Most organizations approach artificial intelligence adoption as a technology challenge.
The conversation has largely focused on model accuracy, data security, governance and risk. Those concerns matter. But our research suggests another obstacle may be emerging inside organizations: Employees may trust AI itself more than they trust one another’s use of it.
In a recent Blanchard survey of leaders and individual contributors, nearly 43 percent of respondents reported observing undesirable AI-related workplace behaviors, ranging from subtle judgment of colleagues who use AI to reliance on AI-generated content without adequate verification. About 24 percent said these behaviors have become normalized in their workplaces, while only 18 percent acknowledged engaging in them themselves.
Respondents were therefore roughly 2.4 times more likely to report seeing these behaviors in others than to acknowledge engaging in them personally. The gap is revealing.
Employees consistently recognize AI-related friction around them far more often than they identify themselves as contributors to it. The result is a growing trust gap—not between people and technology, but among colleagues attempting to navigate a rapidly changing way of working.
This finding points to an overlooked reality of AI adoption: Even when the technology works as intended, the informal norms that develop around its use can become a significant barrier.
When leaders encourage AI adoption but rarely model its use, when employees use AI but avoid acknowledging it, or when people use AI to critique others rather than collaborate with them, ambiguity grows around what is acceptable, expected and safe.
AI is not creating these tensions. It is exposing them.
How AI shadow culture takes hold
What is quietly emerging in many organizations is an AI shadow culture: A workplace dynamic in which people use, judge, hide or avoid AI in ways that remain largely unspoken.
The phenomenon is easy to miss because it is rarely addressed in formal policies or guidelines. Instead, it emerges through everyday interactions: The colleague who quietly uses AI but never mentions it; the manager who encourages experimentation but never models it; the team member who questions the legitimacy of AI-assisted work while privately using AI themselves; the leader who endorses AI publicly but rarely or never uses it.
AI shadow culture develops when employees are uncertain about the informal rules surrounding AI use. They may understand the technology, yet remain unclear about what is acceptable, respected, rewarded or safe.
The consequence is not primarily technological. It is cultural.
AI is increasingly embedded in everyday work, yet conversations about how it is being used often remain limited, inconsistent or avoided altogether. Employees are left to interpret expectations on their own, creating mistrust and missed opportunities for collective learning.
Workplace norms shape AI adoption
Many organizations have formal policies governing AI use. Far fewer have established shared norms.
Policies answer questions of compliance. Norms answer questions about culture:
- Can I openly discuss how I used AI?
- Will my work be judged differently if I disclose it?
- Is using AI viewed as resourceful or as a shortcut?
- Can I challenge AI-generated output without appearing resistant to change?
When organizations fail to establish clear norms around transparency, accountability and collaboration, employees create their own norms. These norms emerge through small interactions, casual comments and observed behaviors rather than formal guidance.
Over time, these informal norms generate the shadow culture that shapes AI adoption.
5 workplace archetypes that undermine trust
Across our research, five recurring workplace archetypes emerged. They are not fixed identities. Most people move in and out of these patterns depending on circumstances. Yet each reflects a behavior that can either accelerate or undermine trust.
The judgmental observer
The judgmental observer signals—often subtly—that AI-assisted work is less legitimate than work produced without AI assistance. The behavior rarely takes the form of direct criticism. More often it emerges through skepticism, dismissive comments or social cues that imply using AI reflects a lack of expertise or effort.
For some employees, the attitude may reflect fears about job displacement. For others, it reflects ethical concerns or a belief that AI-assisted work lacks authenticity. Regardless of its origin, the effect can be similar: Employees become less transparent about how they work.
Forty-seven percent of respondents reported observing colleagues judge or diminish others for using AI, making it the most frequently observed of the five behaviors measured in the survey.
When employees anticipate judgment, AI use does not disappear. It becomes less visible. Organizations lose opportunities for shared learning, experimentation, and norm-setting.
A better alternative: Curiosity
Replace assumptions with curiosity. Ask how AI was used, what role it played and what ideas the individual contributed. Focus on the quality of the work rather than the mere presence of the tool.
2. The competitive user
The competitive user uses AI to critique, revise or improve upon a colleague’s work without meaningful collaboration. The intention is often efficiency. The effect can be diminished trust.
When employees run a colleague’s idea through AI and return an improved version without discussion, they inadvertently communicate that speed matters more than partnership. Over time, people become less willing to share unfinished thinking, reducing collaboration precisely when it is most needed. Forty-two percent of respondents reported observing this behavior.
AI can improve ideas. But when it replaces conversation rather than supporting it, it can erode trust.
A better alternative: Collaboration
Use AI as a collaborative tool rather than a competitive advantage. Before significantly revising another person’s work, involve them in the process and make your intentions explicit.
3. The overconfident adopter
The overconfident adopter mistakes efficiency for accuracy.
As generative AI becomes more sophisticated, polished results can make it increasingly difficult to distinguish between confident language and sound judgment. Well-structured output can create an illusion of expertise even when the underlying reasoning is incomplete or flawed. Forty-two percent of respondents reported observing colleagues relying on AI-generated content without sufficient verification.
The risk extends beyond errors. Excessive reliance on AI can gradually diminish confidence in a person’s judgment. Colleagues may begin questioning whether weak output reflects poor reasoning, insufficient expertise, or overdependence on technology.
The most effective AI users treat AI as an input into their thinking—not a substitute for it.
A better alternative: Accountability
Maintain human accountability. AI can accelerate analysis, but individuals remain responsible for evaluating, defending and improving the work associated with their names.
4. The silent explorer
The silent explorer regularly uses AI but avoids discussing it.
Not every use of AI requires disclosure. But when employees conceal meaningful AI involvement in analysis, recommendations, or decision-making, teams lose opportunities to learn from one another.
Forty percent of respondents reported observing this behavior.
Secrecy creates the illusion that everyone is navigating AI independently. Transparency creates opportunities to develop shared standards, improve collective capability and accelerate learning.
A better alternative: Visibility
Normalize conversations about AI use. Visibility creates learning opportunities and helps teams establish common expectations around responsible use.
5. The sideline sponsor
Few signals are more powerful than leadership behavior. Yet 42 percent of respondents reported observing leaders who encourage AI use while rarely demonstrating their own use of it.
Employees look to leaders for cues about what is genuinely valued and safe. When leaders advocate experimentation but remain absent from it, employees are left to interpret the risks and expectations on their own.
Adoption accelerates when leaders openly discuss how they use AI, where it adds value and where human judgment remains essential.
A better alternative: Role modeling
Using AI openly as a leader may feel uncomfortable. It requires a willingness to learn visibly, acknowledge limitations and invite scrutiny. Do it anyway and model the behavior you hope to see. Share how AI supports your work, where it falls short and how you evaluate its output.
The risk of normalized shadow culture
Occasionally spotting shadow culture behaviors is not a major cause for concern. The bigger problem is that these behaviors may be becoming normalized. Across the five archetypes, nearly one-quarter of respondents said these behaviors feel like a normal part of workplace culture. That finding deserves leaders’ attention. Once behaviors become normalized, they are harder to challenge because they no longer stand out as exceptions—they become expectations.
This is where the real risk lies. Left unchecked, these dynamics can push AI use further underground, increase confusion about what constitutes responsible use and amplify the anxiety many employees already feel about the technology. Organizations that fail to address these emerging norms may discover that the greatest obstacle to AI adoption is not the technology itself, but the culture forming around it.
The AI self-awareness gap
Perhaps the most revealing finding from our research was not the prevalence of any single behavior, but the gap between what employees observed in others and what they acknowledged in themselves.
Across all five archetypes, respondents were more likely to report seeing these behaviors in colleagues than to admit engaging in them personally. They consistently recognized AI-related friction in others’ behaviors, while rarely identified themselves as participants in the same pattern.
This pattern matters because it suggests the challenge is not simply a handful of resistant employees or overly enthusiastic adopters. The dynamics are more complex—and more human.
Most people do not wake up intending to shame colleagues, over-rely on technology, conceal their AI use or undermine collaboration. Yet many of these behaviors emerge when people are navigating changing expectations and competing beliefs about what constitutes good work.
AI is exposing a familiar organizational phenomenon: We readily recognize problematic behavior in others while overlooking our own contribution to the same dynamic.
That insight shifts the conversation. The question is no longer, “Who is creating the problem?” It becomes, “What norms are we collectively reinforcing?”
When employees experience friction around AI, is the concern really about who is creating the problem? Or is it about accountability, transparency, ownership, quality and fairness?
Those distinctions matter because they lead to different conversations.
If the issue is weak work, the discussion should focus on standards and performance. If the issue is lack of transparency, the conversation should focus on disclosure. If the concern is accountability, leaders should clarify expectations around ownership and judgment.
In each case, the most productive response is to focus less on the person and more on the work.
3 leadership practices that build trust around AI
Organizations build trust when AI use becomes visible rather than hidden, accountable rather than ambiguous and collaborative rather than competitive. Those shifts are primarily cultural, and leaders must model them everyday. The following three leadership practices will turn those principles into shared expectations.
1. Make AI use visible. Transparency reduces ambiguity. When AI meaningfully contributes to work, explain how it was used and what role it played.
Disclosure is not about compliance. It is about providing context that enables others to understand, evaluate and learn from the work.
2. Maintain human accountability. AI can assist. It cannot be accountable.
Individuals remain responsible for evaluating, defending, and improving the work associated with their names. The organizations that benefit most from AI are those that reinforce human judgment rather than outsource it.
3. Use AI collaboratively. AI should strengthen collaboration, not bypass it.
Before using AI to significantly critique, revise or pressure-test another person’s work, involve them in the process. Transparency and consent help preserve trust while improving outcomes.
Culture will determine AI’s value
Organizations are investing heavily in AI capabilities. Far fewer are investing in the culture that will determine whether those capabilities create value.
As AI becomes embedded in daily work, organizations that focus on the people side of AI adoption will be more likely to realize greater value from the technology than those focused solely on technical adoption.
The future of AI at work will be shaped not only by what the technology can do, but by how well we use it together.
