Seven critical weaknesses slowing AI adoption in government systems

The analysis shows that most studies focus on frameworks and conceptual models rather than documented implementations. E-government systems themselves are still maturing, and in many cases lack the structural sophistication required to support advanced AI tools. The review categorises AI–e-government integration into three states: potential, actual and desired.

Developing countries face a widening digital governance gap as they attempt to integrate artificial intelligence into public administration systems, according to a major new review that captures the state of global readiness. The study shows that most nations remain at the earliest stages of AI adoption, constrained by fragile infrastructure, weak regulatory systems and limited institutional capacity.

The study, titled “E-Government/AI Integration State and Capacity in Developing Countries: A Systematic Review,” published in Administrative Sciences, examines 50 peer-reviewed studies drawn from an initial pool of 1431 research papers. Their findings suggest a significant mismatch between the potential benefits of AI-enhanced governance and the actual readiness of institutions expected to deploy it.

AI integration remains largely theoretical despite expanding digital ambitions

According to the review, developing countries currently occupy what the authors describe as a potential rather than actual state of AI integration. Governments widely acknowledge the promise of AI in improving administrative efficiency, delivering personalised public services, enhancing transparency and supporting evidence-based policymaking. Yet these ambitions have not translated into real-world deployment at the scale required to transform governance.

The analysis shows that most studies focus on frameworks and conceptual models rather than documented implementations. E-government systems themselves are still maturing, and in many cases lack the structural sophistication required to support advanced AI tools. The review categorises AI–e-government integration into three states: potential, actual and desired.

The potential state dominates across low- and middle-income regions, where the benefits of AI are discussed extensively but remain constrained by infrastructure gaps, weak digital ecosystems and delayed institutional reforms. Countries such as India, China, Morocco and Indonesia show more progress than others, with limited AI deployments in service delivery, public engagement and administrative tasks. These early examples suggest promising efficiency gains, reduced corruption risk and stronger public-sector responsiveness, but they remain exceptions rather than the norm.

The review’s desired state represents a future vision of public governance in which AI-enabled systems provide real-time services, predictive analytics for resource allocation, data-driven policy development and personalised citizen engagement. In this scenario, governments leverage automation, accessible platforms, integrated digital identities and secure, interoperable systems to reshape how services are delivered. But the study concludes that reaching this level of integration will require a significant transformation of institutional capacity, legal frameworks, infrastructure and human capital.

A key takeaway is that AI integration is not simply a technological upgrade. It demands a recalibration of public institutions, robust ethical and regulatory safeguards, and long-term investment in administrative capacity. Without these foundations, the benefits of AI may remain inaccessible to the populations that need them most.

Seven capacity domains determine progress and exposure to digital inequality

The seven capacity domains determine whether developing countries can progress from theoretical AI adoption to practical implementation. These domains form a comprehensive framework for assessing readiness and identifying bottlenecks.

The first domain, governance, regulation and ethics, is one of the most underdeveloped across the reviewed countries. Effective AI–e-government integration requires legal clarity on algorithmic accountability, data protection, user privacy, system transparency and oversight. Many countries lack dedicated AI policies or have fragmented regulatory environments. The absence of ethical frameworks increases the risk of biased decisions, misuse of citizen data and erosion of public trust.

The second domain focuses on strategic and implementation planning. The study finds that while many governments have high-level digital transformation agendas, few possess detailed AI adoption roadmaps or structured deployment plans. Successful integration requires feasibility studies, phased implementation, evaluation cycles and budget planning. The lack of coordinated strategies has already led to stalled pilot projects and inconsistent government-wide adoption.

The third domain is technology and infrastructure. AI requires robust data ecosystems, interoperable platforms, cybersecurity defenses and computing capacity. Many developing countries lack this foundation. Limited connectivity, outdated legacy systems, poor data quality and inadequate cybersecurity readiness constrain what is technologically feasible. Even in countries with stronger infrastructure, challenges remain in integrating AI into complex administrative systems that were not designed for automation.

Organisational capacity development forms the fourth domain. This includes change management, innovation culture, performance management and inter-agency coordination. Public institutions in developing countries often operate with rigid bureaucratic structures that resist change. Without organisational flexibility, even well-designed AI systems fail to deliver intended outcomes.

A fifth domain, human capital and expertise, reveals another major gap. AI integration requires a skilled workforce capable of developing, maintaining and overseeing digital systems. Many countries face shortages of data scientists, AI engineers, cybersecurity specialists and public managers trained to work with digital technologies. Continuous training, research development and partnerships with academic institutions are essential to building long-term capacity.

The sixth domain focuses on the adoption and impact of AI tools. The review highlights limited evidence of successful deployments beyond chatbots, analytics platforms and early-stage automation tools. Scaling requires structured evaluation, user feedback mechanisms, transparent system design, and strong monitoring of outcomes. Without these, AI adoption risks becoming superficial or counterproductive.

The final domain is citizen engagement and participation. Public trust is fundamental to digital governance, and the study notes that many developing countries face challenges in digital literacy, accessibility and inclusivity. Citizens cannot benefit from AI-enhanced services if they lack the skills or resources to interact with them. Engagement strategies must close literacy gaps, protect user rights and ensure equitable access.

Together, these seven domains offer a roadmap for governments seeking to build the institutional conditions necessary for AI-enabled governance. They also highlight the risk of widening digital divides if capacity-building efforts lag behind technological ambition.

Transformation pace, transformation certainty and the uneven path ahead

The study introduces two analytical concepts to measure country-level readiness: transformation pace and transformation certainty. Transformation pace refers to the speed at which countries move from conceptual interest to actual implementation. Transformation certainty measures the likelihood that integration will succeed given current capacities.

Upper-middle-income countries, particularly China and Indonesia, show faster transformation pace due to stronger investment, national AI strategies and coordinated digital ecosystems. These countries are already testing or deploying AI systems in public management, social protection, transportation and healthcare. Their progress reflects stronger institutional capacity and clearer policy direction.

Lower-middle-income countries demonstrate slower transformation pace, with attention focused on foundational issues such as interoperability, data governance and digital literacy. Their progress is uneven and often disrupted by political instability, insufficient funding or fragmented policymaking.

Low-income countries appear farthest from realising AI–e-government integration. The review notes a significant underrepresentation of research from these countries, suggesting that practical implementation is even less advanced than documented. Weak infrastructure, limited budgets and high dependency on external partners hinder progress.

Despite these uneven trajectories, the study argues that transformation is possible with coordinated investment and sustained policy commitment. Strategic partnerships, donor support, regional collaborations and capacity-building initiatives can accelerate progress, but only if anchored in long-term planning and local context.