The quick read.
Lewin (three stages)
- Built for
- Understanding why change feels hard
- Strongest at
- Simple shared language: unfreeze, change, refreeze
- Strains with AI because
- AI never refreezes. There is no end state to settle into
Kotter (8 steps)
- Built for
- One big organizational change with a finish line
- Strongest at
- Executive sponsorship, urgency, momentum
- Strains with AI because
- It is episodic. AI change is continuous, not a program that ends
ADKAR (Prosci)
- Built for
- Individual adoption, person by person
- Strongest at
- Diagnosing exactly where each person is stuck
- Strains with AI because
- Light on workflow redesign, governance, and value measurement
McKinsey 7-S
- Built for
- Diagnosing organizational alignment
- Strongest at
- Showing how strategy, structure, and skills interconnect
- Strains with AI because
- It is a snapshot, not a process. No sequence, no cadence
Lewin's three stages.
Developed by psychologist Kurt Lewin in the 1940s, this is the model most others descend from: unfreeze the current state, make the change, refreeze the new state. It is still the clearest way to explain to a team why change feels uncomfortable. Its limit in an AI transition is the third word. Refreezing assumes the ground stops moving, and with model releases arriving quarterly, it does not.
Kotter's 8 steps.
John Kotter published the model in Harvard Business Review's 1995 article 'Leading Change.' Eight sequential steps that run from creating urgency through building a coalition to anchoring the change in culture. It remains the strongest framework for winning executive sponsorship and momentum on a single defined transformation. Its limit is that it was built for change as an event. AI is change as a condition. You cannot run eight steps every quarter.
ADKAR.
Developed by Jeff Hiatt at Prosci in the late 1990s, ADKAR tracks five outcomes for each individual: Awareness, Desire, Knowledge, Ability, Reinforcement. It is the best diagnostic on this list for a stalled deployment, because it tells you precisely where each person is stuck. Is this an awareness problem or an ability problem? Its limits are the things it never claimed to cover: it will not redesign a workflow, set your governance, or tell you whether the change produced business value.
McKinsey 7-S.
Developed at McKinsey in the late 1970s, 7-S maps seven interdependent elements: strategy, structure, systems, shared values, skills, style, and staff. It is a diagnostic lens, not a change process, and that is its value. It shows leaders that buying a platform (systems) without touching skills, style, or shared values is why the platform sits unused. But it gives you no sequence and no cadence. It tells you what is misaligned, not what to do on Monday.
How to choose.
- - One defined change with a hard deadline and executive attention: Kotter.
- - A specific tool has been deployed and people are not using it: ADKAR.
- - Leadership cannot agree on what is actually wrong: 7-S as the diagnostic conversation.
- - A team that needs to understand why this feels hard: Lewin, in one slide.
- - Continuous AI change across many workflows, with spend already committed: none of the above alone. You need a loop, not a line.
Where the classics fit.
We built our seven-layer framework with full awareness of these models, and out of respect for what they get right. We do not repackage them, and we are not affiliated with any of them. What we do is apply the same underlying logic where it belongs: our Enable layer works from the same insight ADKAR is built on, that adoption happens one person at a time and stalls at identifiable points. Our Decide and Measure layers reflect the same discipline about sponsorship and momentum that Kotter codified. Our Diagnose layer asks the same alignment questions 7-S taught a generation of leaders to ask. If your team is already trained in one of these models, nothing in our framework asks you to abandon it. The classics handle the human transition. Our framework adds the layers AI demands around them: continuous redesign, governance, and a measurement loop that survives a model upgrade.
Related reading.
- - An AI change management framework - the seven-layer loop we use, in detail.
- - AI change management tools and frameworks - the full landscape, with the adoption platforms.
- - AI change management software, compared - what to buy and when.
- - AI readiness assessment - the diagnosis layer in detail.
- - Our book, The Elephant in the Algorithm - the longer argument behind the framework.
Questions people ask.
Which change management framework is best for AI?
None of the classics alone. Pick the one that fits your immediate change, then add continuous redesign, governance, and measurement around it, because AI change does not end.
Is ADKAR still relevant for AI?
Yes, for individual adoption it is still the sharpest diagnostic available. It was never designed to cover workflow redesign or value measurement, so pair it rather than stretch it.
What is the difference between ADKAR and Kotter?
ADKAR tracks change one person at a time. Kotter runs change one organization at a time. ADKAR tells you why one person is not using the tool. Kotter tells you why the program lost momentum.
Do I need a new framework for AI?
You need a new wrapper more than a new framework. Keep the classics for what they are good at, and add the layers they leave out: redesign, governance, and a measurement loop.
ADKAR is a registered trademark of Prosci Inc. Kotter's 8-Step Process, Lewin's change model, and the McKinsey 7-S framework remain the work and intellectual property of their respective creators. Average Robot is not affiliated with, certified by, or endorsed by any of these organizations. References here are for comparison and commentary.