From Cloud to Edge and Embedded AI: Why industry leaders are adopting it now

The industrial world is at a turning point. As digital transformation surges across the energy and industrial sectors, there is more pressure to act on data faster without sacrificing resilience, security or sustainability. The question is no longer if we adopt edge and embedded AI, but how.  

Embedded AI is artificial intelligence that operates directly on local devices rather than depending on servers. In other words, it’s an intelligence built into hardware like sensors, thermostats, or industrial systems, allowing them to process data and act on it instantly, right where it’s collected, without needing to transmit it elsewhere. Edge AI operates on local servers, rather than Cloud servers.  

Why Embedded AI is becoming a must-have for modern industry 

Embedded AI used to be a futuristic trend, but the appearance of modern microcontrollers brought this technology to an inflection point. Organizations that wait to implement embedded AI solutions risk falling behind in an increasingly competitive, decentralized market as the industry moves toward faster, smarter and more autonomous systems. Embedded AI is especially valuable in sectors like manufacturing and energy management where milliseconds matter. By embedding intelligence directly at the embedded, organizations can: 

• Reduce latency and increase uptime 
• Lower bandwidth and cloud costs 
• Improve energy efficiency and resilience 
• Maintain functionality and security even when connectivity is limited 

Starting with efficiency benefits, embedded AI plays a critical role in advancing sustainability by enabling smarter, real-time energy and resource optimization. By analyzing data at the point of generation or consumption, embedded-based systems can immediately adjust operations to reduce waste, balance loads, and match usage with renewable availability, without waiting for cloud instructions.  

Benefits from analyzing data right where it is 

This localized intelligence supports more efficient HVAC systems and adaptive equipment control, all of which contribute to lower energy consumption and emissions. As industries transition toward net-zero goals, embedded AI will prove to be a foundational technology for decarbonizing operations at scale. 

Secondly, for some of our customers, top notch security requirements simply disqualify cloud-based solutions. Embedded AI comes as a perfect solution that strengthens industrial cybersecurity by minimizing the need to transmit sensitive data to external servers or the cloud. By processing information locally on devices, it significantly reduces exposure to potential interception or tampering during transit.  

This decentralized approach not only lowers the attack surface but also allows for quicker anomaly detection and threat response directly at the source. In high-risk environments like critical infrastructure, energy grids, and manufacturing lines, this added layer of embedded protection helps safeguard operations from cyber threats while ensuring continuity, even when connectivity is interrupted. 

Edge and embedded AI is already delivering results 

Schneider Electric’s on-device analytics for grid optimization is a prime example of edge AI in action. Recognized by ABI Research as the top-ranked vendor in grid digitalization, Schneider Electric delivers real-time intelligence at the edge. This capability is crucial as energy grids shift to support bidirectional flows, decentralized renewables and fluctuating demand. Edge AI enables local decisions to be made even in low-connectivity or remote environments to ensure safety, efficiency and continuity. 

Another example comes from the buildings field where embedded AI is redefining room control. 

Our SpaceLogic™ Touchscreen Room Controller embeds AI into room-level controls, helping facility managers adapt to real-time occupancy patterns, environmental changes and preferences. 

The solution was awarded the Business Intelligence Group’s 2025 Artificial Intelligence Excellence award in the Energy Management AI product category.  

In a large-scale quantitative analysis at active building sites, our AI at the embedded-enabled systems successfully: 

• Maintained temperature regulation and occupant comfort compliance over 85% of the time (compared to room controllers without AI). 
• Achieved an average HVAC energy savings of 5% daily, with potential savings reaching up to 15% in certain scenarios. 

Cloud + Edge + Embedded: A unified AI strategy

While embedded AI has a lot of potential on its own, it also unlocks new capabilities when paired with cloud AI to provide a cohesive system that blends instant decision-making with broader context, scale, and continuous improvement. At Schneider Electric, we see this blended architecture as one of the potential scenarios for the future. This will empower fast, localized actions while tapping into the expansive processing power of the cloud to support advanced analytics. A hybrid architecture maximizes strengths on both ends: 

• Cloud: Centralized learning, deep analytics, cross-device coordination 
• Edge : Decentralized AI operation on local server dedicated to an application
• Embedded: Instant action, local control, operational independence 

Together, they form a distributed ecosystem where every node, device and platform works in concert to deliver optimized performance. Real-world use cases show how edge, and embedded AI combined with the cloud delivers value – in educational buildings, Schneider Electric’s HVAC solutions learn occupant behaviors and adjust in real time to reduce energy waste and enhance comfort. 

Creating competitive advantage for the future

Schneider Electric continues to invest in next-generation edge and embedded intelligence through product innovation, partnerships, and education, including being an active sponsor of Edge AI Foundation. As we recently discussed with Evgeni Gousev, its chairman, the embedded AI use cases are multiplying and are starting to cause a fear of missing out among companies. In manufacturing, embedded-enabled predictive maintenance uses real-time vibration and temperature data to anticipate failures and minimize unplanned downtime without relying on the cloud. In farming, fish farm owners can now install small sensors under the water and monitor the health of their fish farms. We want to bring these solutions and ideas to larger audience to show the new capabilities and reply to challenges that were unsolved since now. 

By delivering actionable intelligence at every layer, from device to data center, we’re helping organizations make faster, safer and more sustainable decisions. Embedded AI is both advancing operational performance and helping build the infrastructure of the future. 

Explore more about Schneider Electric’s AI strategy, product innovation and use cases by visiting our AI Hub.