Most AI conversation focuses on cloud-based systems - ChatGPT, Claude, enterprise LLM deployments. But some of the most practical AI deployment is happening at the edge: AI running on devices in factories, warehouses, vehicles, and infrastructure.

This isn’t as glamorous as chatbots, but it’s arguably more important for industrial productivity.

Why Edge AI Matters

The case for edge AI in industrial settings is straightforward:

Latency requirements. When a machine vision system needs to detect a defect and stop a production line, you can’t afford the round-trip to a cloud server. Decisions need to happen in milliseconds.

Bandwidth constraints. High-resolution cameras generate enormous data volumes. Streaming raw video to the cloud for processing isn’t practical at scale. Processing at the edge reduces the data that needs to be transmitted.

Reliability. Industrial operations can’t depend on internet connectivity. Edge AI runs even when the network is down.

Data sensitivity. Some organizations don’t want operational data leaving their facilities. Edge AI keeps data on-premises.

Cost. Cloud inference at scale gets expensive. Edge deployment can be more cost-effective for high-volume applications.

What’s Actually Deploying

Let me be specific about where edge AI is reaching production scale:

Quality inspection. Machine vision systems that detect defects in manufactured products. This is probably the most mature edge AI application in industrial settings. The economics work - catching defects earlier saves money - and the technology is reliable enough for production.

Electronics manufacturers use edge AI to inspect circuit boards for soldering defects. Food processors use it to identify contamination or quality issues. Automotive suppliers inspect parts before assembly.

Predictive maintenance. AI that analyzes sensor data from equipment to predict failures before they happen. This is technically challenging - you need enough failure examples to train models - but the payoff is significant. Unplanned downtime is expensive.

The pattern I see: companies with extensive sensor infrastructure and historical data are having success. Companies trying to retrofit sensors onto old equipment and train models with limited data are struggling.

Logistics optimization. AI on forklifts, AGVs (automated guided vehicles), and robotic systems for routing, picking, and inventory management. Amazon’s warehouse operations are the poster child, but the technology is spreading to traditional logistics operators.

Safety systems. Computer vision for worker safety - detecting when someone is in a dangerous zone, ensuring PPE compliance, monitoring for hazardous conditions. The regulatory and liability drivers are strong here.

Energy management. AI for optimizing HVAC, lighting, and equipment operation in industrial facilities. The energy savings compound quickly at scale.

The Hardware Landscape

Edge AI requires specialized hardware. The options:

NVIDIA Jetson. The dominant platform for most edge AI applications. Good performance, strong software ecosystem (CUDA, TensorRT), range of options from embedded modules to more powerful edge servers.

Intel solutions. OpenVINO toolkit and associated hardware. Strong for customers in Intel environments.

Google Coral. TPU-based edge accelerators. Good for specific workloads, less general than NVIDIA.

Custom silicon. For very high volume applications, companies are developing custom ASICs. This only makes sense at massive scale.

FPGAs. Field-programmable gate arrays offer flexibility between general-purpose and custom silicon. Gaining traction for specific use cases.

The trend is toward more capable edge hardware at lower power consumption. What required a server room five years ago can now run on an embedded device.

Integration Challenges

The technology works. The hard part is integration.

OT/IT convergence. Industrial operations have operational technology (OT) systems - PLCs, SCADA, industrial networks - that operate separately from IT systems. Integrating edge AI requires bridging these worlds, which is culturally and technically challenging.

Legacy equipment. Most factories run equipment that’s decades old. Retrofitting sensors and AI requires working around equipment that wasn’t designed for it.

Skilled workforce. Running edge AI systems requires different skills than running traditional industrial operations. The talent gap is real.

Change management. Factory workers and operators need to trust AI-assisted systems. This requires training, demonstrated reliability, and thoughtful introduction.

Maintenance and updates. Edge devices need to be monitored, maintained, and updated. At scale, this becomes its own operational challenge.

Vendor Landscape

Who’s serving this market?

Hyperscalers’ edge offerings. AWS, Azure, and Google all have edge AI products. These are strong when you’re already in their cloud ecosystem.

Industrial automation vendors. Siemens, Rockwell Automation, ABB, and others are adding AI capabilities to their industrial portfolios. Strong integration with existing industrial systems.

Specialized startups. Companies focused specifically on industrial AI for particular applications or industries. Often deeper domain expertise than generalists.

System integrators. Many edge AI deployments require customization and integration work that vendors don’t do themselves.

How to Approach Edge AI

For innovation managers exploring edge AI:

Start with a clear use case. Don’t deploy edge AI because it’s interesting. Deploy it to solve a specific operational problem with measurable value.

Assess your data infrastructure. Edge AI needs training data. Do you have the sensors generating relevant data? Do you have historical data for model training? Is the data quality sufficient?

Pilot before scaling. Edge AI in industrial settings is unforgiving. Systems need to work reliably in harsh conditions. Pilot extensively before committing to scale deployment.

Plan for operations. How will you monitor edge devices? Update models? Handle failures? The operational model matters as much as the technology.

Consider build vs. buy. For common applications (quality inspection, predictive maintenance), commercial solutions may be better than custom development. For unique applications, custom might be necessary.

Involve operations from the start. IT-led edge AI projects that don’t engage operations tend to fail. The people who run the operations need to be partners, not recipients.

The Trajectory

Edge AI is moving from experimental to essential for industrial operations. The technology maturity is there. The value propositions are proven. The question is execution speed.

Companies that build edge AI capabilities now will have operational advantages that compound over time. Those that wait will find themselves catching up to competitors who moved earlier.

It’s not the flashiest part of the AI story. But for industrial companies, it might be the most practically important.