On Making Buildings AI-Ready

Buildings are the world’s largest asset class, yet their spatial data remains largely locked in files, different formats, and siloed systems—making them hard to activate with AI at scale. The key to unlocking this value is high-fidelity, standardized, connected spatial building data that functions as an AI-ready system of record across the entire portfolio and tech stack.

Spatial Data as a Value Multiplier for Existing Data Classes

Organizations already run on data systems for finance, people, and supply chains, but lack an equivalent for buildings; treating buildings as data adds a missing vertical that makes AI-driven workflows possible across operations, workplace, and real estate decisions. A graph-based spatial model turns drawings into queryable information—rooms, assets, adjacencies, metrics—so rules, logic, and automations can run reliably at portfolio scale. In combination with other data classes, this will unlock tremendous value across the physical domain using agentic AI: “Based on your occupancy data, expected headcount, property leases, and spatial analysis, it’s possible to consolidate our Seattle locations into one building. Here is the suggested layout. Would you like to go ahead and initiate the change processes?”

Realizing Value Today

Full-on spatial jiu-jitsu like that isn’t here yet - but there is no reason it can’t be soon. We believe that these agents will run where enterprises already keep most of their data. Archilogic’s job is to ensure that there is a seamless connection between our platform and the places where our customers spend a lot of time managing their companies. We’ve written about this in “Archilogic + Integrations: Closing the Feedback Loop.” This will allow workplace planners to have all data - not just the spatial component - in one place to make the best possible iterations as quickly as possible. 3rd party data flows into Archilogic as Custom Attributes - which are fully queryable as part of Space Graph.

Here’s a sneak peek at running spatial queries in Archilogic. Note that something like “find two adjacent meeting rooms” sounds easy, but it is a task that would require a lot of manual work in systems that are not built on a spatial graph. Getting buildings AI-ready is not a pipe dream - it’s here today.

The path forward

To make the built world AI-ready, treat floor plans as data, not drawings; get them standardized in a graph-based platform and integrate them as a first-class data vertical alongside other enterprise data classes. The moto should be to digitize once, keep it live, and let every application—and AI—consume and contribute to a continuously improving spatial record.

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