Your organization has invested in a new AI tool. Leadership is enthusiastic, employees have received training, and managers have been asked to encourage adoption across their teams.
Then an employee says something nobody particularly wants to hear:
"I don't think this is actually making our work better."
It's a familiar challenge in organizational change. A new initiative is introduced, some employees embrace it, and others express hesitation or frustration.
The instinct may be to interpret that skepticism as resistance. Perhaps the employee is uncomfortable with new technology, reluctant to change established routines, or simply needs more training.
Those explanations are possible. But there's another possibility worth considering.
What if the employee is right?
What if the AI workflow really is creating more work? What if the technology performs well in a controlled demonstration but struggles with the complexity of everyday operations? What if the productivity gains being celebrated at the organizational level aren't translating into meaningful improvements for the people using the tools?
Not everyone who questions AI is resisting change. Sometimes they're providing valuable information about how the change is actually working.
And knowing how to recognize that distinction is becoming an increasingly important leadership capability.
The pressure to drive adoption
For many organizations, AI adoption has become a strategic priority. Leaders want employees experimenting with new tools, identifying efficiencies, and finding better ways to work.
Managers play an important role in making that happen.
They're often responsible for reinforcing the organization's direction, helping employees navigate new workflows, addressing concerns, and maintaining momentum.
But this responsibility can create a difficult tension.
When managers are expected to drive adoption, they may begin to interpret questions or criticism primarily as obstacles to overcome. An employee who challenges the value of a new AI workflow can start to look like someone who needs additional encouragement, reassurance, or training.
Sometimes that's appropriate. New technology often comes with a learning curve, and employees may need support before they can use it effectively.
But if managers assume every objection is a change-management problem, they risk overlooking evidence that the technology or workflow itself needs attention.
The challenge is distinguishing between the two.
The employee closest to the work may see what leadership doesn't
Consider a team that has introduced AI to accelerate the creation of client-facing reports.
From a leadership perspective, the rollout looks promising. Employees can generate an initial draft in a fraction of the time it previously took to write one manually.
That's a meaningful improvement.
But an employee who works with those reports every day notices something else.
The AI-generated drafts frequently require corrections. Certain types of information are presented inconsistently, and reviewing the output takes longer than expected. In some cases, the employee finds it easier to complete the work using the previous process.
When they raise these concerns, the manager faces a choice.
They can interpret the feedback as reluctance to change and encourage the employee to keep using the tool.
Or they can recognize that the employee may be describing a real operational issue worth understanding.
The employee's experience doesn't automatically prove the rollout is failing. Their workflow might be unusual, or the issue might be resolved through training or changes to the process.
But their concerns shouldn't automatically be dismissed either.
The people closest to the work often have information that isn't visible in an adoption dashboard or executive presentation.
That information can be valuable, particularly when organizations are still learning where AI creates genuine improvements and where it introduces new complexity.
Adoption and effectiveness aren't the same thing
One of the challenges of evaluating AI initiatives is that different measures can tell different stories.
An organization might see growing usage and conclude that adoption is progressing successfully.
Employees may be logging into the tools, completing training, and incorporating AI into their workflows.
Those are useful indicators. But they don't necessarily establish that the work itself is improving.
A tool can be widely used without producing meaningful efficiency gains. A workflow can generate output faster while requiring additional review. Employees can comply with an adoption initiative while privately believing the new process is less effective.
Conversely, a tool that initially generates frustration may become valuable once employees gain experience or the workflow is refined.
This is why employee feedback matters.
It can reveal questions that usage statistics alone don't answer.
Is the work actually getting faster?
Is quality improving?
Are employees spending less time on the task—or simply moving that time into review and correction?
Does the tool work equally well across different types of work?
Are the problems caused by the technology, the process, or the way people are using it?
These are operational questions, not simply questions about employee attitudes.
And managers are often the first people who hear the answers.
Skepticism isn't automatically evidence, either
There's an important distinction here.
The fact that an employee dislikes an AI initiative doesn't necessarily mean the initiative is flawed.
Some concerns may reflect unfamiliarity with the technology, discomfort with changing responsibilities, or expectations that don't align with what the tool was designed to accomplish.
Others may identify genuine performance problems.
Both deserve attention, but they aren't the same.
The goal isn't to treat every skeptical employee as an authority on whether an AI initiative should continue. It's to avoid making the opposite mistake: assuming skepticism has no value simply because it challenges the direction of the rollout.
A manager needs to be able to navigate that uncertainty without immediately choosing a side.
That's particularly difficult when organizational expectations are clear and employees are looking to their manager for an answer.
The manager is caught between two realities
Imagine the employee from our earlier example approaches their manager after another frustrating experience with the new reporting workflow.
They've spent an hour correcting AI-generated content and are convinced the previous process was better.
At the same time, the manager knows leadership expects the team to adopt the tool. They've been asked to reinforce the rollout and help employees move past initial challenges.
Now the manager has to respond.
They need to represent the organization's direction without dismissing what the employee is experiencing. They need to understand the concern without automatically concluding that the rollout is failing. And they may need to acknowledge uncertainty about how the process should evolve.
That isn't simply a conversation about technology.
It's a conversation about leadership judgment.
The manager is navigating competing priorities: supporting organizational change, maintaining credibility with employees, understanding operational evidence, and recognizing when an issue needs further attention.
There's no universal script that resolves all of those tensions.
Why this is a leadership development challenge
Organizations can teach managers about AI strategy, adoption goals, governance policies, and available tools.
All of that is valuable.
But understanding the organization's AI strategy doesn't necessarily prepare a manager for the moment an employee challenges whether that strategy is working in practice.
Real conversations are rarely as straightforward as the examples presented during training.
Employees may be frustrated. Their concerns may be incomplete or difficult to articulate. The manager may not know enough about the technology to immediately evaluate the claim. Organizational expectations may create pressure to reach a conclusion before all the information is available.
In those moments, managers need more than a set of talking points.
They need the ability to navigate disagreement, uncertainty, and competing perspectives while maintaining credibility.
Those are leadership capabilities, and they're worth developing deliberately.
Practice for the conversations created by AI adoption
This is one of the challenges addressed by Leading Through AI Change from Practice Ground.
Rather than focusing on prompting techniques or product features, Practice Ground helps leaders rehearse the human conversations that emerge as AI changes how organizations operate.
Through realistic, AI-powered simulations, managers can encounter consequential workplace situations, try different approaches, experience how conversations respond, and receive immediate, evidence-informed feedback.
The goal isn't to memorize a perfect response to employee skepticism. Different situations may require different leadership moves, and a good conversation won't always end with agreement.
Instead, practice gives managers opportunities to develop the judgment and communication capabilities needed to navigate those situations more effectively.
That matters because AI adoption isn't a single event. It's an ongoing process of experimentation, learning, adjustment, and organizational change.
Managers will encounter new questions as tools evolve, workflows change, and employees gain experience.
Preparing them to lead those conversations is part of preparing the organization for AI.
The most useful feedback may not be the most enthusiastic
Successful AI adoption requires people who are willing to try new things. It also requires organizations that can learn from what happens when those experiments meet the realities of everyday work.
Sometimes that learning comes from enthusiastic employees who discover an unexpectedly valuable use case.
Other times, it comes from someone who says:
"I don't think this is working."
Neither reaction tells the entire story on its own.
The important question is whether managers are prepared to understand what the feedback means and respond appropriately.
Because skepticism isn't always resistance.
Sometimes it's the beginning of a conversation that helps an organization make AI adoption work better.
Practice Ground by Virbela helps leaders rehearse the human conversations created by AI adoption before they're real.
Explore Leading Through AI Change >
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