AI-Powered Recruitment & Talent Acquisition
Deploy AI to screen candidates, match skills to roles, and reduce time-to-hire by 50% while improving quality of hire.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Recruiters manually screen 200-500 resumes per role, spending 6-8 seconds per resume on initial screening. Unconscious bias influences shortlisting. Time-to-hire averages 45-60 days. Quality of hire is inconsistent because screening criteria vary by recruiter. Passive candidate sourcing is time-intensive and opportunistic.
After
AI screens all applications against role requirements in minutes, ranking candidates by fit score. Bias in screening is reduced through structured, criteria-based evaluation. Time-to-hire drops to 20-30 days. AI identifies passive candidates from internal and external talent pools proactively.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Define Hiring Criteria
2 weeksWork with hiring managers to define structured, measurable criteria for each role family: required skills, experience levels, competencies, and cultural fit indicators. These become the AI's scoring rubric. Remove criteria that introduce bias (e.g., specific university names, exact years of experience).
Configure AI Screening
2 weeksSet up AI recruitment platform (HireVue, Pymetrics, Eightfold, or ATS-integrated tools). Train screening models on your criteria. Configure: resume parsing, skills matching, experience scoring, and automated outreach. Integrate with your ATS for seamless workflow.
Implement AI Interview Tools
2 weeksDeploy AI-assisted interview tools: structured interview scorecards, AI-generated competency questions, automated scheduling, and video interview analysis (if approved by your legal/compliance team). Train interviewers on using AI insights without over-relying on scores.
Pilot on Live Roles
4 weeksRun AI screening in parallel with traditional screening for 5-10 open roles. Compare: shortlist quality, diversity metrics, time-to-shortlist, and hiring manager satisfaction. Adjust AI scoring based on which candidates actually perform well after hire.
Scale & Optimise
OngoingRoll out to all open roles. Build talent pools with AI-identified passive candidates. Implement AI-powered internal mobility matching. Track: time-to-hire, quality of hire (90-day performance), diversity metrics, and candidate experience scores.
Tools Required
Expected Outcomes
Reduce time-to-hire by 40-50%
Screen 100% of applications consistently (vs. recruiter fatigue)
Improve quality of hire by 20-30% (measured by 90-day performance)
Reduce unconscious bias in initial screening
Free recruiter time for relationship-building and candidate experience
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
AI can reduce bias when properly designed — by evaluating candidates against structured criteria rather than gut feeling. However, AI trained on biased historical data can perpetuate bias. Key safeguards: audit AI decisions for demographic parity, remove proxy variables, use diverse training data, and maintain human oversight for final hiring decisions.
In many jurisdictions, yes. EU AI Act, US state laws (e.g., Illinois BIPA, NYC Local Law 144), and emerging Asian regulations require disclosure of AI use in hiring. Even where not legally required, transparency builds candidate trust. Include AI disclosure in your application process.
Ready to Implement This Workflow?
Our team can help you go from guide to production — with hands-on implementation support.