Your Team Isn't Successfully Using AI (And It's Partially Your Fault)

I talk with real estate companies every day about AI adoption. Property managers, developers, investment firms, lenders. And I keep seeing the same pattern.

There's enthusiasm at the top. The CEO or COO gets it. They believe AI is going to transform the industry. Some have purchased licenses for AI assistants. Some have brought in consultants or hosted training sessions.

But when I dig beneath the surface? Reality doesn't match the enthusiasm.

I was on a call last month with a property management company that had purchased ChatGPT Plus licenses for their entire 50-person team three months earlier. The CEO was frustrated—he knew people weren't using it, but he wasn't sure what to do about it.

So I asked him: "Did you ever provide any training? Did you teach anyone how to actually talk to and collaborate with their AI assistants?"

Silence.

Then I asked, "How fluent are you with AI?"

"Pretty good, actually," he said. "But I've been using it for six months."

"That's exactly my point," I said. "It took you six months of regular use to get fluent. But you bought licenses for your team three months ago, and you're frustrated they haven't figured it out—without any training, without any guidance, or even a basic understanding about how it could be specifically helpful to them and their role."

This company was spending roughly $15,000 per year on licenses, and much of that investment was going unused. But the wasted money wasn't even the real problem. The real problem was the opportunity cost. The productivity gains they weren't getting. The competitive advantage they weren't building.

Why AI Is Different

Here's the part that's not your fault.

AI tools look simple. There's a text box. You type. You get an answer. No complex menus. No multi-step workflows. No certifications on where to click. Just type and go.

So you applied the same playbook that worked for 30 years. Buy the tools. Have IT set them up. Do basic training. Let people figure out the rest. That's how you rolled out email, CRM, project management, communication tools. Why would AI be different?

Because that simple text box is deceptive.

Every previous technology was a tool that executed commands. Click this, get that. The complexity was in learning the interface. Once you learned where things were, you were competent.

AI flips this. The interface is simple, but competency is in the conversation. It's more like a colleague than a calculator—it needs context, guidance, and feedback. Two people using the same AI tool get completely different results based on how they communicate with it.

The old playbook doesn't account for this, and you get:

• Leadership isn't AI-fluent. You can't guide your team through an AI transformation if you don't understand AI yourself. Many executives use something like ChatGPT a few times a week, but haven't developed real fluency and they don't know the difference.

• Teams lack real AI education. One training session doesn't build capability. Many people try AI a few times, get mediocre results, and give up or just use it for the easy stuff. They don't know how to make things better or what they should even be using it for.

• It's happening in the shadows. Without leadership understanding and systematic training, AI adoption is happening dangerously with unapproved tools seeing your company's sensitive data every day.

Problem 1: Leaders Aren't AI-Fluent

It starts with you.

CEOs and COOs understand that AI is important. They've read the articles, used ChatGPT. But many haven't developed real fluency themselves.

They can't tell good AI use from bad. They don't recognize opportunities. And their teams are taking cues from them. If leadership isn't using AI, why should anyone else?

I was on a call with a real estate investment firm that had brought their summer intern to the meeting. The senior partners asked her to describe what she'd built: a Zapier workflow that automatically screened incoming deal emails against their initial investment criteria and forwarded the qualified opportunities to partners with a summary.

It never occurred to them this could be automated. She solved it in two weeks.

The partners were so impressed, but only because they couldn't have built it themselves. If leadership had been AI-fluent, this wouldn't have seemed remarkable. It would have been obvious and replicable.

That intern graduated and left. I imagine the system still works, but nobody knows how to modify it or build the next one. Hopefully their next intern will be as resourceful.

You can't set the strategy for something you don't understand. And make no mistake, this isn't a technology strategy. It's a corporate strategy, and you need to be involved.

Problem 2: No One Taught Your Teams How to Work With AI

Only about 20% of the companies I've talked to have done company-wide training. But even those companies see mixed adoption.

When I talk to employees, I hear the same three things:

• "I don't have time to learn this"
• "For many tasks it's faster to do it myself"
• "I don't know what I should be using it for"

Think about learning a new language. You don't take a four-hour Spanish class and become fluent. You need continuous practice and you need an expert to go back to and ask "How do I say this?".

AI is the same.

A bootcamp teaches the basics: how to write prompts with context, how AI thinks, what's possible. People leave excited and try things.

Some responses are helpful. Some are close but not quite right. They go back and forth, trying to refine it, but it starts to feel like a waste of time to get the AI's response good enough.

A property manager takes the bootcamp and tries using AI to draft their quarterly market update to owners. They provide context: the occupancy rates, rent trends, comparable properties. They go back and forth with the AI to add the right analysis. They refine the prompt. They try again. After twenty minutes, they think "I could have done this faster myself." The excitement fades and it's back to the old way next quarter.

Think about training an intern. You'd write documentation explaining what you want them to do, give them examples. But with AI, people write a few sentences and get frustrated when it doesn't perform the way they think it should.

Working with AI requires clear communication and effective delegation. These skills can be learned, but it takes experimentation and ongoing guidance.

AI training gets people started. Ongoing support builds capability. This isn't something you can outsource to HR.

Problem 3: It's Happening in the Shadows

I sat down with a real estate developer who had set a policy saying employees shouldn't use AI without approval. But when I asked what "approval" meant or which tools were approved, they couldn't answer.

The partner told me: "We know people are using this stuff, we just don't know which tools or how many people. We'd like to figure out who's using AI so we can get them licenses and make it official."

Leadership knows it's happening but has no visibility into which tools people are using, which data is being shared, or what risks they're exposed to.

And their solution to buy licenses for people already using it misses the point. This isn't about access. It's about building capability with proper oversight.

Without leadership fluency and clear direction, experimentation happens in the wild, in isolation. The company can't learn from experiments it doesn't know about. Can't replicate what works or prevent what's risky.

What you need: clear policies on what's encouraged versus restricted, official tools with support, and a culture where experimentation is expected and visible, not hidden.

The AI Adoption Playbook

I recently met with a multi-family developer (about 75 employees) that's doing this well. I wasn't meeting with the CEO. I was meeting with the person they'd put in charge of AI transformation. They'd made it a formal role.

The CEO's message at the beginning of the year was clear: AI is critical to the future of the company, and we're going to figure it out together as a team.

They Addressed the Leadership Needs First

Before rolling anything out company-wide, they put their leadership team through several workshops. Leaders became AI-fluent first.

Now their leaders can recognize good AI use, spot opportunities, and help when people get stuck. They model the behavior they want to see.

They Built Fluency Systematically

They officially encouraged experimentation. If someone wanted to try a tool, the company paid for it. But they didn't stop at access:

• Culture shift that using AI to help you with your job is expected and a good thing when done responsibly and collaboratively
• Regular AI sharing sessions, not just wins, but honest talk about fears and challenges too
• Protected time in schedules for AI learning and experimentation
• A Slack channel where teams shared effective prompts and prompting strategies they had figured out for specific tasks

It's a feedback loop of continuous learning.

They Eliminated Shadow AI

Because experimentation was officially supported, nothing happens in the shadows. Clear guidance on approved tools. A process to evaluate new tools. Support for learning.

AI innovation happens in the open. The company captures the learning, manages the risks, builds on what works.

The Results

Not everyone is AI fluent yet, but enough people that productivity gains are substantial. Every quarter, more people become fluent. Every month, they discover new ways to use AI.

When someone leaves, capability stays. When someone joins, they enter the learning system. When they discover a use case, it gets shared.

This isn't magic. It's leadership, change management, and systematic capability building.

The Gap Is Widening

The gap between companies doing this well and everyone else is growing every month.

Boston Consulting Group research quantifies it: AI leaders achieve 1.7x revenue growth, 3.6x shareholder returns, and 1.6x higher profit margins compared to companies lagging behind. This isn't marginal. It's a fundamental competitive advantage that compounds.

Think about the next 12 months.

Company A has 80% of their team who tried AI a few times and went back to the old way. Company B has 30% fluent and growing every quarter. They have people who are doing in hours what used to take days.

Which company do you want to be?

The question isn't whether your competitors are investing in AI tools. Many are. The question is: Have they made AI adoption a core part of their business strategy? Are they teaching their teams to use those tools effectively and supporting them along the way?

That's where the real competitive advantage lies. The few companies figuring it out are building something very hard to catch up with.

So Where Do You Start?

Ask yourself: How many people on your team can describe three specific ways they used AI this week to do their actual work?

If it's fewer than five people, you have a capability problem, not a tool problem.

Here's how to fix it:

1. Leadership becomes AI-fluent first. You can't guide what you don't understand. Block time for your leadership team to develop fluency through systematic practice.

2. Build ongoing learning systems. Not one-time training. Regular sessions, feedback loops, protected time for experimentation. Make it part of how your organization operates.

3. Create clear policies for experimentation. Remove the shadows. Make it clear what's encouraged, what tools are supported, and how people get help.

This is partially your fault. Which means you have the power to fix it.

Building AI capability across your organization isn't something you figure out on the weekend. If you want to talk about what this looks like for your company specifically, get in touch with us.

---