There’s More AI in Investment Than Meets the Eye: Roundtable

As AI tools evolve, more CRE investors are looking to implement and utilize them. From streamlining processes and automating document management to analyzing demographic shifts and external factors, using AI for investment can be a good strategy to generate added value.

Commercial Property Executive reached out to four experts who are familiar with implementing AI at the CRE investment level, to better map out the industry’s level of adoption of this new technology and figure out where it can provide the most value, as well as its most common pitfalls, drawbacks and challenges.

Here’s what Yao Morin, chief technology officer, JLL; Julie Ingersoll, chief investment officer, CBRE Investment Management; Jonathan Leiner, director of financial planning and analysis, Quinn Residences; and Ben Reinberg, founder & CEO, Alliance Consolidated Group of Cos., had to say.

Compared to other sectors, real estate has generally been slower to adopt new technologies. How are AI tools, as part of investment strategies, faring so far?

Ingersoll: AI tools in general have taken off with impressive speed across sectors, but let’s draw a distinction between applying AI tools to general productivity or business operations and applying AI directly to investment strategies. AI will take time to directly influence the investment process. But investment thesis development shouldn’t be the only focus: AI has tremendous potential to unlock efficiency and opportunity across the entire real estate investing value chain, from research, to tenant engagement, to report generation.

“AI is a great equalizer. Smaller investors who leverage AI can make data-driven decisions at a fraction of the cost as it once took, allowing them to compete on deal sourcing and market intelligence.” —Ben Reinberg, Founder & CEO, Alliance Consolidated Group of Cos.

Today, AI is integrated into our services, streamlining workflows and improving decision-making to create real value for our clients. There are also several other workflows outside of direct investment strategies where we’re already seeing value creation in leveraging AI, such as predictive model development for trend and asset performance forecasting, and integrated facilities management.

Leiner: We’re seeing this trend start to change as the industry integrates AI tools. Many large firms are working to incorporate AI projects and initiatives into their strategies. Down the line, we expect to see how leveraging this new technology potentially transforms the way investments are managed, and properties are developed.

On the construction and development side, the human-driven nature of building and constructing real estate has been a bit of a barrier for implementing AI tools. However, we’re beginning to see an increase in manufactured housing, which leverages supply chains for automation.

“Nearly 90 percent of leaders believe AI can help them solve major CRE challenges.” —Yao Morin, Chief Technology Officer, JLL

Morin: While adoption is a part of the puzzle, many companies and developers mistake AI adoption as the goal, rather than a means to solve challenges. Technology has always been a driver of change for real estate, but traditionally our industry has not offered best-in-breed products. At the end of the day, if you create an application that’s useful, and if you spend the time and resources to train your people on using the technology, then you’ll naturally see higher adoption.

Additionally, technology adoption is affected by both supply and demand. Previously, we didn’t have many dedicated tech solutions designed for real estate use cases and CRE decision makers would be hesitant to commit to new technologies without proven value adds. However, over the past few years, our industry has evolved to view technology as an important value driver, and as a result, we’re witnessing a growing ecosystem of real estate technology suppliers.

“Startup costs of LLMs are decreasing for consumers of AI. A commonly cited stat says that the cost of generating insights with AI has been decreasing—and will continue to decrease—by 10x every year. However, while AI itself is becoming cheaper, implementation, change management and the talent required to harness it remain significant investments.” —Julie Ingersoll, Chief Investment Officer, CBRE Investment Management

AI adoption is in full swing within CRE, with more than 700 companies actively developing AI-powered tech solutions—growing the supply—and 61 percent of companies starting to pilot different CRE use cases—demonstrating the demand.

Reinberg: CRE has historically been slower to adopt new technologies due to its relationship-driven nature and the complexity of deal structuring. However, AI is changing that, and Alliance went all in on retooling our entire operation on a multi-year strategy with a custom-built enterprise AI agent platform.

Like any new technology, it’s critical to pair automation with human processes. This means putting checks and balances in place to ensure accuracy for any AI-driven decisions being made. —Jonathan Leiner, Director of Financial Planning and Analysis, Quinn Residences

We’re looking to lead in our peer set, as we see a competitive window closing as AI tools are being integrated more rapidly now, especially in underwriting, asset management and market analysis. More and more firms are recognizing the competitive advantage in speed and accuracy.

What are some of the best use cases for AI by real estate investors, and why?

Reinberg: AI excels in predictive analytics, risk assessment and automation. We will use AI to analyze market trends, forecast property valuations, automate offer letter scoring for both on and off-market listings as well as identify emerging investment opportunities before they become mainstream.

Additionally, AI-driven automation improves operational efficiencies—whether it’s lease abstraction, tenant screening, or maintenance scheduling—saving time and reducing errors. It’s also a tremendous low-hanging tool for creating investment-grade content and engagement with our audiences of investors and ecosystem partners through our Substack and publishing arm.

Morin: CRE companies are embracing generative AI, and our research shows that nearly 90 percent of leaders believe AI can help them solve major CRE challenges. This fundamental belief is driving more and more companies to pilot use cases for AI and implement training plans to upskill their workforce. AI is impacting every stage of the investor journey from location strategy—where it can support market positioning, cost analysis and streamlined site selection—to lease administration and automating document management, compliance and auditing.

An example within JLL where we’re seeing high value for our investor clients is through JLL’s Investor Center, an integrated deal execution platform, where we have an AI-powered buyer list that connects sellers to new prospects, expanding our reach, producing more promising bids and leading to more wins.

Leiner: AI has had a transformative impact on real estate investment. One of the key benefits of AI has been the streamlining of revenue management. It’s become more efficient as the technology has helped optimize pricing strategies and rental income predictions.

AI has also been helpful from a customer-facing standpoint. Incorporating AI-driven approaches has helped make a significant impact in companies’ ability to create personalized campaigns that reach our target audience. We’ve seen a boost for customer service with the use of chatbots and conversational AI, which helps address potential resident leads with instant responses, but also help companies automate standard tasks.

Can AI aid smaller investors/firms to compete with the big players, or is it more likely to rather multiply the power of these larger companies?

Reinberg: AI is a great equalizer. Smaller investors who leverage AI can make data-driven decisions at a fraction of the cost as it once took, allowing them to compete on deal sourcing and market intelligence. However, larger firms with proprietary AI models and vast datasets will still have a significant advantage. The key for smaller investors is to integrate AI into their decision-making process without over-relying on it—human experience and relationships still and always will matter.

Leiner: I think smaller firms can definitely leverage AI to create an advantage and be disruptors or first movers. With that said, there is a built-in advantage for the larger companies, both in ability to spend and collective brainpower—dedicated teams working on solely AI and tech full-time.

Smaller companies must be more calculated about what they implement and how they implement it compared to larger companies that are more equipped to take a “fail fast” approach by trying several different things to see what works best.

Ingersoll: The beauty of how AI has progressed in recent years is that many of the efficiencies and capabilities it offers are immediately accessible to the individual—think about an individual analyst making their workflow more efficient using AI tools. Productivity unlocks like this aren’t reserved for big players. However, AI applications that analyze large pools of operational data or enrich interactions with tenants or operators become more impactful as they scale.

For instance, creating in-house GPT-based assistants that tap into proprietary data, like our “Ellis AI,” allows CBRE to provide AI capabilities to our employees with stringent security standards and responsible use of AI. This ensures the preservation of client and proprietary data while leveraging AI’s benefits.

How cost-effective is it to implement AI, in terms of hiring/training talent, developing proprietary tools etc.?

Ingersoll: Startup costs of LLMs are decreasing for consumers of AI. A commonly cited stat says that the cost of generating insights with AI has been decreasing—and will continue to decrease—by 10x every year. However, while AI itself is becoming cheaper, implementation, change management and the talent required to harness it remain significant investments. CBRE views AI often as a way to help our teams focus on the highest-value activities, helping eliminate work like data cleanup or summarization.

Reinberg: The cost-effectiveness depends on the scale of implementation. AI tools and platforms provide immediate value at a relatively low cost. However, developing proprietary AI agent platforms that are agnostic and allow for certainty and adaptation as the infrastructure technology improves with LLMs requires significant investment in data infrastructure, skilled talent and ongoing training. For most real estate firms, a hybrid approach—leveraging third-party AI tools while developing internal AI literacy—is the smartest and most cost-effective strategy.

Morin: JLL’s technology strategy is to “Build, Invest, Acquire and License.” We are very intentional in building what we believe is core to JLL and leveraging partners who excel in what they do. For example, we have built our AI platform, JLL Falcon, which allows us flexibility to build key capabilities while leveraging quickly evolving, best-in-class AI technologies. JLL Falcon enables us to implement AI across JLL in a cost-effective way.

What are some risks of adopting AI in investment strategies, and how can they be overcome? Alternatively, beyond these common risks, what would be a clear reason against the use of AI?

Morin: A key challenge with successful AI adoption is data quality and availability. Ultimately, your output will only be as good as the data you’re inputting into the tools. Also, the sheer amount of data that exists for a single property—let alone for an investor’s entire portfolio—is overwhelming, but AI reasoning can connect multiple data points to streamline insights and accelerate decision-making. I don’t see any reasons why an investor should be deterred from using AI, and in fact, think we are on the brink of realizing AI’s full potential for value creation across the CRE life cycle.

READ ALSO: CRE Investment in the Industrial Age

Leiner: While it’s great to adopt the latest technologies, there are naturally some risks that come along with integrating AI in real estate investment strategies. Most importantly is the financial consideration—it’s critical to ensure that the investments in AI are actually generating a positive return. It’s also important to be wary of cybersecurity and privacy, as inadequate controls can potentially expose the system to security risks and issues.

Like any new technology, it’s critical to pair automation with human processes. This means putting checks and balances in place to ensure accuracy for any AI-driven decisions being made. For AI, it’s vitally important to check that the quality of the data the technology will analyze is good. Similar to how it is for manual analysis, poor data can lead to incorrect or incomplete outcomes.

Lastly, another factor to consider when adopting AI technology is the team’s perspective. With any new technology, there is a level of hesitation or uncertainty. Questions arise, such as “does it impact my job? Am I at risk of becoming obsolete? It’s important during the adoption of AI for companies to provide assurance, training and understanding of how new processes will work to support them.

Ingersoll: Organizations should always take measures to use AI responsibly. One risk is data bias—any model that aims to make a prediction based on a set of underlying data is dependent on the quality of that data.

To give a simplified example, if you look to AI to identify new investment opportunities, and you train it only on your historical investments, you risk missing the same opportunities you missed in the past. We mitigate this risk by focusing relentlessly on improving the scope and quality of our data; we manage an enterprise-grade commercial real estate dataset, comprising 39 billion data points from more than 300 sources.

CBRE’s responsible AI approach involves data governance, human oversight, strategic alignment with business goals, a culture of responsible innovation and experimentation, and an ethical corporate philosophy—all aimed at unlocking AI’s potential while mitigating risks.

Are there any specific types of real estate that can benefit more from such tools, and why?

Reinberg: Asset classes with high transaction volume and operational complexity—such as multifamily, medical office and industrial—benefit the most. AI helps optimize portfolio management, tenant retention and pricing strategies in these sectors. Looking at medical office real estate trends, AI can analyze demographic shifts and health-care demand trends, giving investors an edge in identifying high-growth areas.

Ingersoll: AI offers significant opportunities for properties that require very “high touch” management, such as commercial office and large multifamily residential. The operators of these buildings navigate high levels of turnover that take lots of back-and-forth over lease terms, and ongoing coordination with tenants.

Some of the more straightforward parts of these interactions can be made easier with generative AI. Think, a chatbot that can answer tenant questions about amenities or billing, or an AI assistant for a building manager to generate custom reports on the performance of their properties. AI is also leading to more energy efficient building, with AI-enabled systems to control temperature, humidity etc.

Leiner: Data centers and towers, being more technologically advanced and forward-thinking compared to traditional real estate classes, are likely to benefit the most from AI adoption. Their inherent reliance on technology makes them prime candidates for leveraging AI to enhance operational efficiency and innovation.

Morin: A segment of real estate assets that could benefit greatly from AI tools are buildings that are not efficient and risk obsolescence as we inch closer to 2030 and companies’ targets to reduce emissions. Investors must respond to occupier needs, and smart buildings with sustainable upgrades is becoming a higher priority for them. Taking a look at industrial real estate market trends, two-thirds of top occupiers have commitments to reduce emissions and are focused on energy upgrades and clean energy procurement. AI tools can be instrumental in ensuring buildings are fit for the future.

JLL’s Hank uses machine learning, energy modeling and outside data sources to make real-time micro-adjustments and continuously optimize all HVAC equipment, reducing energy consumption and costs by 20 percent. Another AI-powered technology that is helping make buildings more efficient is VergeSense. VergeSense provides spatial intelligence, giving companies a true understanding of how spaces are being utilized so they can continuously and confidently optimize spaces to reduce costs and improve employee experiences.

The post There’s More AI in Investment Than Meets the Eye: Roundtable appeared first on Commercial Property Executive.