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Edge AI enables businesses to deploy AI applications on smart devices or robots in warehouses, running compute-intensive inference and decision-making models near the data source rather than from public clouds or data centers, significantly accelerating AI performance. When physical AI and autonomous AI are combined, this capability becomes particularly powerful, enabling truly autonomous systems that can perceive, decide, and act with minimal human intervention.
Enhanced industrial robots and smart devices will fundamentally change how we utilize AI at the edge and deepen our understanding of cloud and data centers.
Humanoid robots, smart devices, and autonomous driving are often cited as lucrative business applications for edge AI. However, edge AI computing will liberate AI from centralized servers in data centers and clouds, deploying it in manufacturing plants, operating rooms, and municipal centers to process data in real-time, closer to IoT devices, sensors, and intelligent systems. It also provides low latency and autonomous decision-making capabilities, making AI ubiquitous and enabling fully autonomous industrial facilities, thereby transforming business and daily life.
The CIO of Rockwell Automation, a customer and partner of NVIDIA, is acutely aware of this, as the company is at the forefront of edge AI computing. “The decentralization of AI from cloud-centric architectures to edge-based deployments represents not just a technological evolution,” said Chris Nardecchia, CIO of this digital transformation provider. Last month, the company showcased its Emulate3D advanced factory-scale virtual control testing technology at NVIDIA’s GTC conference. “This fundamentally redefines how AI capabilities integrate into every aspect of our industrial and personal environments.”
The solution integrates with NVIDIA’s Omniverse API, allowing manufacturers to validate automation systems through virtual factory acceptance testing before physical deployment.
For instance, edge AI enables businesses to deploy AI applications on smart devices or robots in warehouses, running compute-intensive inference and decision-making models near the data source rather than from public clouds or data centers, significantly accelerating AI performance.
At a recent conference, NVIDIA continued to push for edge development, launching a series of advanced AI hardware, software platforms, and developer frameworks, including the Jetson Orin, Xavier, and Nano platforms, the Blackwell Ultra AI chip enhanced for advanced edge applications, the Groot N1 AI robot model for autonomous machines, the IGX Orin industrial-grade edge AI platform for industrial and medical needs, and the NVIDIA AI data platform for data analytics at the edge.
In particular, NVIDIA’s core EGX enterprise edge AI platform supports real-time AI workloads for healthcare, manufacturing, and retail, while its Metropolis platform provides edge video analytics for smart cities.
The Jetson Nano super workstation launched at the NVIDIA GTC conference will provide powerful AI capabilities for enterprise users in remote offices or business centers. Its Clara for healthcare, Drive for autonomous driving, and Aerial for 5G networks will also offer real-time monitoring, predictive maintenance, and process optimization features to reduce downtime of industrial assets on-site and improve system performance throughout their lifecycle.
An analyst noted that NVIDIA’s platform and product lineup for physical AI, such as Omniverse for industrial digitalization, highlights that NVIDIA is expanding from a pure semiconductor manufacturer to a provider of hardware, platforms, tools, and frameworks.
“The market has yet to grasp the significance of NVIDIA’s move,” said Chirag Dekate, chief AI analyst at Gartner. “This is truly remarkable. The combination of EGX and Jetson, along with NVIDIA’s Cosmos platform, allows you to develop physical AI environments that combine the advantages of AI and digital twins, creating an environment that helps accelerate the training of intelligence that can be deployed at the edge. The edge is where they are now transforming our robots, smart robots, autonomous vehicles, and humanoid robots. They are forming a new growth area, just as they did with GPUs in data centers.”
Event Sequence
The market initially embraced GenAI for content creation, then shifted to autonomous AI, enabling models to reason and execute tasks. However, the core of the industrial AI revolution is physical AI or AI-enabled robots, which can achieve fully autonomous industrial facilities, most of which are deployed at the edge.
For example, Rockwell’s autonomous mobile robots (AMRs) have measurable impacts on throughput, labor optimization, and time savings. Nardecchia stated that these robots serve as mobile edge computing platforms, processing sensor data locally while feeding aggregated insights back to the company’s FactoryTalk Edge Manager.
NVIDIA’s platform for edge computing will expand, enrich, and expose industrial data, creating new sources of value through applications and analytics. Nardecchia noted that when physical AI and autonomous AI are combined, this capability becomes particularly powerful, enabling truly autonomous systems that can perceive, decide, and act with minimal human intervention. He also mentioned that Rockwell’s strategic acquisitions of Otto Motors and Clearpath Robotics position the company favorably in production logistics automation.
Edge AI is Transforming Industrial Digitalization
According to IDC’s latest forecast across 27 enterprise industries, global spending on edge computing solutions is expected to reach nearly $261 billion this year, with a projected compound annual growth rate (CAGR) of 13.8%, reaching $380 billion by 2028.
Dave McCarthy, IDC’s vice president of research and head of cloud and edge services, stated, “Edge computing is poised to redefine how enterprises leverage real-time data, and its future depends on industry-specific solutions tailored to unique operational needs. We see service providers doubling down on investments to build low-latency networks, enhance AI-driven edge analytics, and establish partnerships to provide scalable and secure infrastructure. These efforts are crucial to unlocking the full potential of edge computing, from smarter manufacturing plants to responsive healthcare systems, ultimately driving a new wave of innovation across industries.”
As a result, CIOs are planning their next-generation AI architectures, leveraging powerful platforms, tools, and frameworks to build robots and IoT devices that require autonomous decision-making capabilities.
“CIOs are certainly planning to use AI for edge workloads,” said Nate Melby, CIO of Dairyland Power Cooperative, who is looking at such advancements to manage the grid during storms and enable systems to perform rapid analysis and decision-making in hazardous environments. There are also expectations that using edge AI devices in other physically challenging environments will create new business opportunities and profit outcomes.
Melby stated, “By pushing AI to the edge, we can reduce reliance on centralized architectures, thereby building resilience and optimizing and processing more sensitive or critical task data by combining cloud resources with local devices for easier scaling and resource flexibility. However, this will take time to mature.”
Interest in Edge Computing is Multiplying
Many top cloud and AI providers, including OpenAI, Google, Amazon, and innovative AI startups, are targeting edge computing. For instance, cloud provider Oracle recently added a GPU-optimized configuration to its Oracle Roving Edge Device. “We see demand from customers for edge computing AI,” said Dave Rosenberg, senior vice president of field and industry marketing at Oracle Cloud Infrastructure.
Amol Ajgaonkar, chief technology officer of product innovation at Insight Enterprises, added that many industries beyond manufacturing will leverage edge AI, but it is not without challenges. If edge is defined as anything not in the cloud—like a laptop, a machine in a manufacturing workshop, or point-of-sale devices in retail—then healthcare, retail, and finance will be primary targets for edge AI, he said.
Ajgaonkar stated, “A significant challenge in using AI at the edge is determining which data is useful for the task at hand and which is not, and setting up processes so that AI agents or a set of agents can be managed without continuous human involvement. When creating predictive models (for example, managing ongoing maintenance of factory workshop machines), if there are certain biases or distorted data present without filtering, it can affect the model and skew the results produced by the agents. Clean data input is always crucial for clean output, but this is a delicate balance.”
Tom Richer, CEO and former CIO of AI consulting firm Intelagen, advised CIOs to closely monitor NVIDIA’s advancements, as NVIDIA dominates AI infrastructure, data center transformation, and edge AI capabilities, all of which directly impact organizational innovation and competitiveness.
He stated, “As edge computing is increasingly used for AI workloads, driven by low latency, bandwidth optimization, and enhanced security needs, strategic decisions must be made between on-premises and service provider deployments, with hybrid approaches often proving most effective in balancing control and scalability. This requires CIOs to develop clear strategies, invest in necessary infrastructure, and stay attuned to evolving technologies.”
Benefiting from the Edge
Implementing physical AI requires robust edge computing capabilities to process sensor data and execute complex algorithms while maintaining minimal latency. Edge computing achieves faster response times, higher data transfer efficiency, and stronger security by bringing data processing closer to the data source—critical factors for applications in robotics and industrial automation, as explained by Rockwell’s Nardecchia.
As platforms and technologies continue to mature, we can expect AI to be increasingly embedded in physical systems within industrial environments.
For companies like Rockwell, this evolution represents an opportunity to integrate edge AI capabilities into their product offerings. Properly managed edge computing can yield significant business outcomes, including affordable access to data, faster software deployment, future-ready analytics platforms, improved security posture, better scaling of digital transformation initiatives, and reduced total cost of ownership.
The Edge AI Foundation states that CIOs and enterprises are looking to automate and implement intelligent devices at the edge. Pete Bernard, CEO of the nonprofit organization, said, “The core of edge AI is running AI workloads where data is generated, and the gravitational pull towards the edge means lower costs, lower power consumption, greater impact, and often enhanced privacy, lower latency, higher flexibility, and clarity. CIOs are responsible for determining information strategies. You want to move computing as close to the data generation point as possible, avoiding cloud ingress and egress fees and operational costs, and overall having more control over your processing.”
As platforms and technologies continue to mature, we can expect AI to be increasingly embedded in physical systems within industrial environments.
Paul Golding, vice president of edge AI at Analog Devices, stated, “The rise of foundational models is extending to the edge through refined and quantized converters and small foundational models. This shift requires dense and compute-intensive infrastructure to be deployed at the edge. Meanwhile, the demand for real-time processing, low latency, and privacy is driving AI closer to the data source—we often refer to this as the sensor or physical edge. Autonomous AI can learn, adapt, and act in real-time on heterogeneous nodes, fundamentally changing task orchestration, as we move from machine automation to machine autonomy, creating new forms of distributed intelligence that enable critical tasks to run at the edge without relying on centralized cloud systems. The frontier of AI remains vast and uncharted.”
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(Source: D1Net)
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