When I work with teams – city departments, nonprofits, small businesses, and schools – I hear the same two statements over and over again.
“We’re not ready for AI.” and “I just don’t trust it.”
People usually think these are separate issues: one about skills, and one about reliability. But after years of teaching adults and guiding organizations through change, I’ve learned something different.
People don’t feel ready because they don’t trust AI. And they don’t trust AI because they don’t feel ready.
It’s a loop. A hesitation cycle. And it’s far more human than technical.
AI doesn’t act like the tools we’re used to. Traditional software is predictable and rule-bound. Press a button, get a result. Do the same thing again, get the same outcome.
AI breaks that expectation in every direction.
It can be brilliant one moment and confidently wrong the next. It can miss crucial details, introduce errors, or generate something that feels slightly off. For people who work in fields where accuracy matters, and that’s everyone, that unpredictability feels risky.
So when someone says, “I don’t trust it,” what they’re really saying is something more vulnerable:
“I can’t put my credibility on the line for a tool that feels inconsistent.”
And when someone says, “We’re not ready,” the underlying message is often:
“I don’t feel confident enough to catch its mistakes.”
Adults don’t resist technology. They resist feeling unprepared using technology.
No one wants to be the person who didn’t notice the error. No one wants to send out a document with a mistake. No one wants to be asked, “Where did you get this information?” and have to answer, “AI made it up.”
This is where readiness and trust collapse into each other. You can’t feel ready for a tool you don’t trust, and you can’t trust a tool you haven’t learned how to use confidently.
Here’s the part people often miss:
Trust doesn’t come from AI becoming flawless. Trust comes from people learning how to work with AI wisely.
When teams understand what AI is good at, and where it struggles, they relax. When they learn to treat AI as brainstorming, not the final answer, the pressure drops. When they know how to evaluate and refine AI-generated work, their confidence grows. And when they build workflows that include accuracy checks, trust stops feeling like a gamble and starts feeling like a skill.
This is the shift that breaks the readiness–trust loop.
I see it every time I facilitate a session. At the beginning, the energy is cautious. People sit back with their arms crossed, waiting to be convinced. They share the stories they’ve heard about AI mistakes. They talk about their fear of relying on something unpredictable.
But once they see how to guide AI, how to question it, how to correct it, how to verify what it produces, something changes.
Curiosity replaces hesitation. Skepticism turns into experimentation. People start leaning in.
A few weeks ago, a participant told me, “I don’t trust any of this,” before the session even began. By the end, she had built a reliable workflow that reduced a weekly task from 45 minutes to under 10. Not because AI became perfect, but because she learned how to stay in control.
She didn’t trust the tool. She trusted her process.
And that made all the difference.
If your organization feels both “not ready” and unsure whether to trust AI, here’s the simplest place to start:
Choose one low-risk workflow.
Model how to check for accuracy. Create a shared practice for verifying output. Build from small, safe successes instead of big, overwhelming expectations.
Readiness isn’t a prerequisite. Readiness is something teams build—through experience, clarity, and support.
The truth is this:
AI doesn’t need to be perfect to be powerful. And your team doesn’t need to feel ready to begin.
What people need is a way to use AI that protects their credibility, strengthens their confidence, and gives them a clear sense of control.
When that happens, trust grows. And when trust grows, readiness follows right behind it.
This is where real AI empowerment begins – not with certainty, but with the courage to take one practical step forward.
