Unlocking the value of artificial intelligence in human resource management through AI capability framework

Introduction

• Artificial Intelligence (AI) plays a crucial role in transforming Human Resource Management (HRM).

• Despite the potential benefits, many organizations struggle to realize gains from AI.

• Aim of the paper: to systematically review literature across multiple disciplines to develop an AI capability framework for HRM.

Key Terms and Concepts

• Keywords:

  • Artificial Intelligence

  • Organizational Resources

  • AI Capability

  • Human Resource Management

  • Systematic Review

  • AI-Employee Collaboration

Importance of AI in HRM

• AI adoption in HR has increased, deemed essential for:

  • Business model innovation

  • Process transformation

  • Competitive advantage

• Statistics:

  • 70% increase in AI adoption in the past 5 years.

  • Global spending on AI projected to rise from $85.3 billion in 2021 to over $204 billion in 2025.

  • WEF predicts AI will displace 75 million jobs yet create 133 million new roles.

Current Limitations of AI in HRM

• Surveys indicate limited positive impact from AI projects:

  • Only 30% report significant benefits.

  • Many fail to integrate AI into existing systems and processes.

Objectives of Review

• Identify organizational resources required for effective AI implementation in HRM.

• Develop the AI capability framework integrating both technical and non-technical resources.

Importance of Non-Technical Resources

• Emphasis on developing capabilities such as:

  • Human skills and competencies

  • Leadership and team coordination

  • Organizational culture and innovation mindset

  • Governance and strategy

  • AI-employee integration strategies

AI Capability Framework

• The framework aims to guide practitioners in assessing readiness for AI and its implementation in HR processes.

• Integrates resource-based and knowledge-based view theories focusing on both technical and organizational dynamics.

Research Methodology

Selection of Articles

• Reviewed literature from 56 journals in HRM, General Management, International Business, and related fields, culminating in 82 qualifying journals.

Search Strategy

• Employed searches using specific keywords focused on AI applications in HRM, utilizing multiple databases to ensure comprehensive literature coverage.

Article Screening

• Analyzed 14,376 articles, utilizing topic modeling algorithms to identify relevant themes and compile structured findings into an extraction document.

Findings

Key Themes Identified

1. Applications of AI in HRM:

   • Various applications from recruitment to employee performance tracking.

2. Collective Intelligence:

   • AI's role in augmenting human intelligence and enhancing decision-making capabilities.

3. Drivers to AI Adoption:

   • Identified factors motivating organizations to integrate AI technologies into HR processes.

4. Barriers to AI Adoption:

   • Challenges such as complexity, ethics, and employee resistance obstructing effective implementation.

Specific Applications of AI in HRM

• AI enhances candidate recruitment, onboarding, employee performance appraisals, skill development, and overall workforce management.

Implications for HR Managers

• Framework serves as a tool for self-assessment and determining readiness for AI integration in HR.

• Emphasis on improving knowledge-sharing mechanisms and interdepartmental collaboration to enhance AI efficiency.

Research Propositions

1. Explore the relationship between organizational resources and valued outcomes.

2. Investigate how to prioritize resources for successful AI adoption.

Conclusion

• The systematic review highlights the importance of integrating organizational resources for successful AI implementation in HRM and presents avenues for future research.

• Clear understanding of AI capabilities concerning organizational context is essential for stakeholders to derive real value from AI in HR.