the changing role of Information Technology (IT) and Artificial Intelligence (AI) in HRM

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7.4 HRM Strategy – Approaches to HRM

Objective: Understand the Changing Role of Information Technology (IT) and Artificial Intelligence (AI) in Human Resource Management (HRM)

7.4.1 Hard vs. Soft HRM

Aspect Hard HRM Soft HRM
Philosophy People are a *resource* to be managed like equipment or finance. People are *assets* whose development drives long‑term success.
Primary Focus Cost control, quantitative performance, short‑term productivity. Commitment, empowerment, learning, employee well‑being.
Typical Tools Work‑force planning, time‑sheet control, financial KPIs. Career development plans, employee surveys, participative decision‑making.
When It Is Most Appropriate
  • High‑volume, low‑skill operations (e.g., fast‑food chains, call centres).
  • Industries where price competition dominates.
  • Knowledge‑intensive or creative sectors (R&D, professional services, tech start‑ups).
  • Organisations seeking innovation, low turnover and high employee engagement.
Pros / Cons (exam‑style table) Pros: Tight cost control, clear performance metrics.
Cons: Low morale, higher turnover, limited flexibility. 		Pros: Higher motivation, better retention, stronger brand as employer.
Cons: Higher short‑term costs, more complex performance measurement.

7.4.2 Flexible and Temporary Employment Arrangements

Arrangement Key Features Advantages (for employer) Disadvantages (for employer & employee)
Zero‑hours contracts No guaranteed minimum hours; work offered on an as‑needed basis. Maximum labour‑cost flexibility; easy to match demand spikes. Job insecurity; potential morale problems; higher turnover.
Part‑time contracts Fixed reduced weekly hours; often regular pattern. Attracts students, parents, semi‑retired workers; predictable scheduling. Limited availability for overtime; may require more roster management.
Freelance / gig‑economy contracts Self‑employed, project‑based work accessed via digital platforms. Access to specialised skills on demand; no long‑term commitments. Less control over quality; tax & legal complexities; variable reliability.
Temporary agency staffing Employees hired through an agency for a defined period. Rapid response to peak demand; agency handles payroll & admin. Higher hourly rates; possible integration & culture issues.
Seasonal contracts Fixed‑term contracts aligned with seasonal peaks (e.g., retail holidays). Predictable workforce for known busy periods. Redundancy risk after season; training costs for short‑term staff.

7.4.3 Performance Management

Measuring Performance
  • Quantitative KPIs – sales volume, error rate, absenteeism, productivity ratios.
  • Qualitative indicators – customer feedback, 360° peer reviews, supervisor comments.
  • Balanced Scorecard – integrates financial, customer, internal process, and learning & growth metrics.
  • Technology aid – real‑time dashboards, AI‑driven trend alerts.
Common Causes of Poor Performance
  • Unclear objectives or role ambiguity.
  • Insufficient training, resources or tools.
  • Poor motivation – inadequate rewards, recognition or career prospects.
  • Personal or health issues.
  • Ineffective supervision, feedback or performance‑review processes.
Consequences for the Organisation
  • Reduced productivity and profitability.
  • Higher error rates and lower customer satisfaction.
  • Increased turnover, recruitment and training costs.
  • Negative impact on team morale and organisational culture.

7.4.4 Strategies for Improving Employee Performance

  • Clear goal‑setting – link individual objectives to organisational strategy (SMART).
  • Regular coaching & feedback – weekly or monthly check‑ins, not just annual reviews.
  • Targeted training & development – needs‑analysis, blended learning, on‑the‑job coaching.
  • Performance‑related pay – bonuses, commissions, profit‑sharing tied to measurable outcomes.
  • Job redesign / enrichment – increase autonomy, task variety, responsibility.
  • Mentoring & peer‑support networks – formal mentorship schemes, communities of practice.
  • Technology‑enabled monitoring – real‑time dashboards, AI alerts on performance trends, gamified performance apps.

7.4.5 Management by Objectives (MBO)

  1. Objective setting – joint agreement on SMART goals.
  2. Action planning – define tasks, resources, timelines.
  3. Monitoring – continuous tracking via HRIS or analytics platforms.
  4. Performance appraisal – compare actual results with targets; discuss successes and gaps.
  5. Feedback & reward – constructive feedback; link achievement to incentives.

Usefulness: Aligns individual effort with corporate strategy, encourages accountability and provides a clear basis for appraisal. AI‑driven analytics supply up‑to‑date KPI data, making the MBO cycle more dynamic.

7.4.6 Impact of IT & AI on HRM

  • Accelerated decision‑making through real‑time data and predictive analytics.
  • Improved employee experience via self‑service portals, mobile apps and AI chatbots.
  • Strategic insights from AI models (e.g., turnover forecasting, talent‑gap analysis).
  • Automation of routine tasks (payroll, leave administration), freeing HR for strategic work.

7.4.7 Future‑Proofing Checklist for Teachers

  • Explain ethical AI principles – bias mitigation, transparency, accountability.
  • Stress the importance of data governance – GDPR compliance, secure storage, data‑quality checks.
  • Develop students’ digital literacy – interpreting dashboards, questioning algorithmic outputs.
  • Introduce change‑management techniques for rolling out new HR technologies.
  • Encourage critical evaluation of cost‑benefit and ROI of HR tech investments.

7.4.8 Key Technologies Transforming HRM

  • Human Resource Information Systems (HRIS) – centralised employee data, payroll, benefits, compliance.
  • Applicant Tracking Systems (ATS) – automates job posting, CV screening, interview scheduling.
  • AI‑Driven Analytics – predictive models for turnover, talent gaps, workforce planning.
  • Chatbots & Virtual Assistants – 24/7 support for routine queries (leave balance, policy info).
  • Performance Management Platforms – continuous feedback, goal‑setting, competency mapping.
  • Learning Management Systems (LMS) with Adaptive Learning – personalises training pathways based on performance data.
  • Robotic Process Automation (RPA) – automates repetitive admin tasks such as data entry and contract generation.

7.4.9 Benefits of Integrating IT & AI into HRM

  1. Operational efficiency – routine tasks automated, reducing errors and processing time.
  2. Data‑driven decision making – large data sets reveal trends, enable forecasting.
  3. Enhanced employee experience – self‑service portals, AI chatbots provide instant support.
  4. Improved talent acquisition – AI matches candidate profiles to job specifications, widening talent pool.
  5. Strategic workforce planning – predictive analytics align talent supply with business growth.
  6. Continuous learning culture – adaptive LMS delivers personalised development pathways.

7.4.10 Challenges and Risks

  • Data privacy & security – compliance with GDPR, secure storage, breach response plans.
  • Algorithmic bias – poorly trained models can perpetuate discrimination; requires regular audit.
  • Skill gaps – HR staff need digital competencies to interpret analytics and manage new tools.
  • Change management – resistance to automation; requires clear communication, training, and stakeholder involvement.
  • Implementation cost – upfront investment in software licences, integration, and staff training.
  • Reliance on data quality – inaccurate or incomplete data leads to misleading insights.

7.4.11 Strategic Implications for HRM

When IT and AI are aligned with the overall business strategy, HRM can evolve from a transactional function to a strategic partner.

  • Link talent analytics to corporate performance metrics (e.g., revenue per employee).
  • Use predictive turnover models to reduce recruitment costs and minimise skill shortages.
  • Design personalised career pathways that support organisational agility and succession planning.
  • Embed a culture of continuous learning via adaptive LMS platforms and micro‑learning.
  • Leverage AI‑enabled scenario modelling for workforce planning under different market conditions.

7.4.12 Comparison: Traditional HRM vs. IT/AI‑Enabled HRM

Aspect Traditional HRM IT/AI‑Enabled HRM
Data Management Paper files, spreadsheets, manual entry. Integrated HRIS with real‑time updates and secure cloud storage.
Recruitment Manual CV screening, limited reach, long cycle. ATS with AI matching, automated outreach, video‑interview analytics.
Performance Review Annual appraisal, largely subjective ratings. Continuous feedback, data‑driven KPIs, AI‑identified performance trends.
Learning & Development One‑size‑fits‑all classroom sessions. Adaptive LMS delivering personalised, on‑demand content.
Decision Making Based on intuition and limited reports. Predictive analytics, scenario modelling, real‑time dashboards.
Employee Experience HR as a gate‑keeper; limited self‑service. Self‑service portals, AI chatbots, mobile access 24/7.

7.4.13 Future Trends in HRM

  1. Deeper integration of HR data with Enterprise Resource Planning (ERP) and Business Intelligence tools.
  2. Natural Language Processing (NLP) for sentiment analysis of employee communications (e.g., internal chat, surveys).
  3. AI‑driven workforce planning that incorporates external labour‑market, economic and demographic data.
  4. Development of sector‑specific ethical AI frameworks to mitigate bias in recruitment, appraisal and promotion.
  5. Growth of “HR as a Service” (HRaaS) – cloud‑based, subscription‑model solutions delivering end‑to‑end HR functions on demand.
  6. Use of immersive technologies (AR/VR) for onboarding, safety training and remote collaboration.

Suggested Diagram (for classroom use)

Technology Adoption Lifecycle in HRM

  • Innovators – early adopters of AI‑driven analytics and RPA.
  • Early Majority – mainstream HRIS, ATS and self‑service portals.
  • Late Majority – integrated ERP‑HR suites, adaptive LMS.
  • Laggards – organisations still relying on manual, paper‑based processes.

Show the flow from isolated tools → integrated platforms → predictive optimisation.

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