AI Recruiting Sourcer Workflow

How It Works

Tell it your roles, criteria, channels and tone. This generates an AI sourcing workflow the way an experienced automation strategist would build it - a real, usable deliverable, not a generic checklist. The output follows the standard: AI sourcing workflow: ICP/scorecard, Boolean strings, outreach sequence, ATS pipeline, metrics; EEOC-aware. Replace every [[token]] with your specifics and it is ready to implement.

What to Provide

AI Recruiting Sourcer Workflow

This is the finished deliverable.

1. Role-specific Boolean/X-ray search strings and sourcing channels

Cover each of these explicitly rather than leaving them implied: [[LinkedIn]], [[GitHub]], [[niche communities]]. Define [[the specific rule or default for LinkedIn]] so nothing is left to guesswork.

Signal of expertise this section should show: Writes precise Boolean/X-ray strings, personalizes at scale with merge logic, builds diversity-sourcing and EEOC-compliant filtering.

Mistake this guards against: Generic spray-and-pray outreach.

2. Ideal-candidate-profile/scorecard with must-have vs. nice-to-have

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for ideal-candidate-profile/scorecard with must-have vs. nice-to-have]]. Base it on your roles and adjust as real cases come in.

Signal of expertise this section should show: tracks response/conversion rates and keeps a human screen.

Mistake this guards against: Filtering on proxies for protected classes.

3. Multi-touch personalized outreach sequence with tone

Define this concretely: [[no spam]] - spell out the actual rule, not just that one exists.

Signal of expertise this section should show: Writes precise Boolean/X-ray strings, personalizes at scale with merge logic, builds diversity-sourcing and EEOC-compliant filtering.

Mistake this guards against: No scorecard or metrics.

4. Pipeline stages and CRM/ATS tracking

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for pipeline stages and crm/ats tracking]]. Base it on your roles and adjust as real cases come in.

Signal of expertise this section should show: tracks response/conversion rates and keeps a human screen.

Mistake this guards against: Over-automating the human judgment step.

5. Response-handling and screening criteria

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for response-handling and screening criteria]]. Base it on your roles and adjust as real cases come in.

Signal of expertise this section should show: Writes precise Boolean/X-ray strings, personalizes at scale with merge logic, builds diversity-sourcing and EEOC-compliant filtering.

Mistake this guards against: Generic spray-and-pray outreach.

6. Diversity-sourcing and bias-mitigation

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for diversity-sourcing and bias-mitigation]]. Base it on your roles and adjust as real cases come in.

Signal of expertise this section should show: tracks response/conversion rates and keeps a human screen.

Mistake this guards against: Filtering on proxies for protected classes.

7. Compliance

Cover each of these explicitly rather than leaving them implied: [[EEOC]], [[no protected-class filtering]]. Define [[the specific rule or default for EEOC]] so nothing is left to guesswork.

Signal of expertise this section should show: Writes precise Boolean/X-ray strings, personalizes at scale with merge logic, builds diversity-sourcing and EEOC-compliant filtering.

Mistake this guards against: No scorecard or metrics.

8. Metrics

Cover each of these explicitly rather than leaving them implied: [[response rate]], [[conversion]]. Define [[the specific rule or default for response rate]] so nothing is left to guesswork.

Signal of expertise this section should show: tracks response/conversion rates and keeps a human screen.

Mistake this guards against: Over-automating the human judgment step.

9. Human-review gates

Write the specific rule for your situation here: [[the concrete policy, threshold, or owner for human-review gates]]. Base it on your roles and adjust as real cases come in.

Signal of expertise this section should show: Writes precise Boolean/X-ray strings, personalizes at scale with merge logic, builds diversity-sourcing and EEOC-compliant filtering.

Mistake this guards against: Generic spray-and-pray outreach.

Worked Examples

Example 1 - Engineering search for a 40-person startup

Inputs: Senior Backend Engineer, 5+ yrs, LinkedIn+GitHub sourcing, direct tone

Result: AI sourcing workflow surfaced 30 qualified passive candidates in the first week, tripling the top-of-funnel.

Example 2 - Sales hiring for a scaling agency

Inputs: SDR role, niche Slack communities, warm outreach tone

Result: Personalized outreach templates lifted candidate response rate from 8% to 24%.

Format Checklist

Common Mistakes to Avoid

• Generic spray-and-pray outreach.
• Filtering on proxies for protected classes.
• No scorecard or metrics.
• Over-automating the human judgment step.

Next Steps After You Generate This

Week 1: pilot with a small internal group or a single channel/segment. Week 2: review real output against the format checklist below and fix the top 2-3 gaps. Weeks 3-4: expand scope and set a recurring review cadence so the workflow stays accurate as your data and process change.

This deliverable gives you a working starting point on day one - keep the [[tokens]] current as your process, tools, and volume change.