AI Adoption Across Mission-Driven Organizations

2025-10-04

AI Summary

Existing research lacks empirical grounding on how mission-driven organizations (MDOs)—particularly resource-constrained, values-oriented entities in the Global South and development sectors—prudently adopt AI. Method: Through 28 cross-regional, semi-structured interviews and thematic analysis, this study systematically examines AI’s practical deployment in content generation and data analytics, identifies adoption barriers, and explores integration aspirations. Contribution/Results: We propose a “Conditional AI Adoption” model, asserting that AI deployment must prioritize organizational sovereignty, mission integrity, and sustained human oversight—rejecting automation-first logic. Findings reveal that MDOs deliberately pause decision-making when efficiency gains conflict with core values, affirming AI’s role strictly as an augmentative tool for human-centered practice. This work addresses a critical empirical gap in AI governance literature across North–South contexts and offers both a theoretical framework and actionable pathways for responsible AI implementation in MDOs.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: AI for Accessibility
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization

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.

Problem

• Limited empirical understanding of AI adoption in mission-driven organizations
• How resource-constrained organizations navigate AI integration across operations
• AI adoption conflicts with organizational values causing decision-making stalls

Innovation

• Thematic analysis of practitioner interviews across organizations
• Selective AI deployment with human oversight in operations
• Conditional AI adoption strengthening sovereignty and mission integrity