TL;DR
Edge AI is infrastructure now — the global market is at ~$25B in 2025, heading toward $165B by 2035, with 97% of US CIOs already planning deployments.
The highest-value use cases are in the most remote places — oil rigs, mine sites, pipelines, farms, offshore turbines — not factory floors and city centers where connectivity is already solved.
Edge AI runs inference locally, not in the cloud — processing on-device cuts latency from 50–100ms to 5–10ms, and reduces what needs to travel the network from raw data streams to small structured outputs like alerts and status pings.
Most devices ship with WiFi, Ethernet, or LTE — each has a hard ceiling: Ethernet needs cable, WiFi needs access points, and cellular needs towers that don't exist where most industrial assets actually operate.
The smarter the deployment, the worse the connectivity — the industries spending the most on edge AI are the ones the existing connectivity stack reaches least.
Edge AI is no longer experimental. It's in the vibration sensors mounted on oil rig drill heads. It's in the cameras inspecting automotive parts at 1,000 units per hour on the assembly line. It's in agricultural monitors sitting in fields hundreds of kilometres from the nearest city. It's in underground mine shafts, offshore wind turbines, and remote pipeline corridors.
The technology has arrived. The market is moving fast. But not all deployments are created equal — and where an edge AI device runs turns out to matter enormously for how it stays connected.
The Market in Numbers
The global edge AI market was valued at approximately $25 billion in 2025 and is projected to reach $119–165 billion by 2033–2035, with compound annual growth rates consistently in the 20–30% range across major research firms including Grand View Research, Precedence Research, and Fortune Business Insights.
Hardware is the fastest-moving segment. Edge AI chips are on track to ship 1.6 billion units globally in 2026 alone. The North American edge AI hardware market is projected to grow from 349.8 million units in 2025 to 716.7 million units by 2030 — a CAGR of 15.4%, (according to MarketsandMarkets). Industrial IoT applications account for more than 33% of total edge computing revenue, making it the single largest segment.
97% of US CIOs had edge AI on their 2025–2026 technology roadmap. This is no longer fringe investment. It's vital infrastructure to allow projects to run as they should.
And the deployments are heading exactly where you'd expect a technology designed for local intelligence to go: into the field, the factory floor, the mine site, the remote installation. Precisely the places where network infrastructure is weakest, where robust connectivity is more critical.
Simply Embedded are connectivity experts, so let's dive into it.
Where Edge AI Actually Lives
The use cases with the highest operational value are almost always in environments that are physically remote, structurally complex, or both.
Manufacturing and industrial automation represent the highest concentration of operational edge AI use cases today. Computer vision systems on assembly lines run defect detection models locally — a single high-resolution industrial camera continuously generates terabytes of raw video, and routing that upstream to a cloud for processing is neither fast enough nor economically viable. These devices live inside factories, where wired Ethernet is typically available and WiFi is engineered in. Connectivity is generally solved here.
Oil and gas is a different story. Vibration sensors on oil rig equipment analyze acoustic patterns to predict bearing failures, running on battery power for months in remote locations. Offshore platforms sit beyond any cellular footprint. Pipeline monitoring assets may stretch across thousands of kilometres of uninhabited terrain.
Mining is actively deploying autonomous systems for equipment maintenance and hazardous material handling. Haul trucks operating 200 miles from the nearest cellular tower cannot pause inference when connectivity drops — they need local AI for safety-critical decisions. But those same trucks still need to report back: telemetry, diagnostics, maintenance flags.
Agriculture is using edge AI for crop health monitoring, soil condition analysis, livestock tracking, and autonomous machinery coordination. Farms in rural areas span thousands of hectares with no fixed network infrastructure within range.
Utilities and energy deploy remote monitoring on transmission lines, substations, and distributed renewable generation assets — wind turbines in particular, often sited in areas specifically chosen for their distance from populated (and networked) areas.
Smart cities and public infrastructure represent a different connectivity challenge: devices deployed across a city grid, some on Ethernet backhaul, some on municipal WiFi, but many mounted in locations where running physical cable is impractical.
The pattern across all of these: the more operationally valuable the edge AI deployment, the more likely it is to exist somewhere that WiFi doesn't reach and Ethernet isn't practical.
The Shift Away From Cloud Compute
None of this would matter much if the cloud model still made sense for these deployments. It increasingly doesn't — and the reasons are directly because of connectivity.
The original cloud AI model assumes that a device captures data and streams it to a remote server for processing. That works fine when bandwidth is cheap and abundant, and if the data is small packets. But in remote environments, or when we are talking large packets of industrial data - it quickly becomes a huge bottleneck. It breaks down fast in the environments edge AI actually serves.
Latency is the first issue. Edge inference can improve round trip response times significantly. For applications like robotic arms on a production line or autonomous vehicle decision-making, that gap is the difference between a system that works and one that doesn't.
Bandwidth is the second, and arguably bigger, problem for remote deployments. A single industrial camera running continuous video analytics generates a genuinely large volume of raw footage. Streaming that footage to the cloud for processing — every frame, every camera, continuously — quickly becomes both technically and financially impractical.
That's the real shift. Edge AI doesn't just move compute closer to the data — it fundamentally changes what needs to travel over the network. Instead of sending every piece of raw data to the cloud, to be computed and sent back - which requires high bandwidth and latency - an edge AI device computes it at the device itself - and only transmits the final output.
The Default Connectivity Stack
Most edge AI devices today ship with one or more of three connectivity options: WiFi, Ethernet, and sometimes cellular (LTE/4G). Each has a role. Each has a ceiling.
Ethernet is the gold standard for fixed industrial deployments. It's reliable, high-bandwidth, and deterministic — exactly what you want in a factory or a data hall. But it requires physical cable infrastructure. You cannot run Ethernet to a haul truck, a remote pipeline sensor, or a weather station on a mountaintop. It solves a real problem in a bounded set of environments.
WiFi extends the reach of wired infrastructure over short distances. WiFi 6 and similar advancements have improved reliability and throughput significantly. But WiFi range is fundamentally constrained — typically 30–100 metres indoors under ideal conditions, less through industrial materials. It tethers devices to existing access point infrastructure, which itself requires either Ethernet or cellular backhaul. WiFi is excellent for structured indoor environments. It doesn't exist in a remote field.
Cellular (LTE/4G/5G) meaningfully extends edge AI connectivity beyond fixed infrastructure. An LTE-equipped sensor can report from a construction site, a logistics hub, or a semi-remote installation. But cellular networks are designed around population density. They exist where people live — not necessarily where industrial assets operate.
Industries With The Highest Operational Need Also The Least Served
The most interesting thing is this:
The industries with the highest operational need for edge AI — oil and gas, mining, remote agriculture, maritime, utilities — are also the industries least served by the existing connectivity stack.
Factory floors and urban deployments are well-covered. The hard problems are in the field. And it's precisely in those remote, high-value environments that a smart edge device that can't communicate its findings back to an operations center is only doing half its job.
In our next article, we will look at exactly how large that coverage gap is, why LTE alone doesn't close it, and what satellite connectivity — specifically new hybrid LTE/satellite standards — means for the deployments that need it most.
Simply Embedded designs and manufactures Edge AI solutions, custom embedded systems and IoT connectivity solutions (including satellite connectivity) for remote and industrial applications.