AI in Commercial Real Estate: How Unified Data Helps Teams Work Smarter

Commercial real estate professionals spend a significant amount of time looking for information that already exists.

A property manager may gather updates from engineers, vendors, tenant requests, and financial systems before preparing a report for ownership. An asset manager may compare spreadsheets and operating reports to understand why expenses changed. An engineer may review equipment histories and work orders to determine whether a problem is isolated or part of a recurring pattern.

The analysis and judgment involved in this work are valuable. The hours spent locating, organizing, and summarizing the underlying information are often not.

That is where artificial intelligence can create practical value in commercial real estate.

The immediate opportunity is not to replace experienced operators or create fully autonomous buildings. It is to reduce repetitive work, make information easier to access, and give teams more time to focus on tenants, building performance, risk, and long-term asset value.

The firms that gain a competitive advantage will not necessarily be those that adopt the most AI tools. They will be the ones that use reliable data and clearly defined workflows to help experienced people work more effectively.

The Problem Is Disconnected Data

Commercial properties already generate substantial amounts of information. Building controls, meters, maintenance platforms, tenant requests, accounting systems, leasing records, and asset management tools each provide part of the operating picture.

The challenge is that this information often remains divided among systems, properties, vendors, and departments.

Each platform may perform its intended function well. The difficulty appears when someone needs to understand what is happening across several of them.

Employees often become the connection between those systems. They export reports, update spreadsheets, compare records, request clarification, and manually assemble information for different audiences.

Commercial real estate does not simply need more data or another dashboard. It needs better ways to turn the information it already has into timely, useful insight.

Large language models may help bridge some of these gaps because they can interpret, organize, and summarize different types of information. Traditional system integrations, data governance, and human oversight will still matter. AI can provide an additional layer that makes existing information easier for teams to use.

AI Can Give Time Back to Experienced Teams

The strongest early applications of AI may be the least dramatic.

They include recurring tasks such as assembling reports, summarizing work orders, comparing results, reviewing documents, organizing notes, and finding information across multiple systems.

With reliable access to the right data, AI may help teams:

• Compile recurring property reports
• Summarize maintenance activity and open work orders
• Compare results across properties or reporting periods
• Identify unusual changes and recurring patterns
• Organize tenant requests by issue or urgency
• Extract information from leases and operating documents
• Prepare initial updates for review

These applications do not remove accountability from the process. Reports still need review. Recommendations require context. Decisions remain with qualified professionals.

The benefit is reducing the manual work between information and action.

A property manager who spends less time assembling a report can spend more time communicating with tenants, following up with vendors, and anticipating ownership needs. An engineer who receives an earlier indication of unusual equipment behavior can investigate before the issue becomes a larger disruption. An asset manager who can compare operating and financial information more efficiently can spend more time evaluating risk, controlling costs, and identifying opportunities to improve performance.

This supports the broader purpose of effective property management: protecting asset value while delivering responsive oversight and a strong tenant experience. 

AI can also make information more dynamic.

Dashboards remain useful, but they typically reflect questions selected in advance. When an unexpected issue arises, a team member may still need to export information or prepare a separate analysis.

With well-organized data, an operator may eventually be able to “ask a building a question.”

Why did energy consumption increase last week? Which maintenance issues have appeared repeatedly? Are tenant comfort requests concentrated in one area? Which property requires attention before the next ownership report?

The value is not the novelty of the interaction. It is the time saved locating and organizing the information needed to investigate.

Building performance is one area where this capability may be especially useful. Energy Star estimates that, on average, 30% of the energy consumed in commercial buildings is wasted. More accessible information could help operators compare energy use with equipment activity, schedules, occupancy patterns, weather, and maintenance records to identify potential inefficiencies sooner.

AI does not determine the correct operational response on its own. It can give an experienced operator a better starting point.

Where Do You Start?

You do not need to begin with a broad AI transformation initiative. It can begin with one recurring problem.

Which report takes too long to prepare? Where do employees repeatedly reconcile the same information? Which questions require searching across several platforms? What routine work prevents experienced staff from focusing on more valuable responsibilities?

One useful approach is to imagine a highly capable intern.

What repetitive task could that person perform with clear instructions, access to the right information, and defined review criteria?

Tasks that involve summarizing, organizing, comparing, categorizing, or preparing an initial draft may be strong candidates for generative AI.

Beginning with a narrow use case makes the value easier to evaluate. A firm can measure the time saved, review the accuracy of the output, identify missing information, and improve the process before applying the technology more broadly.

The operating problem should come first. The tool should come second.

Data Readiness Matters More Than Hype

AI is not a magic button.

It cannot automatically understand an organization’s properties, reporting requirements, approval processes, ownership objectives, or ways of working. It also cannot consistently produce reliable results from incomplete, outdated, or poorly organized information.

Before implementing an AI Agent, firms should understand what data they collect, where it is stored, who maintains it, and which systems are considered authoritative.

They should also establish clear expectations for access, security, accuracy, and human review. The National Institute of Standards and Technology’s voluntary AI Risk Management Framework provides guidance for organizations seeking to manage reliability, transparency, accountability, security, and other risks associated with AI systems.

The same discipline should apply when evaluating an AI partner.

Owners and operators should ask what has already been developed, where the product has been deployed, how it will connect with existing systems, and what will require customization. They should understand how feedback will be incorporated and where human oversight remains necessary.

A polished demonstration may show what is possible in a controlled setting. The more important test is whether the product can solve a defined problem within the realities of a live commercial real estate operation.

The Competitive Edge Is Better Execution

AI adoption alone will not distinguish one commercial asset from another. The advantage will come from execution.

Organizations that understand their workflows, improve access to reliable data, and apply AI to specific operating problems will be better positioned to respond faster and use their people more effectively.

The goal is not to remove people from operations. It is to reduce the time they spend searching for information, reconciling reports, and completing repetitive tasks. That gives experienced professionals more time for the work that requires judgment, communication, creativity, and accountability.

Rising Realty Partners’ investment management approach is built around using technology, disciplined strategy, and operational alignment to create value for investors, tenants, and the assets we steward. AI can become another tool within that operating model.

Its most meaningful near-term contribution may be straightforward: helping experienced people spend less time assembling information and more time acting on it.