How AI is boosting efforts to cut buildings’ energy use

As companies look for ways to cut their energy use across their real estate, evolving AI tools are playing a growing role in identifying opportunities for efficiencies and optimizing operations.

At least 90% of buildings in the world’s most developed cities are over a decade old, often falling short of today’s energy standards. Improving energy efficiency could make the second-largest impact on reducing carbon emissions over the next decade.

With many buildings now equipped with sensors and smart technologies, a wealth of real-time data on systems and equipment offers significant potential for AI to analyze and optimize energy use.

“Tackling energy efficiency is the most tangible path to real estate decarbonization, but many building owners lack a clear roadmap. The value of AI lies in its ability to learn the energy demand patterns of building assets and optimize energy distribution,” says Ramya Ravichandar, Vice-President of Product Management, Smart Buildings & IOT.

Energy audits and modeling are areas where AI is already having a significant impact.

Today’s products can enhance energy audits by identifying energy and cost-saving opportunities and modelling demand fluctuations under different scenarios, such as weather events.

“AI solutions can analyze disparate data sources to develop algorithms for predictive maintenance and HVAC optimization, supporting facilities managers by setting energy efficiency parameters that are balanced with tenant comfort,” says Vidhya Balakrishnan, Vice-President of Software Engineering, JLL.

For example, JLL’s Hank platform analyzes occupancy and external data to optimize heating, ventilation and air-conditioning (HVAC), cutting energy use by 20% while supporting comfortable conditions for building users. It can also scale back energy consumption during peak pricing, leading to cost savings.

There are AI tools that create benchmark energy models for assets, allowing owners to leverage existing building data in their energy strategy. These benchmark models also help identify energy saving opportunities across portfolios without the time and cost of auditing each asset.

“AI can integrate location, climate conditions, energy sources and externally available information to model energy use for similar assets or newer technologies lacking granular data. This allows building operators to start benefiting from more advanced energy controls before they perform a full audit,” says Yuehan Wang, Global Research Associate – Real Estate Technologies, JLL.

In energy planning, AI tools can also inform strategies to combine renewable energy with traditional sources and battery storage, supporting resilience through price spikes and power outages.

Energy retrofits are becoming crucial for buildings to remain competitive as tenant demand shifts towards more sustainable spaces as well as avoiding non-compliance amid tightening regulations.

“Enhanced energy efficiency attracts the “green premium” from the growing number of tenants who value sustainability, helping futureproof real estate portfolios,” says Wang.

JLL research found that a light to medium retrofit – addressing elements from lighting to mechanical, electrical and plumbing equipment – can reduce energy consumption by 10%-40%.

AI is increasingly helping owners and investors to take a more informed, data-driven approach on their overall energy retrofit strategy and better navigate uncertainties in cost-effectiveness and payback periods.

Highly specific, AI-driven energy models can guide retrofits to improve a building’s energy efficiency, supporting the creation of detailed digital twins. These simulate building energy demand under different design parameters and can be a crucial factor in accelerating retrofits to align with net-zero targets.

“Mitigating devaluation and stranding risks is a key benefit of energy retrofits. AI-powered energy modelling can help establish a data-driven investment strategy not only for single buildings but across portfolios as owners work toward decarbonization targets over the next five years,” says Balakrishnan.

While many real estate owners see AI’s potential to meet energy efficiency goals, implementation can be challenging.

“Implementing AI is more than a tech upgrade; it requires reorganizing building workflows to support an AI-driven model,” says Wang. “Leadership must also ensure every level of their organization is engaged with the AI solution.”

Green leases, which align tenants and owners on sustainability goals, can encourage AI adoption for energy efficiency. Current government subsidies such as the U.S.’ Inflation Reduction Act and the EU’s Green Deal also lower the cost barriers to AI implementation.

Still, broader incentives are essential to engage all building stakeholders in AI adoption.

“Regulations are increasing investor demand for AI to address energy efficiency challenges,” says Ravichandar. “The key is finding additional incentives to encourage all users in an organization to embrace AI.”

Though AI adoption for energy management varies between regions and local governments, the growing availability of AI products and falling costs are likely to help shift mindsets and drive uptake.

“With new energy sources and AI-driven solutions entering the market, the benefits of AI in energy planning will become clearer, especially as real estate stakeholders seek to futureproof their assets,” says Balakrishnan.

This could convince more companies AI is a core facilitator to achieve their decarbonization goals.

“AI will bring a paradigm shift in building operations,” says Ravichandar. “The technology is here – now we need to integrate it into processes and equip people to unlock its full potential.”

Find out more about how AI is supporting sustainable real estate in our latest research:
Streamline sustainable asset enhancement with AI

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