did they build? What was their specific contribution?
  • Skill Adjacency: We understand that a background in logistics might make for a phenomenal operations manager, even if they've never held that exact title.
  • Career Trajectory: We analyse the velocity and direction of their career. Are they consistently taking on more responsibility and solving harder problems?

This deep analysis allows us to surface candidates who are a phenomenal match in substance, not just in syntax. We find the person who can do the job, not just the person who has the right words on their CV.

Tired of your ATS filtering out your best hires? Schedule a demo to see how our capability-first approach can deliver the talent you've been missing.