The Benefits of an AI-First Business Model for Commercial Real Estate Firms

The speed at which Artificial Intelligence (AI) has integrated into many facets of real estate during the past few years has led some industry experts to advocate for organizations to pivot to AI or risk being left behind.

Some commercial real estate (CRE) firms are boldly charging ahead with their implementation while others have chosen to move toward AI adoption more incrementally. Others are still trying to understand the basics of AI and whether its use is even feasible for their organization.

There are challenges for CRE firms when it comes to technology implementation for several reasons, including size of the organization and the unique needs of brokers. Data is not only sensitive, but also extremely valuable. Getting buy-in from brokers to share data and centralize it could be met with resistance.

Regardless of whether a CRE firm is an early adopter or still understanding the basics, a comparison between a traditional business model and one that is AI-centered can reveal new ways to address market challenges, drive innovation, and unlock value.

The Value of a Business Blueprint

An initial business blueprint should align a company’s long-term goals with its operational and technological capabilities. It should encourage coordination across business functions, avoid silos, and promote integrated decision-making. It can also be a bridge that integrates AI seamlessly into a firm.

Figure 1 is an example of a blueprint that emphasizes customer value and organizational agility. The customer value component focuses on value creation for clients and value capture for the firm. It should reflect how an organization develops its specific products or services. The organizational agility component focuses on how the firm can execute its business model by scaling its operations, broadening its scope, and continuously improving through learning and adaptation. The result should be an organization whose vision can be put into practice.

Advantages of Integrating AI Into a CRE Business Blueprint

CRE firms face many challenges, including market volatility, increasing customer expectations, and operational inefficiencies. Integrating AI functions into the business blueprint may result in some significant improvements and solve those challenges.

First is agility enhancement, where AI-powered predictive analytics allow CRE organizations to anticipate market trends and adjust strategies proactively. Enhanced decision-making using real-time data and insights can provide quick responses to changing customer needs or market conditions.

AI technologies can augment data-driven functions, such as lease abstraction or predictive maintenance. Manual processes can be automated, reducing errors and improving operational efficiency. Advanced analytics help uncover useful insights, improving performance metrics like occupancy rates, tenant satisfaction, and energy efficiency.

AI functions can be trained to focus on the customer, where tools like conversational AI, recommendation engines, and personalized marketing improve tenant and customer experiences. Integrating customer data into the business model can create tailored services and solutions that can build loyalty and trust.

Data can be acquired by CRE firms from three primary sources. The first option is public data such as census or openly available market data. Second is internally generated private data, such as tenant information, property performance, or any operational metrics. Third is subscription data where datasets are purchased from external providers for use in market insights, trends, or analytics.

AI can impact company performance by refining the search for new business processes, such as dynamic leasing or demand-based pricing. The result can be increased innovation and value creation. Continuous learning through AI feedback loops can improve a CRE firm’s operations, enhancing the company’s scope and scalability while driving profitability.

By embedding AI into the customer-value and organizational- agility components of a business blueprint, CRE organizations can take an initial step toward acquiring a competitive edge, helping to position them as market leaders.

Is Your Firm Using a Traditional Business Model?

Figure 2 represents a traditional CRE business model where structure is built around hierarchical layers and functional silos. The figure offers insight into the challenges faced by many CRE organizations looking to adopt AI technologies.

In a traditional business model, leadership represents the central decision-making authority, overseeing the entire CRE organization. The core consists of multiple lines of business, such as leasing, property management, development, or investment. Each line operates semi-independently within its defined scope. Shared services include cross-functional departments that support all lines of business, including:

• Human Resources: Manages personnel and recruitment.
• Legal: Handles contracts, compliance, and legal matters.
• Finance: Manages budgets, accounting, and financial reporting.
• Information Technology: Provides technological infrastructure and support.
• Other: May include marketing, procurement, or risk management.

The Trouble with a Traditional Business Model

Geographic divisions are physical silos the organization operates under. Each geography typically aligns with a specific region, market, or portfolio. The traditional siloed model often presents numerous challenges. Business is often conducted in isolation. Each division, function, and line of business may use its own software, systems, processes, and data. As a result, this model may create significant barriers to the scope, scale, and learning component that can generate long-term value for customers.

Such a fragmented structure makes it challenging to share insights and best practices across the different silos. Firms can experience knowledge transfer issues, and a lack of unity between geographic divisions and departments can lead to inefficiencies. Isolated teams can struggle to adopt new technologies or innovations when knowledge transfer and coordination are more difficult.

A traditional structure can also result in redundant processes, missed opportunities for collaboration, and delays in decision-making. Different systems and software can hurt the ability to centralize and analyze data for strategic insights.

The Alternative: A Platform “AI-First” Business Model

An AI-first business model may provide the necessary integration to connect organizational agility to customer value. This model is designed to address inefficiencies and offer several advantages over the traditional model. It can provide a holistic view of operations, resulting in more informed decision making. Figure 3 illustrates a model with AI at its heart. This alternative is a technology- driven approach to transforming the traditional CRE business structure.

A critical component of this model is an AI-platform virtuous cycle containing a core feedback loop. Within the loop, increasing amounts of data are generated and collected as the platform is used. With more data, a firm’s specifically tailored AI algorithms (machine learning) will improve through continuous learning and refinement. Improved algorithms will provide more value for the firm and enhance the customer experience. More value will encourage more usage, which feeds back into generating more data, completing the cycle. The AI-platform virtuous cycle ensures ongoing refinement of AI algorithms and processes, keeping the organization competitive and innovative.

The brain of the model within the central core-technology layers is the Core AI, which leverages AI tools for decision-making, automation, and analytics across all CRE processes. Next are integrated systems that connect various CRE functions, ensuring seamless communication across all platforms. Finally, a unified data layer acts as an automated data warehouse that collects and organizes all CRE-related data, eliminating silos.

The green boxes in Figure 3 represent CRE functions that are enhanced by Application Programming Interfaces (APIs) linking them to the AI platform. An API is a set of protocols or routines that act as a bridge, enabling communication between different software applications. APIs enable each function to interact with the core AI and other systems, ensuring real-time data exchange and collaboration. They also ensure that the system can adapt and scale up to include new functions, markets, or technologies with the least amount of disruption.

In CRE, APIs can serve several key functions:

• Improve lease administration and tenant analytics.
• Provide predictive insights to foster better tenant relationships.
• Streamline project workflows and organize resources.
• Improve planning, procurement, and inventory control.
• Provide data-driven property valuation and investment insights.
• Enhance capital allocation and risk assessment.

Data Can Be the Catalyst in Many CRE Firms

Figure 4 outlines an AI-driven CRE company “shop” that manages and uses data as the foundation for business strategy and operations. The figure illustrates a comprehensive outline for CRE companies where data drives the business. For this model to work, the data must be free of errors and inconsistencies. It must also be adequate in volume for analysis, standardized, consistent, and reliable.

Appropriate algorithms must be created to take advantage of AI’s learning methods used to process data and derive insights. The first method of learning is unsupervised, meaning hidden patterns can be detected without more expensive “labeled” data (where tags or classifications must be assigned to each individual datapoint). Supervised learning is more sophisticated, predicting outcomes based on labeled data. Finally, reinforcement learning optimizes decisions by using feedback loops.

The basic components enabling AI operations, the “enterprise infrastructure,” are organization, the internal setup that helps a company manage and oversee its AI projects; platform, a central system where data is processed and AI applications run; and software, the specific tools and programs used to analyze and work with the data. The final product should lead to better results across all activities, creating value for the CRE firm.

Questions to Ask Before Adopting AI

1. Does your firm have a data strategy?
2. Does your firm have the right people?
3. Does your firm have the right business blueprint?
4. Are you asking the right questions?
5. Can your firm develop the algorithms to answer your questions?
6. Can your firm test and validate the answers?
7. Can your firm commercialize the products & services?

Transition to AI

Firms with no desire to go through the AI discovery process directly should at least identify goals for AI adoption. This could include the desire to reduce costs, improve tenant experiences, or gain competitive insights. At a minimum, research the basics of AI to better understand its potential.

Next, find AI experts and strategists who can align specific business needs with AI solutions and identify areas where AI could create value for the firm. Before hiring any firm or individual, look for a proven track record and list of satisfied clients. Business process consultants with experience in real estate can evaluate workflows, identify inefficiencies, and recommend how AI can address any gaps. However, they may not provide the actual AI tools or systems.

Technology vendors specializing in AI applications for real estate firms are another option. Reonomy, Yardi, and CompStak all offer off-the-shelf AI tools for tasks such as tenant behavior analysis, property valuation, or improving operational efficiency. CoStar Group offers AI-based products that can conduct property valuation, predictive analytics, image recognition, and natural language processing.

CRE firms can also opt for in-house data scientists or AI engineers who have the skills to analyze existing data and develop custom AI tools. At some point, legal and compliance experts will be necessary to ensure any AI implementation follows all industry regulations and data privacy laws.

Endless Possibilities

Expectations are extremely high for AI’s impact.

“Sometimes people say that data or chips are the 21st century’s new oil, but that’s totally the wrong image,” according to Mustafa Suleyman, CEO of Microsoft AI. “AI is to the mind what nuclear fusion is to energy: limitless, abundant, world changing.”

Time will tell if that’s the case, but AI is no longer just a product. It has already become a phenomenon that could potentially change a CRE firm’s thoughts, actions, and identity.

Harold D. Hunt, Ph.D. ([email protected]) is a research economist with the Texas Real Estate Research Center; Stephen A. Ramseur is executive professor for Texas A&M’s Master of Real Estate program and holds the Julio S. LaGuarta Professorship in Real Estate; and Bucky Banks ([email protected]) is associate director and executive assistant professor for Texas A&M’s Master of Real Estate program in Mays Business School.