Insight

Every infrastructure operator today wants to make data “AI-ready.” But what does that really mean? Most organizations still treat AI as something you plug on top of your data – as if algorithms can magically find structure in spreadsheets, CAD files, GIS layers, and sensor streams. The reality is: if your data isn’t organized around meaning, AI can’t use it. That’s where ontology comes in.

For decades, plant and process teams have relied on two essential, but disconnected, views of their assets: - 2D schematics that capture system logic, flow, and connectivity, and - 3D models that represent the physical world.

In asset-intensive industries, AI rarely fails because models are weak – it fails because data lacks context.

Digital twins have come a long way. What began as a way to see assets has become a way to understand them. But most twins still stop short of true intelligence. They visualise geometry -- they don’t model knowledge.

Digital twins are everywhere in the conversation today — from airports to rail networks, utilities to manufacturing. But not all twins are created equal. The biggest dividing line? Whether they’re built on file federation or on a true ontology. At Nextspace, we believe this is the single most important choice infrastructure owners and operators will make in the next decade.

Most AI initiatives stall because critical data sources can’t talk to each other. Engineering, spatial, and operational systems all run on incompatible taxonomies and schemas — leaving organizations with silos, blind spots, and unreliable insights.