Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Bajpai, Annanya; Srivastava, Shilpa Title A name given to the resource AI-Driven Real-Time Decision Making at the Edge: Overcoming Latency, Bandwidth, and Scalability Challenges for Smarter Data-Intensive Applications in Healthcare, Manufacturing, and Smart Cities Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Studies in Computational Intelligence;Volume;1267;pp.1-19 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-16711-8_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-16711-8_1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038027860?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038027860?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Bajpai A., Christ University, Bengaluru, India; Srivastava S., Christ University, Bengaluru, India Description An account of the resource Aim and Purpose: This chapter explores the crucial role of artificial intelligence (AI) in enabling real-time decision-making at the edge, particularly within data-intensive applications. It aims to identify and address fundamental challengessuch as latency, limited bandwidth, and scalabilitythat frequently hinder the efficient deployment of AI models near data sources. The objective is to propose a coherent and implementable framework to mitigate these obstacles, thereby facilitating the development of intelligent, responsive systems. The chapter emphasizes the transformative potential of edge AI across three key sectors: healthcare, manufacturing, and smart cities, illustrating how localized intelligence can enhance performance, efficiency, and autonomy in time-sensitive environments. Methodology: We adopt a comprehensive methodological approach that includes studying optimization techniques such as model compression, quantization, and distributed inference. Special attention is given to federated learning, which supports collaborative training without the need to transfer raw dataenhancing both privacy and scalability. The examination of edge-optimized hardware accelerators (e.g., NPUs, FPGAs) and streamlined software frameworks will highlight their role in overcoming processing bottlenecks and ensuring low-latency performance. Limitations: Despite the promise of edge AI, challenges persist. These include limited processing and energy resources, security vulnerabilities, and device heterogeneity. Managing updates and maintaining consistency across distributed systems complicate widespread implementation further. Applications and Novelty: This chapters novelty lies in its integrated focus on practical, real-world applications of edge AI in healthcare, manufacturing, and smart cities. By presenting targeted solutions to known constraints, it contributes a practical, implementation-ready perspective to the growing body of edge AI research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Bandwidth; Edge AI; Healthcare; Latency; Manufacturing; Real-time decision making; Scalability; Smart cities Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 1860949X; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online