AI solutions could improve agriculture, disaster prediction, and urban planning in fragile areas

Artificial intelligence has already transformed sectors such as finance, healthcare, and logistics in advanced economies, but its potential role in fragile territories is now drawing growing attention from researchers and international development organizations.

In the study Governing Artificial Intelligence for Sustainable Territorial Development in Fragile Contexts: Insights from North Lebanon, published in the journal Administrative Sciences, researchers analyse how AI could support sustainable territorial development in fragile regions while examining the structural barriers that limit technological adoption. Using North Lebanon as a case example, the study evaluates how stakeholders perceive the opportunities and challenges associated with integrating AI technologies into development strategies.

AI as a tool for sustainable development

According to the study, AI has the potential to support sustainable development across multiple sectors in fragile territories. Stakeholders identified several areas where AI-driven systems could improve resource efficiency, strengthen infrastructure planning, and support economic growth.

Agriculture emerged as one of the most promising areas for AI applications. In many fragile regions, agriculture remains a primary source of employment and income. However, farmers often face challenges related to climate variability, water scarcity, and limited access to modern technologies. AI-powered systems can analyze environmental data, weather patterns, and soil conditions to help farmers optimize irrigation, predict crop yields, and manage pests more effectively.

Precision agriculture tools supported by machine learning algorithms can also help reduce the use of water, fertilizers, and pesticides, improving both productivity and environmental sustainability. By providing farmers with real-time information about crop conditions and environmental risks, AI technologies can help improve food security in regions that depend heavily on agricultural production.

Urban management represents another area where artificial intelligence could play an important role. Many cities in fragile regions struggle with rapid population growth, outdated infrastructure, and inefficient transportation systems. AI-driven traffic management platforms can analyze vehicle movement patterns and optimize traffic signals to reduce congestion and improve mobility.

Data-driven urban planning systems can also help authorities identify areas where infrastructure investments are most needed. By analyzing demographic data, transportation flows, and service demand, AI technologies can support more effective planning for housing, public transportation, and utility networks.

Environmental monitoring and disaster preparedness are also key areas where artificial intelligence could contribute to sustainable territorial development. Predictive analytics tools can analyze environmental data to detect early warning signals for natural disasters such as floods, droughts, or wildfires. Early detection systems allow authorities to respond more quickly to emerging risks, potentially reducing damage to infrastructure and protecting vulnerable communities.

AI can also support environmental sustainability by monitoring pollution levels, tracking land use changes, and identifying patterns of resource consumption. These insights can help governments develop policies that balance economic development with environmental protection.

The study also suggests that digital infrastructure projects powered by AI technologies could serve as catalysts for broader regional development. Investments in smart infrastructure, data platforms, and digital services can attract new businesses, encourage innovation, and create employment opportunities in emerging technology sectors.

Structural challenges limiting AI adoption

The study also identifies several structural barriers that limit the adoption of AI in fragile territorial contexts. One of the most significant challenges involves digital infrastructure. Many regions that could benefit from AI technologies lack reliable electricity supply and stable internet connectivity, both of which are essential for operating data-intensive systems.

AI applications rely on continuous data collection, high-performance computing resources, and secure communication networks. Without these technological foundations, it becomes difficult to deploy or maintain advanced digital systems.

Financial constraints represent another major obstacle. Implementing AI-based solutions often requires significant investment in hardware, software platforms, and specialized expertise. In fragile regions where governments and businesses operate with limited budgets, funding large-scale digital transformation initiatives can be difficult.

The study also highlights policy and governance challenges that complicate the deployment of AI technologies. In many countries, regulatory frameworks for AI are still evolving. The absence of clear policies governing data management, privacy, and technological innovation can create uncertainty for organizations considering investments in digital infrastructure.

Another barrier involves the shortage of skilled professionals capable of developing and managing AI systems. Artificial intelligence requires expertise in fields such as data science, machine learning, and software engineering. In regions where educational institutions have limited capacity to train specialists in these areas, organizations may struggle to find qualified personnel.

Digital literacy also plays an important role in determining whether AI technologies can be adopted successfully. Even when advanced systems are available, users must understand how to interpret data outputs and integrate them into decision-making processes. Without adequate training programs, technological solutions may remain underutilized.

Institutional coordination represents an additional challenge. Development initiatives often require collaboration between multiple stakeholders, including government agencies, private companies, academic institutions, and civil society organizations. In fragile governance environments, achieving this level of coordination can be difficult.

These structural challenges show the gap between the theoretical potential of artificial intelligence and the practical realities of implementing digital technologies in resource-constrained environments.

Case example highlights opportunities and governance needs

The study examines a proposed infrastructure initiative in North Lebanon as a case example illustrating how artificial intelligence could be integrated into territorial development projects. The redevelopment of René Mouawad Airport in Klayaat has been discussed as a potential catalyst for economic growth in the region.

Integrating AI-powered systems into airport operations could improve efficiency and safety by analyzing air traffic patterns, optimizing resource allocation, and monitoring infrastructure performance. Predictive analytics tools could help airport managers anticipate maintenance needs, improve scheduling, and manage passenger flows more effectively.

Apart from transportation improvements, such projects could also stimulate broader economic development. Modern transportation infrastructure often attracts investment in tourism, logistics, and trade, creating new employment opportunities and strengthening regional connectivity.

However, the case example also illustrates the governance challenges associated with large-scale digital infrastructure projects. Implementing AI-enabled systems requires coordinated planning across multiple institutions, as well as sustained financial investment and regulatory support.

Stakeholder engagement is also essential for ensuring that technological innovation aligns with local development priorities. Successful projects must involve collaboration between policymakers, community leaders, private sector partners, and international development organizations.

The researchers interpret their findings through two theoretical frameworks that help explain how new technologies influence development processes. The first is the concept of sustainable development, which emphasizes the need to balance economic growth, environmental protection, and social well-being.

AI can contribute to all three of these objectives by improving productivity, enabling more efficient use of natural resources, and supporting better decision-making processes. However, the benefits of digital technologies can only be realized if they are integrated into governance structures that promote transparency, inclusiveness, and accountability.

The second framework involves the diffusion of innovation, which examines how new technologies spread across societies. According to this perspective, technological adoption depends not only on the capabilities of the technology itself but also on factors such as infrastructure availability, institutional readiness, and public awareness.

In fragile territorial contexts, these factors often create barriers that slow the adoption of advanced technologies. Without supportive policies, investment in digital infrastructure, and training programs that build local expertise, the diffusion of artificial intelligence may remain limited.

AI technologies can improve agricultural productivity, strengthen urban planning, enhance disaster preparedness, and support more efficient allocation of public resources. However, realizing these benefits will require more than technological innovation alone. Governments must invest in digital infrastructure, establish regulatory frameworks that encourage responsible AI deployment, and develop educational programs that build digital skills within local communities.

International cooperation may also play an important role in supporting these efforts. Development organizations, research institutions, and technology companies can help fragile regions access the expertise and financial resources needed to implement digital transformation initiatives.