AI guidelines for sustainable rural development and climate resilience in resource-constrained regions

Artificial intelligence (AI) has begun to permeate the strategies adopted for rural development and climate adaptation, especially in those regions where economic and infrastructural constraints are most acute. Rather than offering mere technological panaceas, the current landscape reveals a mosaic of partial successes and persistent obstacles, frequently shaped by the unique realities of small producers and community organizations. This study offers a critical examination of the concrete difficulties faced when deploying AI-driven solutions in domains such as agriculture, water governance, and renewable energy management. Particular attention is paid to the sometimes underappreciated influence of regulatory environments and the subtle interplay of local incentives. Throughout the analysis, it becomes apparent that the true value of AI lies not only in algorithmic sophistication but in the ability to design energy- and computation-conscious systems that remain sensitive to the diversity of local contexts. The findings suggest that progress in resource-limited settings depends equally on participatory approaches, digital literacy, and the adaptation of methodologies—ranging from climate prediction to financial risk assessment—to specific community needs. As a result, the work proposes a set of actionable principles, in harmony with the Sustainable Development Goals, that may serve as a practical guide for those seeking to translate technological promise into tangible benefits at the local scale.