Analysis of Artificial Intelligence on Economical Sustainability

AI applications in economic frameworks offers transformative potential in advancing economical sustainability through productivity augmentation, resource allocation optimization, and innovative financial solutions. But, the challenges and obstructions in our ways toward the AI implementation are however many lessons learnt after 2023. Data privacy and security issues, integration with existing infrastructure, high energy consumption of AI models, regulatory and legal challenges, and social and cultural barriers are some of the major obstacles ailing the deployment of AI for economical sustainability, which this paper critically engages. Data privacy and security are still a major concern with rising cyber threats and stringent compliance demands. Moreover, there are legacy systems and lack of standardization that hinder smooth integration of AI. Deep learning models typically require a lot of computing power, resulting in high energy consumption which is counter to sustainability targets since energy is typically use by large equipment. In addition, unclear regulatory directives and societal fear of AI adoption add fuel to the resistance to change. This paper delivers an extensive insight into the hurdles and offers potential solutions through advanced encryption methods, modular architectures, energy-efficient AI models, adaptive regulatory frameworks, and inclusive stakeholder involvement. Understanding of these constraints is crucial to maximising the potential for AI to drive green economic growth.