Clean Data, Smart Decisions: Implementing AI Successfully in CRE

Commercial real estate firms are rapidly increasing technology budgets to adopt artificial intelligence, yet a significant majority remain unprepared for strategic integration due to foundational data issues. While 87% of companies are boosting tech spending according to JLL, the industry's historical focus on deal speed over data consistency has created a "dirty data" problem that undermines AI effectiveness. Establishing robust data governance and common business definitions is now a critical prerequisite for firms looking to leverage AI for competitive advantage in investment modeling and property management.

Dave DuVarney, a principal at Baker Tilly, emphasizes that the commercial real estate (CRE) sector’s transition from a "deal house" culture to a data-driven one is fraught with challenges related to data quality. According to JLL, while 87% of firms are increasing their real estate technology budgets, 60% are currently unprepared for strategic AI integration. DuVarney notes that AI cannot fix "dirty data"—it only makes existing errors more visible—and warns that a lack of shared business language, such as inconsistent definitions for Net Operating Income (NOI), often leads to decision-making breakdowns across metro areas like Milwaukee.

To build a successful AI strategy, CRE companies must first establish a solid data foundation through governance and an operating model that ensures data is sourced and aggregated consistently. Data often breaks down as properties move between different systems for investment, asset, and property management, making tracking and consistency difficult to maintain. DuVarney argues that accountability on the input side is essential; without it, AI-powered results will be unreliable, regardless of the technology's sophistication.

AI currently adds the most value to CRE in back-office workflows, investment risk modeling, and contract management by making previously cost-prohibitive tasks like large-scale data aggregation feasible. The technology is being utilized for automated property valuation models and due diligence, though DuVarney stresses the importance of a "human in the loop" to provide domain knowledge and accountability. Ultimately, firms that prioritize data literacy, cybersecurity, and iterative governance over chasing isolated pilot projects will be best positioned to gain a competitive advantage in the evolving landscape.