AI and Robotics Reshape Real Estate Work and Labor

Artificial intelligence is accelerating commercial real estate transactions while investment in physical robotics expands, creating new opportunities and challenges for businesses and workers. At Commercial Observer's Power Tech & AI Forum at 10 Grand Central in New York on October 19, 2026, industry leaders discussed current productivity gains and predicted that humanoid robots could significantly change real estate operations beginning in 2027.

Separately, global investment in robotics and physical AI reached $33.4 billion in the first half of 2026, according to Business Insider reporting published by Yahoo Finance. The sector attracted less than $5 billion in funding during all of 2020, highlighting the scale of its recent expansion.

At the Commercial Observer event, industry participants described how AI is reducing the time required for legal reviews, property analysis, and construction planning. Their discussions also highlighted concerns about implementation costs, reliability, and the implications of automation for employment and working hours.

Abhinav Somani, founder of Cadastral and head of real estate for Legora, identified humanoid robotics as a major development expected to affect the property industry.

"For real estate in particular, the big shock is going to be the humanoid robots that will start getting released next year," Somani said.

He distinguished physical AI from existing systems that primarily process text, pointing to the potential of technologies that interpret visual information, speech, and sensory inputs.

"That is really going to change everything we've done. From 2D text-based AI to 3D vision, voice and sensory details, this has more long-term implications for real estate than producing a legal bill," Somani said.

Somani also described document processing as an immediate application for property professionals handling complex transactions.

"When we think about practical uses of AI, you can make it through transactions much faster by expediting reviews of documents and negotiations," Somani said.

He recommended starting with established, straightforward applications rather than attempting to automate every business function at once.

"When we think about giving practical advice, we say, go for the lowest hanging fruit possible. Today, what we're seeing is a tremendous amount of adoption of legal AI technologies, both to law firms and in-house counsel, to basically accelerate real estate transactions," Somani said.

Zach Aarons, co-founder and general partner at MetaProp, said concerns about the accuracy of AI-generated results had previously limited its usefulness. His firm has focused investment on applying AI to preconstruction work, where large volumes of technical documentation must be reviewed.

"No matter how cool AI was two years ago, it was worthless to [most people] because you questioned the accuracy of the output," Aarons said.

Advances combining computer vision and generative AI are enabling systems to examine construction plans, specifications, and related documents for inconsistencies and potential errors.

"millions of pages of construction documentation, spec sheets and more," Aarons said, describing the material AI agents can process.

He cited Handoff, a MetaProp portfolio company that performs material takeoffs for residential construction projects. According to Aarons, the system achieved 97 percent accuracy in testing, compared with 94 percent for a human team performing the same task, while completing the work 20 to 50 times faster.

"We backtested it," Aarons said.

Joseph Sipala, managing director at JLL, reported similar efficiency gains in property analysis and lease reviews after AI tools were incorporated into the company's platform.

"Because of tools like Legora, Claude and others that we've built into our JLL platform, we can turn around portfolio analyses and single lease reviews in a quarter of the time," Sipala said.

He said tasks that previously required a week could now be completed in a day, making this level of efficiency an expected capability rather than a distinguishing advantage.

For property owners, Nick Durst, principal and director of operations at the Durst Organization, described how AI can speed up assessments of potential development acquisitions.

"We can load a data room into a program and be able to understand the ins and outs of a property in minutes," Durst said.

Somani expects businesses to move beyond general-purpose AI products toward systems customized with company-specific expertise.

"Companies are going to ask, 'How can I give our AI tools some of our institutional knowledge and some of the things that make us special so that we can have an edge in the market?' versus just having AIs go back and forth between each other," Somani said.

Aarons likewise anticipates companies will select different models for individual tasks, emphasizing cost efficiency rather than relying on one tool across all operations.

"You're going to see people in our industry getting more comfortable with using different models for different parts of their workflow," Aarons said.

The broader robotics industry faces obstacles beyond software accuracy. Yahoo Finance's report identified the difficulty of replicating human hand dexterity, the expense of developing physical hardware, and the challenges of scaling manufacturing without multiplying production errors.

Nvidia CEO Jensen Huang has valued the potential physical AI market at $50 trillion, according to the report. However, substantial investment does not eliminate the engineering and manufacturing barriers facing companies developing robots for construction, industrial work, and domestic tasks.

Employment remains another unresolved issue. Companies frequently describe AI as a means of supporting human workers, but robots designed to perform physical tasks introduce different questions about how much labor can be automated.

Edward Mehr, CEO of robotics startup Machina Labs, previously told Business Insider that his new factory employed the same number of human workers as a comparable facility without robots.

Meanwhile, a study of 200 employees by researchers at UC Berkeley, described in an opinion article published by The Daily Illini, found that workers using AI produced more output and worked longer hours than employees who did not use it. The findings highlighted how efficiency gains can translate into increased workloads rather than additional free time.

The Daily Illini article also cited a BambooHR survey in which American full-time workers reported feeling pressure to appear productive. Its argument centered on whether employers should allow workers to finish earlier or shorten the workday when AI reduces the time needed to complete standard responsibilities.

At the Commercial Observer forum, moderator Cher Chang, founder and CEO of Literock Capital and partner and head of capital markets at JLJ Capital, contrasted the industry's earlier enthusiasm for AI with its expanding practical applications.

"Three years ago, I did an AI panel for the first time. At the time, there was a lot of excitement, no applications and a lot of predictions," Chang said.

She said the industry's position had changed substantially as businesses began implementing AI in everyday workflows.

"Today, it's a completely different ballgame," Chang said.

Commercial Observer Deputy Editor Tom Acitelli also noted that earlier technologies had delivered uneven results across real estate, while AI appeared capable of bringing broader changes to the industry.

"In my tenure covering real estate, I've watched dozens of technologies arrive on the scene and promise to shake up the business," Acitelli said.

He contrasted technologies that gained traction with others that failed to establish themselves, citing the Metaverse as an example.

"Some have done just that. Others, like the Metaverse, never found their footing. But I don't think any have brought the full scope of upheaval and disruption that AI has," Acitelli said.