Prioritize Transparency and Inclusion

Consider this finding from Microsoft's 2024 Work Trend Index:

At the same time, 75% of knowledge workers reported using AI at work, and 53% said they worry that using AI on important tasks makes them look replaceable.

People are already using AI. Many just do not feel comfortable telling their employer.

That is not an adoption problem. That is a trust problem.

I saw what that looks like. I was working with a client beginning to experiment with generative AI. Leadership was excited, and we had spent the morning demonstrating practical use cases. During a break, one of the employees pulled me aside. She did not ask about prompts or models. She asked:

I asked what she was worried about. She said she was worried that if her manager saw that AI helped her finish something in an hour that used to take her three, they would think the job should only take an hour from then on.

That is not a fear of being replaced. It is a fear that efficiency will be repaid with a permanently higher bar. The organization had an acceptable-use policy. What it had never answered was the question she actually had: what happens to me if I use this?

The question for leaders should not simply be, "How do we get our employees to use AI?"

It should be: "What would make our employees feel safe using AI openly and responsibly?"

Transparency does not mean giving employees every technical detail about an implementation. It means answering the questions that matter to them:

• Why are we using AI?
• What will it be used for?
• What will it not be used for?
• How will it affect my work?
• What does this mean for me?

This is not only about making people feel better. Deloitte found that organizations taking more trust-building actions were 18 percentage points more likely to land in the top third of organizations achieving their expected generative AI benefits, and describes trust as built through:

A 2026 study of workplace generative AI copilots reached a similar conclusion, finding that transparency increases trust and reduces perceived risk. The researchers call transparency an "upstream design lever."

Upstream is the operative word. Transparency is not something we add after deploying AI. It belongs in the adoption strategy from the beginning.

What that looks like in practice

Back to that department. We stopped the training and spent the rest of the day helping leadership define boundaries instead of demonstrating features. What they landed on was short:

• AI usage would not be used to measure individual productivity.
• Human judgment was still expected.
• AI-assisted work still required review.
• Experimenting with approved tools would never become a performance metric.

Then leadership said all of it directly to employees, in the room, in their own words.

The technology had not changed. The trust had.

If you are doing the same, three things matter most.

• 1
  Write it down

  One page, plain language, answering the five questions above. Not a legal document. Not a values statement. Something an employee can read in three minutes and actually understand.

• 2
  Be specific

  "We will not use AI to measure individual productivity" does more for trust than three paragraphs about your commitment to responsible AI. Vague reassurance reads as evasion. Specific limits read as commitment.

• 3
  Give people a name, not an inbox

  Someone who owns AI questions and answers them within a day. Questions sent to a shared mailbox go unanswered, and unanswered questions turn into assumptions.

Transparency tells people what is happening. Inclusion gives them a voice in what happens next.

Sending an email asking for feedback is communication. Asking people where the work actually breaks down is inclusion.

The people closest to the work see what leadership cannot. They know the workarounds. They know which reports nobody trusts. They know which tasks consume hours every week. They know where AI could help, and where it would make things worse.

That knowledge should be part of the transformation. Employee voice in automated decision-making has been linked to stronger trust, higher satisfaction, and a greater sense of control over the work.

People are more likely to embrace a future they helped shape.

What that looks like in practice

• 1
  Run a session, not a survey

  Sixty to ninety minutes with the people doing the work, in a room where their manager is not grading the answers. Surveys give you ratings. Conversations give you the workaround somebody built three years ago that an entire department now quietly depends on.

• 2
  Ask better questions

  "Are you excited about AI?" tells you nothing. What takes you longer than it should? Which report do you not trust? What do you rebuild from scratch every week? And where would you not want AI anywhere near? That last one matters most. Giving people explicit permission to say no is what makes their yes mean something.

• 3
  Close the loop

  Publish what you heard and what you are doing about it, including what you are not pursuing and why. And make your first use case something an employee named, not something a vendor demoed.

Research from the National Bureau of Economic Research studied 5,179 customer-support agents using a generative AI assistant. The result was a 14% average increase in productivity.

The interesting part was where those gains landed.

Novice and lower-skilled workers saw a 34% improvement, while experienced and highly skilled workers saw minimal impact. The researchers found evidence that the AI was helping newer workers benefit from the practices of more experienced employees.

AI was not simply making the best people faster. It was distributing expertise.

That is the vision organizations should pursue. Not: "How many people can AI replace?" But: "How many people can AI empower to do better work?"

And notice what has to be true for that to happen. Expertise only spreads if people work in the open. When half your workforce hides how they use AI on their most important work, the expertise stays hidden with them. Transparency is not a nice addition alongside the productivity gains. It is the condition that makes them possible.