The Impact of Machine Learning on Business Productivity: A Comprehensive Study within the Quality 5.0 Framework

Abstract The data-insight gap is persistent among Small and Medium Enterprises (SMEs): most of them generate mass volumes of data about their operations and customers, but fail to use it analytically and convert to actionable intelligence. SMEs of Saudi Arabia, this paper meets this challenge by formulating and discussing a portfolio of four low-code machine-learning (ML) artifacts sentiment analysis, market basket modeling, geospatial clustering, and predictive classification as a Design Science Research (DSR) contribution. Through the Knowledge Discovery in Databases (KDD) process, the models were used to illustrate the applicability of the concept of Quality 5.0 in the Saudi Arabian emergent scuba diving industry: human centricity, operational resilience, and holistic sustainability through operationalization by SMEs. Findings indicate the portfolio facilitates emotional mapping of experience, personalization of the curriculum, planning of sites on safety grounds, and ecological mitigation of risk. This research contributes to the applied literature of Quality 5.0 by making the theoretical constructs practical and experienceable through providing a set of guidelines and interpretation, making it a replicable roadmap of introducing AI on data-heavy service-based small and medium-sized enterprises.