Re-screen the same hiring pool against an outcomes scorecard — and hire
After the cancellation mess, Curtis wrote a one-page scorecard for the role (mission, five measurable first-year outcomes, six competencies), pasted it into the prompt and had the AI map each candidate's track record to each outcome with evidence from the résumé. With a human making every cut and applications closed before booking, the process ended in a Director of Ecommerce hire who has already optimized the company's Amazon listings and helped launch a product — and a declined candidate was later hired as a TikTok consultant.
How it works
- Write the scorecard first: mission, measurable outcomes, competencies
- Paste the scorecard into the screening prompt
- The AI maps each candidate's track record to each outcome and cites evidence from the résumé, noting where evidence is missing
- She makes every cut herself; AI ranks, she decides
- Close applications before booking interviews
- Send fast, personalized no's to declined candidates
- Hire
Why it’s worth knowing
Same pool, same tool — the fix was a one-page scorecard that turned 'strong match' into 'has delivered X, no evidence of Y'.
Where it’s from
Summarized from a public post on inc.com. See the original ↗