A study on factors affecting employee productivity through the use of generative AI: a case study of an industrial plant at Map Ta Phut Industrial Estate

In today’s digital era, organizations increasingly adopt Generative AI to stay competitive on the global stage, yet clear metrics are needed to verify its impact on strategic goals. In large-scale industrial settings such as the plants at Map Ta Phut Industrial Estate employee productivity is a key performance indicator, but complex, specialized processes demand precise technical data and reliable decision support tools. This study argues that to truly enhance operational efficiency, Generative AI must be carefully tailored to the specific knowledge, skills, and workflows of industrial plant workers, ensuring both practical fit and measurable contributions to the organization’s vision and mission.This study investigates the factors influencing employee productivity through the use of Generative AI at a large-scale energy plant in the Map Ta Phut Industrial Estate. A mixed-methods approach was adopted, combining an online survey of 57 employees with in-depth interviews of four operations managers and executives. Quantitative analysis revealed that although employees use Generative AI only moderately, they hold positive views regarding its usefulness, organizational readiness, and its ability to enhance productivity. However, perceptions of ease of use, task-technology fit, and communication and knowledge sharing remained neutral, indicating usability challenges and gaps in alignment with specific job tasks. One-way ANOVA results showed significant differences in productivity gains across demographic groups age, education level, job position, and department highlighting the need for tailored implementation strategies. Exploratory factor analysis distilled fifteen variables into four components, of which Perceived Technology Effectiveness and Organizational Support emerged as the strongest predictors of productivity enhancement. Generative AI Utilization and Communication & Knowledge-Sharing were not statistically significant. Qualitative findings corroborated these results, with interviewees endorsing Generative AI for routine tasks but expressing concerns about data security, lack of contextual understanding, and insufficient training and communication policies.Based on these insights, organizations should implement targeted, role-specific AI training to build proficiency and confidence, allocate resources and foster an experimental culture for sustained adoption, develop domain-specific AI tools for specialized tasks, and establish clear data security and risk management policies linked to measurable KPIs. These steps will ensure that Generative AI effectively enhances productivity across diverse employee groups.