AI-Powered Recruitment & Talent Acquisition

Work 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). Replace vague requirements like 'strong communicator' with measurable indicators such as 'presented to groups of 10+ in previous role' or 'managed written client communications.' In ASEAN markets, be explicit about language requirements (English proficiency level, Mandarin preferred) rather than using university prestige as a proxy.

Set 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. Run a bias audit on the initial screening model by checking shortlist rates across gender, age band, and university tier before going live. If the model disproportionately favours candidates from specific universities, re-weight the skills and experience criteria and reduce the influence of education features.

Deploy 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. If using video interview analysis, ensure candidates are informed in advance and provide opt-out options where local regulations require it (Singapore PDPA, Malaysia PDPA). Focus AI scoring on structured competency responses rather than facial expression or tone analysis, which carry higher bias risk.

Run 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. Select pilot roles that receive 100+ applications per posting to give the AI enough volume for meaningful comparison. Track not just shortlist quality but also candidate experience scores; a system that improves efficiency but frustrates candidates will damage your employer brand.

Roll 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. Feed 90-day and 180-day performance review data back into the model to close the loop between hiring signals and on-the-job outcomes. Use internal mobility matching to surface existing employees for open roles before external sourcing, reducing time-to-fill and improving retention.