Generative AI has moved quickly from buzzword to everyday toolkit, and its influence on commercial real estate (CRE) is already visible. In a field built on physical assets and in-person relationships, bringing in AI that can create, analyze, and communicate is driving a real shift in how work gets done. McKinsey estimates that generative AI could unlock roughly $110–$180 billion in value for real estate.
Leaders are taking note and putting money behind it. In Deloitte’s 2024 survey, more than 72% of owners and investors say they’ve committed, or will commit, funds to AI-enabled solutions. This is no longer a distant concept. It’s becoming a core part of strategy, reshaping property operations, leasing, and investment decisions.
We will cover the practical ways generative AI shows up in CRE, the business impact you can expect, the hurdles to watch, and a simple path to start integrating these tools.
Generative AI is a branch of AI that produces original content—text, images, video, audio, and even code—using patterns learned from existing data. That’s different from traditional AI, which focuses on analyzing historical data to predict or classify.
In CRE, that difference matters. A traditional model might forecast rents or cap rates from trend data. A generative system can:
Because it creates context-aware outputs, generative AI can take on creative and cognitive tasks that used to demand specialized time and attention.
Momentum is backed by capital and clear use cases. Since 2017, venture investors have put about $7.2 billion into AI and machine learning companies relevant to real estate. Where is the spend going? Deloitte’s data points to AI/ML for listings, sales, and leasing (42%) and for investment and valuation (20%) as priority areas.
KPMG finds many Canadian real estate firms channeling generative AI toward customer experience (40%) and into R&D for new applications (38%). Together, these choices signal a push to raise efficiency internally while improving client-facing interactions.
Source: Deloitte
Generative AI isn’t a single product. It’s a flexible capability that now reaches across the CRE value chain, with use cases maturing to proven practice.
Tenant service is now instant and operates 24/7. Automated chat tools field common queries, log maintenance requests, and run payment transactions without interruption, allowing managers to focus on outlier issues and relationship building.
Coupled with IoT-enabled buildings, the models capture occupancy trends and dynamically optimize heating, cooling, and lighting, delivering both savings and greener operations. JLL’s AI platform, Hank, for example, lowered HVAC energy use by about 45% in a highly leased building. Additionally, Kanda optimized workspace efficiency in our work with a leading tenant and employee experience platform.
Document-heavy work is being rethought. Lease abstraction can be reduced to minutes with automated extraction and summarization. As Chris Zlocki of Colliers noted, "What used to take a lease administration team five to seven days now takes minutes."
Acquisition teams can prompt copilots to sift portfolio data and market reports, surface targets that match defined criteria, and assemble the rationale in one place.
Content at scale is now feasible. Tools generate listing copy, social posts, and investor materials in hours instead of weeks. On the design side, text-to-image workflows let architects and marketers spin up hundreds of variations quickly, while virtual staging and photorealistic visuals help prospects grasp a space’s potential remotely. Listings can hit the market significantly faster, in some cases reported as 73% quicker.
A KPMG survey shows how real estate companies are approaching generative AI adoption.
Source: KPMGDone well, generative AI drives both productivity and profitability, and improves experience for clients and teams.
Automation removes repetitive work so people can concentrate on higher-value effort. JLL notes its JLL GPT cut the time to draft a partnership memorandum from four to six weeks down to under five hours. KPMG reports that 52% of employees using generative AI save one to five hours each week.
By compressing the time required to close a deal, thanks to AI-aided underwriting and faster appraisal, transaction velocity jumps. Predictive maintenance and data-driven energy management further reduce overheads.
This double-benefit explains why “increasing profitability” consistently appears among the top anticipated gains of generative AI in real estate. The evolution of AI in finance-adjacent sectors offers a parallel, showcasing the future of FinTech and banking with AI, ML, and blockchain.
With stronger, data-driven insights, teams make better calls across investment, asset, and portfolio management. For clients, personalization, instant answers, and immersive tours raise satisfaction and conversion. You can learn more about how to leverage these insights through Kanda's expert data and analytics services.
Adoption has real hurdles: data risks, cost, and change management. Addressing each one early pays off.
Generative systems thrive on data volume and breadth, which heightens privacy and security concerns. In KPMG’s research, 92% of real estate firms worry staff might paste sensitive information into public AI tools. A practical response is offering a secure, private environment so proprietary data stays inside the organization.
Upfront spending on software, infrastructure, and training can be costly since many organizations still depend on older systems. Deloitte reports that more than 60% of CRE companies struggle to adopt emerging tech because of legacy dependencies. Budgeting for integration and upskilling is part of the plan, not an afterthought.
Models reflect their inputs. Poor, incomplete, or biased data leads to inaccurate outputs or unfair outcomes, including in valuation or tenant screening. Many firms are establishing clear policies and adding roles that define responsible use standards and bias monitoring.
Source: JLL
Rolling out generative AI works best with a focused plan, measured experiments, and clear guardrails. Here’s a simple path.
Start small. Pick a handful of high-impact use cases that can show value quickly. McKinsey suggests a “2×2” approach: two near-term wins that build momentum, and two bigger bets that can reshape the business over time. Define owners, metrics, and timelines upfront.
Different routes offer different tradeoffs in cost, control, speed, and privacy:
Choosing the right platform and tools is also key; the decision often comes down to a comparison of major ecosystems, similar to the debate over Google AI Studio vs. Azure AI Studio. For complex implementations, collaborating with a dedicated partner that specializes in AI and machine learning services can bridge internal skill gaps and accelerate development.
Technology is only half the job. Upskill your teams, set usage policies, and keep a human-in-the-loop mindset. Less than half of firms mandate generative AI training today, which leaves value on the table. Make it clear that AI augments expert judgment and does not replace it. If you need additional capacity, dedicated development teams can help you move faster while transferring knowledge to your staff.
Implementing generative AI in CRE involves combining new platforms with legacy systems, cleaning and governing data, and building secure, user-friendly applications. A partner with deep software and data experience can make that journey smoother.
Talk to our experts to explore how we can help you modernize operations and move from traditional processes to intelligent automation with confidence.
Generative AI is reshaping CRE. Firms are using it to raise efficiency, improve decisions, and deliver better experiences for clients and teams. The opportunities are significant, and so are the responsibilities around data, cost, and ethics.
Companies that move with intent, strengthen talent and technology, and track performance will create lasting advantage. The migration from physical to digital is accelerating; opportunity now lies with those prepared to act.
