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erguson Partners, a leading talent management and strategic advisory firm for the global real assets industries, has released its first Artificial Intelligence Pulse Survey. The study aimed to understand AI adoption, application, and evolution in the commercial real estate sector.

    Thirty-three real estate investment firms participated in the survey, representing both private and public organizations of varying sizes, strategies, and locations. The results show a growing trend towards exploring AI.

    "The key takeaway from this survey is that real estate professionals need to consider embracing AI to stay ahead," said Justin Pellino, Director at Ferguson Partners. "Successful organizations will be those that draw boundaries early and encourage experimentation with AI technology."

    Major findings of the survey include:

    * 84% of respondents are actively experimenting with or implementing AI:

     + Nearly a third (30%) have already implemented AI in select business practices.

     + Roughly a quarter (24%) reported using AI for personal productivity, with Microsoft Copilot being the most common tool.

    * Participants are exploring AI in areas such as CRM database analysis, tenant work orders, financial and accounting processes, leasing analysis, customer service, rent and asset valuation predictive analytics, lease administration, and more.

    * 87% of respondents allow employees to use AI tools, but with restrictions around usage:

     + 48% enforce restrictions, particularly concerning data privacy and public AI models like ChatGPT.

     + 12% prohibit AI tools entirely in day-to-day operation.

    * Increased efficiency and productivity are the top motivations for incorporating AI (100%). Secondary motivations include cost reduction (67%), improved decision-making (61%), enhanced risk management (48%), and better customer service (39%).

    * Document abstraction is believed to be the business area with the greatest potential for AI impact (64%).

    * The primary challenges in AI adoption cited by survey participants include a lack of understanding of AI applications (67%) and difficulty demonstrating clear use cases or ROI (58%). Other reasons include data quality and availability issues, concerns over information security, and lack of skilled talent.