The research aims to measure the influence of information technology (IT) on employee productivity by examining tools like cloud computing and AI-driven analytics. It focused on identifying productivity metrics impacted by IT, such as task efficiency and error reduction through collaboration. The research aimed to propose strategies for enhancing workforce performance by optimizing IT investments. To achieve these goals, the authors employed regression analysis and K-means clustering. Logistic regression predicted productivity levels based on IT factors like system reliability, usability, and training effectiveness. Meanwhile, K-Means clustering categorized organizations by IT efficiency and productivity, helping identify trends across industries. These methods were chosen for their ability to assess IT’s indirect impact while recognizing industry-wide patterns. The research revealed that system effectiveness, user-friendliness, and comprehensive training significantly influence productivity. Additionally, organizations were grouped based on IT adoption and productivity, enabling tailored recommendations for improvement. The research also emphasized the negative impact of IT breakdowns on productivity, highlighting the need for reliable, responsive IT infrastructure. These insights contribute to the understanding of IT’s role in workforce efficiency, offering a framework for businesses to align digital strategies with employee needs and operational goals. By investing in effective IT solutions, organizations can enhance productivity, minimize errors, and foster better collaboration, ultimately driving business success.