The rise of edge computing in AI is predicted to increase from 5% to 50%, driven by factors like high data center usage, low latency demands, and real-time operations. Currently, only 5% of AI workloads run on the edge, with the rest on central data stations. Schneider Electric anticipates a doubling of total data center power consumption by 2028, reaching 93 GW, with AI power consumption expected to rise from 4.5 GW in 2023 to 14.0-18.7 GW by 2028. The shift to edge computing is attributed to factors like limited space, high data center costs, and the need for decentralized networks to support AI, IoT, and innovation. Experts emphasize that the transformation should focus on practical data management, adaptability, and accommodating IoT-AI convergence. Bringing data closer to the user may not be a new concept. What is new is the amount of data that flows in our world.