Abstract:
This study aimed to examine the impact of using artificial intelligence (AI) applications on enhancing strategic management performance in industrial companies in the southern governorates of the West Bank. The researcher adopted the descriptive-analytical method and relied primarily on a questionnaire distributed to the study population of 135 administrative employees, of which 126 valid responses were retrieved (a recovery rate of 92.3%), in addition to conducting interviews with some administrative staff. Data were analyzed using the SPSS program. The findings revealed that the level of AI applications utilization was high, with a relative weight of 75.49%, where predictive analytics ranked first, followed by data analysis platforms, automation tools, decision support systems, and finally machine learning algorithms. The results also indicated that the level of improvement in strategic management performance was high (75.44%), with decision-making enhancement ranking first, followed by operational efficiency and productivity, customer satisfaction and engagement, market competitiveness, innovation, and lastly financial performance. The study further identified major challenges hindering AI integration in these companies, including weak technological infrastructure, shortage of qualified personnel and training, high investment costs, and limited strategic awareness among management, which explains the limited strategic impact of some applications despite their widespread use. Hypothesis testing showed a significant effect of machine learning algorithms and decision support systems on strategic management performance, while predictive analytics and other tools showed no significant impact. Moreover, significant differences were found in favor of employees with 6–10 years of experience, bachelor’s degree holders, and those who had attended training programs, while no substantial differences were recorded based on gender or specialization, except in some dimensions. Based on these findings, the study recommended building human capacities through specialized training programs in AI applications and shifting their use from an operational to a strategic level, developing digital infrastructure and adopting integrated data analysis systems, and applying automation selectively in cost- or time-intensive areas. It further recommended enhancing integration between different tools, supporting decision-making through advanced AI tools capable of simulating business scenarios and forecasting risks, addressing financial challenges through partnerships with universities and business incubators, improving resource allocation using smart analytics, strengthening customer satisfaction by leveraging AI to understand customer behavior and design responsive innovation strategies, and adopting internal policies that foster a culture of innovation and reduce employee resistance by involving them in digital transformation initiatives and clarifying long-term benefits.
