This process is designed to be followed in order. Complete each step before moving to the next to avoid rework and integration problems.

1. Define the skills and competencies required for each role

   Work with hiring managers to list the specific skills that predict success in each role. Separate required skills from preferred ones. This list becomes the foundation for your AI screening criteria.

2. Remove credential requirements that are not genuinely predictive

   Audit job descriptions for degree requirements, years of experience minimums, and credential requirements that are not tied to actual job performance evidence. Remove them. They narrow your pool without improving hire quality.

3. Configure your AI parsing tool to score against skill criteria

   Set up your resume parsing tool to extract and score skills mentions against your required and preferred skills list. Build scoring rubrics that weight skills by importance to the role.

4. Validate AI scoring against your best current performers

   Run your top 10 performers’ resumes through the new AI scoring criteria. Confirm the system scores them highly. If it does not, refine the criteria before using it on live applications.

5. Run the first hiring cycle with skill-based screening and measure results

   Deploy the skill-based screening process on one active role. Track shortlist diversity, hiring manager satisfaction with shortlist quality, and time-to-fill compared to the previous approach.

6. Iterate criteria based on 90-day post-hire performance data

   At 90 days, assess how hires from the skill-based screen are performing. Connect performance scores back to the screening criteria that selected them. Use this data to refine scoring weights for the next cycle.

Go Deeper

See the full implementation guide: step-by-step HR automation resource.