Data remains a key factor in the value AI brings to real estate. Gathering sufficient, high-quality data is vital to develop trustworthy AI models. Though data bias isn’t typical in data generated by smart buildings, the information fed into AI tools must accurately describe building conditions.

“Having enough data points is critical. To trust an AI system and minimize constant monitoring, it’s important to be able to identify and access what data is being used, and augment any data gaps,” says Ravichandar.

While having human oversight is critical, AI systems can also be trained to recognise “good” and “bad” data and learn to flag data gaps. Business teams must learn to not only understand AI tools, but equally to trust them to successfully integrate AI insights into building workflows, Ravichandar notes.

Uncertainty over the return on investment is another current hurdle to AI adoption. This is where monitoring select data subsets can validate the accuracy and the tangible impact of AI, helping companies evaluate a tool’s operational viability and potential savings.

As AI costs fall, more owners and occupiers will embed AI into smart buildings to increase energy efficiency – with a vital boost to sustainability outcomes, Ravichandar believes.

“AI in smart buildings can identify specific ROIs of different decarbonization measures at asset and portfolio level. As the ROI becomes evident, AI can be instrumental in developing more holistic decarbonization strategies with portfolio-level impact.”

In years to come, the link between smart buildings and sustainability will only strengthen. “In light of the urgent need to tackle emissions, we must accelerate AI adoption if we are to meet targets for real estate decarbonization,” Ravichandar concludes.