“We’re trying to solve issues with yesterday’s solutions. We need to rethink business models.”
This is the year of the operator. For real estate companies, success depends on management strength, scale, and operational excellence. AI can be a critical enabler of all three. In fact, PwC’s AI performance study showed that, across industries, the most AI-fit companies deliver AI-driven revenues and efficiencies 7.2 times as high as those of other companies.
Capital rewards demonstrable improvement. PwC’s 2026 mid-year Global M&A industry trends report showed dealmaking activity remains strong for assets that enable the AI economy. AI—which Emerging Trends survey respondents named the top disruptor in real estate—is reshaping the industry in two ways. It’s changing how companies operate assets, and it’s creating demand for the power, data centre capacity, logistics infrastructure, and specialized industrial space the AI economy needs. Yet many real estate organizations still face significant constraints to achieving measurable AI value. These include data quality, cost, cybersecurity, and workforce reskilling.
Where Are Real Estate Companies Deploying AI?
Most companies recognize the need to invest time, effort, and money in AI but as some sit on the sidelines or delay decisions, the gap between leaders and others is widening.
For some, AI tools have significantly reduced time spent on analysis and document preparation. One interviewee said AI has cut the time needed to prepare a confidential information memorandum (CIM) from roughly two weeks to two days, while another said they’re using AI to overcome translation barriers and aggregate global property data. Others said they’re using AI to help with lease review, underwriting, and data management.
AI use and impact in the real estate industry looks different depending on the asset class and type of organization. Brokerages are among the earliest adopters, where tight margins combined with access to data mean AI has a considerable impact. Many brokers have already begun using AI to improve the lead-to-lease workflow. Some are also using AI to organize building and zoning information, generate renderings, and create immersive storytelling for customers.
One brokerage platform described how AI is embedded across its business model and has become core to how it builds and delivers products. Real-time data access allows the company to shift with supply and demand and make immediate business decisions. AI has also accelerated the pace of building new tools. One interviewee said they’ve cut engineering timelines from two years to six months, or roughly 75 percent.
Construction companies are also leading the way on AI adoption. Beyond automation and robotics, they’re focused on supply chain management and using AI to coordinate scheduling, reduce waste, compress timelines on delivery and building, estimate costs, and improve workflows—making sure they have people at the right place at the right time.
Real estate developers and operators are also using AI: to improve the use of space in their buildings, find ways to cut construction costs without lowering quality, and shorten development timelines. One interviewee highlighted using up to 80 design scans per building to develop fully coordinated, permit-ready layout plans in minutes.
From Digital Intelligence to Physical AI
The next phase of AI adoption will move beyond screens and documents into the physical environments real estate companies build and operate. This is physical AI, technology that enables robots and autonomous systems to sense, reason, and act in the real world. In real estate, physical AI applications range from drones for site surveys and inspections to robots performing tasks in high-risk construction areas.
One construction-technology firm said they’re building physical AI infrastructure for homebuilding. They noted two robots producing a single wall execute more than 240 discrete tasks using approximately 230 sensors per robotic cell. Meanwhile, residential developers are exploring AI-driven service robots for tenant-facing operations.
While physical AI acts in the real world, the intelligence coordinating these actions is increasingly agentic. Unlike generative tools that create or summarize content, agentic AI can take a multi-step objective, reason through a workflow, call approved systems, and complete or escalate tasks within defined controls. In real estate, that could mean an agent that reviews a lease, flags anomalies against a standard playbook, and routes exceptions to legal. Agentic AI changes workflow design and decision rights, making governance, authority limits, and human accountability central to deployment.
Moving from Efficiency to Measurable ROI
Many companies are still unclear on the extent to which their AI investments are delivering business value. Companies must understand outcomes, determine key success factors, and set up indicators to measure return on investment (ROI) beyond efficiency gains and quality improvements. The organizations best positioned to benefit will be those that identify specific operating constraints, design workflows around people and technology, and measure outcomes in terms of safety, service quality, energy performance, asset uptime, and NOI.
Where are early leaders emerging? One industrial property owner and manager that’s been investing heavily in data analytics and AI for years said they’ve found measurable value in predictive leasing analytics, such as identifying tenants likely to expand. They said they’re also using AI for market rent forecasting and energy optimization across their portfolio. A pension fund operating in the areas of office, retail, industrial, and rental housing said measurable value is emerging for them in logistics site intelligence, underwriting and portfolio management workflows, and sourcing, screening, and asset-management decisions.
Another area where leaders are emerging is tenant experience. They’re using AI to support faster leasing, reliable service, better comfort, and proactive maintenance. One investment firm said they’re exploring digital assistants to handle tenant payments, manage service requests, and monitor building access.
How AI Leaders Are Navigating Common Constraints
Accessing the full benefits of AI requires overcoming real constraints. Interviewees cited data quality, cost, cybersecurity and privacy, and talent as top barriers to AI adoption. Some also mentioned the continued need for human oversight. And many larger firms must overcome the hurdle of legacy systems.
Before organizations can use data to make decisions, they must first improve their data consistency, structure, and governance. As one interviewee said, “Before AI can deliver real value, the data has to be clean, standardized, and usable.” A data strategy is a commercial strategy. Organizations need to make sure they own the data generated by their buildings, tenants, operating systems, and service providers. Without that, even a technically successful AI pilot is difficult to scale across a portfolio or convert into lasting competitive advantage.
Smaller or newer companies may be more agile, as they don’t have as much legacy data. In fact, in a reversal of past tech adoption trends, some private companies are leading the way in AI adoption, as many larger enterprises grapple with older systems, privacy concerns, and regulatory issues. This divide is set to compound through the upcoming generational transfer, with many younger leaders more willing to fail fast and move quickly. As the real estate industry sees increased consolidation, private companies that invest in AI and incorporate new ways of doing business will be more competitive and better positioned to attract capital.
One constraint that’s top of mind for interviewees this year is cost. Now that many AI providers have moved to token-based pricing, companies are realizing they need to be more disciplined in how and where they spend their money. And with the pace of technological change, some are afraid to make the wrong bet. One interviewee captured it well: “Things are changing and evolving so fast that you have to ask yourself, is now the time to invest or do I watch it evolve a little bit more first?”
When evaluating costs, it’s important to consider the entire life cycle. This includes start-up and ongoing operations, monitoring as AI and agentic AI evolve, data portability, vendor exits, and potential decommissioning costs.
Cybersecurity is another quickly growing concern. In addition to navigating customer data privacy, many companies worry powerful technologies could find vulnerabilities in their systems and expose them to increased cyber risks. As buildings, equipment, and autonomous systems become more connected, cyber risk moves beyond information technology. Owners and operators will also need to protect their operational technology, including building controls, access systems, life-safety interfaces, sensors, and robotics. Breaches to operational technology systems can disrupt physical operations as well as compromise data.
Risk management must be an integral part of a company’s AI strategy, with resources allocated to managing multiple risks—including cybersecurity, privacy, reputational damage, and loss of trust.
Investing in People—Not Just Technology
While many AI initiatives start from a technology function or are led by a single individual, several interviewees noted true transformation starts at the executive, board, or ownership level and brings multiple parts of the organization together. One interviewee described AI implementation as being led top-down with executive sponsorship, project management, and department-level accountability for outcomes.
As one real estate developer put it, “It’s an organizational reinvention.” Organizations have to build new project management teams to use technology well. Instead of departments, interdisciplinary teams tackle projects together, resulting in big gains.
As competition intensifies, some companies are struggling to find the right talent. PwC’s Global AI Jobs Barometer found that while investing in AI skills is essential, building judgment and leadership is just as important. Reskilling people also requires dealing with resistance to change. As one interviewee noted, the right training helps employees using AI become more comfortable with prompts and outputs. Another acknowledged some people find moving between different AI models challenging, highlighting the need for sustained support.
Next Steps: Delivering Even Stronger Outcomes with AI
The first wave of AI improved the work done around real estate. The next wave will improve and automate parts of the physical work of building, operating, maintaining, securing, and experiencing real estate. While AI may provide competitive advantage to early movers today, over time, adoption will be a requirement to compete.
How do organizations move from experimentation to measurable results? Three transitions will define the next phase: copilots to workflows, insights to decisions, and digital to physical. AI will be embedded in repeatable, high-volume business processes rather than individual productivity tools. Governed agentic AI will be used to identify opportunities, trigger tasks, and escalate exceptions while maintaining human accountability. Physical AI will be deployed in buildings, job sites, industrial facilities, and operational environments where improvements will create direct value.
Tomorrow’s real estate leaders will combine better data, human judgment, and intelligent physical systems to deliver the outcomes that matter most: faster decisions, safer operations, lower energy use, more reliable service, stronger tenant experiences, and higher-performing assets.
