Artificial intelligence is fundamentally altering the commercial real estate (CRE) sector, shifting focus from routine tasks to high-value strategic analysis. Industry leaders believe AI will enhance productivity and provide deeper insights by processing vast amounts of data, ultimately changing how professionals work and make decisions.
According to executives at global real estate advisory firm Avison Young, the integration of AI is not about replacing human experts but empowering them with more powerful tools. This transformation hinges on overcoming significant data challenges and strategically embedding AI into daily operations to unlock new efficiencies and predictive capabilities.
Key Takeaways
- AI is expected to significantly boost productivity by automating time-consuming data analysis in commercial real estate.
- The primary challenge for AI adoption in CRE is the poor quality and unstructured nature of existing industry data.
- Future roles for real estate professionals will likely shift from transactional work to strategic advisory and client engagement.
- AI's ability to analyze non-traditional data sources will provide deeper, more comprehensive insights into property performance.
- Firms are moving toward becoming "AI-native," embedding AI tools directly into their workflows for predictive analytics and scenario modeling.
AI's Dual Impact on Real Estate Operations
Artificial intelligence is poised to deliver two primary benefits to the commercial real estate industry: enhanced productivity and expanded knowledge. Martin Jepil, Chief Information Officer at Avison Young, explained that AI models can analyze diverse databases to identify patterns and connections that are difficult for humans to spot quickly.
This capability allows real estate teams to reduce the time spent on data-heavy projects, freeing them to pursue more opportunities and focus on client-centric activities. "With the potential to decrease the time intensity of projects, AI will increase the number of opportunities that can be handled by our teams," Jepil noted.
Beyond efficiency, generative AI offers significant gains in knowledge. It can answer complex questions about asset performance by cross-referencing multiple data sources that were previously siloed or too cumbersome to analyze manually.
Practical Applications of AI Insights
For property owners and investors, AI can simplify complex analyses. For example, it can determine peak traffic areas within an office building to optimize space utilization or identify cyclical patterns in energy consumption to improve building efficiency. These insights, derived from both quantitative and qualitative data, lead to better-informed decisions about asset management and location strategy.
The Road to an AI-Native Future
Real estate advisory firms are now charting a course to integrate AI deeply into their core functions. According to Jepil, who joined Avison Young from Hewlett Packard, the vision extends beyond simple adoption to becoming fully "AI-native." This involves embedding prompt-based features and machine learning capabilities directly into the firm's operational technology.
"Our next step is to become AI-native," Jepil stated, outlining a plan to automate routine tasks and shift the company's technological focus toward advanced analytics.
"This allows us to shift our people’s focus to high-value work such as business development, service delivery, and client engagement, while deepening our technology’s focus on predictive analytics, scenario modeling, and, ultimately, delivering better decision-making."
This transition will leverage AI's strengths in summarization, translation, and deep research. Given that the CRE industry relies heavily on unstructured written data like contracts and market reports, these AI applications are particularly valuable. The goal is to evolve the role of real estate advisors from transactional agents to strategic consultants who can provide more sophisticated advice.
Overcoming the Data Quality Hurdle
The most significant obstacle to widespread AI adoption in commercial real estate is the quality of available data. While the industry possesses a massive amount of information, much of it is unstructured, inconsistent, and not properly indexed for machine analysis.
"In our industry and beyond, the biggest hurdle continues to be quality of data," Jepil emphasized. This includes traditional CRE data points as well as a wealth of untapped, non-traditional sources from across the internet that can provide context about a property.
Integrating Diverse Data Sources
Effectively training AI models requires organizing and integrating various types of information. This includes:
- Traditional Data: Sale and lease comparables, property listings, and zoning codes.
- Non-Traditional Data: Demographic trends, local business activity, and online sentiment.
- Organizational Data: Internal company information and client-specific metrics.
Interestingly, AI itself can be part of the solution to this problem. AI-powered tools are now being used for data extraction and interpretation of unstructured documents, helping to build the organized database infrastructure necessary for more advanced machine learning applications.
AI as an Empowerment Tool
Industry leaders are keen to frame AI not as a replacement for human jobs but as a powerful tool that enhances human capabilities. Mark Rose, Chair and CEO of Avison Young, compares the introduction of AI in CRE to the adoption of power tools in the construction industry.
The Power Tool Analogy
When power tools were introduced, they didn't eliminate the need for skilled builders. Instead, they made builders more efficient, improved safety, and enabled them to tackle more complex and ambitious projects. Similarly, AI is expected to augment the skills of real estate professionals, not make them obsolete.
This perspective suggests that AI will handle routine data analysis and automation, allowing brokers, developers, and property managers to concentrate on areas where human skills are irreplaceable. These include building relationships, strategic negotiation, and understanding the nuanced needs of clients and building occupants.
"AI is not here to replace brokers, developers, or property managers but to give them more powerful tools," Rose and Jepil concluded in their discussion. The ultimate value will come from combining AI-driven data insights with human expertise and interpretation.
Strategic Adoption and the Human Element
The long-term success of AI in commercial real estate will depend on thoughtful and strategic implementation. According to Rose, firms that thrive will be those that develop a clear vision for how employees' roles will evolve alongside the technology.
This strategy involves more than just purchasing software; it requires building the right IT infrastructure and carefully selecting AI tools that align with business goals. Employees who embrace these new tools will gain a deeper understanding of market dynamics and their impact on real estate assets.
Ultimately, the human element remains the key differentiator. "We believe that people, not technology alone, will be the key differentiator in the age of AI," Rose affirmed. The success of any property is determined by the satisfaction of its tenants and users. Therefore, the priority will always be on understanding and anticipating human needs—a task that requires on-the-ground presence, personal relationships, and intuitive judgment that technology alone cannot provide.
