As sustainability targets tighten, regulatory demands increase and occupants expect more personalised, enhanced environments, data is the key to delivering better buildings.
Our head of digital buildings, James Thomas, argues that AI will become not just an enhancement, but a necessity to ensuring commercial developments can achieve the operational goals set out in their design and construction.
Commercial developments increasingly rely on digital intelligence to deliver buildings that are not only well constructed but continually optimised throughout their operational life. 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 the industry has made major strides in delivering integrated smart buildings, the true value of these systems only emerges once the building is occupied. This is where AI can prove to be a vital bridge between construction and long‑term operation.
Creating cohesion
Central to maximising the AI opportunity is the data produced by building systems, most importantly, how it is structured. Commercial assets generate significant volumes of operational data – from HVAC performance and energy consumption to environmental conditions and occupancy. When 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 the building is performing from an enterprise and energy/carbon perspective.
While the potential for AI in this space is huge, it is currently restricted by a lack of cohesion. The successful delivery of a digital building relies heavily upon a Master Systems Integrator (MSI) bringing order to disconnected systems, but the variance in approach is massive, there are conflicting data models (Brick, Haystack, DBO etc.), and scope can be difficult to define. Once a building is handed over, data ownership becomes fragmented across IT, FM and asset management teams and this disconnect between the design, delivery and operation, means that much of the intelligence embedded during construction goes untapped.
Here, emerging AI‑enabled platforms can ingest and normalise disparate building protocols, effectively sorting the unstructured into a single and consistent independent data layer. This methodology not only enhances operational visibility but also reduces the historic reliance on time-consuming and costly manual data engineering.
Predicting efficiencies
Once the datasets from construction and operation are coherent, they can be analysed continuously to unlock the real value of AI.
For example, comparing real‑time energy consumption directly to the as-designed energy performance model allows AI to alert operators to emerging variances before they become major issues that create a performance gap, such as identifying unexpected occupancy patterns, issues caused by seasonal variances or inefficient operation caused by plant and equipment malfunction. These insights are key to achieving performance certification such as NABERS, where post‑occupancy evaluation is undertaken to achieve, maintain and even optimise the targeted star rating.
Likewise, AI has the power to transform FM by identifying operational patterns that precede equipment failures, flagging where interventions are needed before problems escalate. This reduces downtime, can prevent total loss and ensures systems maintain their as-designed efficiency. 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 extensive property portfolios, the benefits can scale even further. Through the data analysed from a flagship digital building, AI can identify usage patterns from that scheme 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, appropriation of decarbonisation budgets and the prioritisation of upgrades, enabling investors and owners to drive continuous performance improvements and perhaps as importantly, increase enterprise value and rental yield across the estate.The importance of data validation was a main talking point. We first need to ensure that the systems and technology that generate building performance data are commissioned and tested correctly and that the data we capture in real time is meaningful. Certification schemes such as NABERS that monitor energy efficiency in operation and live energy performance assessments can help us identify under-performing systems and equipment before the official NABERS rating process is undertaken.
Early engagement from concept design is key. Developers appear to be leaning towards main contractors getting involved earlier to ensure consistency, collaboration and mitigate risk from the outset. Using schemes such as WiredScore and SmartScore to certify a buildings connectivity and smart credentials not only ensures we are upholding construction requirements but also improves quality, customer satisfaction and helps to attract and retain tenants.
The future is limitless
As AI advances, the industry is also seeing a shift from platforms that just apply analytics to ones that will initiate intelligent automation. Today, many platforms still rely on human operators to act on recommendations generated by machine learning. But more systems are beginning to support autonomous optimisation, automatically adjusting plant operation times or the environmental control strategy to curtail operational energy consumption and reduce carbon emissions. While widespread adoption will depend on operator confidence, this trajectory is already reshaping expectations of how buildings can self‑optimise in the future.
While the industry now recognises that a digital building’s value is realised once completed, AI gives us the capability to maintain that value, amplify it and adapt it as user needs and technologies evolve. By bridging the longstanding divide between design, delivery and operation, it is helping shape a new generation of commercial buildings – ones that can truly learn, predict and optimise from the moment the doors open.
This article was originally published in Property Week
