Why Does My Staff Hate Using It?

When a team resists a new tool, the problem usually falls into one of two categories: the rollout went sideways, or the tool itself wasn’t a good fit. And sometimes, it’s both.

Let’s talk about the fit first because a lot of real estate teams have been handed tools that simply weren’t built for them.

Most AI assistants weren’t designed with real estate in mind. They’re trained on the general web or broad business data. That’s great if you’re asking simple, universal questions, but it falls apart fast when your team asks something specific like, “What’s the rule for temporary off-market status in my MLS?” or “How do I submit a compliance request if I’m a secondary member?”

Tools like Salesforce Einstein, Microsoft Copilot, or Zoom’s AI Companion aren’t inherently bad. They just don’t know your documents, your policies, or how your people work. They don’t understand the stack of edge cases your staff navigates daily, or the fact that someone might be juggling support calls, a broker onboarding, and a leadership meeting at the same time.

When AI isn’t trained on your materials, it can sound confident while delivering answers that are flat-out wrong. And when that happens, your team ends up wasting time trying to fix it. That’s not a failed adoption. That’s a system mismatch.

According to a global AI survey by eGain this month, they found 61% of respondents point to “erroneous or inconsistent answers” as the top barrier to AI adoption. That checks out… no one wants to spend time correcting a tool that was supposed to save them time.

Last month CFO.com released an article stating that only 17% of employees use AI tools daily and most fall into the “a few times a year” category. If this is the case, how are adoption hurdles not a barrier to the growth of AI? Exploding Topics estimates the U.S. AI sector at $74 billion, with a CAGR of ~27% forecasted through 2031. The gap between excitement and actual usage is wide.

Sometimes, it really is about how the AI was rolled out.

Even when the tech is solid, adoption struggles if the launch is rushed, the team isn’t looped in, or no one really understands the tool’s role.

Here are a few of the most common rollout mistakes:

• Skipping the “why”
  If your team doesn’t know why the tool is being introduced or what it’s supposed to make easier, they’re less likely to try it.
  
  

• Skipping real-world testing
  Launching without running it through the actual questions your team hears every day almost guarantees frustration.
  
  

• No feedback loop
  If people report problems and nothing changes, trust in the tool erodes quickly.
  
  

• No internal owner
  Someone needs to be responsible for keeping the tool accurate, up to date, and aligned with your business.
  
  

And here’s the hidden cost of getting this wrong: failed AI rollouts don’t just stall progress, they can burn trust, time, and budget. 

So what does good adoption actually look like?

• It starts with the right data. Train the AI on your own documents… your help articles, policy PDFs, onboarding materials, benefit guides, and even past support emails (with private info removed). The more specific the inputs, the more accurate the outputs.

• You test before you launch. Ask the questions you hear every day. If it fumbles, don’t scrap it. Fix the source.

• You assign an internal champion. Someone who owns the tool, keeps it updated, and helps your team feel supported, not replaced.

• You measure what matters. Track support volume, time saved per interaction, and how often your team or members find the AI helpful. Let data guide the improvements.

• You normalize improvement. No one expects a new employee to be perfect on day one. Your AI assistant is no different. It should learn and improve with every interaction.

Here’s the thing: your staff doesn’t actually hate AI. They hate tools that waste their time.

AI works when it’s trained on your data, answers real questions, and gets a thoughtful rollout. That’s how it becomes a tool your team trusts, not one they avoid. Adoption starts with the right tool, the right training, and treating it like part of the team. 

UP NEXT WEEK: 

What Should We Train It On?

If you’re wondering where to start, you’re not alone. Next week, we’ll break down what documents, tools, and materials make the best training set for your AI assistant and what to skip. Spoiler: you already have everything you need.