By Leslie Rohonczy, IMC™, PCC
Executive Coach | Leadership Development Expert | Author | Speaker | ©2026 | www.leslierohonczy.com
Have you ever noticed how tempting it is to accept an AI tool’s answer without questioning it? It looks and sounds so convincing, and heaven knows we’re all overloaded these days. Who has time to question something that already looks thoroughly thought through? After all, “AI checked it” can feel like permission to act.
But what did it check, against what information, and who examined the answer? Our curiosity needs to survive that moment, before the decision comes back to bite us in the butt.
I've noticed a common theme with many clients over the past six months. They talk about how quickly these convincing AI answers get accepted without challenge. With teams increasingly busy and expected to do more with less, an answer that looks and feels complete can be awfully tempting. Their concern, and mine too, is what happens to our human curiosity when we start accepting that appearance as enough.
Those conversations helped shape my newest keynote and interactive workshop, Critical Curiosity in the AI Age, which I developed for a senior leadership group I’m working with.
In the session, we discover where to find powerful questions, how to dial up our innate curiosity, and how to fine-tune the questions that help us decide if a recommendation actually holds up. We also explore how to help the person who’s bringing the recommendation forward do more of that thinking themselves, so they get better at spotting what needs checking before their next recommendation lands on your desk.
WHEN AN ASSUMPTION ACQUIRES A BUDGET
See if this scenario is familiar: customer satisfaction has declined, and a senior leader says, “We don’t have enough people answering customer inquiries.” It sounds reasonable. The team is busy, customers are unhappy, and hiring seems like a sensible response.
By the next meeting, it has become “our customer service staffing problem.” It suddenly appears as a headline in the quarterly update deck, then acquires a budget request, and AI is asked to create the staffing plan. Back comes a polished recommendation with recruitment priorities and measures of success.
But nobody has examined what customers are actually unhappy about. Are they waiting too long for a response, or contacting the organization repeatedly because nobody resolves their issue? More people might help, but the team has started solving a problem it hasn’t adequately defined or understood.
This is the echo effect to watch for: an interpretation gets repeated until people begin treating it as an established fact. And when that explanation comes from someone who is senior, it’s even more likely that the next person will treat it as a conclusion rather than one possibility.
AI can become another voice repeating the original assumption, sometimes even the loudest voice. A recommendation built around that assumption can feel like independent confirmation. Using AI to develop a recommendation doesn’t, by itself, challenge or verify the assumptions behind it.
We used to call this “GIGO” in the old days: garbage in, garbage out. Today, the output can come back beautifully packaged and stated with such certainty that it makes questionable input hard to spot.
THE HUMAN SKILL BEHIND THE DECISION
This is the kind of conversation participants practice in Critical Curiosity in the AI Age, using my ECHO Check process. It gives leaders and the people presenting recommendations a shared structure for examining the thinking behind them.
Each element serves a distinct purpose:
EXPLAIN: Make the reasoning behind the recommendation visible, in their own words, including why the person presenting it finds it convincing.
CHALLENGE: Examine the assumptions and evidence, bringing what’s missing or uncertain into view.
HONE: Improve the recommendation in light of what is revealed during the Challenge conversation.
OWN: Clarify who is accountable for the decision, how its impact will be assessed, and when to reconsider it.
The person presenting the recommendation stays involved throughout the process. They identify what needs checking and work out how the findings should change their proposal, with the leader and others helping them think more deeply.
In IF AI SAVES TIME, WHO GETS THE TIME BACK?, I explored how leaders can preserve opportunities for employees to develop judgment as AI takes on more work. These conversations give us a way to do that within the work that already needs doing.
For an already-busy leader, that might sound like another demand on limited time. But skipping these questions won’t necessarily save you time. You may end up spending far more time and effort solving the wrong problem, then dealing with new issues your solution creates. A few powerful questions up front can help you understand what you’re agreeing to before you commit the budget and everyone’s sweat equity.
Of course, that depends on whether your questions actually invite people to think. Sometimes we’re just asking them to agree with us.
A QUESTION CAN OPEN THINKING OR CLOSE IT
A leader might say, “Don’t you think we should look at the process first?” It has a question mark at the end, but it’s clearly just a suggestion dressed up in a question suit.
There’s a different possibility when the leader is prepared to learn something. A genuinely curious question can help the employee notice a gap in their own understanding, and their answer can reveal where the conversation needs to go next.
That requires good listening chops and a willingness to reconsider your own position. Asking questions becomes much less useful if you’ve already decided which answers you’re willing to accept.
We sometimes put these capabilities under the dreaded “soft skills” label. But they’re relevant to decisions involving real budgets and consequences for customers. There’s very little softness about discovering that your expensive solution addressed the wrong problem.
As I explored in WHO WILL DISAGREE WITH YOU TO YOUR FACE?, the leader’s response matters. If uncertainty receives an impatient dismissal, asking people to be more curious won’t accomplish much.
WHAT ARE WE SECRETLY HOPING FOR?
This next part might be a bit uncomfortable, but stay with me. Our own preferences belong in this examination too. Sometimes we’re drawn to a recommendation because it confirms what we’re hoping for.
I occasionally give a coaching client a simple practice when they’re trying to determine how they really feel about two choices. We take a coin and assign one choice to heads and the other to tails, then toss the coin. I ask them to notice, while the coin is spinning in the air, which result they’re secretly hoping for. I’ve found that moment remarkably useful for bringing an unacknowledged preference into view.
The coin doesn’t decide for them, and their preference doesn’t establish which choice is best. It reveals where they’re already invested in the answer, which can get in the way of weighing the options.
We can bring that awareness to a recommendation. Perhaps hiring feels more manageable than investigating where the existing process is breaking down. Or maybe the proposed action supports a position we’ve already announced. How might that preference affect what we’re willing to question? Our preferred answer deserves a good hard look, too.
FROM UNDERSTANDING TO PRACTICE
In Critical Curiosity in the AI Age, participants work through a scenario together, practicing questions and listening to what the answers reveal. They compare what different groups noticed and examine how those discoveries affect the proposed action.
Everyone begins with the same scenario, but different questions can bring different uncertainties into view. Another group may notice something yours overlooked or ask a question that changes how you understand the problem. That comparison allows participants to examine the limits of their own questioning.
Understanding the four elements of the ECHO Check gives people a starting point. The workshop gives them practice using it when an answer sounds convincing and the pressure is on to make a decision. Participants then choose where to apply the learning in their own work and develop the powerful questions they intend to use after the session.
YOUR COACHING CHALLENGE
Over the next week, notice the decisions moving through your team. Pay particular attention to recommendations that arrive polished and ready to approve, whether they come from AI or another source.
Where can you see the reasoning behind them? Where does everyone seem to be relying on someone else having checked? Where is curiosity missing?
Then consider where a session like this could help your organization:
Where have you had to revisit a decision because an assumption wasn’t examined, and what did that cost in time, resources, and missed opportunities?
Who would benefit from practicing together, including those presenting recommendations and those approving them?
What would you want them to do differently afterward, both in making decisions and in how they develop employees’ judgment?
Choose a specific decision that’s worth discussing with your colleagues and explore where stronger questions could help. Their perspectives may help you spot assumptions or question something before it becomes accepted as fact.
If you’re planning a leadership event or seeing these patterns in your team’s decisions, I’d welcome a conversation about bringing Critical Curiosity in the AI Age to your organization. Tell me where convincing recommendations are moving faster than the questions they deserve. Get in touch.
For more leadership articles, connect with Leslie Rohonczy on LinkedIn, or visit www.leslierohonczy.com