A.I. & Commercial Real Estate: A Q&A with Niraj Patel From Greystone

In the rapidly evolving landscape of commercial real estate, artificial intelligence is emerging as a game-changing force. However, as with any transformative technology, its true potential remains largely untapped. To shed light on this critical juncture, MBA NewsLink sat down with Niraj Patel, AI and Digital Transformation Executive at Greystone, to discuss the challenges and opportunities that AI presents to the industry.

Niraj Patel is a business executive with Board of Director and C-level experience in global roles ranging from startups to Fortune 500 companies. He has both corporate and start-up experience in technology, operations, business ecosystems and internal/external innovations.

Patel is a pioneer in the design and management of digital enterprises powered by RPA, data, analytics and AI across the enterprise. His previous leadership roles include Chief Strategy Officer, CIO, Board Member, Adjunct Professor, co-founder, Diversity & Inclusion Champion and president. He also has experience with private equity and venture-backed companies.

MBA NewsLink: The commercial real estate industry is just beginning to grasp the implications of AI. How should various stakeholders be approaching this new reality?

Niraj Patel: We’re at a fascinating inflection point in the commercial real estate industry. AI isn’t just another tool; it’s a paradigm shift that has the potential to redefine how we operate. However, we’re facing a significant challenge: many in our industry are still thinking about AI in outdated terms.

There’s a common misconception that AI is all about crunching large datasets to make predictions or decisions. People often ask, “I have a lot of data, can you help me make a decision?” This approach, while valuable, is rooted in traditional analytics and machine learning models that require extensive training on thousands of files.

But the landscape has dramatically shifted. The new frontier of AI is dominated by Large Language Models (LLMs) and Small Language Models (SLMs) that can be fine-tuned for specific tasks. These models don’t necessarily need massive amounts of company-specific data to be useful. Instead, they come pre-trained on vast amounts of general knowledge and can be adapted to understand and assist with industry-specific tasks.

This shift means we need to fundamentally rethink how we approach AI in our industry. Instead of just looking at our data and asking what insights we can extract, we should be asking: “What knowledge-intensive tasks can we now automate or augment? How can we leverage these models to enhance our decision-making processes, even in areas where we don’t have extensive historical data?”

MBA NewsLink: What resources or training tools do you believe will be crucial in bridging this understanding gap?

Niraj Patel: Education is absolutely critical, but we need to update our educational approach to reflect the new AI paradigm. We need comprehensive training programs that go beyond explaining traditional data analytics and delve into the capabilities and applications of LLMs and SLMs.

These programs should cover how these models work, their strengths and limitations, and most importantly, how they can be applied to solve real-world problems in commercial real estate. We need to shift from a data-centric mindset to a task-centric one: “What tasks can these models help us perform more efficiently or effectively?”

We should create experimental environments where employees can interact with these AI models, fine-tune them for specific tasks, and see the results firsthand. This hands-on experience is invaluable in demonstrating the power and flexibility of modern AI.

Moreover, IT departments need to step up and become evangelists for this new approach to AI. It’s not enough to simply deploy tools; IT needs to actively demonstrate how these models can be fine-tuned and applied to various aspects of our business, from property valuation and market analysis to contract review and customer service.

MBA NewsLink: How do you see the competitive landscape in commercial real estate shifting as AI becomes more prevalent?

Niraj Patel: The competitive landscape is going to be dramatically reshaped by this new paradigm of AI. The winners in this new era will be those who can effectively leverage LLMs and SLMs to create unique value propositions.

We’ll likely see a widening gap between those who understand and embrace this new approach to AI and those who remain stuck in the old paradigm of data-heavy analytics. Companies that can effectively fine-tune and deploy these language models will gain significant advantages. They’ll be able to automate complex, knowledge-intensive tasks, provide more personalized services, and make more informed decisions even in areas where they don’t have extensive historical data.

For example, a company could fine-tune an LLM to understand the nuances of commercial lease agreements. This model could then be used to rapidly analyze and summarize hundreds of leases, identify potential risks or opportunities, and even suggest optimizations. This level of analysis would be incredibly time-consuming for humans and difficult for traditional analytics tools.

We are seeing the emergence of new players who have built their entire business models around these fine-tuned AI models. These could be companies that provide AI-powered services for specific niches within commercial real estate, leveraging the power of LLMs to offer insights and capabilities that were previously unattainable.

MBA NewsLink: As AI continues to evolve, what should market participants in commercial mortgages be focusing on?

Niraj Patel: For those in the commercial mortgage space, it’s crucial to start thinking about how LLMs and SLMs can be applied to various aspects of the business. Here are some key areas to focus on:

Document Analysis: These models can be fine-tuned to understand and analyze complex mortgage documents, potentially revolutionizing the due diligence process.

Market Intelligence: LLMs can be used to continuously monitor and analyze market trends, news, and reports, providing real-time insights to inform decision-making.

Risk Assessment: While traditional data will still play a role, LLMs can incorporate a wider range of factors and nuances into risk assessments, potentially leading to more accurate and comprehensive risk profiles.

Customer Interaction: AI-powered chatbots based on these models can provide sophisticated, context-aware support to clients, improving customer experience dramatically.

Regulatory Compliance: These models can be fine-tuned to understand complex regulations and help ensure compliance across operations.

The most crucial thing for market participants to focus on isn’t any specific application, but rather on developing a deep understanding of what these new AI models are capable of. It’s about shifting from a data-centric mindset to a task-centric one: “What complex, knowledge-intensive tasks in our business could benefit from AI assistance?”

This requires a fundamental shift in how we think about AI in our industry. It’s not just about analyzing the data we have; it’s about leveraging these powerful, adaptable models to enhance every aspect of our operations. The companies that will thrive are those that can creatively apply these technologies to solve real business problems and create new value for their customers.

In conclusion, while AI presents enormous opportunities for the commercial real estate industry, realizing its full potential requires more than just implementing new technologies. It requires a fundamental shift in how we think about AI itself. The future belongs to those who can effectively leverage the power of LLMs and SLMs, fine-tuning them to the specific needs of commercial real estate and using them to reimagine what’s possible in our industry.

(Views expressed in this article do not necessarily reflect policies of the Mortgage Bankers Association, nor do they connote an MBA endorsement of a specific company, product or service. MBA NewsLink welcomes your submissions. Inquiries can be sent to Editor Michael Tucker or Editorial Manager Anneliese Mahoney.)