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#edge computing Open access

Intelligent Digital Transformation in Electrical Engineering and Business Management: AI, IoT and Data-Driven Strategies for Smart Energy Systems

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

The energy sector is undergoing a profound transition from conventional, centrally managed electricity systems toward intelligent, connected and increasingly decentralized smart energy ecosystems. This transformation is being accelerated by Artificial Intelligence (AI), the Internet of Things (IoT), advanced data analytics, cloud and edge computing, digital twins, automation, renewable energy technologies and intelligent decision-support systems. In electrical engineering, these technologies enable real-time monitoring, load forecasting, renewable-energy integration, predictive maintenance, fault detection, power-quality management, asset optimization and automated control. In business management, the same technologies influence investment planning, cost management, customer engagement, operational strategy, risk management, workforce capability, digital business models and evidence-based decision-making. The present paper examines intelligent digital transformation as an integrated engineering and managerial phenomenon and explains how AI, IoT and data-driven strategies jointly contribute to the development of smart energy systems. The study follows a conceptual and literature-based approach and synthesizes findings from research on smart grids, digitalization, energy management, predictive analytics, organizational transformation and cybersecurity. The review indicates that AI functions as the analytical intelligence of the system, IoT provides sensing and connectivity, and data-driven management converts technical information into operational and strategic value. The study further identifies continuing challenges involving interoperability, cybersecurity, data quality, high implementation cost, legacy infrastructure, digital skills, regulatory uncertainty and the gap between laboratory or simulation studies and large-scale field deployment. An integrated conceptual framework is proposed in which AI, IoT and data capabilities influence electrical-system intelligence and organizational digital capability, which in turn shape operational efficiency, reliability, sustainability, innovation and business performance. The paper concludes that successful digital transformation requires more than the acquisition of advanced technologies; it requires coordinated investment in infrastructure, people, processes, governance, cybersecurity and organizational learning.

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