Authors: Dalia Ali, Muneeb Ahmed, Hailan Wang, Arfa Khan, Naira Paola Arnez Jordan, Sunnie S. Y. Kim, Meet Dilip Muchhala, Anne Kathrin Merkle, +1 more
Organizations: Technical University of Munich Munich, Germany · Princeton University Princeton, New Jersey, USA · WWF Mumbai, India · WWF Berlin, Germany
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.
Figures & tables
Current AI Practices
Barriers in Adoption
Future Outlook
Goals
Analyzing how MDOs deploy AI across their operations
Identifying the organizational, ethical, and infrastructural barriers that constrain adoption
Exploring practitioners’ visions of AI’s role in strengthening missions while preserving values
Questions
In what application areas are MDOs currently adopting AI across their operations?
What mission- and context-specific barriers do MDOs face in adopting AI?
How do MDOs envision mission-aligned and values-consistent future uses of AI?
Analytical Focus
Inductive thematic coding of interview data to identify domains of AI use and practices
Inductive clustering of barrier-related data into higher-order categories through constant comparison
Narrative and thematic coding of future reflections, synthesized into organizational outlooks
Table 1. Our study framework: Goals, Questions, and Analytical Focus \new{ \parThe above table illustrates our study framework, which comprises four themes. These themes are Current AI Practices, Challenges in Adoption, Future Outlook, and Recommendations. As illustrated in the table, the rows represent our Goals, Questions, and Analytical Focus. Our Goals illustrate the objective of each theme, while the Questions represent the study questions for each theme. Analytical Focus, on the other hand, illustrates the analysis techniques applied. As demonstrated in the table, our study framework comprises four columns and three rows. \par}
ID
Position
Work Area
Organization Type
Region
P1
Chief Regional Advisor
Development
IGO
Middle East & North Africa (MENA)
P2
Global Head Data & AI
Environmental
International NGO
Europe (Switzerland)
P3
Head Digital Transformation & Data
Environmental
International NGO
Europe (Switzerland)
P4
Experiments Team Co-Lead
Environmental
International NGO
Europe (UK)
P5
Director of ICT
Environmental
International NGO
Europe (UK)
P6
Data & Technology Global Lead Scientist
Environmental
International NGO
North America (USA)
Table 2. Participant demographics and organizational context This table summarizes the demographic and organizational characteristics of the 15 participants interviewed. It lists each participant’s ID, their role or position, their primary work area, the type of organization they work for, and the global region in which they are based. Participants come from international NGOs, local NGOs, IGOs, and technology consortia, with work spanning environmental, development, and humanitarian domains. Regions represented include Europe, North America, South Asia, the Middle East and North Africa, and Asia-Pacific. The table contains 15 rows of participants and five columns describing their profiles.
Figure 1. AI adoption patterns across environmental, humanitarian, and development MDOs, showing reported patterns of use, organizational constraints, and future priorities for human–AI collaboration.
Themes
Sub-themes
Representative Codes
Current AI practices
Outreach and External Communication
AI for Donor Intelligence & Engagement; AI for Outreach & External Communication; AI for Data Accessibility & Open Information; AI Supporting Advocacy & Public Messaging
Organizational Efficiency and Planning
AI for HR & Administrative Processes; AI for Resource Management; AI for Daily Workflow & Knowledge Access; AI for Organizational Efficiency & Planning
Data-Driven Insight Generation
AI for Research, Text Analysis & Reporting; AI for Data Cleaning & Integration; AI for Pattern Recognition in Large Datasets
Mission-Critical Monitoring and Response
AI for Risk Monitoring & Anticipation; AI for Crisis Mapping & Humanitarian Response; AI for Combating Illegal Wildlife Trade; AI for Wildlife & Ecosystem Monitoring; AI for Sustainability & Green Transformation
Barriers in Adoption
Implementation Gap
Skills & Expertise Shortages; Struggling to Keep Pace with Rapid AI Development; Over-reliance on Basic Tools; Personal Anxiety & Job Security Concerns; Limited Collaboration Between Organizations
Institutional Inertia
Leadership Skepticism & Reluctance; Process & Organizational Alignment Barriers; Financial Constraints & Business Case Challenges; Difficulty Measuring Impact; Country & Context Alignment Challenges
Table 3. Thematic analysis results: Four main themes with sub-themes and representative codes This table presents the results of the thematic analysis, summarizing four main themes that structure the findings: Current AI Practices, Barriers in Adoption, Future Outlook, and Recommendations. Each theme is broken down into several sub-themes, and each sub-theme lists representative qualitative codes derived from interview data. The table is organized into three columns: the first column shows the major themes, the second lists related sub-themes, and the third provides example codes illustrating patterns observed in the data. In total, the table contains four themes, seventeen sub-themes, and multiple representative codes that reflect how participants described AI use, adoption challenges, anticipated futures, and practitioner-driven recommendations.