One Team, Four AI Archetypes: How Leaders Can Tailor Their Guidance

Key Takeaways

• Secret User: Bring unspoken AI use into the open with clear boundaries and review
• Enthusiast: Channel experimentation into workflows colleagues can understand and run
• Skeptic: Turn quality concerns into a real review role
• Onlooker: Discover the barrier first, then start with one familiar task
• Every leader: Know your own default, then adapt your message to all four archetypes

During a team meeting, a leader suggests using AI to draft weekly status reports and gets four different reactions. One employee has been using it for weeks without telling anyone. Another has already built an agent. A third asks how often AI gets things wrong, and a fourth nods and says nothing while thinking, Everyone else understands this except me.

They heard the same suggestion, but each heard a different message. One may wonder whether using AI makes their work look less valuable. Another sees a chance to move faster, and a third worries about protecting quality and professional judgment. Behind each reaction sits a shared question: Will this change make my role more valuable or easier to replace?

AI adoption goes beyond access to tools and technical training. It's also a human conversation about confidence and trust. Gallup’s July 2026 research found that among AI users in organizations that have integrated AI , 33% who strongly agree their managers actively support AI use rate its productivity impact as extremely positive, versus 9% who agree less. The association isn’t proof of causation, but manager support is hard to ignore.

These reactions reflect four AI adoption archetypes: the Secret User, Enthusiast, Skeptic, and Onlooker. People's behaviors shift between them depending on the task's stakes and their experience with it. Avoiding stereotypes allows leaders to reframe behavior and determine what each person needs from them.

Align, Learn, Apply turns an understanding of behavior into action: align on expectations and concerns, learn the skills that close real gaps, and apply them to the work that matters most. Each step works best as a conversation.

Give Your Secret User a Reason to Share

The Secret User already uses AI but rarely talks about it. They may have found useful prompts and faster ways to complete work. Yet they may worry that colleagues will discount AI-assisted work or question their contribution. When usage rules are unclear, staying quiet can feel safer than explaining their process.

What they bring:

• Real-world experience
• Self-directed learning
• Useful practices the team hasn’t seen

Potential blind spot:

• Unreviewed work: Private workflows may never get a second look.
• Unapproved tools: Unclear rules can lead people to use tools nobody has approved.
• Lost learning: The rest of the team may never hear what worked, and what didn’t.

BCG’s 2025 AI at Work survey of more than 10,600 workers found that 54% would use AI tools even if their company hadn’t authorized them.

For the Secret User, the sequence turns silence into conversation:

• Align: Make it safe to talk about the process. Ask: “What helped you here, and did anything about using AI feel hard to share?” Name approved tools and off-limits data, and say when review or disclosure applies. Routine internal drafting needs less oversight than customer-facing or decision-shaping work.
• Learn: Build judgment alongside prompting skill. Ask: “How did you check the output, and what would you do differently next time?” Teach verification and data boundaries, including the policy on sensitive information, and when human expertise must lead.
• Apply: Invite contribution and keep it voluntary. Ask: “Is there one useful practice you’d be comfortable showing the team?” A voluntary demo can turn a personal shortcut into a repeatable team practice.

The message is straightforward: Using an approved AI tool responsibly doesn’t diminish your contribution. Your judgment is still what makes the work valuable.

Help Your Enthusiast Bring Others Along

The Enthusiast sees possibility everywhere. They test new tools, build agents, redesign processes, and spot opportunities before their peers do. Their energy is valuable, but it can exceed organizational readiness. A solution that works well for its creator can cost a teammate more effort than the task it replaces or prove too risky to scale.

What they bring:

• Curiosity
• Technical confidence
• Experimentation
• Early discoveries

Potential blind spot:

• Speed over value: People can mistake personal speed for team value.
• Change overload: New ideas arrive faster than people can absorb them.
• Left-behind colleagues: Less-confident teammates fall behind.

Scaling an experiment is as much a people problem as a workflow problem. The Enthusiast may want to help, while coworkers experience a constant stream of new ideas as pressure to keep up. OpenAI Academy separates personal AI skill from helping a team adopt it. Leaders should reward both achievements.

The same sequence channels the Enthusiast’s energy:

• Align: Connect the excitement to a shared need. Ask: “What problem are we solving, and who benefits? How will this help the people who do the work?” Agree on approved tools and risk boundaries, then pick one experiment worth testing before the next idea.
• Learn: Develop evaluation and engagement skills. Ask: “Could someone who didn’t build this run it? What could go wrong? How would we know it improved quality or saved time after review and rework?” Teach documentation and measurement, plus how to invite feedback without overwhelming others.
• Apply: Move from inventor to enabler. Ask: “Will you walk a teammate through it and adjust the workflow based on what confuses them?” Pair the Enthusiast’s creativity with the Skeptic’s rigor and bring in the Onlooker’s fresh view. Scale once colleagues can run it and it proves worth the effort.

The leadership move is to broaden the Enthusiast’s definition of success from “I built something impressive” to “Our team can use something valuable.”

Give Your Skeptic a Voice in the Decision

The Skeptic asks what AI gets wrong and who’s accountable when it fails. Those questions often come from a commitment to customers, quality, professional standards, or expertise built over years. Their concern may also be personal: Will leaders expect us to trade judgment for speed?

What they bring:

• Critical thinking
• Domain expertise
• Risk awareness
• Willingness to challenge assumptions

Potential blind spot:

• One error as the verdict: A single AI mistake can lead people to rule out every use case.
• Skipped comparison: Judging a tool without weighing it against the limits of current processes.

A 2024 Asana Work Innovation Lab study found that validating skeptics’ concerns produced lasting attitude change, while urging them to become enthusiasts didn’t.

For the Skeptic, the sequence builds trust through evidence:

• Align: Start with what needs protection. Ask: “Where would an error matter most in your work, and what would you need to see before you felt comfortable testing AI?” Confirm that human accountability stays in place and that pausing or rejecting the tool is a legitimate outcome.
• Learn: Give them evidence and a method to challenge it. Ask: “What would a fair comparison with the way we work today look like?” Train the Skeptic to weigh accuracy, reasoning, audience fit, risk, and ownership against both the hype and the isolated failures.
• Apply: Put their judgment to work. Ask: “Would you help us test this on a low-risk task and tell us whether to proceed, revise, pause, or reject it?” Give them a real review role and make sure the workflow owner addresses what they uncover.

A Skeptic doesn’t have to become an Enthusiast. Their critical thinking may help the team trust an AI-supported process for the right reasons.

Help Your Onlooker Find a Place to Begin

Often unnoticed, the Onlooker attends training and may agree that AI could be useful but seldom tries it. Their hesitation points to missing support: they’re unsure what the team expects, lack protected time, fear asking a basic question, or worry that everyone else is ahead.

What they bring:

• Existing job knowledge
• A realistic beginner’s perspective
• The ability to expose confusing assumptions in a rollout

Potential blind spot:

• Silence read as understanding: Quiet can look like comprehension.
• False progress: Copied prompts, with no way to judge the result, create a false sense of progress.

BCG research found that only 25% of frontline employees felt they received sufficient leadership guidance on using AI well. Telling an Onlooker to “go experiment” may add pressure without removing the obstacle.

For the Onlooker, the sequence starts small:

• Align: Discuss the barrier without embarrassment. Ask privately: “What’s getting in the way right now?” Listen before assigning training, then agree on one low-risk task and make room to try it.
• Learn: Teach one useful skill at a time. Ask: “What’s something you do every week that takes longer than it should?” Use this familiar work to practice a clear prompt and review the output together, including how to spot errors. Job expertise matters more than technical vocabulary.
• Apply: Turn an attempt into confidence. Ask: “What was useful, and what still needed your judgment? What would you change in the next prompt?” Repeat the task with feedback before introducing another tool or use case.

For the Onlooker, progress starts with one guided attempt on one task and the experience of doing it again.

Know Your Own Archetype, Then Learn to Speak All Four

Leaders have defaults, too. A Secret User leader may avoid discussing AI, an Enthusiast may move faster than the team can follow, a Skeptic may lead with quality questions, and an Onlooker may pass along guidance without knowing how to apply it. Recognizing your default matters. The skill is reading the room and adapting your message while staying aligned on a common direction.

Leaders can speak to all four archetypes in a team meeting:

“We’re exploring how AI can improve our work and support the people doing it. If you’ve found a useful approach, let’s create a safe way to share it. If you’re excited to build something, help us make sure others can use it.

If you’re concerned about quality, help us define what good looks like and where we need safeguards. If you’re getting started, we’ll make time to practice on a task you already know. We’ll learn together and ensure people stay accountable for the results.”

The message addresses each archetype’s needs at the same time: permission for the Secret User, purpose for the Enthusiast, evidence for the Skeptic, and encouragement for the Onlooker. Meeting those needs is the leader's part: AI brings efficiency, and leaders bring effectiveness by connecting people to the tools.

A leader’s approach for each might look like this:

Employee	How to show up	What to watch for
Secret User	Make responsible AI use safe to share and protect the value of employee judgment	Heavy caution can turn responsible AI use into something people keep to themselves
Enthusiast	Match optimism with evidence, and ask how others will use the workflow	Personal speed can leave teammates behind or move work into use before anyone has tested it
Skeptic	Welcome hard questions and give their expertise a real review role	Pressuring them to feel enthusiastic can silence useful concerns
Onlooker	Be patient, discuss their barrier privately, and make the first step small	Big promises or public pressure can reinforce uncertainty and hesitation

Align, Learn, Apply works as a cycle because people move between archetypes as the task changes. An Enthusiast building a reporting agent can become a Skeptic when the same technology informs sensitive customer decisions. A shift like this calls for a fresh conversation about expectations and concerns.

Give Each Person Their Next Useful Step

In successful AI adoption, people start in different places and still move together. The team speaks up about how it uses AI, tests ideas with care, protects human judgment, and improves how it gets work done.

Start this week with one question: “What does each person on my team need from me to take their next useful step?” Then ask a second: “How can I bring those different strengths together?” A leader who asks both leads people through change.

New Horizons builds its AI training for organizations on Align, Learn, Apply: a 90-minute launch and alignment session, live training customized to your team’s tools and roles, and a Prompt-a-Thon where teams apply AI to real work.

Watermark Learning adds the judgment to go with AI skills, teaching leaders to validate AI output and redesign workflows with human review built in.

Which person on your team needs their next useful step first?

Start with the people and work that matter most: build your team’s AI program with New Horizons and sharpen your own AI judgment with Watermark Learning.