AI in HR takes center stage at Vikrama Simhapuri University
A two-day National-Level Workshop on Artificial Intelligence in Human Resource Management kicked off at the Department of Business Management, Vikrama Simhapuri University, Nellore. The programme is supported by the Central Government's PM-USHA (Pradhan Mantri Uchchatar Shiksha Abhiyan) scheme and focuses on practical adoption for HR teams.
The workshop was inaugurated by University Registrar Dr K Suneetha, who highlighted how AI is now central to recruitment, training, and performance appraisal. Vinod Nair-AI and Behavioral Science expert and Founder of "Humalytics Samasta"-delivered the keynote, drawing a clear line from theory to day-to-day HR decisions.
Who was in the room
- Resource Person: Vinod Nair, Founder, Humalytics Samasta
- Leadership and Faculty: Prof. Ch Vijaya (College Principal), Dr G Sai Sravanti (Organizing Secretary), Dr J Vijetha, A Gayathri (Co-Coordinator), Dr P Srivalli, and Kota Neelamani Kanta
- Faculty members and research scholars from multiple institutions
Why this matters for HR
AI is moving from experimentation to standard practice in HR. The message was clear: start small, stay ethical, measure impact, and keep humans in the loop.
Immediate actions HR leaders can take
- Recruitment: Use AI to pre-screen resumes, match skills to roles, and flag red flags. Pair every model output with structured human review to cut bias and errors.
- Learning & Development: Deploy adaptive learning paths that focus on role-specific skills. Track skill lift instead of just course completion.
- Performance: Shift to frequent, data-informed check-ins. Use AI to summarize peer feedback and spot coaching opportunities-without automating final ratings.
- Workforce planning: Forecast attrition and hiring needs with simple models first. Validate predictions against historical outcomes before scaling.
- Compliance & privacy: Keep PII encrypted, limit access, and document data sources. Maintain an audit trail for every AI-assisted decision.
Simple rollout plan
- Pilot one use case (e.g., resume screening for a single function). Define success upfront: time-to-shortlist, interview-to-offer ratio, hiring manager satisfaction.
- Set guardrails: bias tests, opt-out paths for candidates/employees, and human approval for all consequential decisions.
- Train your team: recruiters on prompt writing and reviewing model outputs; HRBPs on interpreting insights; managers on ethical use.
- Pick tools that integrate with your ATS, LMS, and HRIS to avoid duplicate work and shadow data.
- Review monthly: compare outcomes to pre-AI baselines; adjust prompts, policies, or workflows based on the data.
Metrics to track (and keep)
- Time-to-hire, quality-of-hire (90-day performance or ramp speed), and candidate experience scores
- Learning completion rates, skill assessments before/after, and manager-reported skill application
- Performance review cycle completion, calibration variance, and coaching actions taken
- Attrition predictions vs actual, and the impact of interventions
- Bias audits: selection rate parity, score distribution across demographics, and override rates
Bottom line
This workshop puts a spotlight on practical, responsible AI in HR-starting with recruitment, training, and performance. The opportunity is real, but so is the need for clear policies, better data hygiene, and upskilling across HR teams.
Keep learning
- AI learning paths by job role - find focused options for HR, recruiting, L&D, and people analytics.
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