Artificial intelligence readiness among audiovisual media professionals in Saudi Arabia and its barriers and facilitating conditions

Abstract

Artificial intelligence is reshaping audio-visual content production, yet adoption among media professionals in Arab contexts remains under-researched, and existing studies rarely integrate individual, structural, and institutional explanations within a single analytical framework. This study examines the barriers to and facilitating conditions for AI integration among audio-visual media professionals in Saudi Arabia, together with the demographic correlates of AI readiness. A cross-sectional survey of 400 media professionals was conducted in 2025 using purposive sampling. Theoretically, the study advances a Barrier–Enabler Ecosystem Model integrating the Technology Acceptance Model, Digital Divide Theory, and Institutional Theory, conceptualising adoption not as a discrete event but as a continuously negotiated balance between constraining and enabling forces operating at individual, organisational, and institutional levels. Adoption was near-saturated: 383 respondents reported using AI tools and 17 did not. Within this small non-user subsample, privacy and security concerns were the most frequently reported barrier, followed by subscription costs; because the subsample comprises only 17 respondents, this ranking is descriptive and exploratory and is not generalisable to the Saudi media sector. Across the full sample, training programmes and specialised workshops were the most frequently endorsed facilitating condition. AI readiness varied significantly by age, occupational sector, geographic region, and monthly income, but not by gender or education. The study contributes a cross-level account of how macro-institutional pressures associated with Vision 2030 are perceived and internalised as individual readiness, and demonstrates that in near-saturated markets the analytical question shifts from whether professionals adopt AI to the depth, quality, and equity of that adoption. Practically, the findings support investment in training ecosystems, targeted subsidies for independent practitioners, and Arabic-language AI resources.