AI Adoption Challenges in Developing Countries' Public Sector
Join us for an insightful webinar exploring the adoption of AI in the public sector of developing countries, featuring research that unpacks the complexities and challenges faced throughout the AI life cycle.
Key Facts
- Strong leadership in AI projects boosts design phases, indicating a need for effective governance.
- Limited infrastructure and financial instability hinder AI deployment, revealing vulnerabilities in developing markets.
- Talent attrition impacts AI initiatives, highlighting a competitive disadvantage for public sector organizations.
- The integration of TOE and TACT frameworks suggests strategic shifts needed for successful AI adoption.
- AI adoption challenges in developing countries contrast with developed nations, indicating market disparity.
Summary
The upcoming webinar hosted by the United Nations University – Operating Unit on Policy-driven Electronic Governance (UNU-EGOV) on October 13, 2026, will focus on the adoption of artificial intelligence (AI) in the public sector of developing countries. This event, featuring Charmaine Distor, aims to shed light on the unique challenges faced by these nations as they navigate the AI life cycle—from design to deployment. Understanding these dynamics is crucial, as AI increasingly shapes governance and public service delivery globally.
Distor's presentation will draw on her research paper, which examines seven AI initiatives within the Philippine public sector through a qualitative case study. This study employs the Technology-Organization-Environment (TOE) framework alongside Technology Affordances and Constraints Theory (TACT) to analyze the factors influencing AI adoption. By conducting semi-structured interviews with ten stakeholders involved in these projects, the research reveals both the opportunities and barriers present in the AI deployment landscape in developing countries.
Key findings indicate that while strong leadership can drive initial design efforts, systemic issues such as inadequate infrastructure, financial instability, talent attrition, and weak governance significantly impede the development and deployment phases. These challenges contrast sharply with those encountered in developed nations, where resources and governance structures are typically more robust. This disparity highlights the need for tailored strategies that consider the unique contexts of developing countries.
The implications of this research extend beyond academic inquiry. For business leaders and policymakers, the findings underscore the importance of fostering strong leadership and investing in infrastructure to facilitate AI adoption. Organizations looking to engage in AI initiatives within developing markets must be prepared to navigate these complexities. The research suggests that successful AI integration requires not only technological solutions but also a comprehensive understanding of the organizational and environmental factors at play.
As AI continues to evolve, the strategic landscape for public sector innovation will likely shift. Companies that can offer solutions addressing the specific constraints identified in Distor's study—such as scalable AI platforms that require minimal infrastructure or training—will find significant opportunities in these markets. Furthermore, the growing emphasis on citizen participation and transparency in governance may drive demand for AI tools that enhance public engagement and service delivery.
Looking ahead, the integration of AI into public governance in developing countries could catalyze broader economic and social advancements. As these nations begin to harness AI's potential, the ripple effects could lead to improved service delivery, increased efficiency, and enhanced citizen trust in government institutions. For businesses, this signals an evolving market ripe for innovation and investment, particularly in sectors that support public administration and civic technology. Engaging with these developments proactively will be crucial for companies aiming to establish a foothold in emerging markets where AI adoption is gaining momentum.
Entities Mentioned
Technologies
People
Organizations
Key Concepts
Definitions
- AI life cycle
- The stages involved in the adoption and implementation of artificial intelligence, including design, development, and deployment.
- Technology-Organization-Environment framework
- A theoretical framework that examines how technological, organizational, and environmental factors influence the adoption of technology.
- Technology Affordances and Constraints Theory
- A theory that explores how technology can enable or limit actions within a specific context.
- qualitative multiple-case study
- A research method that involves in-depth analysis of multiple cases to understand complex phenomena.
- talent attrition
- The loss of skilled employees from an organization, which can hinder project development and implementation.
Use Cases
- →AI initiatives in the Philippine public sector
- →improving public services through automation
- →promoting citizen participation via data science
- →developing the Philippine ICT industry ecosystem
- →local governance enhancements using AI
Frequently Asked Questions
What is the focus of the webinar hosted by UNU-EGOV?
The webinar focuses on how public-sector organizations in developing countries are adopting artificial intelligence and the challenges they face throughout the AI life cycle.
Who is Charmaine Distor?
Charmaine Distor is a Research Consultant at UNU-EGOV and a doctoral student at the University of Lausanne, specializing in artificial intelligence in public sector organizations.
What are the main challenges identified in AI adoption in developing countries?
The study identifies challenges such as limited infrastructure, financial instability, talent attrition, and weak governance that hinder the development and deployment of AI.
What theoretical contributions does the study make?
The study integrates the Technology-Organization-Environment framework and Technology Affordances and Constraints Theory with a life cycle perspective on AI adoption.
How can practitioners benefit from the findings of this study?
The study offers actionable recommendations for practitioners to navigate the challenges of AI adoption in developing countries, enhancing their implementation strategies.