From Afraid to Adopted: How to Move Your Team Through the Six Stages of AI Readiness

Every organization introducing AI to its workforce is dealing with the same fundamental challenge. Your people aren’t all in the same place. Some are already building custom tools in Gemini on their lunch breaks. Others are sitting with their arms folded, hoping the whole thing blows over before it becomes unavoidable. Most are somewhere in the middle. They’re curious but uncertain, often using it at a basic level but cautious about integrating it more into their day-to-day work. 

There’s a five-stage AI adoption framework that gets referenced fairly often in HR and organizational development circles. It maps employees from initial awareness through active integration. When my team began working through how to apply that framework in practice, we realized two key things were missing:

1. A “stage zero” also exists, so we added it to the framework.

2. As much as senior leaders need to understand where their people are, mid-level managers can much more easily identify and address employees at various stages, and so we added a manager response to each stage.

Here’s the framework and some tips on how to apply it to your workforce. 

Stage Zero: The Waiters

Stage zero employees aren’t hostile in an aggressive way. They’ve simply decided that AI isn’t for them, at least not yet. The most common version of this is someone close to retirement who feels that if they can hold out long enough, they won’t have to change. There are also other employees in this stage, perhaps those who have developed a fixed sense of their own identity as non-technical, and who experience AI as a threat to that identity rather than an expansion of it.

The critical thing to understand about stage zero employees is that they will not come to you. They won’t attend the voluntary AI webinar or raise their hand during the information session. Policy announcements about AI tools and use cases don’t land with them either. To get through to people in this group, you need something more personal, like a conversation with a coworker or manager.

Manager Action Item: Successful AI adoption starts with middle management. Invest in giving managers the language and confidence to have direct, personal conversations with stage zero employees. The goal is a one-on-one discussion where the manager can acknowledge the employee’s concerns and meet them where they are.

Stage One: The Skeptics

Stage one employees are aware that AI exists and are paying attention, but their primary orientation is doubt. They’ve read the articles about hallucinations and job displacement. They’ve seen colleagues get burned by taking AI output at face value. Their skepticism is often well-founded, and that’s worth acknowledging directly.

The instinct with skeptics is to flood them with success stories and optimistic projections, but that tends to backfire. Skeptics are aware that AI is transformative. What they need is to see that it can be used responsibly and that the organization has thought carefully about guardrails. Transparency about risks, clear accountability structures, and honest communication about what AI does and doesn’t do well are more persuasive to this group than enthusiasm.

Manager Action Item: Make accountability explicit and visible. Establish and communicate clearly that if an employee uses AI to produce something and then communicates it, the output is theirs. They can’t attribute errors to the model. That kind of organizational clarity actually reassures skeptics, because it shows them that no one is asking them to abdicate their judgment.

Stage Two: The Curious

Stage two employees are interested. They’ve tried a few things, probably asked ChatGPT a handful of questions, and maybe used it to draft an email or summarize a document. They haven’t yet connected it meaningfully to their actual work, but they’re open to the possibility.

This group is where deliberate education pays off most directly. They’re already curious, so they don’t need inspiration. They need practical exposure to specific use cases relevant to their role. The more tangible the demonstration, the better. Abstract conversations about AI’s potential don’t move stage two employees forward. Watching a colleague in a similar function walk through exactly how they used a tool to solve a real problem does.

Manager Action Item: Promote peer examples over expert presentations. Connect stage two employees with colleagues in similar roles who have found practical, specific AI use cases. The more unglamorous and functional, the better. There’s an “anyone can do this” quality that emerges when the person sharing the example is a non-technical employee who simply had an idea and figured it out.

Stage Three: The Experimenters

Stage three employees are actively trying things. They’re running prompts, testing tools, discovering what works and what doesn’t. This stage is genuinely exciting, and it’s also where organizations need to pay the most attention.

The risk with stage three employees is that they’ll adopt AI too quickly and without sufficient precaution. Hallucinations are a real problem at this stage. Someone runs a prompt, gets a confident-sounding answer, and acts on it without verifying. That kind of error is preventable, but only if employees at this stage have internalized the principle that AI output requires human review before it becomes a communication or a decision. They’ll also benefit from guardrails, as any good experiment can sometimes lend itself to scope creep, or end up taking a lot more time than intended.

The other thing stage three employees need is community. They’re learning rapidly, and they benefit from having peers to share discoveries with. Internal showcases, informal Slack channels, or structured learning communities all accelerate this stage significantly.

Manager Action Item: Provide guardrails for their experimentation that keep them focused and aligned with company policies. Develop learning cohorts that give stage three employees and a feedback loop that keeps experimentation visible enough to catch problems early.

Stage Four: The Integrators

Stage four employees have moved from experimentation to integration. AI is now part of how they work. They’ve developed judgment about when to use it and when not to, and they’re producing better output more efficiently as a result.

A manager’s job at this stage is largely to stay out of the way while maintaining awareness of what these employees are working on. I designate AI champions on my team at this stage. These are employees who are pushing the boundaries of what’s possible. I check in with them regularly to stay current with what they’re discovering and to catch anything that might create risk before it becomes a problem. 

Manager Action Item: Designate AI champions and keep lines of communication open with them. Leaders don’t need to know everything their most advanced AI users know. They do, however, need to know enough to exercise appropriate oversight.

Stage Five: The Advocates

Stage five employees aren’t just integrating AI into their own work, they're actively bringing others along. They’re the colleagues who show up in the showcases, answer questions in internal channels, and help stage two and three employees find their footing. They’re organizational assets in a very concrete sense.

Manager Action Item: Build formal pathways for these employees to teach. Give them dedicated time in team meetings and learning sessions, connect them directly with stage one and two employees, and recognize their contribution publicly. Visible recognition tells the rest of the organization that this kind of engagement is valued and worth pursuing.

Making the Framework Work in Practice

A few things have made this framework genuinely useful for us, rather than just a conceptual map.

First, we built it explicitly into manager training. Our quarterly managers meeting now includes a segment on AI adoption. It’s become a regular part of how we think about team development. Managers need to know where each of their people is on the path to AI enablement and have a concrete plan for supporting their movement forward.

Second, we made adoption visible and even fun. We ran a competition within my people team where each member was asked to identify a real work problem and propose an AI-based solution. The submissions came back as short presentations that were evaluated by a panel of judges. The winner got to attend a conference focused on AI and HR. Eighteen people submitted ideas, including people I wouldn’t have predicted would engage. Gamification works, particularly at stages two and three.

Third, we pair education with evidence. The antidote to fear is knowledge, but knowledge in the abstract is less powerful than knowledge that’s based on what the organization is actually doing. When employees can see that AI is already contributing to creating business value that directly supports job stability, it makes the fear more manageable.

Our job as HR leaders is to help employees develop enough knowledge and confidence that they can move forward without needing to see the whole picture. They need to trust that the organization is navigating this alongside them, not ahead of them.

That trust is built one conversation, one showcase, and one curious experiment at a time.

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Matt Poladian is the Chief People Officer at Liferay, a global technology company. In that capacity, he is responsible for all areas of HR across the company's worldwide offices and several hundred remote employees. Before joining Liferay, Matt held various HR business partner and HR manager roles at large companies, most recently within Disney's Animation studios. He has degrees from UC Irvine (MBA) and Claremont McKenna College (BA). Matt keeps busy outside of work holding several non-profit leadership positions. His most important role is as husband to Jenny and dad to their three young kids.