The Slowest AI Adopters Often Build the Most Useful Rollouts
People are feeling as if they don't know what they are good at anymore. They knew the systems, they knew how to work things, and they were very good at it. They're still very good at it, but now they have a competing partner."
Simran Kaur
Founder
Soul-Led CEO
The standard pitch for AI adoption treats hesitation as a skills problem, something a training session fixes. Move fast, get everyone using the tools, keep pace. But plenty of the people dragging their feet understand the technology just fine. They built careers on being the person with the answer, and a tool that produces a comparable answer in seconds unsettles the exact thing that made them valuable. The organizations getting the most out of AI are the ones treating that hesitation as information rather than resistance.
Simran Kaur is a Lead Business & Functional Analyst with a decade of experience with the North Carolina Department of Health and Human Services, author of Learning Gratitude with Journaling, and Founder of Soul-Led CEO, where she coaches senior women on staying irreplaceable as AI moves into their work. Watching herself and the women around her go quiet as adoption pressure ramped up, she put a name to a feeling most rollouts never account for: the sense that the expertise defining your career has just been handed a competitor.
“People are feeling as if they don’t know what they are good at anymore,” Kaur says. “They knew the systems, they knew how to work things, and they were very good at it. They’re still very good at it, but now they have a competing partner.” That reaction shows up unevenly. BambooHR’s Clarity over Chaos report found 60 percent of men use AI daily compared to 40 percent of women, a split that tracks more closely with who feels free to experiment out loud than with capability.
A competing partner
The people hit hardest are the ones who built a reputation on having answers, the person a team turned to when something broke. Now something answers just as fast, and they start questioning themselves before they question the tool. FuturePath founder Terri Horton described the same identity threat, where employees quietly wonder who they are once a tool takes over part of their work.
That inward turn is where Kaur thinks the gender pattern comes from. Women internalize the uncertainty rather than voice it, because voicing it carries a cost they have learned to track. “If I speak up and say I’m not sure, people can read that as saying I’m not capable,” she says. “Women have to work double and prove themselves double before they’re seen as equal. So even with AI, they tend to ask questions before jumping in.” She started posting about it publicly once she recognized the silence in herself. “If I’m feeling that, I’m pretty sure a lot of other women are feeling it too.”
The instinct to interrogate a tool before trusting it reads as reluctance, and Kaur argues it functions as diligence. A generic tool can produce content all day, but until it learns a team’s values, goals, and voice, the output stays generic. “You need to ask it questions before you say this is good enough for us,” she says. “That doesn’t mean you’re not ready to adopt it. It means you’re making sure you get the best value out of it.” The failure she sees most often is a rollout decided at the top and handed down without input. “If you’re telling people how to use the tool and they feel they can do it better than the tool can, they’re never going to adopt it. But if you take their feedback and see where they actually need help, adoption becomes very easy.”
Two teams, same tool
Kaur draws the difference with a scenario. Take two content teams of five, each person producing about one researched piece a week. AI enters, and the same work takes a day. Both teams save four days, and what they do with the time splits them. One cranks the volume up because the capacity is there. The other keeps AI on the research and holds onto the writing.
“I let AI do the research, but I take that information, discuss the angles with the team, and write from my own perspective,” Kaur says. Hand a topic to a tool and accept whatever comes back, and the voice goes with it, along with any sense of what the research actually said. Her own habit is to hand AI a couple of lines and press it. “I tell it to pressure-test what I’m writing, tell me what I’m missing, give me the blind spots,” then she weighs the answer against her own thinking before it goes anywhere.
Her day job supplies the version she wishes leaders would picture. As an analyst, a five-minute update can cost a week because the material sits scattered across dozens of documents. “If I had an assistant who could point me to the documents I need, it would take so much less time,” she says, freeing hours for the design work and conversations that need her judgment. “If I gave you an assistant, what would you have it do?” is, in her view, the question companies should ask before they implement anything. That kind of trust is what earns cooperation on adoption, built over time rather than announced in a memo.
Keep a human in the loop
Kaur waves off HR as outside her expertise, then goes straight to what happens to a resume before a person ever sees it. Candidates now submit applications no human ever reads, screened in or out before anyone speaks to them, and companies lose people who would have fit well. She understands the volume problem. Her argument is about sequence. “Maybe they need a way for AI to research a candidate, look at their background, but still keep a human in the loop for at least a quick conversation before deciding this person isn’t relevant.”
For HR teams at growing companies, that is the assignment in miniature: deciding which parts of the work AI should speed up and which parts need a person. The pattern shows up across the function as HR gets pulled in to translate adoption into strategy. BambooHR’s 2026 Employee Happiness Report found that men still report happiness 6.6 eNPS points higher than women, a gap that narrowed this year without closing. Who gets asked what they need, and who gets handed a mandate, tends to track the same lines.
The tools move fast for everyone now, which is exactly why the decision about what to keep matters more than the decision to adopt. “Use AI to pressure-test your ideas, show you your blind spots, and speed up the work that doesn’t require your expertise,” Kaur says. “But don’t outsource the thinking that makes your work uniquely yours.”
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TL;DR
Simran Kaur
Soul-Led CEO
Founder
Founder
The standard pitch for AI adoption treats hesitation as a skills problem, something a training session fixes. Move fast, get everyone using the tools, keep pace. But plenty of the people dragging their feet understand the technology just fine. They built careers on being the person with the answer, and a tool that produces a comparable answer in seconds unsettles the exact thing that made them valuable. The organizations getting the most out of AI are the ones treating that hesitation as information rather than resistance.
Simran Kaur is a Lead Business & Functional Analyst with a decade of experience with the North Carolina Department of Health and Human Services, author of Learning Gratitude with Journaling, and Founder of Soul-Led CEO, where she coaches senior women on staying irreplaceable as AI moves into their work. Watching herself and the women around her go quiet as adoption pressure ramped up, she put a name to a feeling most rollouts never account for: the sense that the expertise defining your career has just been handed a competitor.
“People are feeling as if they don’t know what they are good at anymore,” Kaur says. “They knew the systems, they knew how to work things, and they were very good at it. They’re still very good at it, but now they have a competing partner.” That reaction shows up unevenly. BambooHR’s Clarity over Chaos report found 60 percent of men use AI daily compared to 40 percent of women, a split that tracks more closely with who feels free to experiment out loud than with capability.
A competing partner
The people hit hardest are the ones who built a reputation on having answers, the person a team turned to when something broke. Now something answers just as fast, and they start questioning themselves before they question the tool. FuturePath founder Terri Horton described the same identity threat, where employees quietly wonder who they are once a tool takes over part of their work.
That inward turn is where Kaur thinks the gender pattern comes from. Women internalize the uncertainty rather than voice it, because voicing it carries a cost they have learned to track. “If I speak up and say I’m not sure, people can read that as saying I’m not capable,” she says. “Women have to work double and prove themselves double before they’re seen as equal. So even with AI, they tend to ask questions before jumping in.” She started posting about it publicly once she recognized the silence in herself. “If I’m feeling that, I’m pretty sure a lot of other women are feeling it too.”
The instinct to interrogate a tool before trusting it reads as reluctance, and Kaur argues it functions as diligence. A generic tool can produce content all day, but until it learns a team’s values, goals, and voice, the output stays generic. “You need to ask it questions before you say this is good enough for us,” she says. “That doesn’t mean you’re not ready to adopt it. It means you’re making sure you get the best value out of it.” The failure she sees most often is a rollout decided at the top and handed down without input. “If you’re telling people how to use the tool and they feel they can do it better than the tool can, they’re never going to adopt it. But if you take their feedback and see where they actually need help, adoption becomes very easy.”
Two teams, same tool
Kaur draws the difference with a scenario. Take two content teams of five, each person producing about one researched piece a week. AI enters, and the same work takes a day. Both teams save four days, and what they do with the time splits them. One cranks the volume up because the capacity is there. The other keeps AI on the research and holds onto the writing.
“I let AI do the research, but I take that information, discuss the angles with the team, and write from my own perspective,” Kaur says. Hand a topic to a tool and accept whatever comes back, and the voice goes with it, along with any sense of what the research actually said. Her own habit is to hand AI a couple of lines and press it. “I tell it to pressure-test what I’m writing, tell me what I’m missing, give me the blind spots,” then she weighs the answer against her own thinking before it goes anywhere.
Her day job supplies the version she wishes leaders would picture. As an analyst, a five-minute update can cost a week because the material sits scattered across dozens of documents. “If I had an assistant who could point me to the documents I need, it would take so much less time,” she says, freeing hours for the design work and conversations that need her judgment. “If I gave you an assistant, what would you have it do?” is, in her view, the question companies should ask before they implement anything. That kind of trust is what earns cooperation on adoption, built over time rather than announced in a memo.
Keep a human in the loop
Kaur waves off HR as outside her expertise, then goes straight to what happens to a resume before a person ever sees it. Candidates now submit applications no human ever reads, screened in or out before anyone speaks to them, and companies lose people who would have fit well. She understands the volume problem. Her argument is about sequence. “Maybe they need a way for AI to research a candidate, look at their background, but still keep a human in the loop for at least a quick conversation before deciding this person isn’t relevant.”
For HR teams at growing companies, that is the assignment in miniature: deciding which parts of the work AI should speed up and which parts need a person. The pattern shows up across the function as HR gets pulled in to translate adoption into strategy. BambooHR’s 2026 Employee Happiness Report found that men still report happiness 6.6 eNPS points higher than women, a gap that narrowed this year without closing. Who gets asked what they need, and who gets handed a mandate, tends to track the same lines.
The tools move fast for everyone now, which is exactly why the decision about what to keep matters more than the decision to adopt. “Use AI to pressure-test your ideas, show you your blind spots, and speed up the work that doesn’t require your expertise,” Kaur says. “But don’t outsource the thinking that makes your work uniquely yours.”