Knowing When To Trust AI Output Becomes The Judgment Call That Sets Strong Teams Apart
AI can teach you what formula to use in Excel. But a mentor teaches you how to think when the answer isn't obvious.
Rhonnie Reeves
Director of Operations
EMSAR
Generative AI tools can lay out the steps for a task in seconds and get every one of them right on paper. What the technology cannot supply is the judgment to know which step does not apply, or the moment the output is quietly wrong. That discernment comes from doing the work, from the years of small corrections that teach a person what to check before trusting an answer. Entry-level work used to be where people built that judgment, so as companies automate that work away, fewer employees develop the instinct to question an answer. Bad numbers travel farther up the chain before anyone catches them.
Rhonnie Reeves, Director of Operations at EMSAR, a national medical device field service company, runs as much of her operation as she can through AI, while still spending her days on the parts of the job the technology cannot handle. She oversees enterprise systems and the company’s fleet, and she credits coaching for how quickly she rose through the field. “AI can teach you what formula to use in Excel. But a mentor teaches you how to think when the answer isn’t obvious,” Reeves says.
The gap shows up the moment conditions stop being ideal. A report can be built on the wrong inputs and still come back looking polished. The numbers add up, but don’t match what’s happening on the ground, and only someone who has done the work can spot it.
Skepticism is a learned skill
The habit worth teaching a team is when to push back on a confident answer. Nothing in the output signals which results are sound, so the discipline has to come from the person, and it matters most on anything headed for a decision. Reeves models it on her own work. “I question my Claude all the time, and it tells me, ‘Oh yeah, you were right to do that.’ Even on their worst day, a human with 30 years of corporate experience is going to be able to say, ‘No, that’s not real life, that’s not what’s going on,'” she says.
An employee who has never built the report by hand has no baseline for when the automated version drifts, and no feel for the context the model was never handed, like a contract ending next quarter or a policy that changed last month. The danger is a false comfort, where people decide the tool is easy and trust what it gives back.
AI is strong on the data side of HR, and reaches its limit the moment a decision turns on a person. A model can sort a clean dataset, but it cannot read a room or weigh how a choice lands on someone across the table. “There’s still going to be situations where a human is the better decision maker, especially when there are people involved. You can’t control people, and AI can’t predict people,” Reeves says.
Where mentoring earns its keep
The harder thing to automate is how people grow into judgment. The leaders who develop talent push back on ideas and explain the reasoning behind a no, so the lesson holds longer than the decision. An employee who learns the job mainly through a tool that tends to agree rarely meets that kind of pressure, and Reeves seeks it out. “I learn from disagreements, and I think it’s healthy,” Reeves says. The pattern is already showing up in the data. BambooHR’s State of the Workforce 2026 report found that 43% of tech workers say senior engineers now spend more time reviewing AI-generated code than mentoring, a trade-off that saves time today and hollows out the pipeline tomorrow.
The other half is making sure the thinking behind today’s process does not walk out the door with the people who remember it. When a team changes how it works, a long-tenured employee can explain what came before and why it was dropped, context no model can reach. Reeves treats passing that down as what mentoring is for. “If we make a change in process, someone who has been there a long time will say, ‘We used to do it that way, and this is why we switched.’ AI doesn’t know that. That legacy is how you progress in your career, and it’s how you prepare the people you end up mentoring,” she adds. Kept current, that knowledge spares a team from relearning the same lesson, and it turns experienced employees into the people others want to learn from.
The cost of a lonely onboarding
Automation has made it easy to get a new hire productive fast, which tempts teams to call that the whole job. A self-serve stack of videos and prompts can teach the tasks well, but on its own it never tells someone why the work matters or who to ask when something breaks, and a new employee can read that silence as a signal about how invested the company is in them. The tools are not the problem. What they leave room for is someone who connects the daily tasks to the bigger picture, so a new hire sees more than a queue of tickets.
That connection is what turns a hire into someone who stays. When people understand how their work ladders up and feel trusted to own a piece of it, they invest back, and when they are left to punch a clock against goals nobody explained, the training spend walks out with them. “If everyone is working on the same goals, we’re all going to succeed. But if a person doesn’t feel empowered enough to understand the goals, they’re not going to stay. As a company, you’re wasting your time and your investment in training them,” Reeves says.
What keeps someone is rarely the content. An automated flow answers the questions a newcomer knows to ask, and the person beside it answers the ones that only surface on the job.
Teaching responsible use
The list of things a mentor is expected to teach now includes the tool itself. Nobody has fully figured out responsible use, leaders included, and Reeves treats that as a reason to learn it shoulder to shoulder with a team. In practice, that means working through the technology together and reviewing AI-assisted work so the person understands what they are sending, with no reply going out straight from the chatbot unread. Work that skips that step tends to announce itself, and it chips away at trust between colleagues. “Someone replies, and you think, ‘Yup, that’s ChatGPT,’ and it makes you lose trust in what they’re doing. Even if the output is right, it lacks context if a human hasn’t looked at it,” Reeves says.
The next group of managers inherits that balance, the technology carrying the execution while a person supplies the judgment to question what it returns. She puts the responsibility on the people learning to lead. “We have to ensure we aren’t relying on AI to do our thinking, but using it as the tool that it is,” Reeves says.
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TL;DR
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Rhonnie Reeves
EMSAR
Director of Operations
Director of Operations
Generative AI tools can lay out the steps for a task in seconds and get every one of them right on paper. What the technology cannot supply is the judgment to know which step does not apply, or the moment the output is quietly wrong. That discernment comes from doing the work, from the years of small corrections that teach a person what to check before trusting an answer. Entry-level work used to be where people built that judgment, so as companies automate that work away, fewer employees develop the instinct to question an answer. Bad numbers travel farther up the chain before anyone catches them.
Rhonnie Reeves, Director of Operations at EMSAR, a national medical device field service company, runs as much of her operation as she can through AI, while still spending her days on the parts of the job the technology cannot handle. She oversees enterprise systems and the company’s fleet, and she credits coaching for how quickly she rose through the field. “AI can teach you what formula to use in Excel. But a mentor teaches you how to think when the answer isn’t obvious,” Reeves says.
The gap shows up the moment conditions stop being ideal. A report can be built on the wrong inputs and still come back looking polished. The numbers add up, but don’t match what’s happening on the ground, and only someone who has done the work can spot it.
Skepticism is a learned skill
The habit worth teaching a team is when to push back on a confident answer. Nothing in the output signals which results are sound, so the discipline has to come from the person, and it matters most on anything headed for a decision. Reeves models it on her own work. “I question my Claude all the time, and it tells me, ‘Oh yeah, you were right to do that.’ Even on their worst day, a human with 30 years of corporate experience is going to be able to say, ‘No, that’s not real life, that’s not what’s going on,'” she says.
An employee who has never built the report by hand has no baseline for when the automated version drifts, and no feel for the context the model was never handed, like a contract ending next quarter or a policy that changed last month. The danger is a false comfort, where people decide the tool is easy and trust what it gives back.
AI is strong on the data side of HR, and reaches its limit the moment a decision turns on a person. A model can sort a clean dataset, but it cannot read a room or weigh how a choice lands on someone across the table. “There’s still going to be situations where a human is the better decision maker, especially when there are people involved. You can’t control people, and AI can’t predict people,” Reeves says.
Where mentoring earns its keep
The harder thing to automate is how people grow into judgment. The leaders who develop talent push back on ideas and explain the reasoning behind a no, so the lesson holds longer than the decision. An employee who learns the job mainly through a tool that tends to agree rarely meets that kind of pressure, and Reeves seeks it out. “I learn from disagreements, and I think it’s healthy,” Reeves says. The pattern is already showing up in the data. BambooHR’s State of the Workforce 2026 report found that 43% of tech workers say senior engineers now spend more time reviewing AI-generated code than mentoring, a trade-off that saves time today and hollows out the pipeline tomorrow.
The other half is making sure the thinking behind today’s process does not walk out the door with the people who remember it. When a team changes how it works, a long-tenured employee can explain what came before and why it was dropped, context no model can reach. Reeves treats passing that down as what mentoring is for. “If we make a change in process, someone who has been there a long time will say, ‘We used to do it that way, and this is why we switched.’ AI doesn’t know that. That legacy is how you progress in your career, and it’s how you prepare the people you end up mentoring,” she adds. Kept current, that knowledge spares a team from relearning the same lesson, and it turns experienced employees into the people others want to learn from.
The cost of a lonely onboarding
Automation has made it easy to get a new hire productive fast, which tempts teams to call that the whole job. A self-serve stack of videos and prompts can teach the tasks well, but on its own it never tells someone why the work matters or who to ask when something breaks, and a new employee can read that silence as a signal about how invested the company is in them. The tools are not the problem. What they leave room for is someone who connects the daily tasks to the bigger picture, so a new hire sees more than a queue of tickets.
That connection is what turns a hire into someone who stays. When people understand how their work ladders up and feel trusted to own a piece of it, they invest back, and when they are left to punch a clock against goals nobody explained, the training spend walks out with them. “If everyone is working on the same goals, we’re all going to succeed. But if a person doesn’t feel empowered enough to understand the goals, they’re not going to stay. As a company, you’re wasting your time and your investment in training them,” Reeves says.
What keeps someone is rarely the content. An automated flow answers the questions a newcomer knows to ask, and the person beside it answers the ones that only surface on the job.
Teaching responsible use
The list of things a mentor is expected to teach now includes the tool itself. Nobody has fully figured out responsible use, leaders included, and Reeves treats that as a reason to learn it shoulder to shoulder with a team. In practice, that means working through the technology together and reviewing AI-assisted work so the person understands what they are sending, with no reply going out straight from the chatbot unread. Work that skips that step tends to announce itself, and it chips away at trust between colleagues. “Someone replies, and you think, ‘Yup, that’s ChatGPT,’ and it makes you lose trust in what they’re doing. Even if the output is right, it lacks context if a human hasn’t looked at it,” Reeves says.
The next group of managers inherits that balance, the technology carrying the execution while a person supplies the judgment to question what it returns. She puts the responsibility on the people learning to lead. “We have to ensure we aren’t relying on AI to do our thinking, but using it as the tool that it is,” Reeves says.