The Impact of AI-Driven Predictive Scheduling on Employee Engagement and Customer Satisfaction

EasyChair Preprint 14933

Abstract

AI-driven predictive scheduling is transforming workforce management by optimizing employee scheduling and improving operational efficiency, with significant implications for employee engagement and customer satisfaction. This abstract explores how predictive scheduling algorithms, powered by artificial intelligence (AI) and machine learning (ML), can balance business needs with employee preferences, creating a more responsive, flexible, and efficient work environment. The study further investigates how these advancements in scheduling technology directly influence employee morale, engagement, and customer satisfaction in service-oriented industries.

Traditional scheduling methods often fail to account for real-time fluctuations in customer demand, employee availability, or individual work preferences, leading to inefficiencies such as understaffing, overstaffing, and increased employee turnover. AI-driven predictive scheduling systems, however, analyze historical data on employee performance, customer demand patterns, and other relevant factors to generate optimal schedules. These AI systems forecast demand and dynamically adjust schedules to match the workload, ensuring the right number of employees are available at peak times while allowing for flexibility during slower periods.

From an employee engagement perspective, predictive scheduling benefits workers by offering more consistent and fair schedules, minimizing last-minute changes, and considering personal preferences like preferred shifts and work-life balance. When employees feel valued and have more control over their schedules, job satisfaction increases, leading to higher retention rates and improved performance. By reducing scheduling conflicts and burnout, predictive scheduling helps maintain a more motivated and engaged workforce.

Keyphrases: AI-driven predictive scheduling, Demand Forecasting, Employee Engagement, Employee Retention, Workforce Management, customer satisfaction, machine learning, operational efficiency, service quality, work-life balance

@booklet{EasyChair:14933,
  author    = {Oluwaseyi Oladele},
  title     = {The Impact of AI-Driven Predictive Scheduling on Employee Engagement and Customer Satisfaction},
  howpublished = {EasyChair Preprint 14933},
  year      = {EasyChair, 2024}}