How Do You Run a Successful AI Pilot?

At Apartmentalize, Nurture Boss's CEO, Jacob Carter led a session with Courtney Bastian, Sr. Director of Marketing at Dayrise Residential. They discussed a question many operators are wrestling with today: How do you run a successful AI pilot?

Throughout the session, attendees participated in live polling that offered a real-time snapshot of where multifamily stands with AI adoption. While there has been no shortage of discussion about artificial intelligence over the last two years, the results revealed something surprising. The industry's biggest challenge is no longer deciding whether to adopt AI. It's figuring out how to evaluate it.

Multifamily Has Moved Beyond AI Exploration

The first poll asked attendees where their organizations currently stand with AI. A majority of respondents—51%—said they had already deployed AI and were now focused on optimization. Another 26% reported actively running a pilot. Only 21% said they had explored demos without committing to a solution, while just 3% were still researching their options. Not a single respondent selected "I'm overwhelmed and not sure where to start."

Taken together, that means 77% of the room was already using AI or actively testing it.

That number is significant because it challenges the perception that multifamily is still in the early stages of AI adoption. In reality, many operators have already moved beyond the exploration phase. The conversation has evolved. Instead of asking whether AI belongs in property management, operators are now trying to determine how to maximize the value of the technology they've already implemented.

The Biggest Fear Isn't AI Failure. It's Wasting Money.

The second poll explored what concerns operators most when selecting an AI partner. If the industry were still skeptical of AI itself, you might expect fears around resident experience, employee adoption, or even vendor lock-in to dominate the results.

Instead, the top concern was far more practical.

Thirty-eight percent of respondents said their biggest concern was wasting budget on something that doesn't move the needle. Another 25% worried about not being able to measure whether the technology was actually working. Concerns about resident experience came in at 21%, while resistance from onsite teams accounted for 13%. Only 3% cited getting locked into a bad contract as their primary concern.

What stands out is how business-oriented these concerns have become. Operators are no longer evaluating AI based on hype or novelty. They're evaluating it the same way they would any other investment. Will it improve performance? Will it save time? Will it generate measurable results? Most importantly, can those results be proven?

The industry's primary concern is not whether AI works. It's whether a specific AI solution creates enough value to justify the investment.

The Industry's Biggest Challenge Is Measurement

That focus on measurement appeared again in the session's final poll, which may have produced the most revealing insight of the day.

Attendees were asked what would prevent them from running a head-to-head AI pilot tomorrow. The leading response, selected by 36% of participants, was that they didn't have clear success metrics defined. Another 33% said they lacked internal buy-in, while 21% admitted they wouldn't know how to measure results objectively. Only a small percentage struggled with selecting properties for a pilot or finding vendors to compare.

Taken together, these responses reveal a challenge that has little to do with artificial intelligence itself.

Operators don't appear to be struggling to find AI solutions. They don't appear to be struggling to identify use cases. And they certainly aren't waiting for the market to mature before taking action.

Instead, they're struggling to answer a more fundamental question: How do we know whether an AI pilot succeeded?

This is where many organizations get stuck. They spend weeks evaluating vendors, attending demos, and comparing features, but very little time defining what success actually looks like before implementation begins. Without clear goals and agreed-upon metrics, every pilot becomes subjective. Conversations become opinions rather than decisions informed by data.

The Next Competitive Advantage Isn't Better AI. It's Better Evaluation

One of the key themes from Jacob's presentation was that successful AI pilots begin before any software is implemented.

Organizations often start by evaluating vendors. The strongest operators start by defining outcomes.

They identify the business problem they're trying to solve, establish baseline metrics, and determine what measurable improvement would constitute success. Only then do they evaluate technology. When success criteria are clearly defined from the outset, measuring results becomes significantly easier.

The poll data suggests that this discipline may become the next competitive advantage in multifamily. As AI adoption continues to increase, the differentiator will not be access to technology. Most operators already have access to AI tools.

The differentiator will be an organization's ability to evaluate, measure, and optimize those tools over time.

What Multifamily Should Know

What makes these results particularly interesting is what they say about the industry's future.

For the last several years, the conversation around AI has largely been about adoption. Vendors focused on education, demonstrations, and helping operators understand what was possible.

That phase is ending.

The next phase will be defined by optimization, measurement, and operational discipline. The companies that gain the most value from AI won't necessarily be the ones that buy the most tools. They'll be the ones that build the best frameworks for testing, measuring, and improving those tools over time.

The Apartmentalize audience made that abundantly clear. AI adoption is no longer the primary challenge facing multifamily. The real challenge is determining what success looks like and proving you've achieved it.