SKN | AI Adoption in Property Management Is Outpacing Office Policies and Training

A survey of 3,662 U.S. property management and real estate professionals found that 58% use artificial intelligence in their work, while only 8% reported having a formal written AI policy.

Formal training is also lagging, with just 26% of respondents saying they had received AI training despite growing use in leasing, presentations, invoicing, and other administrative tasks.

The gap creates a governance issue because respondents identified accuracy and reliability as leading barriers, while human review remains important for payments, lease terms, fair housing, and tenant communications.

The Public Assumption

The rapid adoption of AI in property management can make it appear that the industry is already adapting its operating procedures to the technology. In practice, the survey suggests that employees are adopting AI faster than many organizations are establishing formal rules around its use.

The September 2026 report from the Institute of Real Estate Management and AppFolio found that 58% of respondents use AI in their jobs. Yet only 8% reported having a written AI policy, while 26% had received formal training.

The survey was conducted online in May and June among 3,662 U.S. property management and real estate professionals through IREM’s membership and AppFolio’s contact database.

The Economic Breakdown

Property management professionals reported using AI for tasks including drafting responses to leasing inquiries, preparing presentations, and processing invoices. These applications can reduce administrative workloads, but the economic benefit depends on whether the resulting work is accurate and useful.

Among AI users who expressed an opinion, 85% reported improved productivity. Separately, 48% reported a positive effect on net operating income or profitability.

Those figures indicate that many users perceive operational benefits, but they do not establish that AI itself caused higher profitability. The survey does not provide a controlled comparison between properties or companies using AI and those that do not.

The report also identifies accuracy and reliability as the leading obstacles to more effective AI adoption, followed by insufficient training. That creates a potential gap between the apparent time savings from automation and the cost of correcting inaccurate output.

The Hidden Picture

The more consequential issue may be liability rather than productivity. Property managers operate across areas where errors can affect tenants, owners, payments, leases, and regulatory compliance.

David Barrow, managing director of WatchPoint Commercial Real Estate, emphasized in the report that liability remains with property managers regardless of whether calculations are performed by AI or by a person.

The report therefore points toward a model in which AI assists employees rather than replacing accountability. James Scott of MIT’s Center for Real Estate recommends retaining human involvement in decisions involving payments, lease terms, fair housing, and tenant communications.

For companies, that distinction matters. A written policy can define where AI is permitted, where it is prohibited, and when employees must verify its output. Training can then turn those rules into practical procedures rather than leaving individual employees to determine acceptable use independently.

The source recommends evaluating AI beyond hours saved, including service quality, customer satisfaction, and financial results. Without those measures, higher AI usage does not necessarily demonstrate better property management.

Closing

If property managers are adopting AI faster than their companies can establish policies and training, the more important question is whether offices are measuring the technology’s benefits quickly enough to identify where automation creates efficiency—and where it creates new liability.