Artificial intelligence in commercial real estate isn’t new—but its role is evolving in ways that are starting to meaningfully impact occupier decisions.

For much of the past two years, the conversation has focused on speed: faster underwriting, faster research, faster marketing.

But speed alone doesn’t solve one of the industry’s most persistent challenges: understanding tenants.

Many landlords, corporate real estate decision-makers and brokers still operate with limited visibility into how occupiers are actually using space. That’s beginning to change. A new generation of AI platforms is helping pinpoint tenant workplace preferences, centralize data, surface actionable insights and guide decision-making in real time.

Information once required input from siloed teams from leasing to design to construction to finance. Now through AI, this can all be accessed and applied through a single, integrated workflow.

From Hype to Practical Use

A year ago, much of the industry was still experimenting with tools like ChatGPT. Today, adoption has broadened but fragmented.

“There’s been a huge shift from even a year ago,” says Rick Chatham, a Senior Associate for Lee & Associates and a Member Associate of SIOR. “Some are still using ChatGPT, but a lot of users have moved to platforms like Claude for deeper analysis. Others are using Microsoft Copilot or Google Gemini because they integrate directly into their workflows.”

Despite differences in platform, usage patterns are strikingly consistent. Across the industry, AI is being used primarily for lease abstraction, marketing content, and underwriting.

In other words, AI is firmly embedded in the transaction process. Where there is less consensus: its role in understanding and planning the tenant experience itself.

“Regarding using AI for renovation and space planning, this is being done a LOT by a wide range vendors,” said Chatham. “I would say these tools are in their infancy right now, but as they mature, they will become crucial for us as we tour tenants/buyers through a space.”

Utilizing AI tools, Chatham expects most all brokers will soon be able to walk into a space with a client, use a phone or iPad to scan and create a 3D model, and then use AR technology to overlay different design/layout options onto a live view or photos of the space in real time. Tools he has seen moving the industry closer to this reality include qbiq and CubiCasa.

The Tenant Visibility Gap

Traditionally, landlords and brokers have relied on lagging indicators—lease renewals, tenant surveys, and anecdotal feedback—to understand occupier behavior.

AI has the potential to change that by transforming fragmented inputs into continuous insight. At its core, AI excels at identifying patterns across large datasets—something CRE has historically struggled to unify.

“AI is really good at pattern matching,” Chatham says. “I’m sure a lot of portfolio managers are already feeding their assets into these systems to identify which properties might need to be repositioned or even divested.”

At the tenant level, that same capability can surface early signals:

• Declining in-office attendance
• Underutilized space within a suite
• Increased service requests or comfort complaints

These are not just operational metrics; they are leading indicators of tenant satisfaction.

Additionally, SIOR professionals note that AI is improving how brokers engage with occupiers, reducing friction and enabling more tailored recommendations.

“The broker is not likely going away, and your value can become even more evident in an AI-enabled environment if you are high-touch and bring strong market relationships,” says Barry Murphy, SIOR, of Cushman & Wakefield. “AI tools streamline behind-the-scenes tasks, giving brokers more time to focus on strategy and client interaction. They also make it easier to deliver more targeted, actionable options to tenants.”

Murphy points to tools like ScoutSpace, which aggregates property data into more accurate and user-friendly surveys, and BrokerVisibility.ao, which helps AI platforms like Chat, Gemini, and Grok connect prospective tenants with brokers who have the necessary and relevant expertise.

Over time, this type of intelligence could allow landlords and brokers to move from reactive leasing strategies to predictive tenant retention.

From Insight to Action: A New Leasing Workflow

While many AI applications remain analytical, some platforms are beginning to directly reshape how deals come together.

One example is an AI platform named “MEG”, founded by three industry experts from REVISE, representing backgrounds in brokerage, workplace strategy and design, and technical architecture and construction innovation. The platform wasn’t built in a vacuum but reflects how the team actually works: conversational, iterative, and focused on helping people think through complex real estate decisions.

“We saw too many projects where critical decisions were happening downstream, after time and money had already been spent,” says REVISE Co-founder Peter Mugford. “And at that point, the ability to influence the deal is already limited.”

The founding team came together after working across brokerage, workplace strategy, and construction, and repeatedly running into the same issue: a lack of clarity and connected information needed to move decisions forward.

Bringing expertise from brokerage, workplace strategy, architecture, and technology—including a team with significant experience at firms like CBRE, HOK, Unispace, Gensler, and IBM—REVISE created an AI leveraging LLMs to advance integrated, proactive occupier decisions.

The firm’s AI tool ‘MEG’ is already influencing significant corporate real estate decisions.

“Meg acts as a force multiplier,” says Co-Founder Megan Campbell. “It brings together market research, workplace strategy, financial analysis, construction analytics, and business case development into a single, intelligent workflow. For brokers, that means getting to a clear, defensible deal strategy faster.

Unlike general-purpose AI tools, MEG is purposefully built for commercial real estate. It can generate test fits, renderings and financial scenarios almost instantly, allowing stakeholders to evaluate options much earlier in the process.

That speed has real-world implications.

Last year, REVISE worked with Oxford Properties in evaluating workplace use and needs of one of its tenants, a large tech company with approximately 1,000 employees, against the owner’s broader portfolio. Findings were used to support the tenant’s decision to relocate its headquarters to 125,000 square feet in a different Oxford Properties building in the same market, with an expanded footprint that was repositioned for the tenant’s specific needs. What would traditionally take months of iterative research, design, pricing and negotiation was compressed into a significantly shorter timeline.

Using AI to Enhance the Tenant Experience—Before and After the Lease

Tools like MEG use AI to better act on a truth that many in the industry already know: tenant experience begins before a lease is signed.

By clarifying requirements like space needs, buildout costs, and workplace strategy early on, AI helps ensure that shortlisted properties are viable from both a financial and functional perspective. That reduces friction, aligns stakeholders, and improves outcomes for both landlords and occupiers.

But the opportunity doesn’t end there.

For occupiers, AI can also provide deeper insight into behavior:

• Work-from-home and in-office patterns
• Commute dynamics and geographic preferences
• How employees actually interact with space

This data can inform not only real estate decisions, but broader workplace strategy.

Over time, platforms that capture and learn from this information could continuously refine how space is designed, leased, and managed.

For example, REVISE led a workplace strategy engagement with the Public Policy Institute of California (PPIC) in San Francisco's Jackson Square, leveraging its proprietary tools to layer in-office attendance patterns, commute analysis, space utilization data, and construction costs to determine exactly how much square footage the team actually needed in a hybrid model. Armed with that insight, PPIC made the confident call to sell and relocate, a transaction led by Revise and closing one of the highest-PSF Class A office sales in San Francisco since the pandemic.

Challenges: Security, Integration and Regulation

Despite its potential, AI adoption in CRE-particularly for understanding tenant behavior-comes with challenges.

Security remains a top concern, particularly as widely used AI tools may not offer enterprise-grade protections.

“Almost everyone is using at least one AI platform,” Chatham says. “But legal compliance and privacy are still major concerns.”

There are also regulatory considerations. Rules around AI-generated content and automated communications are evolving quickly, and compliance requirements may vary by market.

“We’re still very much in the Wild West,” he adds.

There is also room for some hardware innovations to help really feel the impact of AI on the tenant experience, according to SIOR experts. Specifically, brokers suggest that more widespread and advanced motion sensors linked to data collection systems could use traffic patterns to pinpoint where maintenance and janitorial staff is most needed, as well as to better manage heating and cooling systems. This will generate cost savings for the owner, greater comfort for the users, and reduced environmental impact.

The Broker’s Role in an AI-Driven Industry

If anything, AI is reinforcing—not replacing—the role of the broker.

While technology can surface patterns and generate scenarios, it cannot fully interpret the human factors behind them: company culture, leadership priorities, or employee sentiment.

“The more computer-generated outreach people receive, the more they want to talk to a real person,” Chatham notes.

For brokers, this creates an opportunity to evolve into more strategic advisors—translating data into insight and guiding both landlords and occupiers toward better decisions.

“This technology isn’t changing the core of the business—it’s changing how efficiently we can operate within it,” adds Murphy. “The more you automate the workflows, the more time you have to focus on relationships, strategy and understanding what your clients actually need.”