Sep 2026· Business Technology & Innovation Studies Journal· 0 citations
TL;DR
This paper provides a streamlined overview of Edge AI’s technological foundations highlighting how they enable low‑latency, privacy‑preserving, and context‑aware intelligence on resource‑constrained devices.
Abstract
Edge Artificial Intelligence is emerging as a critical paradigm that brings AI capabilities closer to data sources, addressing the limitations of cloud‑centric architectures. This paper provides a streamlined overview of Edge AI’s technological foundations (its architectures, enabling hardware, model‑adaptation methods, and distributed frameworks) highlighting how they enable low‑latency, privacy‑preserving, and context‑aware intelligence on resource‑constrained devices. It also examines the Edge AI Global Value Chain, outlining how value is created across data, compute, models, and applications, and emphasizing the importance of supporting infrastructures such as semiconductors, telecommunications networks, and cloud–edge platforms. The analysis explores business‑model dynamics, including cost‑efficiency levers, differentiation strategies, vertical integration, and stakeholder expectations. Finally, the paper discusses market trends, data‑center decentralization, and the evolving competitive landscape, showing how Edge AI is poised to become a foundational component of future digital infrastructures.
The rapid evolution of digital infrastructure has catalyzed the emergence of novel computing
paradigms that integrate cloud computing with edge computing within complex
collaborative architectures. This paper presents a comprehensive survey of recent literature
(2010–2026) encompassing three principal dimensions: (1) c...
I. M. Suartana, R. E. Putra, R. Bisma· ITEGAM- Journal of Engineeri...· 0 citations
This study proposes an integrative analytical architecture grounded in three interlocking layers: enabling technological foundations, intelligent sense-making processes, and context-sensitive deployment pathways that provide a holistic, implementation-ready framework for building intelligent systems that are not only h...
Edge AI refers to the deployment and execution of artificial intelligence (AI) algorithms on decentralized, local computing devices (e.g., Internet of Things) rather than relying on centralized cloud‐based computing infrastructures. The literature has addressed the increasing importance of AI in supply chain and oper...
Dmitry A. Ivanov· International Transactions i...· 0 citations
The swift growth of Cloud-Edge infrastructure to support IoT and artificial intelligence workloads with low latency is exceeding the capacity of our management to control the environmental impact. Whereas there are energy-efficient hardware and software, the measures of sustainability, such as lifecycle assessment (LCA...
Mustafa Abdulkadhim, S. Répás· Széchenyi István University...· 0 citations
It is shown that passive data accumulation does not directly contribute to brand recall unless it is combined with active ML processing and interactive consumer platforms, and a conceptual framework of the AI edge in e-commerce and digital marketing is synthesised.
V. Nagavalli· Scholar Journal of Commerce...· 0 citations
This paper presents a comparative review and synthesis of the hyperscale data analytics cloud architectures of Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). The review characterizes storage, processing, machine learning, data lake, governance and business intelligence services depending on...
Harish Kasireddy· International Journal of Int...· 0 citations
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