Most Real Estate Organizations Fail to Train Employees on AI
Commercial real estate is not short on AI enthusiasm. Walk through any industry conference, read any proptech publication, or sit in on any technology vendor presentation and the message is consistent: AI is transforming the way buildings are managed, deals are underwritten, and portfolios are operated. That enthusiasm is reflected in adoption numbers that have climbed rapidly across the industry.
What is less visible, and considerably less discussed, is what happens after organizations deploy these tools and whether the people using them actually understand how to get results from them. A new survey from MRI Software suggests the answer for most organizations is no.
The MRI Software 2026 Commercial Real Estate Pulse Check surveyed nearly 600 CRE professionals across roles ranging from company leaders and property managers to accountants and asset managers. The findings paint a picture of an industry that is simultaneously racing toward AI and failing to prepare its workforce for it. Sixty-two percent of respondents report that their companies are preparing for AI tools in some way, a number that sounds reassuring until you read what sits alongside it. Fifty-four percent of respondents report that their organizations do not offer any AI training at all. The two data points together describe an industry that has convinced itself it is getting ready for a technology it is doing almost nothing to actually teach its people to use.
The most common forms are usage guidelines, with responsible use policies a close second. Both matter, but neither is what determines whether an employee gets real value out of an AI tool. Source validation (the ability to critically evaluate AI outputs) was offered by only 16% of organizations. Prompting exercises, which teach employees how to construct queries that produce reliable and repeatable results, were offered by just 14%.
The gap between what organizations are teaching and what actually drives AI effectiveness is significant. “We are finally starting to understand the importance of asking AI the right questions in the right way,” said Carla Hinson, VP of Solution and Innovation at MRI Software. The ability to prompt well is not an instinctive skill. It is learned, and it has a direct bearing on whether the outputs an employee receives are useful, accurate, and consistent enough to be acted on. When organizations skip that kind of instruction, they are leaving the quality of AI outputs largely to chance.
So far AI has largely been used individually as a way to enhance individual workflows. Over half of all respondents plan to use AI primarily for improving personal performance over the next 12 months, with automating administrative tasks coming in second at 45%. The pattern holds across roles, with executives, property managers, and property accountants all citing personal productivity as their primary AI use case at rates between 53% and 56%. When AI benefit concentrates at the individual level rather than flowing across teams and departments, it becomes harder to get the full return on investment of AI training and tools.
The consumer-facing AI tools that most CRE professionals encounter first are designed to feel intuitive. They respond to plain language, produce polished outputs quickly, and create the impression that effective use requires no particular expertise. That perceived accessibility has led a significant number of organizations to treat AI deployment as largely self-service. “The excitement and perceived ease of use of AI has given the impression that training is unnecessary,” Hinson said. “In order to get the true benefits of AI, users need to trust it, and that trust comes through having a better understanding of how it works and the limitations, which requires training.” An employee who has used an AI tool enough times to feel comfortable with it may still have no reliable framework for knowing when the outputs are sound and when they require additional scrutiny. That distinction matters enormously in a field where AI is increasingly being used for operational decision making and all areas where an undetected error carries real consequences.
The survey’s open-ended responses reflect that the skepticism is already present and widely felt. Respondents cited concerns about data accuracy, security, and overreliance on AI outputs. One described the technology as dangerous and warned of dependency on inaccurate information. Another pointed to skill gaps with employees as the primary obstacle to valuable AI adoption. These responses are consistent across experience levels and roles, which suggests the issue is not resistance to change but a legitimate and unresolved concern about reliability. The industry has largely framed AI adoption as a question of willingness. The data suggests the more pressing question is readiness, and that readiness is a function of training, not time.
What the report ultimately reveals is that AI is proliferating in commercial real estate faster than the organizational infrastructure to support it. The tools are being deployed. The workflows are being changed. But the deliberate investment in helping employees understand what these tools can do, where they fall short, and how to use them in ways that produce consistent and trustworthy results has not kept pace. As AI takes on a larger role in how CRE organizations analyze data, manage properties, and serve tenants, the gap between having the technology and using it well will become an increasingly consequential distinction between the organizations that get the most out of it and those that don’t.
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