As Buildings Become Smarter, AI Unlocks Critical Insights for Energy Efficiency, Notes JLL

AI is Evolving

As companies seek ways to reduce energy consumption in real estate, evolving AI tools are playing an increasingly significant role in identifying efficiency opportunities and optimizing operations.

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

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

“Addressing energy efficiency is the most tangible pathway to real estate decarbonization, yet many building owners lack a clear roadmap. AI’s value 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.

A New Era of Energy Efficiency

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

Today’s solutions can enhance energy audits by identifying energy and cost-saving opportunities while modeling demand fluctuations in various scenarios, such as weather events.

“AI solutions can analyze disparate data sources to develop algorithms for predictive maintenance and HVAC optimization, helping facility managers set energy efficiency parameters 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), reducing energy consumption by 20% while maintaining comfortable conditions for building users. It can also reduce energy use during peak pricing periods, leading to cost savings.

AI tools can also create benchmark energy models for assets, allowing property owners to leverage existing building data in their energy strategy. These benchmarks help identify energy-saving opportunities across portfolios without the time and expense 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 without granular data. This allows building operators to start benefiting from advanced energy controls before conducting a full audit,” says Yuehan Wang, Global Research Associate – Real Estate Technologies, JLL.

In energy planning, AI tools can inform strategies for combining renewable energy with traditional sources and battery storage, supporting resilience against price surges and power outages.

Retrofitting for a Low-Carbon Future

Energy retrofits are becoming essential for buildings to remain competitive, as tenant demand shifts toward sustainable spaces and regulatory pressures tighten.

“Enhanced energy efficiency attracts a ‘green premium’ from the growing number of tenants prioritizing sustainability, helping future-proof real estate portfolios,” says Wang.

JLL research found that a light-to-moderate retrofit—addressing components from lighting to mechanical, electrical, and plumbing systems—can reduce energy consumption by 10%-40%.

AI is increasingly helping property owners and investors adopt a data-driven approach to their energy retrofit strategy, improving decision-making on return on investment and payback periods.

Highly specific AI-driven energy models can guide retrofits to improve a building’s energy efficiency, supporting detailed digital twin creation. These models simulate energy demand based on different design parameters, playing a crucial role in accelerating retrofits to align with net-zero goals.

“Mitigating risks of depreciation and asset obsolescence is a key benefit of energy retrofits. AI-driven energy modeling helps establish a data-backed investment strategy not only for individual buildings but also for portfolios, as owners work toward decarbonization goals over the next five years,” says Balakrishnan.

Overcoming Barriers to AI Adoption

While many real estate owners recognize AI’s potential to achieve energy efficiency goals, implementation remains challenging.

“AI adoption is more than a technology upgrade; it requires restructuring workflows to support an AI-driven model,” says Wang. “Leadership must also ensure every level of their organization engages with the AI solution.”

Green leases, which align tenants and landlords on sustainability goals, can encourage AI adoption for energy efficiency. Government incentives, such as the U.S. Inflation Reduction Act and the EU Green Deal, are also helping lower the cost barriers to AI implementation.

The Need for Broader Incentives

However, broader incentives are crucial to engage all stakeholders in adopting AI for energy efficiency.

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

While AI adoption for energy management varies by region and local government policies, the growing availability of AI-driven products and declining costs could help shift mindsets and drive adoption.

“As new energy sources and AI-based solutions enter the market, the benefits of AI in energy planning will become more evident—especially as real estate stakeholders look to future-proof their assets,” says Balakrishnan.

This could convince more companies that AI is a key enabler in achieving their decarbonization goals.