1Assistant Professor (c), Department of Business Management, Krishna University, Machilipatnam, India.
Email: gnaneswari11@gmail.com
2Professor, Department of CSE, Sree Dattha Group of Institutions, India.
Email: santhoshkumar.aucse@gmail.com
3Senior Assistant Professor, Department of ECE, CVR College of Engineering, Telangana, India.
Email: malli.akurati@gmail.com
4Associate Professor, Department of CSE, School of Computing, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India.
Email: rajalingam35@gmail.com
5Associate Professor, Annamacharya University, Rajampet, India.
Email: kasigariprasad@gmail.com
6Associate Professor, School of Management and Technology, MALLA REDDY (MR) DEEMED TO BE UNIVERSITY, India.
Email: pushpa.kamineni@gmail.com
The rapid adoption of Internet of Things (IoT) technologies in smart enterprises has generated massive volumes of real-time data from sensors, connected devices, and operational systems. However, traditional cloud-centric analytics models often introduce latency, bandwidth overhead, and privacy concerns, limiting their effectiveness for time-critical managerial decision-making. This paper proposes an Edge AI-based IoT framework that enables intelligent, low-latency data processing directly at the network edge to support real-time enterprise management. The proposed framework integrates distributed IoT sensing, edge-level data preprocessing, lightweight artificial intelligence models, and adaptive decision-support modules to deliver actionable insights with minimal delay. By leveraging edge computing, the system reduces cloud dependency, enhances data security, and improves responsiveness in dynamic enterprise environments. Experimental evaluation demonstrates significant reductions in decision latency and network traffic while maintaining high predictive accuracy. The proposed framework provides a scalable and efficient solution for smart enterprises seeking real-time, data-driven managerial intelligence.
Keywords: Edge AI, Internet of Things (IoT), Smart enterprises, Real-time decision-making, Edge computing, Distributed intelligence, Predictive analytics, Industrial IoT, Decision support systems, Enterprise automation.
How to cite this article: Gnaneswari P, Santhoshkumar R, Akurati M, Rajalingam B, Prasad K, Pushpa Latha K. Edge AI-based IoT framework for real-time managerial decision-making in smart enterprises. Int J Drug Deliv Technol. 2026;16(8s): 35-40; DOI: 10.25258/ijddt.16.8s.7
Source of support: None.
Conflict of interest: None