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Operational Frameworks 5 min read

The 3 Main Change Management Models

Lewin, Kotter, ADKAR: the 3 change management models explained for executives. How to choose and apply the right framework in your organization.

Naïm Bentaleb

Naïm Bentaleb

AI Strategy & Governance Advisor

What Are the 3 Main Change Management Models?

The three major change management models are Lewin’s model (Unfreeze, Change, Refreeze), Kotter’s 8-step model, and ADKAR (Awareness, Desire, Knowledge, Ability, Reinforcement). Each fits a different context. Lewin structures, Kotter mobilizes, ADKAR anchors change at the individual level.

These are not academic theories. They are tools that leaders use to prevent their projects from stalling before they produce any result.

And today, with AI integration reshaping operational processes, change management has moved back to the center of executive attention. Companies deploying conversational agents, automating HR workflows, or rolling out e-invoicing systems need a framework. Not goodwill.

Lewin’s Model: Simple, Solid, Often Underestimated

Kurt Lewin formalized his model in the 1940s. Three phases: Unfreeze, Change, Refreeze.

Unfreeze means creating the conditions for change. Challenging habits. Showing why the status quo costs more than moving forward. This is a phase many leaders cut short, and it is often where projects lose momentum.

Change is the transition itself. New tools, new processes, new accountabilities.

Refreeze means anchoring. Making the new behavior the norm. Not a pilot. An operational reality.

Lewin works well when the change is targeted and clearly scoped. A tool migration, a team reorganization, a new approval process. It is less suited to complex transformations that affect multiple organizational layers simultaneously.

Kotter’s Model: For Changes That Require Mobilization

John Kotter developed his 8-step model, published in his reference work in 1996. It remains one of the most widely used methodological frameworks in large organizations.

The 8 steps:

  1. Create a sense of urgency
  2. Build a guiding coalition
  3. Form a strategic vision
  4. Communicate the vision
  5. Remove obstacles
  6. Generate short-term wins
  7. Sustain acceleration
  8. Anchor in culture

What sets Kotter apart is the emphasis on coalition. You cannot drive change alone. You need relays inside the organization, people who carry the message on your behalf.

In the AI integration projects I work on, step 2 is usually what is missing. The CEO is convinced. The CTO is convinced. But middle managers are not, and they are the ones who make or break adoption.

Kotter fits cultural change, mergers, and large-scale technology deployments. It requires time and sustained communication.

I built a 6-dimension diagnostic framework to assess exactly this kind of organizational readiness. Download the AI Board Pack 2026.

ADKAR: When Change Happens at the Individual Level

ADKAR was developed by Prosci. The acronym stands for:

  • Awareness: the person understands why the change is necessary
  • Desire: they want to participate
  • Knowledge: they know how to do it
  • Ability: they can actually do it in practice
  • Reinforcement: the behavior is sustained over time

Where Lewin and Kotter reason at the organizational level, ADKAR goes down to the individual. That is both its strength and its limitation.

Its strength: it allows you to diagnose precisely where someone is stuck in their adoption process. Do they not understand the why? Do they lack the skills? Are they not motivated? The answer determines the intervention.

Its limitation: it is harder to deploy at scale without a solid HR structure to manage it.

In an AI upskilling context, ADKAR is particularly relevant. Maroc Cloud recently announced the launch of Gemini Enterprise in Morocco to govern AI use within organizations. This type of deployment illustrates exactly the challenge ADKAR addresses: resistance to change does not always reflect deliberate refusal. It often reflects a lack of understanding or apprehension about the unknown. Those two situations require different responses.

As I explained in my analysis of AI in recruitment, tools are not enough. What determines adoption is how you support the people using them.

Which Model Should You Choose?

There is no universal answer. Here is how I think through it with clients:

Lewin if the change is scoped and you need a simple structure to manage it.

Kotter if you need to mobilize an entire organization and resistance is coming from middle management.

ADKAR if you have heterogeneous teams and want to understand precisely where each person is stuck.

These three methodological frameworks combine well: Kotter for vision and collective mobilization, ADKAR for individual follow-through, Lewin to structure the project phases.

What I observe with clients is that the model chosen matters less than the rigor with which it is applied. A good model poorly executed delivers the same result as a bad model. Which is: nothing.

If you are a CHRO or CEO looking to structure your change management approach, especially in an AI integration context, request a free diagnostic.

For more on the skills these transformations require, read my analysis of AI roles hiring in 2026.

FAQ

What is the difference between Kotter and ADKAR?

Kotter structures collective mobilization: the leadership coalition, communication, and shared vision. ADKAR diagnoses the specific barriers each employee faces in their adoption journey. The two operate at different levels and complement each other.

Is Lewin’s model still relevant in 2026?

Yes. Its simplicity is its strength. For targeted changes, it remains one of the most effective frameworks. It is less suited to complex transformations that simultaneously affect culture, processes, and tools.

How do you apply ADKAR in an AI integration project?

Start by assessing where each team sits across the five dimensions. Some employees lack understanding of the why (Awareness). Others have the understanding but not the skills (Knowledge or Ability). The response differs: communication in the first case, training in the second.

Should you pick one model or combine them?

Most complex projects benefit from a combination. What matters is execution consistency: each model should fill a precise role, not overlap with the others without clear logic.

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