Convergence of Artificial Intelligence and Cloud Computing in IoT Innovation with a Decision-Making Approach

Abstract

This study aims to explore the convergence of artificial intelligence (AI) and cloud computing within the Internet of Things (IoT) and its impact on innovation through a decision-making approach. The focus is on understanding how the integration of these technologies enhances the capabilities and performance of IoT systems across various sectors. A qualitative research design was employed, utilizing semi-structured interviews to gather data from experts in AI, cloud computing, and IoT. Participants were selected through purposive sampling to ensure relevance and expertise. Thematic analysis was conducted using NVivo software to identify key themes and insights. The study reached theoretical saturation, ensuring comprehensive coverage of the topic. The integration of AI and cloud computing in IoT systems facilitates technological synergy, improved data management, scalability, cost efficiency, and flexibility. Enhanced decision-making capabilities are achieved through data-driven analytics, algorithm selection, risk assessment, real-time processing, stakeholder involvement, and feedback loops. Significant innovations were observed in healthcare and industrial sectors, supported by cloud infrastructure. However, challenges such as cybersecurity, scalability, and ethical considerations, including data privacy and algorithmic bias, were identified. The convergence of AI and cloud computing with IoT drives substantial innovation and enhances decision-making processes, offering significant benefits in efficiency, scalability, and intelligence. Addressing challenges related to cybersecurity, scalability, and ethical considerations is crucial for realizing the full potential of this integration. Future research should focus on quantitative studies, long-term impacts, emerging technologies, and ethical implications to further understand and optimize this convergence.