Most of us want the same things at work: to do meaningful work, be treated with respect, and know our contributions matter.
That is why new technology can bring up complicated feelings. AI may save time and open new possibilities. It may also leave people wondering: Will my work still matter? Will a machine make decisions about me? And will my manager still see me as a person?
Those questions deserve honest answers.
Integrating AI well is not just a technology project. It is a leadership test. The tool may be new, but the need for trust, clear communication, and human judgment is not.
I have worked in IT long enough to know that a system change affects more than systems. Early in my career, I took part in a major technology migration. I felt the weight of the change because people depended on the work being done well. There were details to learn, problems to solve, and questions that did not always have easy answers.
I did not know everything. I had to listen, keep learning, and take responsibility for the work in front of me.
That experience shaped a lesson I still carry: technology can support people, but it cannot replace the care and judgment people bring to change.
AI calls for that same care. How can we use it to make work better without letting it speak for us, decide for us, or distance us from one another?
Authenticity is not the absence of technology
Sometimes people talk about authenticity as if it means doing everything by hand. That is not what it means to me.
Authenticity means your actions line up with your values. It means people can see what you believe, how you make decisions, and whether you take responsibility for the results.
You can use AI and still be authentic. You can also work without AI and still avoid honest communication. The tool alone does not determine the outcome. The choices around it do.
A leader might use AI to organize meeting notes, suggest a first draft, or summarize a long report. That can be helpful. But if the leader sends a sensitive message without reviewing it, or lets a tool make a decision about someone’s future without human oversight, the issue is not efficiency. It is a failure of judgment and care.
The bottom line: AI should support your work, not impersonate your values.
I have written more about the leadership side of this question in How to Integrate AI with Authentic Leadership. The same principle applies across roles and organizations: use technology with a clear purpose, and keep people accountable for the impact.
Two leaders, two different paths
Imagine two managers, Maya and Luis. Both are asked to introduce an AI tool to their teams.
Maya announces that the company is adopting the tool because it will make everyone more efficient. She shares a polished list of benefits. When employees ask what information the system uses, she says she is sure it will be fine. She does not invite questions because she wants the rollout to move quickly.
Luis also believes the tool could help. But before he introduces it, he asks his team where repetitive work is getting in the way. He explains what the tool may help with, what it cannot do, and which decisions will remain with people. He invites employees to test it and report problems. When he does not know an answer, he says so and finds out.
Which leader sounds more trustworthy?
Luis is not perfect because he has every answer. He earns trust because he is honest about what he knows, listens to people affected by the change, and stays accountable.
That is the difference between announcing a technology and leading a change.
If leaders only talk about speed, employees may hear, “We care more about output than you.” If leaders explain the purpose and invite honest feedback, employees have a better chance to see how the technology might serve the work.
Research supports this focus on trust. A 2025 Work Trend Index report from Microsoft described a gap between leaders’ and employees’ familiarity with AI agents. When leaders understand a tool better than the people expected to use it, clear communication and practical learning matter even more. A rollout cannot depend on enthusiasm alone.
Let AI help with the work, not replace the relationship
Think about what happens when a child learns to ride a bicycle. A parent can explain balance and check the tires. The parent can even run beside the child. But the child still needs room to try, wobble, and learn.
Good leaders take a similar approach to new tools. They provide guidance, create a safe place to practice, and stay close enough to help. They do not pretend that a tool removes the need for learning.
AI can help a manager prepare for a conversation. It can organize information or suggest questions. But it cannot replace the conversation itself. It cannot fully understand an employee’s history, sense what is left unsaid, or take responsibility for how its words affect a person.
That matters most in moments that call for empathy, context, and care. A thank-you, a performance conversation, a difficult change, a customer complaint, or a conflict should not be treated as just another task to automate.
Five questions to ask before using AI
Before adopting an AI tool, pause and ask a few practical questions. You do not need a complicated framework to begin. You do need honest answers.
- What problem are we trying to solve?
Be specific. “We want to use AI” is not a business purpose. “We want to reduce the time employees spend formatting routine reports” is clearer. - Who benefits from the change?
Does the tool help employees, customers, leaders, or all three? If a process saves time for one group but creates more work or risk for another, name that trade-off. - What must remain a human decision?
Identify decisions that affect people’s opportunities, privacy, safety, or dignity. Decide where human review is required before the tool is used. - What information goes into the tool?
Employees need clear guidance on what can and cannot be entered. Sensitive personal, customer, or company information needs careful handling. - How can someone question an output?
Make it clear who reviews the result, how errors can be reported, and how a person can ask for a decision to be reconsidered.
If you cannot answer these questions yet, then you may not be ready to roll the tool out widely. That is not a reason to give up. It is a reason to learn before you scale.
A practical way to keep AI human-centered
A simple three-zone approach can help teams decide how much review a task needs. It is not a substitute for your organization’s policies. It is a useful starting point for discussion.
Green zone: low-risk support
AI may be useful for tasks such as brainstorming, organizing non-sensitive notes, formatting, or preparing a first draft for internal review.
Even here, check the result. A polished answer can still be wrong or incomplete.
Yellow zone: careful human review
Some work can benefit from AI but needs a person to check its accuracy, fairness, and context. Examples may include drafting customer responses, creating policy language, summarizing performance information, or making recommendations.
The person reviewing the work should understand the task. They should be able to explain why they accepted, changed, or rejected the tool’s suggestions.
Red zone: human ownership is essential
Some situations carry serious consequences. They may involve sensitive personal information, employment decisions, disciplinary action, or communication during a personal crisis.
These situations call for human accountability and close review. In many cases, AI should not make the decision at all.
The labels may differ from one workplace to another. What matters is that people know where the boundaries are. If a tool is involved in work that affects someone’s life or livelihood, then leaders need to make the human decision-maker visible.
Seven habits that protect authenticity
1. Say why you are using AI
Do not announce a new tool without explaining the reason. Tell people what problem it is intended to solve and how you will know whether it helps.
“We want to test whether this can reduce repetitive data entry so our team has more time for customer questions” is more meaningful than “We are modernizing our workflow.”
If the purpose is clear, employees can judge whether the tool is actually serving it.
2. Be honest about AI’s role
If AI materially helped create a message or recommendation, do not imply that every word came directly from you. Use disclosure that fits the situation and your organization’s policies.
You do not need to describe every minor edit. But people should not be misled about who created important content or made a meaningful decision.
Authenticity does not require you to avoid tools. It requires you to be truthful about how you used them.
3. Keep your voice in important messages
AI can suggest words. You must decide what you mean.
That difference matters in a message of recognition, feedback, apology, or change. If the message is supposed to express your care or judgment, read it aloud. Does it sound like something you would actually say? Does it reflect what you believe? Does it address the person in front of you?
If it sounds polished but empty, rewrite it.
4. Invite questions before people lose trust
Do not wait for frustration to become silence. Ask employees what concerns them. Give them a way to raise questions without embarrassment or penalty.
Then close the loop. Tell them what you heard, what you changed, and what you still need to learn.
An invitation to speak is only meaningful if people can see that their input matters.
5. Make learning part of the work
A single training session rarely prepares people for every situation. Give teams time to practice with real tasks, share examples, and ask for help.
Leaders should learn, too. If employees are expected to adapt, managers should be willing to test the workflow, discuss what went wrong, and adjust the plan.
If learning only flows from the top down, then the organization may miss important knowledge from the people closest to the work.
6. Review the output, not just the speed
Saving time is useful. It is not the only measure of success.
Check for accuracy, fairness, usefulness, and the effect on the people who do the work. Did the tool reduce repetitive effort? Did it create new errors? Did employees feel supported, or did they feel monitored and pressured?
If the work gets faster but trust falls, the process needs attention.
7. Name who is accountable
A tool cannot take responsibility for a decision. A person or team must.
Make it clear who reviews an output, who approves it, and who responds if something goes wrong. If no one owns the result, then accountability has disappeared into the process.
Bottom line: Faster work is not automatically better work. The goal is useful technology with visible human responsibility.
A practical 30-day action plan
You do not need to transform your entire organization at once. Start with one meaningful use case and learn from it.
Week 1: Listen and choose
Ask employees where routine work takes too much time or creates avoidable frustration. Choose one task that is specific and low-risk enough to test.
Write down the goal. For example: “We will test whether AI can help organize non-sensitive meeting notes while keeping action decisions with the team.”
Also identify what information must not be entered into the tool.
Week 2: Set boundaries and prepare
Agree on who will use the tool, who will review its output, and how people can report a mistake. Provide a short guide with examples.
Do not assume that everyone has the same knowledge or confidence. Ask what people need to feel ready to try the process.
Week 3: Run a small test
Invite a small group to use the tool for the selected task. Ask them to keep track of what worked, what needed correction, and what felt unclear.
Leaders should take part. If managers expect employees to experiment, they should be willing to learn alongside them.
Week 4: Review and decide
Compare what happened with the goal you set. Did the tool help? Did it introduce errors or extra work? Did employees feel informed and able to raise concerns?
Decide whether to keep testing, change the process, or stop. Stopping a tool that is not helping is not failure. It is responsible leadership.
Measure trust along with productivity
Teams often measure adoption by asking how many people used a tool. That can be useful, but it does not tell the whole story.
Ask employees whether they understand how the tool is being used. Ask whether they know what information is appropriate to share. Ask whether they can challenge an output or get help when something seems wrong.
You can also track accuracy, rework, time saved, customer experience, and the effect on team workload. Look at the results together.
A 2025 review of research on trustworthy AI and organizational trust emphasizes that trust takes ongoing work. It is not a one-time statement or a checklist completed before launch.
If people do not trust the process, then adoption numbers may hide a problem. If people trust the process but the tool is not useful, then the technology still needs to change.
The lesson is simple: pay attention to both the work and the people doing it.
Your voice still matters
As a woman who built a career in IT, I have learned that technology changes quickly, but people still need leaders who are willing to listen, learn, and make thoughtful decisions. I have also learned that you do not need to know everything before you take the next responsible step.
You can be curious without pretending to be certain. You can use new tools without giving up your judgment. You can move forward and still invite people to tell you when something is not working.
If AI helps you make room for better work and stronger relationships, then use it with care. If it starts to make people feel invisible, pause and listen. If you are unsure, ask questions before you make a promise.
The bottom line: Authenticity is not about resisting change. It is about staying honest, human, and accountable while you change.
I challenge you! Choose one task where AI might help. Ask the people who do the work what they need. Set clear boundaries. Test the tool. Review the results together. Then make the next decision with both courage and care.
Do not let the pressure to move quickly take your voice, your judgment, or your responsibility away. Technology can help you reach further. Your values must guide where you go.
Jeannette L. Collazo is the President and CEO of Lurdez Consulting Group. She is a speaker, author, and consultant who helps leaders build authentic, human-centered cultures. Learn more at jeannettecollazo.com.