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Institutional Readiness and the Boundaries of AI Adoption: Evidence from UAE Newsrooms

Year: 2026

Type: article

Abstract: As Artificial Intelligence (AI) becomes embedded in newsroom operations, organizations invest in AI readiness on the assumption that preparedness travels with successful outcomes. Readiness frameworks typically aggregate diverse outcomes into composite performance measures, leaving it unclear whether readiness explains each equally well. This study examines the explanatory scope of institutional AI readiness in UAE newsrooms using a cross-sectional survey of a purposive sample of 336 media professionals. Three collection streams were harmonized and outcomes analyzed separately rather than aggregated. The seven readiness indicators showed high internal consistency (α = 0.919) and a dominant first component accounting for 67.7% of item variance; the four conceptual domains behind the instrument were not recovered as separable dimensions, a pattern that may partly reflect a general evaluative disposition toward the organization. A one-standard-deviation increase in readiness was associated with 2.59 times higher odds of deeper adoption (95% CI [1.88, 3.56]) and with greater training provision and perceived return on AI investment, but not with practitioners’ perceived quality of Arabic-language AI output, measured with a single item (ρ = 0.097). Because one readiness indicator concerns current AI use, all estimates were re-estimated without it: the odds ratio was 2.38 (95% CI [1.74, 3.25]), the organizational associations remained significant, and the association with perceived Arabic-language output quality remained non-significant (ρ = 0.095). Institutional readiness, therefore, appears to have a bounded explanatory domain in these data, showing stronger associations with organizational implementation outcomes than with perceived output quality. Because the design is cross-sectional and not all respondents answered outcome items, these are statements of association, not of effect. Studies relying on composite measures may overstate their explanatory reach.

Source: Journalism and Media

DOI: 10.3390/journalmedia7040212

Authors: Elsayed Darwish, Ahmed Al Mansoori

Institutions: Zayed University, Al Ain University, United Arab Emirates University

Language: English

Topic: Journalism and Media Studies

Subfield: Communication

Field: Social Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $1,207

Funder: Zayed University