IMPACT OF AI DRIVEN PREDICTIVE SCHEDULING ON EMPLOYEES ENGAGEMENT AND PRODUCTIVITY
Keywords:
Aidriven Predictive Scheduling, Workforce Management, Employee Engagement, Workforce Forecasting, Operational Efficiency, Worklife Balance, Algorithmic BiasAbstract
The integration of Artificial Intelligence (AI) driven predictive scheduling systems represents a significant innovation in workforce management, with potential implications for employee engagement and productivity. This abstract examines the impact of these systems on organizational dynamics, specifically focusing on how predictive scheduling influences employee engagement and productivity. Drawing on existing research and empirical evidence, this study explores the mechanisms through which AI-driven predictive scheduling affects employee attitudes, behaviors, and performance. AI-driven predictive scheduling systems leverage advanced algorithms and predictive analytics to forecast staffing needs, optimize workforce schedules, and align staffing levels with anticipated demand. By analyzing historical data, real-time metrics, and external factors such as market trends and seasonality, these systems enable organizations to proactively al- locate resources and streamline operational processes. This proactive approach to scheduling has the potential to enhance employee engagement by providing greater predictability and flexibility in work schedules, reducing instances of overwork, burnout, and work-life conflict. In conclusion, AI-driven predictive scheduling systems have the potential to significantly impact employee engagement and productivity by optimizing workforce schedules, enhancing predictability and flexibility, and fostering a culture of empowerment and accountability. By leveraging data-driven approaches to scheduling, organizations can unlock new opportunities for operational efficiency, employee satisfaction, and competitive advantage in today’sdynamic business landscape.
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Copyright (c) 2023 Sangeetha Govindarajan, Balaji Ananthanpillai (Author)
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