How can AI accelerate the decarbonization of real estate?

As more companies set sustainability targets and standards, another important aspect of AI is supporting compliance and reporting. Tools such as JLL's Canopy gather utility and environmental data to monitor performance, with an automated reporting feature helping companies benchmark progress against recognized frameworks.

Other AI tools are helping real estate owners stay on top of complex, evolving sustainability regulations by highlighting actions to comply with incoming laws.

One emerging area is aiding sustainability decision-making across real estate portfolios. New AI tools are scanning data such as leasing information and asset emissions to offer data-driven recommendations about which buildings to sell and which ones to retrofit - while flagging decarbonization solutions tailored to the condition of buildings.

The JLL-GPT tool, for example, uses market data, external business insights and internal commercial real estate data to generate insights for decision-making, including reducing carbon footprints.

Another growth area is tackling embodied carbon. AI is helping to make new construction and fit-outs more sustainable by taking on the laborious task of pinpointing materials with a lower carbon footprint.

With so many AI tools already on the market and more to come, companies can find it difficult to select the most relevant ones for their business.

A tailored decarbonization solution might take advantage of multiple AI tools, says Ravichandar. “Finding the best solution for a given business means leveraging an ecosystem of tools that work with the right data streams – whether these are compliance-related or building-related.”

“It’s a nascent area, but eventually, AI can help create more holistic, data-driven strategies that steer portfolio decisions, including the acquisition of greener buildings.”

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.