The Best AI Strategy Is Not the Middle Path. It Is a Fast, Disciplined One.

I recently received an email promoting a conference that described three possible paths for multifamily companies: radical change, maintaining the status quo, or taking a measured “middle path.”

The email made many points I agree with. AI adoption cannot simply be delegated to the IT department. Companies need to think about how work should change, where people still add the most value, and how to bring employees along. Buying more software does not automatically produce better outcomes.

But I disagree with the idea that the safest or most effective approach is to move down the middle.

The better path is to start early, pilot with a small group, learn quickly, and then expand across the organization. That is not reckless change. It is how companies develop the knowledge and experience they need to make good decisions while the technology is still evolving.

A Pilot Is Not the Same as Chasing Every New Tool

There is an important difference between experimenting deliberately and trying everything without a plan.

A good pilot starts with a real business problem. It gives a small group access to a tool, establishes expectations, provides training, and measures what happens. The company learns where the technology is useful, where it creates risk, and what processes need to change before a broader rollout.

The goal is not to prove that AI works. We already know that generative AI can help people research, analyze data, draft content, summarize information, write code, improve customer communications, and complete many other common tasks.

The goal of the pilot is to learn how AI works inside your company.

That means asking practical questions:

• Which tasks become faster or better?
• Where does the output still require human review?
• What information can employees safely enter?
• Which tools fit the company’s security and privacy requirements?
• What training helps employees get consistently good results?
• Which workflows should be redesigned instead of simply adding AI to the existing process?

These questions are best answered through use, not through months of discussion.

The Real Risk Is Allowing the Pilot to Become Permanent

Starting with a handful of employees makes sense. Staying there too long does not.

Some multifamily companies are still giving tools such as Claude to only a few people. That may have been a reasonable first step a year or two ago. Today, it can be a sign that the organization has become stuck in pilot mode.

If five people are learning how to use AI while another 500 are waiting for permission, the company is not truly adopting AI. It is running an extended experiment.

This matters because the advantage does not come from owning an AI license. It comes from employees learning how to work differently. They need time to develop judgment, understand the limitations of the technology, and discover use cases that leadership may never identify from the top down.

Those capabilities compound. An employee who uses AI regularly becomes better at identifying the right tasks, providing better instructions, evaluating responses, and building AI into everyday work. A company that delays broad access also delays that learning.

Broad Access Still Needs Guardrails

Moving quickly does not mean handing every employee every tool with no oversight.

Companies should select approved platforms, establish clear data policies, provide role-specific training, and require human review where accuracy, compliance, fair housing, privacy, or resident experience could be affected. They should also monitor adoption and outcomes instead of assuming that access equals effective use.

But guardrails should make responsible use possible. They should not become an excuse for preventing use altogether.

There is also no reason every rollout must happen companywide on the same day. Expansion can occur by team, job function, region, or workflow. A marketing department may be ready before property operations. A centralized leasing team may identify different applications than an accounting team. Each rollout can build on what the organization learned in the previous one.

The model is simple:

• Choose a meaningful problem or workflow.
• Run a short pilot with a representative group.
• Measure time saved, quality, adoption, and risk.
• Fix the process and establish guardrails.
• Expand access to the next group.
• Repeat continuously.

This is not radical change, and it is not a cautious middle path. It is disciplined acceleration.

Multifamily Cannot Wait for AI to Be Finished

AI tools will continue to change. Models will improve, prices will shift, and some products will disappear. Waiting for the market to settle sounds prudent, but there may never be a clean moment when the technology feels finished, and every risk has been resolved.

Meanwhile, the people using AI today are building experience that cannot be purchased later with a software subscription.

This is especially important in multifamily, where teams are being asked to do more across leasing, marketing, resident communication, renewals, maintenance, collections, reporting, and asset management. AI will not improve all of those functions simply because a company purchases an enterprise platform. The value will come from learning which work should be automated, which work should be assisted, and where human attention matters most.

At Respage, we believe AI adoption should be intentional, but intentional does not have to mean slow. We have been developing and using AI in multifamily since 2017, well before generative AI became a boardroom topic. One lesson has been consistent: you learn more by putting useful technology into the hands of real users and learning from your mistakes than by debating it from a distance.

Start small enough to manage the risk. Start early enough to learn before your competitors do. Then expand quickly enough that the knowledge reaches the organization, not just a select group of early adopters.

The companies that lead the next phase of multifamily will not be the ones that tried every tool, and they will not be the ones that waited for a perfect roadmap. They will be the ones that built a repeatable way to test, learn, and scale.