Barriers to Adopting Artificial Intelligence Applications in Supporting Decision-Making "An Applied Study on the Gaza Governorates Electricity Distribution Company"

Abstract

Artificial intelligence (AI) is among the most transformative technologies that have significantly enhanced organizational performance by improving operational efficiency, enabling advanced data analytics, and extracting decision-supportive insights. Its role becomes particularly evident in strengthening decision-making quality by providing accurate and timely information that assists decision-makers in selecting the most effective alternatives. In this context, the present study aimed to analyze the impact of technical, human, organizational, financial, and external barriers on the adoption of AI applications for supporting decision- making processes at the Gaza Electricity Distribution Corporation (GEDCO). To achieve the study’s objectives, a mixed-method approach was employed, incorporating a structured questionnaire alongside The study encompassed all employees occupying supervisory positions at different managerial levels (senior, middle, and lower), totaling 316 individuals. A stratified random sampling technique was applied, whereby questionnaires were distributed to a sample of 190 employees, of which 174 were retrieved, representing a response rate of 91.5%. The results of the study showed that the level of obstacles to adopting artificial intelligence applications in the Gaza Electricity Distribution Company was high, with a relative weight of (73.2%), while the level of decision-making support was low, with a relative weight of (47.8%). The results also showed a clear negative impact of these obstacles in their various dimensions on the adoption of artificial intelligence applications in supporting decision- making, and they came in the following order: external obstacles, followed by financial obstacles, followed by human obstacles, then technical obstacles, and finally organizational obstacles. The results also revealed the presence of statistically significant differences between the averages of the respondents’ responses regarding the obstacles to adopting artificial intelligence applications attributed to variables: gender, age, educational qualification, and years of service. In contrast, statistically significant differences were found in the averages of Their responses regarding the level of decision-making support are attributed to the following variables: gender, age, academic specialization, and years of service. Key recommendations include establishing an internal multidisciplinary AI unit reporting directly to decision-makers; adopting a digital roadmap adaptable to GEDCO context; implementing gradual AI adoption through pilot projects, developing strategic partnerships, allocating dedicated AI budgets, creating comprehensive training programs (especially for mid-qualified staff), enhancing data integration; and updating regulatory policies