While I have been somewhat silent and have not published anything on AI yet, I've been keenly following the rapid development of the domain as well as conducting some hands-on experience with the latest models. The development is amazingly fast, and most likely after no more than half a year, there will be more use cases than these, with the current ones having been completely renewed already.
Since the potential use cases are almost limitless in real estate, I'll focus here only on those relevant to Investment and Asset Management within Commercial real estate. I want to be extremely clear that most of these are not created by me, but by noteworthy practitioners in the area, such as Nikodem Szumilo, Scott Dunphy, Kyle Walker, among others. All credit goes to the original authors. Then, I am not an advocate for any specific model, since most of the latest models - or a combination of - can do the heavy lifting for a number of use cases. Now, let's examine these five use cases.
The investment analysis landscape in commercial real estate is being revolutionized by AI tools that process vast amounts of market data in seconds. As highlighted by Nikodem Szumilo, AI can now analyze investment opportunities with unprecedented depth and accuracy.
These systems evaluate property metrics, market trends, and risk factors simultaneously — tasks that would take human analysts days to complete. They identify patterns in historical data that might escape human attention, leading to more informed investment decisions.
What makes this valuable is the ability to run multiple scenario analyses instantaneously. Asset managers can test how different market conditions might impact their investments, allowing for more robust risk management strategies. The AI doesn't replace human judgment but augments it by providing deeper insights and helping eliminate cognitive biases.
Nikodem Szumilo has demonstrated how AI has potential to transform the underwriting process by automating the journey from document review to discounted cash flow (DCF) model creation.
Today's AI tools scan offering memorandums and market reports, extracting critical financial data and property metrics in seconds. This information is then automatically organized to populate DCF models, dramatically reducing the time required for initial analysis.
What previously took junior analysts days to compile can now be accomplished in minutes, with higher accuracy. This speeds up the underwriting process and allows investment teams to evaluate more potential deals, improving the chances of finding optimal investment opportunities.
Scott Dunphy has showcased how tools like Google's Gemini can integrate with Google Maps to provide unprecedented insight into location-specific data, while Kyle Walker has demonstrated powerful applications combining large language models with statistical analysis tools.
These AI systems can analyze neighborhood dynamics, track tenant migration patterns, monitor competitive properties, and identify emerging market opportunities before they become widely recognized.
The power lies in AI's ability to synthesize information from diverse sources—public records, satellite imagery, social media trends, and economic indicators—creating a comprehensive view of market conditions impossible for human researchers to compile manually.
For asset managers, this means having access to deeper market intelligence that informs both acquisition decisions and ongoing portfolio management.
Document processing is perhaps one of the most immediately practical applications of AI in commercial real estate asset management. The industry has always been document-heavy, with critical information buried in leases, contracts, invoices, and financial statements.
I've seen at least five cases where AI has been applied to extract data from documents into a coherent structured dataset. Two cases in Germany involved extracting data from "Exposés" to streamline the acquisition pipeline. Another German case focused on extracting data from energy bills for the use in ESG reporting.
Lease extraction has proven valuable in multiple markets—with implementations in Denmark, Finland, and Germany. I believe lease extraction offers one of the highest ROIs among especially LLM applications, though it remains challenging to perfect.
At Assetti, we've been applying AI in extracting data from financial statements of different legacy systems from multiple property managers to create coherent numbers for Soll-Ist-reporting. The business impact is substantial: faster data processing, elimination of manual entry errors, and more accurate property information.
The most exciting development is the emergence of "agentic workflows" as demonstrated by Thomas Burns. These AI agents can autonomously execute multi-step processes with minimal human intervention.
Unlike simple automation tools, AI agents can make decisions, adapt to changing conditions, and coordinate multiple systems to complete complex tasks. For example, an AI agent could monitor property performance, identify upcoming lease expirations, analyze market rents, generate renewal proposals, draft tenant communications, and track responses.
This level of automation frees asset managers from routine tasks, allowing them to focus on strategic decisions. The technology is particularly valuable for organizations managing large portfolios where routine tasks can overwhelm human teams.
While I promised to stay focused on asset management, I can't help but mention an additional generative AI use case. If you want to understand the difference between predictive and generative AI, follow Antony Slumbers, who has done groundbreaking work in this area. The case worth mentioning of the latter is the enhancement of property imagery, giving brokers a powerful tool for marketing properties more effectively.
These five use cases represent just the beginning of AI's transformation of commercial real estate asset management. This is not an exhaustive list. More will emerge as the technology evolves.
If you're interested in streamlining your asset management operations with the support of AI, we are ready to assist. The first step is to reach out about your specific needs.
The future of commercial real estate asset management will belong to those who can effectively harness AI to enhance human expertise — not replace it. The combination of human judgment and AI capabilities creates a powerful synergy in today's complex and competitive market.
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