AIM 2026: Turning Sentiment into Steps: The Next Frontier of AI

Turning Sentiment into Steps: The Next Frontier of AI 

The discussion brought together Maria Banks of AMLI Residential, Tiffany Walsh of Nolan Living, and Dom Beveridge of 20for20 to examine two increasingly connected applications of AI: understanding resident sentiment and using those insights to drive smarter automation. Rather than viewing AI simply as another communication tool, the session challenged operators to consider the intelligence being created through thousands of everyday resident conversations.

Traditional surveys provide only a limited view of resident sentiment. AI-powered conversations can be more dynamic, asking follow-up questions based on what residents actually care about and gathering insights continuously rather than only after predetermined events. Research presented during the session demonstrated the difference: while residents complained about numerous aspects of Wi-Fi, outages were the issue actually correlated with renewal probability.

Walsh described seeing this dynamic across Nolan Living. In just 30 days, its conversational AI generated more than 26,000 text messages, with 22% of residents responding in paragraphs. Those conversations have surfaced maintenance issues, operational problems and even potential CapEx needs that conventional surveys might never uncover.

Resident sentiment is also beginning to influence marketing. Authentic resident conversations can become detailed reviews, while AI search increasingly synthesizes information from reviews, Reddit and other sources to tell prospects what living at a community is actually like. This makes the substance of resident feedback potentially as important as the traditional star rating.

The second major opportunity is turning those insights into action. AI can learn communication preferences, coordinate resident touchpoints, automate processes such as delinquency follow-up, and identify which steps actually improve outcomes. Banks described the future as moving beyond simply telling technology what process to automate toward allowing AI to help determine which processes are actually necessary.

The panel was equally clear that successful automation still requires people. Complex or emotional situations need employees with empathy, judgment and problem-solving skills. AI should augment those teams—not eliminate the human connection.

Key Takeaways

• Conversations may reveal more than surveys. AI can follow what residents actually care about instead of forcing everyone through the same questions.
• Sentiment becomes powerful when it drives action. Operators can connect feedback to maintenance, retention, CapEx and service decisions.
• Reviews are becoming AI-search data. Authentic resident commentary increasingly shapes how AI describes a community to prospects.
• Automation should learn, not just repeat. AI can test which follow-ups, channels and processes produce better outcomes.
• Treat AI like a new employee. It requires training, oversight and trust before teams or residents will fully embrace it.
• Human connection remains essential. The goal is technology when residents want it and skilled people when they don't.