Trust: The Foundation of Employee AI Adoption

Part 1 of our series exploring what happens to trust when AI becomes part of relationships.

I remember when my boss at a previous job first encouraged me to start using AI.

I was curious and genuinely excited about the possibilities. Who wouldn’t welcome a little help taking work off their plate or getting through certain tasks more efficiently?

I started with Microsoft Copilot, but this was early enough that the outputs weren’t particularly impressive. When I mentioned that to my boss, they suggested I try ChatGPT instead.

“My own personal account?” I asked.

“Yes.”

And so I did.

But here’s what I remember most about that experience. Every time I entered a prompt or considered uploading something, I had this nagging concern in the back of my mind.

Should I be sharing this? Is this information safe? What if I’m inadvertently exposing something I shouldn’t?

Having spent much of my career in public relations and crisis communications, I instinctively knew to avoid sharing intellectual property or sensitive, non-public information. But there was still so much gray area. I held back from certain tasks, spent extra time second-guessing what was appropriate and worried that a well-intentioned decision might expose my organization to risk.

I wasn’t resistant to AI. I was curious. I wanted to learn how to use it well. When Copilot wasn’t giving me what I needed, I wanted to compare experiences with colleagues, share what was working and collectively figure out how to get better results.

But those conversations weren’t happening.

And I don’t blame my former boss. We were all exploring something new, without much of a roadmap. It really did feel like the Wild West.

Today, I work with organizations on AI policy, governance and Human + AI workflow design. And one of the things that concerns me most is how often I still encounter versions of that same experience.

Employees are encouraged to use AI without always having the clarity, guidance or shared practices they need to feel confident about their decisions. We’ve learned a great deal since those early days, yet some teams are still navigating uncertainty largely on their own.

And I wonder what that’s costing organizations. Not simply in potential data exposure or inefficient workflows, but in employee confidence and trust in the organizations asking them to change how they work.

Employees are consumers, too

Here’s something I think leaders sometimes overlook: our employees are consumers, too.

They are experiencing AI outside the workplace, sometimes in ways that delight them and sometimes in ways that leave them deeply uncomfortable. They are reading about jobs being eliminated, hearing concerns about privacy and encountering AI systems in places where they once expected to interact with another person. Those experiences don’t simply disappear when they log in to work.

Consider the new Axios-CivicScience AI Sentiment Index, released October 30, 2026. Based on online polling of more than 7,300 U.S. adults, it reported an overall AI sentiment score of 41.8 out of 100. Sentiment about AI contributing to economic growth was positive at +10, while sentiment about AI creating more jobs was negative at -30.4.

These findings reflect public attitudes rather than the experiences of employees within specific organizations. Still, they raise an important consideration for leaders: people can believe AI will create economic value while questioning whether they themselves will benefit.

So when leadership announces a new AI agent, introduces an AI-enabled process or talks about increasing productivity, employees may bring more to the conversation than their immediate experience with the technology. They bring their hopes, concerns and assumptions about what AI means for their future.

And those feelings can change. Someone initially excited about AI may become apprehensive when a familiar workflow is automated. Another employee who begins with serious reservations may gain confidence through training and experience. Someone can become highly proficient with AI while growing less confident in how their organization intends to use it.

Adoption, confidence, trust and fear are different things. They don’t necessarily move in the same direction. If we measure only whether employees use AI, we may miss what they’re actually experiencing.

Trust in leadership is different from trust in AI

I want to make an important distinction here. Building trust around AI does not mean encouraging employees to believe everything AI produces.

We want employees to be appropriately skeptical, question outputs, verify important information, understand limitations and exercise their own judgment. That is part of responsible AI use.

But employees also benefit from knowing their organization is making thoughtful decisions about how AI is introduced, what it can access, where it belongs and how people will be affected. They should feel able to raise concerns, ask questions and challenge an output without those actions being mistaken for resistance to change.

I think back to my own experience and realize I wasn’t asking someone to promise that AI was perfectly safe. I wanted direction, a shared understanding of what was acceptable and a way to navigate the gray areas.

I also recognize that leaders are navigating considerable uncertainty themselves. They are being asked to demonstrate value, manage risks, prepare their teams and make decisions about technology that keeps changing. They may be learning many of the same lessons as their employees.

The answer isn’t to pause every decision until everyone feels completely comfortable. But unclear boundaries have operational costs of their own. Employees may hesitate, duplicate efforts, avoid useful applications or make inconsistent decisions. Clarity and dialogue can help teams move forward more effectively, rather than becoming another layer of work competing with adoption.

An AI policy is important. So is an organizational AI POV.

An AI policy provides necessary boundaries. It helps employees understand approved tools, appropriate data sharing, human oversight requirements and other expectations for responsible use.

But there’s another foundational element I believe organizations need: a clearly articulated organizational AI Point of View (POV).

An AI POV communicates what the organization believes about AI and how those beliefs will guide its decisions. Why are we adopting AI? What do we want it to make possible? What role should people continue to play? How will we approach automation, employee development, privacy and human judgment?

These are leadership questions, and leaders don’t have to have every answer in advance. A shared POV gives people principles to work from while the organization continues to learn.

Those principles become far more useful when they are communicated across the organization and employees can see how they shape real decisions. An AI POV isn’t a promise that the approach will never evolve. It is a commitment to explaining the reasoning and values behind decisions as circumstances change.

Its credibility comes from how consistently the organization acts on it, including its willingness to explain difficult choices, acknowledge uncertainty and revisit assumptions.

Dialogue is how organizations keep learning together

One of the things I wish we’d done differently in my previous workplace was to make time for honest, practical conversations about the experience of using AI. What was working? Where were we getting stuck? What questions needed to be raised with leadership? What would help us use these tools responsibly and effectively?

That is why Dialogue is a cornerstone of HDAI’s 4Ds of Shared AI Practice™. Governance establishes expectations, but employees need opportunities to explore what those expectations mean in real work, share lessons, identify gray areas and shape practices that make sense for their teams.

Those conversations also give leaders valuable insight into where support is needed and whether changing AI capabilities are altering employees’ expectations. A team comfortable with an AI writing assistant today may feel very differently when an autonomous agent begins completing tasks or interacting with other systems on its behalf.

Dialogue is not a one-time launch activity. It is part of how organizations keep up with the changing relationship between employees, leadership and AI.

And while a policy is essential, it’s through dialogue, shared decisions and continued leadership accountability that those boundaries become meaningful.

Trust is an ongoing leadership responsibility

AI adoption will look different across organizations, and the right approach depends partly on the culture, work and people already in place. But whether the starting point is a mature AI program or a team still finding its footing, trust deserves attention alongside capability, efficiency and risk.

An organizational AI POV establishes direction. Policies provide boundaries. Training builds capability. Shared practices and continuing conversations help leaders and employees translate those commitments into everyday decisions.

Looking back on my own experience, I wasn’t asking my organization to have all the answers. I was hoping we could figure them out together.

People need clarity, agency and a voice in how AI enters their lives. When organizations make room for those things, they create stronger conditions for responsible, sustainable employee AI adoption.

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Human Driven AI helps organizations move from scattered AI experimentation to shared, scalable practice. We build the governance foundations, redesign workflows around human and AI strengths, and deliver custom training programs that teach your teams how to put those systems into practice.

Whether you need an executive Lunch-and-Learn, a hands-on offsite workshop, an enterprise AI transformation program or a strategy to strengthen your brand’s visibility through GEO, we help you turn AI capability into better ways of working.

Ready to move from using AI to working differently with it? Let’s talk.

Read more: Trust: The Foundation of Employee AI Adoption