Blog | nearby computing

For years, artificial intelligence has lived predominantly in the cloud: large models, high-performance compute, and centralised data pipelines. But the next wave of adoption is shifting direction. Enterprise AI is increasingly moving closer to where data is generated. In other words, AI is going to the edge.

This shift is not cosmetic — it’s structural. The use cases that truly move the needle for businesses require something the cloud alone cannot always guarantee: instant response, local context, and full control over sensitive data.

What Edge AI really is

Edge AI means executing inference — the part where an AI model analyses new data and makes decisions — on local edge locations such as stores, warehouses, factories, hospitals, ports, vehicles, cameras, and sensors.

It doesn’t replace the cloud; it complements it. Models are trained centrally, while decisions happen locally. This hybrid architecture is becoming the de facto standard for any organisation aiming to operationalise AI at scale.

Three major forces are accelerating this transition:

 R  Time-critical decisions
Computer vision, anomaly detection, robotics, safety systems, and industrial control loops require responses in milliseconds. When every second matters, processing cannot sit hundreds of kilometres away.

R  Data sovereignty and compliance
Many sectors simply cannot send raw data to the cloud — whether for regulatory, security, commercial, or privacy reasons. Edge AI lets businesses process sensitive information locally while syncing only what’s necessary.

R  Cost and efficiency
Continuously sending video streams, sensor data or telemetry to the cloud is expensive and inefficient. Running AI closer to the data reduces bandwidth consumption and ensures autonomous operation even during connectivity issues.

Who truly benefits from Edge AI

Edge AI is not a niche capability — it directly impacts any organisation with distributed operations or significant real-time data generation, including:

R Retail: loss prevention, shopper analytics, stock visibility, in-store automation.

R Manufacturing: visual inspection, predictive maintenance, quality control.

R Energy & utilities: remote site monitoring, safety, inspection and automation.

R Transport & logistics: flow optimisation, security, fleet intelligence.

R Healthcare: local processing of sensitive clinical data for decision support.

Wherever real-time insight meets physical operations, Edge AI becomes transformative.

How to enable this opportunity

The challenge is no longer the AI models themselves, but the distributed operations required to run them. To unlock the full value of Edge AI, organisations — and the service providers supporting them — need to build a foundation that includes:

R a unified orchestration layer spanning edge, on-prem, cloud and networks;

R the ability to deploy and update AI workloads across hundreds or thousands of sites easily;

R automated lifecycle management, security, patching and rollback;

R full-stack observability across all distributed environments;

R an open ecosystem where third-party AI applications can be onboarded without friction.

Platforms such as NearbyOne enable this approach in a subtle but powerful way, giving organisations the control and automation required to run edge environments at scale without adding complexity.

The direction of travel is clear: AI is becoming more local, more contextual, and more distributed. The opportunity is significant — but only those who can operationalise it effectively will capture its full value. The right time to prepare for this shift is not in the future. It’s now.