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AI Adoption Across Mission-Driven Organizations

Dalia Ali, Muneeb Ahmed, Hailan Wang, Arfa Khan, Naira Paola Arnez Jordan, Sunnie S. Y. Kim, Meet Dilip Muchhala, Anne Kathrin Merkle, Orestis Papakyriakopoulos

TL;DR

This study addresses the gap in empirical understanding of AI adoption in mission-driven organizations (MDOs) by conducting semi-structured interviews with 15 practitioners across environmental, humanitarian, and development sectors in Global North and South. Using a Goal Question Method and reflexive thematic analysis, it reveals that AI adoption in MDOs is conditional, with strongest use in internal operations and data-driven insight generation, while mission-critical deployments remain limited pilots governed by human oversight. The work identifies five interconnected barriers—implementation gaps, institutional inertia, ethics dilemmas, data governance challenges, and vendor dependence—that hinder scaling from pilots to organization-wide practice, and outlines a governance-first, sovereignty-preserving path toward durable, mission-aligned AI. Practitioners and researchers propose two streams of recommendations: operational strategies that embed AI literacy, protect human decision-making, and foster cross-sector collaboration; and systemic strategies to build mission-aligned data infrastructure, bridge the implementation gap, and reduce vendor dependence. Collectively, the study contributes an initial, cross-sectoral portrait of how MDOs negotiate AI under resource constraints and value-driven mandates, offering practical guidance for governance, design, and policy to realize responsible AI for social good.

Abstract

Despite AI's promise for addressing global challenges, empirical understanding of AI adoption in mission-driven organizations (MDOs) remains limited. While research emphasizes individual applications or ethical principles, little is known about how resource-constrained, values-driven organizations navigate AI integration across operations. We conducted thematic analysis of semi-structured interviews with 15 practitioners from environmental, humanitarian, and development organizations across the Global North and South contexts. Our analysis examines how MDOs currently deploy AI, what barriers constrain adoption, and how practitioners envision future integration. MDOs adopt AI selectively, with sophisticated deployment in content creation and data analysis while maintaining human oversight for mission-critical applications. When AI's efficiency benefits conflict with organizational values, decision-making stalls rather than negotiating trade-offs. This study contributes empirical evidence that AI adoption in MDOs should be understood as conditional rather than inevitable, proceeding only where it strengthens organizational sovereignty and mission integrity while preserving human-centered approaches essential to their missions.

AI Adoption Across Mission-Driven Organizations

TL;DR

This study addresses the gap in empirical understanding of AI adoption in mission-driven organizations (MDOs) by conducting semi-structured interviews with 15 practitioners across environmental, humanitarian, and development sectors in Global North and South. Using a Goal Question Method and reflexive thematic analysis, it reveals that AI adoption in MDOs is conditional, with strongest use in internal operations and data-driven insight generation, while mission-critical deployments remain limited pilots governed by human oversight. The work identifies five interconnected barriers—implementation gaps, institutional inertia, ethics dilemmas, data governance challenges, and vendor dependence—that hinder scaling from pilots to organization-wide practice, and outlines a governance-first, sovereignty-preserving path toward durable, mission-aligned AI. Practitioners and researchers propose two streams of recommendations: operational strategies that embed AI literacy, protect human decision-making, and foster cross-sector collaboration; and systemic strategies to build mission-aligned data infrastructure, bridge the implementation gap, and reduce vendor dependence. Collectively, the study contributes an initial, cross-sectoral portrait of how MDOs negotiate AI under resource constraints and value-driven mandates, offering practical guidance for governance, design, and policy to realize responsible AI for social good.

Abstract

Despite AI's promise for addressing global challenges, empirical understanding of AI adoption in mission-driven organizations (MDOs) remains limited. While research emphasizes individual applications or ethical principles, little is known about how resource-constrained, values-driven organizations navigate AI integration across operations. We conducted thematic analysis of semi-structured interviews with 15 practitioners from environmental, humanitarian, and development organizations across the Global North and South contexts. Our analysis examines how MDOs currently deploy AI, what barriers constrain adoption, and how practitioners envision future integration. MDOs adopt AI selectively, with sophisticated deployment in content creation and data analysis while maintaining human oversight for mission-critical applications. When AI's efficiency benefits conflict with organizational values, decision-making stalls rather than negotiating trade-offs. This study contributes empirical evidence that AI adoption in MDOs should be understood as conditional rather than inevitable, proceeding only where it strengthens organizational sovereignty and mission integrity while preserving human-centered approaches essential to their missions.

Paper Structure

This paper contains 46 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Technology Uptake in Mission-Driven Organizations
  4. AI Adoption and Organizational Tensions
  5. Mission-Driven Organizations from a Socio-Technical Perspective
  6. Methods
  7. Participant Recruitment and Selection
  8. Study Instrument
  9. Conducting and Analyzing Interviews
  10. Results
  11. RQ1: Current AI Practices
  12. Outreach and External Communication
  13. Organizational Efficiency and Planning
  14. Data-Driven Insight Generation
  15. Mission-Critical Monitoring and Response
  16. ...and 31 more sections

Figures (3)

  • Figure 1: Semi-structured interview instrument comprising three sections: (1) Understanding participants and organizations, (2) AI readiness and organizational use, and (3) Ethical and future considerations. The instrument includes 30 questions and sub-questions designed to explore organizational AI adoption patterns, barriers, and future perspectives.
  • Figure 2: AI adoption patterns across environmental, humanitarian, and development MDOs showing maturation gradient by domain, structural barriers limiting scaling, and strategic future directions emphasizing human-AI collaboration.
  • Figure 3: AI adoption recommendations for MDOs, categorized by practitioner-informed operational needs and researcher-derived systemic challenges.