Why recruiters can’t outsource judgment to AI
AI matching tools promise instant candidate matches, but they don’t understand recruiter priorities. They treat every skill as equal, so a candidate with mostly nice-to-have skills might rank higher than someone with the critical must-haves. Recruitment is not a one-click process. The strongest matches are built through iteration, not automation.
Why AI matching tools fail
What needs to exist first
Your Applicant Tracking System (ATS) must have clean, structured, and current data. Skills, locations, and experience levels need to be classified consistently. Without that structure, both AI matching and manual filters return noise.
How to build better searches
Start with a narrow search that applies all must-have criteria. This creates your precise core pool. Then gradually relax filters to widen the search until you reach an actionable shortlist.
This method balances control and scale. You decide who qualifies, not the software.
Most ATS systems already provide the filters needed for this approach. When your data is complete and consistent, you can achieve the same logic as AI matching directly inside your ATS. You can search, refine, and expand in controlled steps without losing oversight.
Why it matters
When your ATS data is structured, every search and match becomes faster and more relevant. You stop depending on external tools and regain confidence in your own database.
Daidalo keeps this foundation in order by automatically enriching, categorising, and maintaining your ATS data. Once the data is clean, both filters and AI tools finally work as intended.
TL;DR
AI matching is not the problem. Dirty data is. Fix the structure first, and every search becomes meaningful again.
