Industry at the Edge posts

Most conversations about Agentic AI in industry start three levels too high: autonomous factories, agents talking to other agents. This is the small version that actually works, five building blocks that let an operator ask the plant a question and get a real answer.

Everyone is chasing generative AI and autonomous agents, but the industrial AI that already delivers, predictive maintenance and computer vision, never left the factory floor. A field reality check on hype versus what actually works, and why the best engineering starts with the problem, not the technology.

Most people in industrial automation still believe that hardware is inherently more reliable than software. But many of the assumptions behind that belief were formed decades ago, before connected operations, edge computing, and software-defined industrial systems became a reality. In this article, we challenge five of the most common myths about software in industrial environments.

Industrial digitalization has become unnecessarily complex, with AI-first strategies before they even get started. In this article, we explore a simpler approach: start by connecting one machine, collecting one stream of data, building one dashboard, or automating one workflow. Because starting small is often the smartest way.

Every industrial company today is looking for the same profile: someone who understands machines, networks, data, cloud, and AI. Someone who can connect a PLC, deploy a container, troubleshoot a VPN, and explain why the data pipeline is broken. And... surprise, surprise... they can't find it. In this article we explain why.

Many industrial companies have a data strategy, yet very few can honestly say their business is better because it. The reason often isn’t technology or talent, but a missing piece in what we call the Triangle of Digitalization — a simple framework that reveals why most data strategies quietly fail.