When Your AI Co-Worker Teaches Your Team the Wrong Lessons

Most leaders think about AI as a productivity tool, separate from their team dynamics. But if your team members are using AI daily—and they are—then AI has effectively become a collaborator. And just like with human collaborators, the quality of the interaction determines the quality of the output.

When someone types a question into AI, gets an answer, and moves on without pushing back, they’re not just missing out on a better answer. They’re reinforcing a pattern: accept what authority gives you, prioritize speed over depth, avoid the discomfort of saying “that’s not quite right.”

Do that enough times, and it becomes muscle memory.

Then they bring that same pattern to their interactions with their manager, their colleagues, their direct reports. The person who doesn’t challenge AI’s surface-level response is the same person who doesn't challenge the flawed assumption in the team meeting.

Here’s what I've been noticing lately: when people accept AI’s first response without question, or without slowing down, or without pushing back, they’re practicing the very behaviors that undermine team psychological safety and trust.

In our trust-building work with teams, we teach three foundational agreements that create the conditions for psychological safety:

1. Being present

Real presence means bringing your full attention and genuine curiosity to the interaction. It means noticing when something feels off, even if you can’t articulate why yet.

With AI, presence looks like: actually reading the response instead of skimming it. Noticing when it sounds confident but vague. Asking yourself, “Does this actually answer my question, or does it just sound smart?”

Most people don't do this, or if they do it, they do it once or twice within the conversation, not continually. Presence is an embodied practice, something you intentionally bring into the moment. Without presence, you are liable to accept the illusion of “competence” in your AI projects and quickly move on.

2. Suspending judgment

This doesn’t mean accepting everything at face value or not providing feedback or critique—it means staying open long enough to explore whether there’s another version of the story available to you. Suspending judgment means deliberately holding back your default assumption and remaining open to the possibility you may learn another side of the story.

With AI, this means treating the first response as a starting point, not an endpoint. If working with AI is giving you a headache, and your default response is “AI doesn’t have the capabilities I need,” or simply “this won’t work” you are letting judgment override the possibility for more. It means being willing to say to AI, “That's interesting, but what if we approached it from this angle?” and iterating multiple times. Revealing more and more of the story.

Instead, most people either accept the first answer uncritically or dismiss AI entirely—thinking it simply cannot keep up with your expertise. Neither approach develops the skill of collaborative refinement, where you continue to peel back another layer, another layer.

3. No fixing each other

When you attempt to “fix” or “rescue” someone from a problem, you do their thinking for them. When you coach, you ask questions that help them think more deeply.

Here’s the thing: AI will happily do your thinking for you if you let it. It will give you the answer, write the email, solve the problem. And every time you let it rescue you instead of using it as a collaborative thinking partner, you’re outsourcing the very cognitive work that builds your capacity to think critically—and to infuse humanity into your AI projects. Not to mention, AI still fails to actually solve the problem much of the time!

The person who uses AI as a shortcut to avoid hard thinking is the same person who “rescues” or “fixes” their team members by rushing in to solve the problems instead of coaching them to solve problems themselves. This doesn’t make them bad people, but it does mean you need to give them the proper training to think differently. Coaching AI can mean asking powerful questions, giving examples, sharing the “why” behind your company’s way of doing things.

While leaders are focused on AI’s efficiency gains, they are losing something valuable in the process—the practice of disagreement. The muscle memory of pushing back. The comfort with saying “I need you to slow down and explain this differently.”

Teams with high psychological safety and trust have what researchers call “learning behaviors”—seeking feedback, sharing information, asking for help, talking about errors, experimenting. These behaviors feel risky because they expose our limitations. But they’re exactly what drives collaboration, innovation, and high performance.

When we train people to accept AI’s first or second response without challenge, we’re training them to skip the learning behaviors. We’re teaching them that speed matters more than depth, that looking competent matters more than being genuinely curious, that efficiency trumps iteration.

And here’s what makes this particularly insidious: it’s invisible. No one’s saying “don't challenge AI.” No one’s explicitly discouraging critical thinking. But the behavior is spreading anyway.

Here’s what's possible: AI could actually become a training ground for psychological safety.

Think about it. AI doesn't have ego. It won’t get defensive when you push back. It won’t hold your questions against you in your next performance review. It’s actually the perfect low-stakes environment to practice the behaviors that feel risky with humans.

But only if you’re intentional about it.

This means teaching your teams that AI collaboration isn’t about getting quick answers—it’s about developing the capacity to think critically, iterate effectively, and maintain your standards even when it’s easier not to. It means modeling what good AI collaboration looks like: asking follow-up questions, challenging assumptions, iterating multiple times, being transparent about what’s not working. It means recognizing that every AI interaction is either building or eroding the behaviors that drive team performance.

I get it—you’re under real pressure to keep pace with competitors who are moving fast. I’m not suggesting you slow down AI adoption. I’m suggesting you get more intentional about how you adopt it.

If you’re serious about psychological safety, you can't treat AI adoption as a separate initiative. The way your team uses AI is teaching them how to show up everywhere—to your team meetings, your strategy sessions, your client conversations, your moments of crisis.

The organizations that will do extraordinary work with AI are the ones who understand that AI collaboration requires the same human development skills as human collaboration: the courage to challenge, the patience to iterate, and the safety to say “I need something different.” That’s what high performance actually looks like.

And it starts with recognizing that your team’s relationship with AI is teaching them something. The only question is: what do you want them to learn?