A digital twin isn’t a static 3D model; it’s a live representation, fed by real sensor and operational data, of an actual building, piece of infrastructure, or process, kept in sync with its physical counterpart closely enough that you can simulate a change on the twin before making it in the real world. In infrastructure and smart-city work, this is where AI shows up most concretely: predictive maintenance, energy optimisation, and traffic or crowd modelling running against the live twin rather than a physical pilot.
The AI element is usually less about generating the twin and more about what runs on top of it: models that learn from the accumulating operational data to predict failures before they happen, or flag anomalies a human monitoring dashboard would miss. The twin is the substrate; the prediction and detection is the AI value.
The commercial shift worth watching is from a one-off engineering deliverable to a continuously learning asset: a digital twin that gets more accurate over time as it accumulates real operational data, rather than a model that’s accurate on day one and stale a year later.