The Biggest Mistake Companies Make When Adopting AI

Key Takeaways

• Successful AI adoption is a people transformation before it is a technology transformation.
• Resistance to AI is often valuable feedback, not a problem to eliminate.
• Employees need clarity, support, and psychological safety to adopt AI with confidence.
• Leadership plays a critical role in helping teams navigate uncertainty during AI change.
• Organizations that invest in readiness, not just technology, are more likely to see lasting success.

If you're leading a team through an AI rollout right now, chances are most of your planning has gone into the technology side, including which platform to use, which workflows to automate, and how to increase productivity. That's not the wrong instinct. Every week brings another announcement about a new AI assistant, a smarter workflow, or another company promising to transform the way we work, and the pressure to move quickly makes it easy to treat implementation as mostly a software decision. Those questions dominate boardrooms, strategy meetings, and leadership conversations across every industry.

Yet after listening to Mindy Honcoop speak at Running Remote 2026, it became clear that leaders may be focusing on the wrong question altogether.

Mindy is the founder of Agile in HR and a recovering CPO with more than 25 years of experience helping organizations navigate change. Her session, From Resistance to Readiness: What Strategic HR Leaders Do Differently, wasn't really about artificial intelligence. It was about what happens when organizations move technology forward faster than they prepare the humans expected to use it. The companies struggling with AI aren't necessarily choosing the wrong software. Many are investing heavily in technology while assuming people will naturally adapt once the tools arrive. As Mindy explained, organizations often assess readiness by looking almost exclusively at systems, processes, governance, and infrastructure, while the people expected to work alongside AI are left out of that readiness conversation entirely. Technology feels measurable. You can compare features, evaluate vendors, estimate costs, and build implementation timelines. Human adaptation doesn't work the same way, though. It unfolds through conversations, uncertainty, learning, and trust, and that's where AI transformations either succeed or quietly begin to fail.

Technology Changes Overnight. People Don't.

Every major shift in the workplace follows a familiar pattern where the technology evolves faster than the people expected to use it. We've seen it with cloud computing, smartphones, collaboration platforms, and remote work itself. AI is simply the latest chapter.

Organizations often assume that once new technology becomes available, employees will naturally find better ways to work. In reality, people need time to understand how the change affects them personally before they can embrace it professionally. That's one of the reasons Mindy's session resonated with so many leaders. Rather than framing AI adoption as a software rollout, she framed it as an organizational change initiative, and those two things aren't the same. Software can be deployed in days. Building confidence in new ways of working takes considerably longer.

Leaders often underestimate how many invisible questions employees start asking the moment AI enters the workplace. Will this change my role? What work am I still expected to do myself? Will using AI make me look less capable? These concerns don't appear on a project implementation checklist, yet they're often what's occupying people's minds while leadership celebrates a successful rollout. The irony is that organizations usually interpret hesitation as resistance to technology when it's often uncertainty about the future.

Mindy challenged leaders to think differently about that hesitation. Instead of viewing resistance as something to overcome, she encouraged organizations to become curious about it. "What if we were to look at resistance as a signal?" she asked. "As a data point... to get curious about, to seek to understand." That shift reframes the entire conversation. Instead of asking why employees won't adopt AI, leaders begin asking what employees need to feel ready, and those lead to very different outcomes.

Readiness Starts Long Before the Rollout

One of the strongest ideas from Mindy's presentation is that readiness isn't something organizations achieve a week before implementation. It's something they build long before anyone logs into a new platform. Too often, readiness becomes synonymous with technical preparedness. Leaders ask whether the infrastructure is in place, whether the data is ready, and whether the security requirements have been met. Those questions matter, but they're only part of the picture.

Organizations also need to ask whether employees understand why the change is happening, how success will be measured, what support they'll receive, and what role they'll play in shaping new ways of working. Without those conversations, even the most sophisticated AI strategy begins on uncertain ground. Perhaps that's why some organizations spend months trying to increase AI adoption after launch. The rollout may have been technically successful, but the people were never invited into the journey early enough to make the change feel like something they owned rather than something that happened to them.

Resistance Usually Has a Story Behind It

One of the easiest mistakes leaders can make is assuming that resistance means employees don't want change. Sometimes that's true, but more often resistance is simply what uncertainty looks like from the outside.

Think about the last major change your organization went through. Chances are, people weren't immediately opposed to the new process or technology itself. They were trying to understand what it meant for them. Would they still be successful? Would they have enough time to learn? Would they be expected to figure everything out on their own? Those questions rarely get voiced directly; instead they show up as hesitation. People delay using the new tool, continue relying on familiar processes, or quietly wait to see what everyone else does first.

From leadership's perspective, it can look like people are resisting progress. From the employee's perspective, they're simply trying to reduce uncertainty before taking a risk. That's why Mindy's suggestion to treat resistance as a signal feels so important. Resistance isn't necessarily something to eliminate; it's employees telling leaders, often without saying a word, that something about the transition still feels unclear or unsafe. Organizations that become curious about that resistance tend to uncover much bigger issues than software adoption, including communication gaps, assumptions leadership didn't realize they were making, and places where people need more clarity, more support, or simply more time. In that sense, resistance becomes one of the most valuable forms of feedback an organization can receive during change.

The Emotional Side of AI Adoption

Another insight from Mindy's session stood out because it shifted the conversation away from technology entirely. She explained that many of the challenges organizations experience during AI implementation trace back to four emotional responses: doubt, overwhelm, isolation, and fear. None of those emotions have anything to do with whether an AI platform is technically capable; they have everything to do with what employees are actually experiencing while trying to adapt.

Doubt tends to appear first. Employees wonder whether they're using AI correctly, question when it's appropriate to rely on it versus completing the work themselves, and even people who are genuinely excited about AI sometimes hesitate because they don't want to appear careless or overly dependent on technology. Overwhelm follows close behind, because for many employees AI isn't replacing existing work, at least not initially. It's being introduced alongside everything else they're already responsible for, and learning new tools, experimenting with unfamiliar workflows, and keeping up with daily responsibilities can quickly become exhausting if organizations don't create space for the learning curve.

Isolation is another challenge that leaders frequently overlook, especially in distributed organizations. Learning something new is difficult when people feel like they're figuring it out alone, and without opportunities to ask questions, compare experiences, and learn alongside colleagues, remote teams can start to feel transactional rather than connected, and uncertainty grows quietly in the background.

Fear is perhaps the most uncomfortable emotion to discuss, yet one of the most common. Ironically, Mindy observed that some of the people who embrace AI the fastest are also among those most concerned about what it means for their future. They understand AI's capabilities better than anyone else, which also means they understand how significantly work could change. Ignoring those emotions doesn't make them go away; it tends to make them harder to recognize before they quietly start undermining adoption.

Psychological Safety Has Become a Business Strategy

When organizations talk about AI readiness, conversations usually revolve around governance, security, compliance, and responsible use. Those are all essential, but there's another form of readiness that receives far less attention, and that's whether employees actually feel psychologically safe enough to engage with something new.

In this context, psychological safety means employees believe it's safe to ask questions, to admit they don't understand something, to experiment, and to make mistakes while they're learning. Without that foundation, curiosity quickly gives way to caution. People stop exploring new ways of working because the perceived risk of getting something wrong becomes greater than the potential reward of trying something new. That's why psychological safety has become less of a leadership concept and more of an AI strategy.

Organizations that normalize experimentation give employees permission to discover better ways of working. They celebrate learning instead of expecting perfection from day one, and they recognize that mistakes aren't evidence of failure but part of the process of adapting to something entirely new. As Mindy explained, leaders need to create environments where people feel safe enough to "fail fast" while learning. That mindset doesn't lower standards; it accelerates growth because people spend less time protecting themselves and more time improving together, and shared experience becomes the missing layer that makes the difference. Technology will keep moving quickly, but human confidence develops on a different timeline, and successful leaders make room for both.

Why Slowing Down Can Help Organizations Move Faster

The pressure to adopt AI has created an understandable sense of urgency, and no leader wants their organization to fall behind. But urgency can easily become the enemy of successful change. One of Mindy's most memorable reminders from the session was surprisingly simple. Sometimes organizations need to slow down in order to move faster.

At first, that sounds almost contradictory, but it reflects something many organizations have already lived through. Rushing implementation often creates months of additional work downstream, as leaders end up addressing confusion that better communication could have prevented, managers work to rebuild confidence after employees feel left behind, and HR teams step in to repair adoption challenges that started long before the technology launched. Slowing down doesn't mean delaying innovation; it means giving people enough context to move forward together. When employees understand why change is happening, how it benefits the organization, and how they'll be supported along the way, momentum builds naturally, and the rollout becomes something people participate in rather than something they endure.

Readiness Is Never Finished

Near the end of her session, Mindy shared an idea that quietly ties everything together. She reminded the audience that readiness isn't something organizations eventually achieve and then move on from. It isn't a milestone at the end of an implementation plan or a box to check before launching another AI initiative; it's continuous. Organizations are constantly adapting as technology evolves, customer expectations change, new tools emerge, roles shift, and teams grow. In that environment, readiness becomes less about preparing for a single change and more about building an organization that knows how to change well.

That matters because AI isn't a one-time transformation. Today's AI workflows will look different a year from now. New capabilities will appear, old processes will disappear, and employees will need to learn, unlearn, and relearn on an ongoing basis. The organizations that thrive won't be the ones that successfully implemented AI in 2026 but the ones that continue helping people adapt in 2027, 2028, and beyond. As Mindy put it, readiness isn't something you arrive at; it's something you continually cultivate as technology and people evolve together.

Perhaps the biggest mindset shift for leaders is moving away from "Are we ready for AI?" and toward "How do we build an organization that's always ready to learn?" That question changes the role of leadership entirely, moving it away from managing technology and toward creating environments where learning, curiosity, and adaptation become part of everyday work, and that's a competitive advantage no software can purchase.

The Real Competitive Advantage Isn't AI

It's tempting to think the organizations winning with AI simply have better technology. After all, new tools appear almost every week, each promising faster workflows, smarter decisions, and higher productivity. Technology certainly matters, but after listening to conversations throughout Running Remote 2026, another pattern became clear. The organizations making the most meaningful progress weren't talking only about AI. They were talking about leadership, communication, culture, and helping people navigate uncertainty together, which is a very different conversation from asking which platform to buy next.

Artificial intelligence will continue changing the way work gets done, but what it can't replace is the leadership required to help people navigate that change with confidence. Employees don't need leaders to have every answer. They need leaders who create clarity when the future feels uncertain, who listen before assuming resistance, and who see curiosity as something worth protecting rather than rushing. Most importantly, they need leaders who remember that every technology transformation is also a human transformation. The organizations that understand this won't simply adopt AI more successfully; they'll build workplaces where people feel confident learning alongside it, and that's the kind of competitive advantage no software can replicate.

Frequently Asked Questions

What is the biggest mistake companies make when adopting AI?

One of the biggest mistakes is treating AI implementation as a technology project instead of a people transformation. Organizations often invest heavily in software while overlooking communication, leadership, employee readiness, and organizational culture.

Why do employees resist AI?

Resistance is often a response to uncertainty rather than unwillingness. Employees may have concerns about changing expectations, job security, learning new skills, or understanding how AI fits into their role. As Mindy Honcoop explained at Running Remote 2026, resistance should be viewed as valuable feedback rather than something to eliminate.

How can leaders improve AI adoption?

Successful leaders involve employees early, explain why change is happening, create opportunities for learning, encourage experimentation, and build psychological safety so people feel comfortable asking questions and making mistakes while adapting.

What is AI readiness?

AI readiness extends beyond technology infrastructure. It includes leadership alignment, employee understanding, organizational culture, change management, governance, and a willingness to learn continuously as AI evolves.

Does AI replace leadership?

No. If anything, AI increases the importance of leadership. While AI can automate tasks and improve efficiency, leaders remain responsible for creating clarity, building trust, supporting employees, and guiding organizations through continuous change.

Sources

Running Remote 2026

• From Resistance to Readiness: What Strategic HR Leaders Do Differently — Mindy Honcoop

Related Reading

• Why Remote Teams Feel More Transactional Than They Used To
• Why Shared Experience Is the Missing Layer in Remote Team Culture
• Why Custom Questions Are the Sneaky-Best Part of Team Culture

The Kind of Team That Handles Change Well

The insight at the center of Mindy's session is that AI adoption goes better when people feel psychologically safe enough to ask questions, try new approaches, and admit uncertainty without worrying about how they'll be seen. That kind of safety isn't something leaders can simply declare. It develops through shared experience, through working alongside each other in ways that build trust over time.

At GoFish, we create multiplayer experiences for remote teams that help build exactly that foundation. Not team-building as a formality, but genuine shared moments that give distributed teams something to draw on when work gets hard or unfamiliar.

If you're leading a team through an AI rollout, a period of change, or just the ongoing challenge of keeping people connected at a distance, GoFish is worth exploring.