Strategic workforce planning has emerged as a critical organizational capability in the dynamic business environment of the 21st century, where technological advancement and competitive pressures necessitate sophisticated approaches to human capital management. This research presents a comprehensive conceptual framework for integrating artificial intelligence technologies with human resource information systems to enhance strategic workforce planning capabilities across diverse organizational contexts. The framework addresses the fundamental challenges organizations face in aligning workforce capacity with strategic objectives while leveraging advanced analytics and machine learning capabilities to optimize talent acquisition, development, and retention strategies. The proposed framework synthesizes theoretical foundations from strategic human resource management, information systems theory, and artificial intelligence applications to create a holistic approach to workforce planning that transcends traditional reactive methodologies. Through extensive analysis of existing literature and contemporary organizational practices, this study identifies key components necessary for successful integration of AI technologies within HRIS environments, including data architecture requirements, algorithmic decision-making processes, and organizational change management considerations. The framework emphasizes the importance of predictive analytics in forecasting workforce needs, identifying skill gaps, and optimizing resource allocation across different organizational units and time horizons. Central to this framework is the recognition that effective strategic workforce planning requires seamless integration between technological capabilities and organizational culture, ensuring that AI-driven insights translate into actionable strategies that support long-term business objectives. The research explores how organizations can leverage machine learning algorithms to analyze historical workforce data, identify patterns in employee behavior, and predict future talent requirements with greater accuracy than traditional forecasting methods. Additionally, the framework addresses ethical considerations surrounding AI implementation in human resource management, including bias mitigation, transparency in algorithmic decision-making, and maintaining employee trust throughout the transformation process. The study contributes to the existing body of knowledge by providing a structured approach to implementing AI-enhanced workforce planning systems that organizations can adapt to their specific contexts and requirements. The framework offers practical guidance for HR professionals, technology leaders, and organizational executives seeking to modernize their workforce planning capabilities while maintaining alignment with strategic business objectives. Through systematic analysis of integration challenges and best practices, this research provides actionable insights that can facilitate successful transformation of traditional workforce planning approaches into AI-driven strategic capabilities.
Strategic Workforce Planning, Artificial Intelligence, HRIS Integration, Predictive Analytics, Human Capital Management, Machine Learning, Organizational Transformation, Talent Management
IRE Journals:
Mayokun Oluwabukola Aduwo , Adaobi Beverly Akonobi , Christiana Onyinyechi Okpokwu
"Conceptual Framework for Strategic Workforce Planning Leveraging Artificial Intelligence and HR Information Systems Integration" Iconic Research And Engineering Journals Volume 4 Issue 5 2020 Page 284-308
IEEE:
Mayokun Oluwabukola Aduwo , Adaobi Beverly Akonobi , Christiana Onyinyechi Okpokwu
"Conceptual Framework for Strategic Workforce Planning Leveraging Artificial Intelligence and HR Information Systems Integration" Iconic Research And Engineering Journals, 4(5)