Commercial real estate has consistently lagged behind other industries in adopting technological innovations, surviving multiple digital waves with minimal structural change. While listings migrated from newspapers to online platforms and CRMs replaced physical Rolodexes, brokers in 2026 still typically navigate five or six disconnected systems before making a single phone call. This fragmentation stems from the industry's relationship-based nature, where data has historically been hoarded as a strategic advantage rather than shared through centralized systems.
Dan Mosher, Co-Founder and CEO of DealGround, explains that commercial real estate's core transaction model—built on personal relationships cultivated during lunches, golf outings, and drinks—never developed a centralized exchange like financial markets. This created an environment where proprietary data became currency, and brokers adopted point solutions like CRMs, comp databases, and property management systems individually, resulting in workflows requiring eight to ten separate tools that don't communicate.
The industry's advanced brokers now operate through remarkably detailed yet cumbersome processes, starting in one system, pulling data into another, sometimes processing it through ChatGPT, dropping results into Google My Maps with color-coded pins, switching systems for contact information, and finally checking their CRM before making an informed call. According to Mosher, the fundamental problem wasn't technology absence but rather the broker's role as connective tissue between specialized tools that each performed single functions well.
Artificial intelligence represents a fundamentally different technological wave because it directly addresses commercial real estate's unique data characteristics. The industry's mountain of unstructured data—including offering memorandums, Excel spreadsheets, lease documents, surveys, due diligence materials, property notes, and contact information—is precisely what AI systems are designed to process. Large language models excel at synthesizing exactly this type of messy, siloed information that previously resisted digitization efforts.
DealGround was built around this insight, creating a single platform where brokers, investors, and analysts can centralize private data, access public property and title records, and query everything through an AI-powered interface. Rather than adding another point solution to the existing stack, the platform aims to become the industry's database of record, tracking everything from new tenants and lease expirations to rent increases and loan maturity dates in one unified system.
Mosher describes this as the third wave of commercial real estate transformation, characterized not by adding AI features to existing workflows but by collapsing the entire technological stack. The envisioned future involves one system that knows everything a broker knows, surfaces relevant insights at appropriate times, and frees professionals to focus on relationship-building and deal-closing activities where humans outperform software.
The platform's objective is shifting broker time allocation from administrative legwork to relationship development by automating research and flagging opportunities like upcoming lease expirations or loan maturities. This enables brokers to contact owners with well-informed, timely propositions. After decades of technological lag, Mosher believes conditions for genuine transformation now exist: the data is available, AI tools are mature, and even traditional brokers are questioning whether better approaches exist.
This shift represents more than incremental improvement—it addresses commercial real estate's fundamental structural challenges that resisted previous technological waves. The fragmentation that frustrated earlier digitization attempts becomes the exact problem modern AI tools solve, potentially giving early adopters significant competitive advantages in an industry where information asymmetry has long determined success.
