The rising costs of AI services and hyperscaler cloud compute are driving more organizations to reduce vendor dependence and expand their edge AI capabilities. However, edge AI is fundamentally different from cloud-based frontier models, and their implementation strategies can’t be replicated one-to-one.
Think of it as different tools in the same toolbox. You don’t use a saw when you need a screwdriver. And the way you operate those tools is different; the screwdriver engages the wrist, the saw works the whole arm and shoulder.
If sprawling, cloud-native LLMs are the saw, then smaller, edge-based AI models are the screwdriver. Enterprises are finding measurable ROI from their edge deployments in highly precise, vertical-specific use cases. Lightweight models trained on clean, focused datasets don’t require the hefty storage capacity and inference architecture that cloud-based models do, making on-device operation more sustainable without sacrificing performance.
Edge AI is also preferable in situations where a cloud loop will cause harmful – and in some cases, catastrophic – latency. Consider a climate control device in a semiconductor clean room, or an AI-enabled robot working alongside humans on an assembly line. Instant response times require instant processing. Every millisecond counts.
But as edge fleets grow, they need additional infrastructure to remain functional and productive. A network layer, meshed between edge devices and the broader enterprise cloud, provides such security. Networking performance is already a critical consideration for managing enterprise AI workloads, but when scale is the goal, a reliable, low-latency network layer becomes the difference between true transformation and a long, expensive experiment.
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Where edge AI is gaining ground
Three industries are making exemplary advances in edge AI deployment: manufacturing, energy, and healthcare. The lessons from each apply beyond their immediate industry, guiding more strategic deployments and operational readiness across the enterprise.
Manufacturing
The manufacturing sector is a longtime leader in process automation and IoT, making the industry a natural first mover in edge AI innovation. Siemens, for example, developed a specialized, edge-native AI to optimize manufacturing operations across a diverse array of manufacturing equipment.
What’s unique about the solution is its integrability: it operates on the edge, but not alone. By working directly with factory hardware and software, it enables intelligent transformation across numerous processes to better optimize, tune, and control equipment in real time. These integration capabilities are a prime example of the importance of a strong hybrid infrastructure: a flexible cloud for software operations and edge reliability combine to achieve consistent performance and deliver results.
Energy
The energy sector relies on the edge to reduce time to reaction for AI-enabled devices. When working with such volatile materials, the difference between success and disastrous failure is a matter of milliseconds – even a small margin of error presents outsized risk.
The advantages of edge AI in energy extend well beyond the sector; effective deployments create efficiencies that reduce environmental impact and promote more sustainable resource consumption.
For example, in the mining industry, AI-integrated monitoring tools have helped to reduce greenhouse gas emissions and water pollution. Edge AI is also improving the grid: in another recent example, Utilidata and electric provider Hubbell partnered to create “smart meters” that deliver more reliable, cleaner power.
Healthcare
In healthcare, edge AI is often an adaptation to regulatory constraints. Sensitive patient data is governed by strict privacy and sovereignty rules that vary across jurisdictions, making many cloud-based inference strategies difficult if not impossible to implement.
One growing use case is in real-time patient monitoring. Early research shows that edge AI devices used in home patient monitoring can facilitate better, timelier care delivery. Similarly, AI-enabled monitoring devices in a clinical setting can alert medical staff to changes in patients’ condition. Running on the edge reduces latency and triggers alerts in seconds, allowing for immediate, life-saving action.
Why the network layer matters
Enterprises need seamless integration between cloud and edge infrastructure to streamline processes from end-to-end. A reliable network layer forms the vital connective tissue that connects the entire fleet of devices and software; edge AI stays “plugged in” to the larger enterprise architecture without losing the advantages of running on-device.
For example, IoT devices can transmit data via a secure cloud for analysis. That same data can then be routed back to edge infrastructure for low-latency processing. The result is a loop for continuous performance, making the most efficient use of each component of infrastructure.
A secure, resilient network layer also ensures that the right machines are talking to each other, and that information remains safe from origin to destination. This keeps edge and IoT devices both functional and compliant, while creating an open throughway for model updates.
Without the connection to the broader network, edge AI is working in isolation and, therefore, not reaching its full potential.
A diversified cloud for verticalized use cases
Enterprises with robust, global edge ecosystems face the twofold challenge of managing infrastructure costs and managing complexity, and they aren’t likely to get sufficient networking support from a single hyperscaler or fully on-prem infrastructure. Hyperscalers can typically handle the complexity quotient, but not without exacerbating costs. Unused features and storage are notorious for bloating computing budgets, and it can be difficult to match hardware to workload on a single hyperscaler plan.
Now, enterprises are shifting from single-cloud models to composable, multi-provider models that allow them to economize workloads across a diverse mosaic of software-accelerated infrastructure. When enterprises match infrastructure to the actual demands of their models and programs, they’re optimizing for cost as well as performance to promote measurable value creation. Similarly, this flexibility allows for more strategic use of edge inference and deployment. With a multi-cloud, software-supported infrastructure strategy, enterprises gain the agility to adapt to the next wave of AI – both at the edge and in the cloud.
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The sovereignty factor: How cloud-based networking enables global scale
The global AI boom has elevated digital sovereignty from a valuable ideal – a “maybe we’ll get there, one day” – to a strategic geopolitical imperative. Nations are exerting stricter control over their data as they build out AI capabilities; meanwhile, new privacy laws and residency requirements are extending beyond the public sector into broad-scale business IT compliance. As regulations evolve, enterprises across the globe must exercise increased diligence when it comes to where their data is stored and processed, who has access to it, and how it’s being used to train AI.
Edge AI clears the residency requirement, making it attractive to industries like healthcare and defense, where AI interacts with highly sensitive data. On-device and on-premises infrastructure offers more control, with the (largely acceptable) caveat of a higher maintenance burden on the organization. However, for global enterprises and public institutions with an international footprint, the need for cross-border coordination exposes the limitations of an edge-exclusive AI strategy.
A low-latency network is foundational for scaling edge AI from a single-location pilot to a robust global ecosystem. The network layer allows enterprises to manage data across operational jurisdictions with respect to data sovereignty and residency requirements, while also ensuring a technological standard across the entire fleet. With security, compliance, and performance covered, organizations can focus on innovation.
The next frontier of the edge
As enterprises work to advance their edge AI projects, two emerging innovations are introducing new layers of complexity – and opportunity – to their strategies: physical AI and AI agents.
Physical AI connects the advanced AI “brain” to the robot “body,” powering fully autonomous operation of warehouse robotics. While physical AI makes robots more adaptive and communicative, and therefore less reliant on human interference, they demand more from their infrastructure. Physical AI models must retain full situational and spatiotemporal awareness to operate; keeping them functional requires consistent training, retraining, and fine-tuning, which on-device hardware typically cannot support without offloading at least a portion of the inference load to cloud-connected hardware. A high-performance networking layer allows for real-time inference across distributed devices.
Implementing physical AI also poses heightened business risk. In the event of property damage or human injury, having a reinforcing layer that remains on even when the robot malfunctions or goes offline is vital for legal accountability. More than that, it minimizes the potential for these risks in the first place, enabling greater visibility and control for timely intervention.
AI agents are also coming to the edge, albeit in highly-focused use cases. In healthcare, for example, agents can elevate the data collected and processed by AI-enabled monitoring devices, turning signals into insights that can inform more personalized care. Unlike generative LLMs that can only respond to user prompts, AI agents act autonomously,
In experimental phases, AI agents may operate in isolation, but as enterprises begin to deploy them for more business-critical processes, they will most likely be part of a multi-agent system. To work across multiple business functions and locations, these systems require seamless integration with not only other distributed devices, but the complete enterprise stack. A reliable cloud layer enables agent-to-agent communication, agent-software interactions, and greater human oversight. Enterprises have more control over the outputs, and therefore more control over the results.
What improves the edge, improves all
Strategic edge deployments are becoming increasingly crucial to sustaining the future of AI at the enterprise level – not only as a cost-saving measure, but as a way to improve model performance, maximize the returns of AI investments, and allow for deployment in hyper-sensitive use cases. However, running on the edge does not mean running in isolation. A cloud-based network magnifies the efficiency and utility of edge AI to improve the quality of its outputs and drive business value. By adopting flexible, hybrid computing infrastructure, enterprises can successfully maintain a fleet of distributed devices with AI capabilities while strengthening their foundation in the cloud.
