Recruiting is one of the highest-leverage AI use cases for small businesses because the volume per hire is so high. Claude compresses each phase by 50-70%. But the bias risks are real and require explicit guardrails. Here is the workflow.
1. Sourcing. Boolean search query generation, LinkedIn search optimization, target company list building.
2. Screening. Resume review against role requirements — but with explicit bias guardrails.
3. Interview prep. Question generation, structured scoring rubrics, interviewer brief creation.
4. Communications. Outreach, scheduling, status updates, rejection emails — drafted and personalized.
5. Reference checks. Question lists, summary writeups from notes.
Screen [N] resumes for [ROLE]. Role requirements (must-haves only): [LIST] Nice-to-haves: [LIST] Deal-breakers: [LIST] Applicant batch: [PASTE RESUMES] For each applicant: 1. Fit score (1-10) against MUST-HAVES only 2. 3 specific evidence points from the resume supporting the score 3. Whether they meet all deal-breakers 4. 1 question to test the riskiest assumption in a screening call 5. Recommendation (advance / borderline / pass) BIAS GUARDRAILS — do NOT use as signals: - Names - Geographic location indicators - Age estimates from dates - School prestige hierarchies - Gendered language patterns - Employment gaps without context Score only on demonstrated qualifications relevant to must-haves and deal-breakers.
Final hiring decisions. AI helps screen and prep; humans interview and decide.
Compensation negotiation. Human work.
Diversity strategy. Cannot be delegated to AI.
Reference check evaluation. AI can synthesize notes; humans interpret signals.