Artificial Intelligence: Real Estate Revolution or Evolution?

89%

C-suite leaders believe AI can help solve major CRE challenges

Future of Work Survey, JLL Research, 2025

700+

companies providing AI-powered Real Estate Tech solutions by end of 2024

JLL Research

2.04M sqm

Real estate footprint of AI companies in the US as of May 2025

JLL Research

As the capabilities of AI agents continue to astonish the world in 2025, AI is becoming increasingly powerful.
 

• AI has enormous potential to reshape real estate, with near and long-term impacts ranging from the emergence of new markets and asset types to innovations in investment and revenue models.
• A rapidly expanding AI ecosystem and its supporting infrastructure will drive demand for real estate in different markets across the globe.
• There are increasing number of AI-powered real estate technology solutions. Organizations will need to consider how they can harness AI strategically and ethically, piloting applications before scaling to deliver value.

The potential for artificial intelligence (AI) to transform businesses, industries and society has been mounting for decades. But recent advancements, have moved the science from niche to mainstream. The technology’s proficiency in writing, drawing, coding and composing has compelled corporate leaders to consider both the opportunities and threats that AI presents for their future.

For commercial real estate, it’s clear that strategically embracing AI could transform the sector. JLL’s latest Global Future of Work survey published in Jan 2025 confirms that AI is already being actively implemented in the business world. More than nine in ten C-suite leaders believe AI will change the way the workforce operates over the next five years. A similar proportion plans to accelerate investment in AI over that period. 

Yet, the survey also shows a lack of thorough understanding of the technology, it’s impact and a systematic approach to its implementation.

First, it’s important to clarify the scope of discussion by differentiating the definitions of definitions of AI, generative AI, and agentic AI. AI, in a broad sense, uses algorithms to perform tasks that require the ability to learn from experience, understand complex concepts, recognize patterns, interpret the nuances of natural language and independently make decisions.

Generative AI is a subset of artificial intelligence that focuses on what its name implies – generating new content, designs or solutions. It employs advanced algorithms to create outputs, including synthetic data, images, text and music.

Agentic AI is believed to be the next phase of AI capabilities. It refers to systems that are not only capable of generating outputs but can also autonomously pursue goals, make contextual decisions, and take actions over time without continuous human prompting. These agents can plan, execute, and adapt tasks based on evolving objectives and feedback from their environment—bringing AI closer to a form of digital autonomy.

In this article, we will focus on AI in a broader context and discuss its impact on real estate, while specifically emphasizing the role of generative AI and agentic AI where applicable.

AI uses machine learning and deep learning algorithms to perform tasks that require the ability to learn from experience, understand complex concepts, recognize patterns, interpret the nuances of natural language and independently make decisions. Generative AI is a subset of AI that focuses on creating new content, designs or solutions.

AI’s impact in the real estate market

AI sector as real estate occupiers

The real estate industry as AI adopter

AI’s impact in the real estate market
 

AI has been deemed a revolution with widespread impact

According to OpenAI, around 80% of jobs are exposed to AI disruption. This number has sparked a wave of concerns about a potential upheaval in the real estate market because of the impending changes in the labor market.

Anxiety around such change is not new. In 1589, Queen Elizabeth of England refused to grant the inventor of the mechanical knitting machine a patent out of fear that it would put knitters out of work. Now, it is universally acknowledged that mechanical knitting machines spearheaded the first industrial revolution that led to explosive economic growth and real estate market expansion.

AI is expected to boost productivity

When technology allows fewer people to achieve the same productivity level, more people are freed to create new areas of opportunity. Goldman Sachs draws on a study by MIT economist David Autor to reveal that more than 85% of employment growth in the U.S. over the last 80 years is explained by the technology-driven creation of new positions.

Additionally, according to Microsoft CEO Satya Nadella, AI service providers are making the conscious choice to explore a human-centric approach, developing “co-pilot” products designed to assist people, as opposed to “auto-pilot” products that aim to entirely replace human roles. This positions AI as a significant productivity booster. On an aggregated level, the increase in productivity is projected to augment global GDP by 14% by the year 2030.

Commercial real estate is set to feel these effects. Historically, there have been five adaptive ways that real estate responded to technological changes over time:

With AI, we anticipate a similar five-fold impact in the long run. While it remains to be seen how AI will be applied to specific sectors like healthcare and how much this growth will generate space demand, some influences are already emerging.

1. Geolocation: AI companies and investments have been observed to cluster around established tech markets. Going forward, growth is likely to be concentrated in locations where AI talent is available, namely tech hubs, innovation centers and universities.

2. Altered demand among assets: AI development calls for more and better data centers, energy grids and connectivity infrastructure.

3. New asset and product types: the birth of the ‘real intelligent building’ is imminent. AI-compliant infrastructure will become a default just as internet connections are a default feature of current buildings. AI will also help deliver net-zero buildings with high sustainability performance.

4. Revenue and investment: AI-powered underwriting and processes will enable faster transactions and more efficient understanding of properties and markets, catalyzing investments at a global scale. AI-compliant infrastructure and the ability to plug in multiple systems could also enable the expansion of ‘space as a service’ models and new revenue streams for landlords and developers.

5. Design and space function: AI will allow for experience-driven design and highly customizable environmental settings.

Read more about this topic in JLL’s Future Vision series - Get set for the 5th Industrial Revolution Real estate strategies for an AI-powered world

AI sector as real estate occupiers
 

The growing AI ecosystem will continue to expand in selected tech hubs

Foundation model developers, such as OpenAI, are just the tip of the iceberg when it comes to the AI ecosystem. Companies involved in semiconductor hardware, cloud computing platforms, model hubs and application development all represent a growing occupier segment. The commercial real estate opportunity looks even brighter when considering the additional companies that will emerge as pre-trained foundation models are modified for specific use cases.

In 2024, private investment in AI within the US alone hit US$109 billion, doubling the amount in 2023. China showed the second largest investment of US $9.3 billion. The AI focus area with the most investment was medical and healthcare; followed by data management, processing and cloud computing; and Fintech. Additionally, enterprise use cases for generative AI represent a substantial opportunity. The GDP related to AI is anticipated to grow with a 18.6% compound annual growth rate (CAGR) through 2030 (PwC). According to our research, the growth in AI is also translating into increased real estate demand from this sector. In the US alone, AI companies nearly doubled their footprint in just two years, occupying more than 2.04 million square meters. This footprint is expected to grow  to 5.2 million square meters in 2030.
 

37% of AI companies are based in the U.S.

Existing AI companies prefer specific locations. In the U.S., for example, 42% of AI companies are concentrated in the San Francisco Bay Area, followed by Boston, Seattle and New York. AI startup growth is expected to continue to center around these major tech hubs in the near future.
 

U.S. sector distribution of AI company occupied

JLL research shows there is an accelerating demand for AI talent, with AI job postings increasing by over 250% since the beginning of 2021. In the longer term, this means growth is likely to be where AI talent is available, namely primary and established secondary tech hubs, innovation centers and universities.Due to specialized skills required to build language models, AI CRE growth may remain in gateway cities for the next several years.

Training and using AI requires considerable resources and supporting infrastructure
 

Generative AI is built upon vast computing power and extensive resources. Training and inferencing AI requires infrastructure such as computing hardware, high-speed connectivity networks, power supply, cloud infrastructure and data storage that all must be housed somewhere. Additionally, the continuous expansion of AI applications will drive the need for more power, more cooling facilities and more data centers. Manufacturers and vendors of GPU and network switches will also grow, and thus require space as occupiers.

Colocation, hyperscale and edge data center markets will continue to expand globally
 

This technological revolution of AI is not merely evolving the digital infrastructure landscape; it's fundamentally redefining it. According to the 2025 JLL Global Data Center Outlook, the exponential progress of artificial intelligence (AI) and machine learning is fueling a wave of transformative shifts in data center design, site selection, and investment strategies. To keep up with the growing demand for computational power, hyperscale data centers are projected to increase their rack density at a compound annual growth rate (CAGR) of 7.8%.

AI infrastructure location criteria gives more weight to lower energy prices and lower land costs. Factors such as competitive energy pricing and energy consumption regulations are driving growth toward less crowded markets such as Atlanta in the U.S., Malaysia and Thailand. While edge data centers continue to grow near major cities to be close to the users, a more dispersed distribution is being observed in the infrastructure underpinning AI.

AI also drives new data center design requirements. Data centers in the AI era need to accommodate evolving hardware needs, such as advanced liquid cooling facilities.

Read more about this topic in JLL 2025 Global Data Center Outlook

The real estate industry as AI adopter
 

The PropTech sector has laid a solid foundation for AI integration into real estate applications

The real estate industry has begun to proactively embrace and adopt new technologies.. In JLL‘s 2024 Future of Work Survey, most companies (90.1%) expect to carry out corporate real estate activities, such as workplace strategy, occupancy management and lease administration, with AI supporting human experts over the next five years. Over 60% have already started piloting different AI use cases in their real estate functions.

This is powered by a maturing PropTech ecosystem. There are now technological solutions for almost every aspect of real estate functions, including investment management, design and construction, building and facility operations and portfolio management. A solid foundation has been laid for AI integration.

Globally, there are over 500 companies providing AI-powered services to real estate and already delivering value in terms of improved efficiency and cost-savings. Top use cases of AI include:

• Document sorting and data standardization for portfolio data analytics and benchmarking
• IoT data mining for automated facility management
• Price modeling and prediction for investment management
• Satellite image processing for asset valuation and risk management
• Reality capture for construction site monitoring
• Scheduling for construction and capital project
• Recommendation and matchmaking for leasing and investment transactions
   

Generative AI applications in real estate are still in the early stages
 

Emerging use cases cover client communication assistance in leasing and property management (such as chatbots to handle tenant queries), floorplan and design generation and summarizing unstructured documents to create reports. More products are expected to come onto the market soon. Large Language Models, in particular, provide the ability to extract insights from vast amounts of text-based documents in real estate, significantly reducing the complexity of multi-lingual, multi-national operations.

However, most companies still lack a systematic approach to their AI implementation. The ability to develop actionable strategy is going to be the key determining factor for successful integration.

Read more about AI adoption in The future of AI in CRE - Why corporate real estate needs a systematic approach to AI

Early adopters of AI-enabled solutions are already seeing returns
 

Royal London Asset Management, a leading UK investment firm, experienced significant improvements in HVAC operations and energy efficiency in an 11,600 square meters commercial office building. By implementing JLL’s AI-powered Hank technologies, the firm has reached a record ROI of 708% and energy savings of 59%, reducing carbon emissions by up to 500 metric tons per year.
 

Case studies: AI creating value for real estate

AI in PropTech will continue to grow. JLL research shows that as of end of 2024, among 7,000 global PropTech companies, about 10% (700 companies) are currently providing AI-powered solutions, including both AI native products and AI-augmented products. Venture capital (VC) is the main driving force backing the development of AI products. Among all AI-powered PropTech companies, around 62% are VC-backed. About 20% of companies are in the very early incubator, angel or seed stage; 25% are at early-stage VC rounds; and 15% are at late-stage VC rounds. Overall, this ecosystem is young and energetic, with most (83%) generating revenues or making profits, signaling long-term potential for the market. 

Read more about AI in PropTech in JLL Spark Featured Research: Where AI demand is creating investment opportunity

Act Now: Harnessing AI strategically and responsibly
 

There are still considerable uncertainties about the future impact of AI, the full range of its rapidly expanding capabilities and how these capabilities will be assimilated into specific industry sectors. It is crucial for real estate investors, developers and corporate occupiers to stay informed and strategic, considering how to leverage the power of AI to support your business objectives and how to do it in a responsible and ethical way.

As the regulatory landscape for AI evolves to keep pace with its growth, businesses must be vigilant about three types of emerging regulations:

• Market standards and protocols concerning data quality, IP rights, privacy and data security.

• Regulations to mitigate societal risks, such as measures to protect the labor market from shock or safety standards for autonomous vehicles.

• Environmental legislation, notably that aimed at mitigating carbon emissions from the growing digital economy.

Organizations will need to reflect on a number of key questions as they consider the right path forward: What does the growth in AI mean for your investment and location strategies across existing (or emerging) asset classes? What existing or future applications of AI do you need to be prepared for and pilot now? What are the potential business and societal risks?

Understanding how artificial intelligence will impact your business and creating a test and implementation strategy will be key to mitigating risk and harnessing the potential for growth.

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