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This research investigates the selective adoption of AI within mission-driven organizations, highlighting how values like sustainability and neutrality often constrain efficiency-focused deployments. The study identifies critical barriers such as fragmented data infrastructure and a lack of in-house expertise that prevent individual enthusiasm from translating into scalable organizational practice. Ultimately, it advocates for institutional control over AI infrastructure to ensure human-centered collaboration aligned with specific humanitarian missions.
AI Adoption Across Mission-Driven Organizations
arXiv:2510.03868v2 Announce Type: replace-cross Abstract: Despite AI's promise, little is known about how mission-driven organizations (MDOs) adopt AI. We conducted semi-structured interviews with 15 experts and leaders within environmental, humanitarian, and development organizations across the Global North and South. We find that MDOs adopt AI selectively, with uses in content creation and data analysis, while maintaining human oversight for mission-critical applications. In case of conflicts between the organization's values and efficiency, participants consider sustainability, neutrality, and community trust as constraints that could limit or prevent AI adoption. Individual adoption of AI is not translated into organizational practice due to fragmented data infrastructure, lack of in-house expertise, inertia, ethical challenges, and dependency on vendors. Participants expected AI use in MDOs to involve institutional control over infrastructure, support for missions, and human-centered human-AI collaboration. We contribute an analysis on how MDOs manage AI adoption, identify barriers to responsible deployment, and provide recommendations for designing mission-aligned AI systems.
Abstract:Despite AI's promise, little is known about how mission-driven organizations (MDOs) adopt AI. We conducted semi-structured interviews with 15 experts and leaders within environmental, humanitarian, and development organizations across the Global North and South. We find that MDOs adopt AI selectively, with uses in content creation and data analysis, while maintaining human oversight for mission-critical applications. In case of conflicts between the organization's values and efficiency, participants consider sustainability, neutrality, and community trust as constraints that could limit or prevent AI adoption. Individual adoption of AI is not translated into organizational practice due to fragmented data infrastructure, lack of in-house expertise, inertia, ethical challenges, and dependency on vendors. Participants expected AI use in MDOs to involve institutional control over infrastructure, support for missions, and human-centered human-AI collaboration. We contribute an analysis on how MDOs manage AI adoption, identify barriers to responsible deployment, and provide recommendations for designing mission-aligned AI systems.
Computers and Society (cs.CY); Artificial Intelligence (cs.AI)
arXiv:2510.03868 [cs.CY]
(or arXiv:2510.03868v2 [cs.CY] for this version)
https://doi.org/10.48550/arXiv.2510.03868 Focus to learn more arXiv-issued DOI via DataCite
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