Slide 1 – Executive Summary

Title: AI-Enhanced HR for Professional Growth, Internal Mobility & Workplace

Culture at MICA

 Current Role of HR (Non-Teaching Staff):

o Serves as a critical support department managing the employee
lifecycle—from recruitment to exit.
o Shapes workplace culture and fosters professional development
for administrative and operational teams.
 Challenges:
o Fragmented HRMS with limited analytics and no integrated
dashboards.
o Siloed processes: recruitment, onboarding, appraisals, and
mobility tracking are disconnected.
o Lack of predictive workforce planning tools to anticipate
future role needs.
 AI Vision:
o Establish an end-to-end AI-integrated HR platform mapping
the complete employee journey.
o Use predictive analytics for internal mobility and professional
growth pathways.
o Free HR personnel from repetitive tasks to focus on culture-
building, grievance resolution, and mentoring.
 MICA Ethos Alignment:
o Preserve the ethos of creative human inquiry in HR decision-
making.
o Integrate ethics into AI deployment, ensuring fairness and
transparency.
o Enhance—rather than replace—critical, emotional, and
philosophical capacities in people management.

Slide 2 – Philosophical Lens

Title: Framing AI in HR through Ethics of Care & Technological Determinism
  Ethics of Care:
o HR is fundamentally about relationships and trust. AI should
be positioned as an augmenter, not a replacer, in areas requiring
empathy—such as conflict mediation, grievance handling, and
mentoring.
o Example: AI can flag early signs of disengagement through
sentiment analysis, but only humans can provide genuine
reassurance and nuanced problem-solving.
 Technological Determinism:
o AI adoption will reshape MICA’s HR processes—moving from
reactive admin support to strategic workforce planning.
o Predictive analytics could anticipate skills shortages, enabling
proactive mobility planning and succession management.
 Posthumanism:
o Views AI as a collaborative partner:
 AI handles structured, data-heavy processes (e.g.,
shortlisting candidates).
 Humans lead in subjective, creative, and ethical decision-
making (e.g., evaluating cultural fit).
 Data Colonialism Consideration:
o Guard against over-collection of personal data and ensure
privacy boundaries are respected under India’s DPDP Act.

Slide 3 – Current State

Title: Operational Gaps Limiting HR’s Strategic Role

 Existing Tools:
o Basic HRMS (limited analytics, lacks integrated dashboards).
o Standalone attendance recording system (not linked to payroll or
performance).
 Current Workflow:
o Recruitment to exit is managed via disjointed systems and
manual interventions.
o Performance appraisals and training records stored in silos.
 Pain Points:
 o No single employee profile capturing complete history and
development journey.
o Delayed decision-making due to fragmented data.
o Absence of AI-driven skill gap analysis means internal talent
potential remains underleveraged.
 Impact:
o Recruitment cycle times extended by 3–4 weeks vs. industry
benchmarks.
o Missed opportunities for internal promotions and cross-
department mobility.
o HR bandwidth consumed by repetitive tasks, reducing time for
strategic culture initiatives.

Slide 4 – AI Strategy Goals

Title: Transforming HR into a Strategic Growth Partner

 Automation Goals:
o Digitize 100% of the employee lifecycle—from role planning to
exit interviews.
o Automate interview scheduling, candidate shortlisting, and
onboarding checklists.
 Analytical Goals:
o Deploy predictive analytics to map internal mobility pathways
based on skills, performance, and aspirations.
o Use AI dashboards to track attrition risk, training ROI, and
employee engagement scores.
 Human Judgment Retention:
o All hiring and promotion decisions remain human-led after AI-
assisted filtering.
o All grievance cases handled exclusively by trained HR staff.
 Measurable Targets (by 2026):
o Reduce recruitment cycle time by 40%.
o Improve internal mobility rate by 25%.
o Increase employee engagement index by 15%.
Slide 5 – Use Cases
Title: AI Applications in Non-Teaching Staff HR

1. Recruitment & Role Design:

a. AI to design customized job descriptions aligned to MICA’s
academic environment.
b. Candidate sourcing using AI filters for skills, location, and
institutional fit.
2. Onboarding:
a. Automated pre-joining tasks: document verification, welcome
kits, compliance training scheduling.
b. Digital onboarding assistant providing FAQs, campus info, and
HR policy guidance.
3. Talent Development & Mobility:
a. AI skill gap analysis to recommend internal training programs
and mobility opportunities.
b. Personalized learning pathways for professional growth.
4. Employee Experience:
a. Chatbots for routine queries (leave balances, benefits, policies).
b. Sentiment analysis on annual engagement surveys to detect
early dissatisfaction.
5. Workforce Planning:
a. Predictive modeling for staffing needs based on retirements,
attrition trends, and role evolution.

Slide 6 – Stakeholder Insights

o Title: Perceptions of AI in HR – Opportunities & Boundaries
 From Dr. Nirja Sharma (Head of HR):
o "AI can support at every recruitment stage—from role
conceptualization to onboarding."
o "It cannot replace human empathy in grievances, mentorship,
and conflict resolution."
o "Our current systems are not end-to-end; integrating AI could
help unify and streamline processes."
 HR Team Operational Perspective:
 o AI could reduce manual workload in recruitment, onboarding,
and routine data management.
o Data-driven dashboards could help track employee growth and
internal mobility.
o Requires careful integration with existing HRMS to avoid
workflow disruption.
 Institutional Considerations (aligned to MICA’s ethos):
o HR must preserve the “people connect” and creative inquiry in
decision-making.
o Any AI adoption must be transparent, ethical, and culturally
sensitive.
o Human oversight remains critical to ensure fairness, inclusivity,
and trust.
 Assumed Concerns:
o Risk of bias in AI-generated recommendations.
o Need for AI literacy training for HR staff.
o Avoiding over-automation to maintain employee trust and
morale.

Slide 7 – Human Oversight Model

Title: Keeping People Central in AI-Augmented HR

 Critical Human-in-the-Loop Points:

o Final hiring and promotion decisions.
o All grievance and misconduct investigations.
o Employee counseling and cultural fit assessments.
 Oversight Mechanisms:
o AI Ethics Officer role to ensure compliance with fairness, bias,
and privacy guidelines.
o Quarterly AI audit committees with HR, legal, and staff
representatives.
 Why It Matters:
o AI lacks the ability to interpret emotional nuance, especially in
sensitive interpersonal issues.
o Protects against “deskilling” of HR staff by ensuring
continuous human involvement in strategic decision-making.
Slide 8 – Ethics & Risks
Title: Responsible AI Deployment for HR at MICA

 Ethical Principles:
o Transparency: Clear reasoning for AI recommendations in
hiring and appraisals.
o Fairness: Bias testing before deployment; periodic algorithm
reviews.
o Privacy: Compliance with India’s Digital Personal Data
Protection Act (DPDP Act).
 Risks:
o Algorithmic bias impacting diversity goals.
o Over-reliance leading to human disengagement in HR decision-
making.
o Misinterpretation of AI outputs without contextual judgment.
 Risk Mitigation:
o Participatory governance with representation from HR staff,
leadership, and legal.
o AI literacy training for HR professionals.
o Transparency reports shared annually with all staff.