Why modern factories need edge: low-latency AI, resilient operations, smarter data flow

A modern production line can look perfectly healthy right up until it isn't. The machines keep moving, the dashboards keep updating—but then a robot cell trips or a quality issue slips through because the signal from an AI-driven monitoring system arrives too late, is incomplete, or is out of context.

It's the kind of failure becoming more common as factories become more autonomous. Fewer people on the floor and more decisions driven by software mean delays can ripple quickly across output, quality, and scheduling.

Deloitte's 2025 Smart Manufacturing Survey found 46% of manufacturers are using industrial Internet of Things (IoT) solutions and 57% using data analytics—meaning more plants are now equipped to capture and act on operational data. But this rising volume creates its own pressure points.

Shifting large volumes of data across networks can introduce costs and delays, and when responses to issues are slowed, the operational impact escalates quickly. Siemens estimates that downtime accounts for as much as 11% of annual revenue loss across the world's 500 largest companies. The infrastructure that connects data to decisions is no longer a background consideration—it's a competitive one.

Why AI changes the infrastructure equation

Edge computing—which places processing close to where factory data is created and used—is becoming central to manufacturers' data strategies as artificial intelligence (AI) implementation grows. Gartner predicted that by the end of 2025, 75% of enterprise-generated data would be created and processed outside traditional data centers or the cloud, reinforcing the need to process data closer to operations. That helps manufacturers make decisions when needed, while staying integrated with the core and cloud systems they rely on.

We're also seeing the emergence of physical AI in manufacturing: systems that go beyond analyzing data to act on it—controlling machines, adjusting processes, or making decisions on live production lines in real time. This raises the stakes considerably around where data is processed and how quickly those decisions can be made. Unlike many digital deployments, AI on the factory floor operates under strict constraints because its outputs directly affect what happens on the production line. An AI-driven workflow might pause a line, adjust a setpoint, or flag a defect requiring intervention—and those decisions often need to occur within microseconds.

That makes infrastructure the foundation. Data must be captured, exchanged, and contextualized reliably enough to support real-time decisions. Distance—whether physical or network-related—is the enemy of that reliability. For manufacturers deploying AI at scale, cloud adjacency is one of the most effective ways to eliminate that distance penalty and unlock the full performance of hybrid IT environments.

The building blocks for edge-ready manufacturing

Edge computing underpins the move towards more autonomous, data-driven manufacturing, but its impact depends on how infrastructure is designed and connected across sites.

A successful edge deployment requires a few critical ingredients: proximity to operations, the ability to move data efficiently between environments, and the flexibility to scale workloads without rebuilding the architecture each time. It also needs the right supporting capabilities—from local compute and storage to secure, high-speed connectivity between the factory floor and wider enterprise systems.

At Digital Realty, we help manufacturers combine their edge and core data center locations with strong connectivity across regions, partners, and cloud platforms. Through PlatformDIGITAL®—our global data center platform—and our Pervasive Datacenter Architecture (PDx®) methodology, we bring critical workloads closer to plants while keeping them integrated with enterprise systems and partner ecosystems. Our PDx approach profiles workloads to reduce latency, overcome Data Gravity™ barriers, and optimize configurations for performance, security, and scalability.

Edge data centers are one part of that foundation. Sitting within a wider network of edge and central environments, they reduce delays for time-sensitive workloads while allowing raw operational data to be processed near the plant, with selected outputs moving upstream for reporting and longer-term analysis. Predictive maintenance is a practical example: anomaly detection runs close to equipment, while trend data feeds into central systems over time.

Colocation is another important component, giving manufacturers access to scalable, high-performance infrastructure without the burden of building and managing it all on-site. That becomes especially important as edge deployments expand across distributed operations. Hybrid setups spanning edge, cloud, and on-premises environments help integrate those systems, add capacity as demand grows, and provide access to the AI tools needed to support more advanced workloads. You can read more about how we support distributed cloud environments in our blog on distributed hybrid IT.

Colocation facilities are typically carrier-neutral, as well, meaning customers can connect to multiple network and cloud providers rather than relying on a single carrier. That adds flexibility as connected factory strategies expand—manufacturers may need efficient routes between edge sites, cloud platforms, and core systems without locking themselves into a single environment. Our manufacturing solutions page outlines how we help manufacturers achieve exactly that, with seamlessly interconnected physical and virtual environments that drive efficiency and enable real-time insights.

What we see for the future

Edge is becoming a standard layer in manufacturing architecture because factory systems can't afford delays when real-time response is required. As more data is created on the factory floor, more workloads will move closer to the source, especially for latency-sensitive operations. Repeatable hybrid patterns will replace isolated deployments, connecting edge, cloud, and on-premises environments in ways that reduce fragmentation and make it easier to scale what works.

That brings clarity to decisions about what runs where. Control loops and safety functions stay closest to the machine. On-site edge handles near-real-time analytics and vision inference. Regional platforms support cross-site optimization and governance. Cloud remains the right environment for model training, large-scale analytics, and integration with enterprise systems and partner ecosystems.

As factories become more autonomous, AI will increasingly coordinate decisions across machines, lines, and facilities. The manufacturers that pull ahead will treat edge as an operating model rather than a series of pilots—using standardized architectures and data flows that can be repeated across plants to scale new capabilities without constant reinvention.

For manufacturers looking to build resilient, scalable edge deployments, three principles matter most:

Identify latency-sensitive decisions: What must happen on the line versus what can wait?
Map the data flow: What stays local, what is summarised, and what moves upstream?
Standardize architectures: Reusable frameworks across plants mean scaling new capabilities doesn't require rebuilding systems each time.

The next step is to turn these principles into action—designing infrastructure that supports faster decisions, stronger resilience, and AI that performs where it matters most.

Let's build your edge strategy together

We're working with manufacturers across the globe to navigate their AI journey, understanding where to start, what infrastructure is required, and how to deploy at scale without unnecessary complexity.

Explore our research on smart manufacturing in the age of AI, or read more about how we're rethinking smart manufacturing on a sustainable global platform. When you're ready to discuss your specific environment, our solutions architects are here to help.

Explore our manufacturing solutions

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