Summary: Real estate owners and operators are moving artificial intelligence (AI) out of pilots and into the work that fills their week: monthly financial reviews, lease abstraction, and acquisition analysis. Since leveraging AI, a task that once took hours now just takes minutes.
In our recent Aprio webinar, what emerged was less a technology story than a management story during the panel discussion. The speakers touched on AI from three directions: the operator putting it to work across nine properties, the accounting leader rebuilding review processes around it, and the strategist standing up data platforms for it.
AI could be a useful tool, but the hesitation around it still stands: legal exposure, uncertain data, and a team already tired of hearing about AI. You and your team most probably have felt both sides of this. In this article, we discuss where the time savings are real today, who stays accountable for the output, and how to start in the next 90 days.
Where Is AI Saving the Most Time in Real Estate Right Now?
AI saves the most time in the most repetitive, high-volume review work, where manual labor takes up majority of the allotted time. Here are five examples from different teams:
1. Property-Level Financial Statement Review
A leader from the team feeds the income statement, general ledger (GL), trial balance, and rent roll for each property into their AI platform and receives a ten-page report tailored to what they want to see, ending with a list of questions for the property management team. Generating it takes about a minute and a half, plus a few minutes of refinement. Previously, that process used to take a few hours per property across nine properties.
Because the report identified most variances by reading actual entries in the GL, the team’s property managers no longer analyzed the statement line by line. Majority of their time went to correcting and explaining variances instead of finding them.
Another team, which sits between owners and third-party property managers, had independently landed in the same place. Their review historically ran a long checklist, an analytical review, and trend analysis, taking a few hours per property and leaving no room to act on what it found. Agents now handle the first pass, so the team can work with property accountants on why a number moved.
2. Rent Rolls and Acquisition Analysis
A 300-unit rent roll is where the high cost of unknowns shows up most clearly. In one acquisition review, the AI analysis flagged that 27% of a project’s units were corporate leases. With the speed of AI, the approach helped the team screen five times more properties than before and reach a go-or-no-go decision quickly.
3. Lease Abstraction, End to End
Abstracting a lease with software is not new. Taking that abstract and writing the data into Yardi or MRI to set the lease up in the system is what turns it into a start-to-finish process, with the team reviewing for accuracy rather than typing.
4. Accounts Payable and Invoice Coding
There are two things that people often mix together. Logging into utility portals to pull invoices every month is robotic process automation (RPA), not AI. The AI contribution is learning how invoices get coded over time and doing it consistently, which frees teams to understand the operation behind the numbers.
5. Underwriting, Budgeting, and Forecasting
On the finance side, underwriting, budgeting, and forecasting are heavy time savers with the use of AI. The process now becomes simple enough to spend the time on the assumptions that actually move the budget.
What Does AI Look Like Across Your Segment of Real Estate?
AI adoption looks different across commercial real estate, affordable housing, and construction, as well as in each company:
- Commercial real estate: using AI heavily on the sales and marketing side to help teams sell more and sell faster in comparables and market analysis.
- Affordable housing: reporting to the U.S. Department of Housing and Urban Development (HUD) and compliance work has become easier, where organizations want a governed data environment holding internal and third-party public data that they can query for insights.
- Construction: more manageable navigation on analytics and reporting on labor, resources, and projects, plus pre-qualification and proposal work.
What Is the Real Return on AI for a Real Estate Business?
Think of it as your time, one of the speakers shared. Save two hours and a team member can leave early, “go pick up your kid, go to the baseball game.” You do not need a multimillion-dollar savings for the return to be real. Another speaker also shared that with AI, it’s easily 100% more productive, and the change had been life-changing when used well.
However, there is a competitive dimension to it. If a company is small, without a five-person analysis team, and often bids against 15 or 20 other acquirers. Faster, more accurate work also builds credibility: come back to a seller with questions that show you went deep, and you are more likely to get detailed information the next time.
Who Is Responsible When AI Touches Your Financials?
AI must not assume all the responsibility, such as signing financial statements. Accountability stays with the firm or the client, which makes the review step important rather than optional. AI should be treated as a useful tool, but one that also still makes mistakes and needs guidance, the way you would manage and guide any team member. Sometimes, AI makes assumptions that call for guardrails and corrections.
If AI handles the day-to-day processing that junior team members once learned from, how do those team members build the fundamentals to review AI’s output meaningfully? What are the controls around journal entries that AI generated automatically?
What Is Holding Real Estate Companies Back?
Four themes came up, and none of them are reasons to sit still. Consider the following:
- Legal and fair housing risk: property management companies are moving deliberately because they are rightly concerned about fair housing exposure from anything algorithmic in the leasing process. Stacked AI disclosures on inbound calls are another live friction point; a prospective renter will not sit through a minute of them.
- Data reliability: multifamily market data has gotten harder to trust since the U.S. Department of Justice (DOJ) action involving RealPage, so acquisition teams have to be careful about the data they rely on.
- AI fatigue: accounting teams are saying roles going unfilled on the assumption that AI will absorb the work. Fatigue, along with genuine fear about jobs, is real, and the answer is education: go to community groups, attend AI sessions, and identify what is real from what is noise.
- Data security: Keep proprietary information out of open models, then build the closed environment, data protections, and privacy controls that give your team the confidence to actually use AI.
How Do You Get Started in the Next 90 Days?
The most practical first step is to identify the business problems you are trying to tackle, whether through a strategy engagement or an internal lunch-and-learn. Then do the two things any organization can start on Monday: run some training, and connect AI to your systems. Regarding tool selection, both speakers said to start with the enterprise resource planning (ERP) system you already run, because that is where your data lives. Determine quickly whether its AI features cover what you need, because ERP AI tends to be strongly accounting-focused, so end-to-end operations and maintenance work often requires looking further.
As for what is coming, the speakers expect data remediation, GL restructuring, and system consolidation to enable AI; audit readiness questions about AI-generated entries; deeper integrations between models and industry data platforms; and continued adoption with measurable ROI over the next 12 to 18 months.
Final Thoughts: How Starting Small With AI Can Transform Your Real Estate Operation
The pattern across all three speakers was the same: nobody waited for their data to be ready, their strategy to be finished, or their industry to settle. They picked one repetitive, high-volume task, put a person in charge of checking the output, and expanded from there. The cost of waiting is the corporate lease concentration you never spotted, the deal you could not underwrite in time, and the week each month your team spends finding variances instead of fixing them.
