# AI Adoption Across Mission-Driven Organizations

arXiv: https://arxiv.org/abs/2510.03868 · HTML: https://mlsanity.com/paper/2510.03868/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 115 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Measuring the Carbon Intensity of AI in Cloud Instances](https://mlsanity.com/paper/2206.05229/) (2022)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [The Datafication of Care in Public Homelessness Services](https://mlsanity.com/paper/2502.09043/) (2025)
- [Knowledge Workers' Perspectives on AI Training for Responsible AI Use](https://mlsanity.com/paper/2503.06002/) (2025)
- [Predictive modeling of movements of refugees and internally displaced people: Towards a computational framework](https://mlsanity.com/paper/2201.08006/) (2022)
- [Data and Technology for Equitable Public Administration: Understanding City Government Employees' Challenges and Needs](https://mlsanity.com/paper/2505.21682/) (2025)

## Built on by (most cited first, 1 of 1)

- [AI Adoption Across a Multinational Workforce: Sociotechnical Conditions for GenAI Acceptance in Human Resources](https://mlsanity.com/paper/2606.17887/) (2026)

## Abstract (arXiv)

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.