Stop Screening Résumés Like It's 1999 (Here's What Actually Works)
Jul 28, 2025
Here's what comes to mind when thinking of hiring managers reviewing résumés: someone sitting at their desk with a stack of applications, a cup of coffee, and a growing pile of "no" candidates that's about ten times bigger than their "maybe" pile. They're methodically going through 180 applications, looking for reasons to eliminate people as quickly as possible.
The irony is striking: we're using incredible AI tools but still thinking like it's 1999. We've got powerful technology at our fingertips, but we're using it to solve the wrong problem entirely.
The real issue isn't that screening takes too long. It's that we're optimizing to shrink our talent pool instead of finding great people.
The Major Flaw in How We Screen Candidates
Here's what happens everywhere: hiring teams spend most of their energy finding reasons to eliminate candidates. Missing a keyword? Gone. Gap in employment? Next. Doesn't have the exact job title we're looking for? Pass.
This elimination mindset is completely backward. You're basically optimizing to make your talent pool as small as possible, which is the opposite of what you want when you're trying to find exceptional people.
But here's why this happens: Post a job on Indeed.com and you'll get a thousand applicants within a day - most of them completely unqualified. It's overwhelming. You have this massive pile and you have to do something to whittle it down to something useful and manageable. So the natural response is to find disqualifiers to just “X” somebody out.
The keyword trap is the worst part of this approach.
Amazing developers get filtered out because they didn't list "Node.js" on their résumé, even though they're Linux hackers who've worked on core operating systems and could do anything they put their mind to. Meanwhile, someone who keyword-stuffed their way through gets an interview despite never shipping anything meaningful.
Here's the unfortunate reality: your best potential hire probably gets eliminated before a human ever sees their résumé. They might be:
Someone who hasn't described their experience effectively or with enough depth.
Someone with the capability for much more, but won't get through screening.
Someone who seems extremely intelligent and can learn anything.
A career changer who learned everything on the job.
Someone who built incredible things at a company you've never heard of.
AI gives us this great opportunity to look for people who don't necessarily check the boxes but could be exceptional contributors.
The "Rule In" vs. "Rule Out" Paradigm Shift
Most screening systems are designed around one question: "What's wrong with this person?" That's the wrong question entirely.
The elimination approach creates a false sense of confidence. You end up with a smaller pool of candidates who all look similar on paper, and you think that means they're better. In reality, you've just filtered out everyone interesting.
The rule-in approach flips this completely. Instead of asking "What's missing?" you ask "What makes this person potentially great?" You're looking for signals of exceptional capability, unique experience, or untapped potential.
This shift started making sense after we hired someone who would have been eliminated by any keyword screening. She had a background in theater management, not tech. But she'd scaled a regional theater from 200 to 2,000 subscribers, managed complex scheduling with dozens of artists, and somehow kept budgets on track despite constant changes. Those are exactly the skills we needed for a project management role, but no keyword filter would have caught it.
The rule-in mindset means looking for interesting career moves that show adaptability. Evidence of rapid learning in any context. Unique combinations of experience that could bring fresh perspectives. Instead of penalizing people for unconventional backgrounds, you start seeing those backgrounds as potential advantages.
What We're Really Trying to Figure Out
This isn't a technique to do a final analysis of a candidate. It's a technique to:
A) Find the needles in the haystack. The people who might otherwise be easy to miss.
B) Be aware of potential problems with any candidate you're considering.
It's about making sure the right ideas are in your head when you're filtering people in or out. This is not a replacement for how to recruit people - it's a supplement that puts the right thoughts in your mind as you review candidates.
It’s a way you can look for A players regardless of specific role qualifications. The theory is simple - if someone's amazing in a few areas, they have a decent chance of being an A player and a real asset to the organization. You could say that 10% of the people in your organization probably contribute 70% of the value.
Here's a framework that's shockingly effective for identifying these people:
High intelligence indicators:
Impressive places they've worked
Good schools they've attended
Evidence of rapid learning and adaptation
The technology signal approach (this works for any discipline):
I used to work with a crude but surprisingly effective system for spotting technical talent. We'd maintain lists of "good" technologies that indicated sophisticated thinking and "bad" technologies that strong technical people typically avoided. A real Linux hacker, for instance, just wouldn't put VB.NET on their résumé - they wouldn't touch it.
The scoring was simple: +1 point for each good technology (colored green), -1 point for each bad technology (colored red). Position didn't even matter - these technology choices revealed something fundamental about how someone approached problems and their general level of technical judgment.
AI transforms this approach completely. Instead of crude point tallies, you can have AI analyze the sophistication of someone's projects, identifying work that seems especially clever and well-designed versus projects that appear unnecessary or inefficient. It can spot unique value and technical insight that a simple good/bad technology list would miss entirely.
The 250-to-25 System That Actually Works
Here's the framework that turns a pile of 250 résumés into 25 people worth talking to, in about 30 minutes total.
You need one AI prompt that does the heavy lifting. Instead of screening people out, it identifies who's interesting and why. This consistently surfaces candidates that would have been missed while filtering out obvious non-fits.
The prompt tells the AI to score each résumé 1-10, but more importantly, to explain what makes each person worth considering. It looks beyond polished résumé writing and keyword stuffing to focus on actual accomplishments and career progression.
The magic happens when you ask the AI to identify "A player potential" candidates - people who might not be traditional fits but have compelling evidence of high capability, unique backgrounds, or demonstrated ability to learn and adapt quickly.
Here's a specific prompt you can copy and use:
You are an expert recruiter helping me identify the most promising candidates for this role. Instead of eliminating people, I want you to find reasons to give candidates extra consideration and highlight their potential value.
THE ROLE: [Paste your job description here]
YOUR TASK: Score each resume 1-10 and provide 2-sentence reasoning focusing on:
Evidence of high capability and intelligence (not just keyword matching)
Unique strengths, interesting background, or A-player potential
What makes them worth extra consideration
Look for indicators of exceptional people who could contribute significant value to the organization, even if they don't perfectly fit the traditional mold.
SCORING CRITERIA:
8-10: Strong A-player potential or compelling unique value
6-7: Solid capability worth considering
4-5: Possible fit but would need strong interview performance
1-3: Poor fit for this specific role
SPECIAL FOCUS: Identify candidates who show:
Evidence of rapid learning and adaptation
Impressive career progression or achievements
High-intelligence indicators (top schools, prestigious companies, sophisticated projects)
Unique combinations of experience that could bring fresh perspective
OUTPUT FORMAT: For each candidate, provide:
Name
Score (1-10)
Why they deserve extra consideration (2 sentences max)
One potential concern (if any)
After scoring all candidates, identify your top 3 "A-player potential" picks - candidates who might not be perfect traditional fits but show compelling evidence of high capability and potential to contribute exceptional value.
Focus on finding reasons to be excited about candidates rather than reasons to eliminate them.
RESUMES TO SCORE: [Upload your resumes or paste them here]
After the AI ranks everyone, spend 15 minutes reviewing only the top-scored candidates. You're not doing a detailed analysis - just a quick human check using three questions:
Does their trajectory show high capability?
Any real red flags?
What unique value might they bring?
This gets you from 250 candidates to 25 phone screens without missing the interesting people.
Why This Changes Your Hiring Game
The immediate benefit is obvious - you save hours of screening time. But the real advantage runs deeper.
You'll start finding people others miss because they don't fit standard patterns. While your competitors are eliminating great candidates for arbitrary reasons, you're discovering them. In a tight talent market, this becomes a significant competitive advantage.
The diversity outcomes improve naturally. When you stop ruling people out for having unconventional backgrounds, you end up with a much more interesting candidate pool. Career changers often bring fresh perspectives that pure industry experience can't match.
Most importantly, you build a reputation for discovering talent others overlook. The best people start seeking you out because they know you actually look at what they've accomplished, not just whether they check the right boxes.
Start With Your Next Hiring Round
You don't need to overhaul your entire process. Pick your next open position and try this approach alongside your current method.
Take whatever applications you've already received and run them through the AI screening. Compare the results to your manual screening decisions. You'll likely find at least 2-3 candidates you would have dismissed who actually deserve phone screens.
Action Step: Take your most recent job posting and the last 20 applications you received. Use the AI prompt above to score them, then compare those results to your original screening decisions. Write down which candidates you would have missed entirely and what unique value they might have brought to the role.
The goal isn't perfect hiring - it's turning your screening process from an elimination game into a discovery process. That's the difference between teams that compete for the same obvious candidates and teams that consistently find talent others miss entirely.