Commercial developments increasingly rely on digital intelligence to deliver buildings that are not only well constructed but continually optimised throughout their operational life.
James Thomas is head of digital buildings at SES Engineering Services
Artificial intelligence (AI) and machine learning are central to this shift, offering new ways to understand building performance, react to changing demands and optimise efficient asset management.
While industry has made big strides in delivering integrated smart buildings, the true value of these systems only emerges when a building is occupied. This is where AI can be a vital bridge between construction and long‑term operation.
Central to maximising the AI opportunity is the data produced by building systems and, most importantly, how it is structured. Commercial assets generate significant volumes of data, from heating, ventilation and air-conditioning performance and energy consumption to environmental conditions and occupancy.
Structured correctly, these are a powerful foundation for AI‑driven building analytics. By understanding how occupants use their space and whether conditions are maintained effectively, developers and operators gain a clearer picture of how efficiently a building is performing from an enterprise and energy/carbon perspective.
While AI’s potential in this space is huge, it is restricted by a lack of cohesion. A successful digital building relies heavily upon a master systems integrator bringing order to disconnected systems, but the variance in approach is massive; there are conflicting data models (Brick, Haystack, Digital Buildings Ontology etc) , making scope hard to define.
The future is now: AI can help to define what a commercial building looks like and how it functions
Once a building is handed over, data ownership becomes fragmented across IT, facilities management (FM) and asset management teams; and this disconnect between design, delivery and operation means much of the intelligence embedded during construction goes untapped.
Here, AI‑enabled platforms can ingest and normalise disparate building protocols, sorting the unstructured into a single, consistent independent data layer. This methodology not only enhances operational visibility but reduces reliance on time-consuming and costly manual data engineering.
Autonomous optimisation
Once the datasets from construction and operation are coherent, they can be analysed to unlock AI’s real value. For example, comparing real‑time energy consumption to the as-designed energy performance model allows AI to alert operators to variances before they become major issues that create a performance gap, such as identifying unexpected occupancy patterns, seasonal variances or inefficient operation caused by plant and equipment malfunction. Such insights are key to achieving performance certification such as NABERS, where post‑occupancy evaluation is undertaken to achieve, maintain and optimise the targeted star rating.
AI can also transform FM by identifying operational patterns preceding equipment failures, flagging where interventions are needed before problems escalate. This reduces downtime, ensures systems maintain as-designed efficiency and can prevent total loss. In commercial environments – particularly those with mission‑critical spaces such as data rooms – avoiding unplanned outages is invaluable.
Creating efficiencies in a single building is just the start. For developers with big property portfolios, the benefits can scale even further. Using data analysed from a flagship digital building, AI can identify usage patterns that can be applied to older but similar asset types in the estate. This portfolio‑wide modelling supports better-informed decisions on green-retrofit viability, decarbonisation budgets and the prioritisation of upgrades, enabling investors and owners to drive continuous performance improvements, increasing enterprise value and rental yield across the estate.
Today, many platforms still rely on human operators to act on recommendations generated by machine learning. But more systems are supporting autonomous optimisation, automatically adjusting plant operation or the environmental control strategy to curtail energy consumption and cut carbon emissions. While widespread adoption will depend on operator confidence, this trend is already reshaping expectations of how buildings can self‑optimise.
While the industry now recognises that a digital building’s value is realised once completed, AI can maintain that value, amplify it and adapt it as user needs and technologies evolve. By bridging the divide between design, delivery and operation, it is helping to shape a new generation of commercial buildings – ones that can truly learn, predict and optimise from the moment the doors open.
James Thomas is head of digital buildings at SES Engineering Services
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