UTILIZATION OF (AI) MACHINARIES ON EMPLOYEES SATISFACTION AT YANYAN INTERNATIONAL (PHILS) INC. IN CALOOCAN CITY

Abstract

This study investigates the effectiveness of artificial intelligence (AI) machinery in enhancing employee satisfaction and operational performance at Yanyan International (Phils) Inc., within the context of increasing digital transformation in the manufacturing sector. Grounded in Lin and Chen’s (2021) theory of employee satisfaction in AI, the research examines key factors influencing job satisfaction, role clarity, and workload management associated with AI implementation. It also highlights barriers to successful integration, including outdated machinery, insufficient employee training, and persistent maintenance challenges. Employing a quantitative-descriptive research design, the study gathered data from 30 purposively selected employees of Yanyan International (Phils) Inc. using a researcher-made survey questionnaire. To analyze the data, statistical tools such as the weighted mean, Kruskal-Wallis H-test, and ranking were applied. The study tested the null hypothesis that there are no significant differences in employee satisfaction based on demographic variables, including age, gender, and length of employment. The findings revealed that the integration of AI machinery led to increased employee productivity, enhanced role clarity, and more efficient task distribution. The highest levels of satisfaction were associated with employees’ adaptability to new AI-driven roles. Statistical analysis indicated no significant differences in satisfaction across demographic variables such as age, gender, and length of employment, suggesting that organizational culture and management practices play a more critical role in shaping employee satisfaction than individual characteristics. The most frequently cited challenge was the need for consistent maintenance of AI machinery, which remains a barrier to seamless operations. The results indicate that AI technologies can significantly enhance employee satisfaction and operational efficiency when implemented with appropriate support systems. To ensure long-term success, a comprehensive strategic plan was developed, encompassing targeted training programs, role realignment, workload optimization, predictive maintenance, and operational streamlining. Each initiative was structured with a defined timeline and assigned stakeholders to support sustainable implementation. The study concludes that effective AI adoption requires not only technological investments but also parallel commitments to employee development. It recommends the adoption of inclusive workplace policies, continuous skills training, and proactive maintenance strategies to cultivate a dynamic, high-performing organizational environment.