Volume 2 - Issue 3, May - June 2026

Software-as-a-Service (SaaS) platforms are widely used by organizations to improve productivity and collaboration. However, many companies face significant financial losses due to underutilized or unused software licenses. Traditional monitoring methods rely on manual tracking and basic reporting, which fail to capture dynamic usage patterns and lead to inefficient resource allocation. To address this issue, this paper proposes an intelligent SaaS wastage detection system using machine learning and data analytics techniques. Initially, usage datasets containing employee activity, tool information, and license details are collected and preprocessed to remove inconsistencies and missing values. Important usage metrics such as average usage, active users, and usage consistency are then calculated. A company-specific baseline is determined using statistical methods to evaluate tool utilization accurately. The K-Means clustering algorithm is applied to group tools based on usage patterns and identify underutilized tools. A hybrid decision mechanism is implemented to generate recommendations such as KEEP, REDUCE LICENSES, or REMOVE. The system also performs cost analysis to estimate wasted expenditure and potential savings. The results are presented through an interactive dashboard for easy interpretation. Experimental results demonstrate that the proposed system effectively identifies software wastage and supports better decision-making compared to traditional methods.

Sasi Kottaiyan S, Usha Devi M,"AI Based Tool Wastage Detection(Saas Tools)" International Journal of Advanced Multidisciplinary Research and Educational Development, V2(3): Page(950-955) May-June 2026. ISSN: 3107-6513. www.ijamred.com. Published by Scientific and Academic Research Publishing.