Case Study in AI Adoption: Property Management

AI adoption looks different for every industry, often changing with the needs of companies on a case-by-case basis. A recent McDermott Will & Emery memo gives insight into how the rental and property management sector is adopting AI, providing a case study in sectoral adoption. The memo gives four examples of how day-to-day property management companies are utilizing AI:

1. “AI-powered chatbots can handle tenant inquiries around the clock, enhancing customer service, shortening response times, and potentially reducing the risk of rent abatements due to delayed responses.
2. Smart energy metering systems leverage AI to optimize energy use and support sustainability initiatives.
3. Predictive maintenance tools detect and prioritize repair needs early, minimizing downtime and associated costs.
4. AI automation handles routine tasks such as processing rental payments and prioritizing maintenance requests, further boosting efficiency.”

The property management sector is using AI in a variety of applications. However, each application uses AI differently and gives rise to distinct risks. To effectively mitigate these risks, we have to understand the shortcomings of AI in certain scenarios. AI-powered chatbots, for example, are prone to errors. Leading some to seek new chatbot insurance and establishing “human in the loop” practices to spot and resolve errors quickly. Also, the housing sector is highly regulated, making it critical to screen any AI use for algorithmic bias.

Some applications also require different types of AI. Tenant chatbots and automation related to tenant inquiries may be done by large language models (LLMS). These tasks revolve around answering queries and prioritizing requests coming in through online submissions. Therefore, companies use LLMs to analyze, and in some cases, respond to written requests.  On the other hand, energy metering and predictive maintenance likely use other machine learning tools more suited to those types of data. The type of AI used for a task changes the risks involved and how companies manage that risk. The property management sector provides a great example for how industries can break down their AI needs into specific tasks. Then evaluate the associated risks of those applications to build robust risk management practices.