Beyond AI Experimentation: Building an Enterprise AI Strategy in Real Estate and Construction

Laurie Lauletta-Boshart for Market Share Blog | October 7, 2026

Artificial intelligence is rapidly moving from an individual productivity tool to an enterprise-level strategy for real estate and construction companies. But as adoption accelerates, the question is shifting from what AI can do to how companies can deploy it effectively, measure its value and ensure it supports the work that matters most.

Will Mitchell, CEO of Rabbet, moderated a panel at CREDA Conference featuring Matt Boras, managing director of RXR Arden Digital Ventures, Ethan Argov, managing director, capital markets and investments of Trailbreak Partners, and Dusti Wofford, global head of digital and technology strategy for Trammell Crow Company. The panelists discussed how their organizations are using AI, where they are seeing measurable returns and what it will take to move beyond fragmented experimentation.

From Individual Experimentation to an Enterprise Strategy

At RXR, a New York-based vertically integrated real estate owner, operator and developer with approximately 30 million square feet of commercial real estate, AI adoption is supported by a roughly 10-person digital lab that works with property management, investment management and development teams to identify business problems that technology can solve.

“We’re pursuing multiple paths at once,” Boras said, including enterprise-wide access and education, department-level consulting, and identifying work that AI can tackle that previously would have been too time consuming.

Denver-based Trailbreak Partners has built an asset management platform from the ground up that consolidates information from multiple property management systems into a single dashboard covering its development and operating portfolio.

“Two years ago, the company looked very different in terms of staffing and overhead, and it’s a direct result of integrating AI into the business,” Argov said.

For Trammell Crow, the challenge is scaling experimentation. Wofford said the company initially encouraged practitioners to “go play” with AI and identify potential applications in their own jobs. That generated a significant backlog of ideas but also exposed the limitations of disconnected tools and agents.

“Having a bunch of agents working independently and not replicating the strategy across your enterprise will only get you so far,” Wofford said. “Today our focus is more on an enterprise AI framework.”