Responsible AI readiness in a humanitarian and development ecosystem: an exploratory case study - Yasmine Attia; Diana Maddah; Al-Anoud Al-Kaabi; Ghalia Seifo; Mohammed Bader Al-Sada; Jesha Mohammed Ali Mundodan; Abdelaziz Khatir Jadeed; Niveen M. E. Abu-Rmeileh; Kwalombota Kwalombota (Frontiers in Public Health, 2026)
Humanitarian and development organizations are increasingly exploring artificial intelligence (AI) to support information-intensive work, yet responsible adoption depends on both staff readiness and organizational enabling conditions. This study provides an exploratory stakeholder-based snapshot of AI readiness within Qatar’s humanitarian and development ecosystem. A cross-sectional, bilingual online survey was administered to humanitarian-sector stakeholders ( N = 83) using purposive and snowball recruitment initiated through a multi-stakeholder network. Readiness was examined using a dual conceptual lens: the Unified Theory of Acceptance and Use of Technology (UTAUT) was used to interpret adoption-related perceptions, and the Organizational Digital Transformation Readiness (ODTR) model was used to structure organizational readiness conditions. Descriptive indicators were summarized, and composite indices were calculated on a 0–100 scale. Adoption-related perceptions were high in this sample (UTAUT-informed index mean: 90.8/100), whereas organizational readiness conditions were more moderate and variable (ODTR index mean: 56.8/100). Governance and safeguards represented the weakest organizational domain (mean: 43.6/100). The most frequently reported medium or major barriers were insufficient staff skills to work with data or AI tools (89.2%), technical complexity or difficulty using AI tools (86.7%), and concerns about data privacy or potential misuse (84.3%). Overall, the findings suggest a layered readiness pattern in which individual openness to AI appears stronger than the visibility and consistency of organizational governance and capability conditions. These results should be interpreted as descriptive and context-specific rather than representative of the wider sector. Even so, they provide an empirical baseline that may help inform future efforts to strengthen governance, safeguards, and capacity-building for responsible AI adoption in humanitarian and development settings.