The short answer.
Every AI change management framework is really answering a different question. ADKAR asks how you move individuals through change. QuantumBlack asks how you deliver AI at industrial scale. People, Process, Platform asks why AI adoption has stalled after the tools are already in place.
AI adoption rarely stalls because the technology fails. It stalls because the people were never brought along, the processes never adapted, and the platform choices drifted from strategy. The right framework is the one that names the gap you actually have.
Prosci ADKAR.
Prosci's ADKAR is the most widely adopted change model in the world. Awareness, desire, knowledge, ability, reinforcement. It gives a clear five-stage frame for moving a person from resistance to sustained adoption, and it has two decades of research behind it. Prosci publishes the methodology at prosci.com.
The strength is the human transition. The limit is that ADKAR pre-dates generative AI. It does not name AI-native failure modes such as shadow tool sprawl, prompt hygiene, model selection, or the confusion between activity and value. Most teams use it as the transition engine and add an AI-specific read on top.
McKinsey QuantumBlack.
QuantumBlack is McKinsey's AI arm. Its approach is delivery-led and enterprise-scale, pairing strategy consulting with data science and engineering to build and deploy AI systems. Strong on technical rigor, use-case prioritization, and the operating model needed to run AI at scale.
The strength is scale and technical depth. The limit is that this model is built for large transformation budgets and heavy delivery teams. For a marketing, creative, or talent function trying to get real value from AI already in the building, the weight of the approach can outrun the problem.
People, Process, Platform.
People, Process, Platform is the framework behind everything we build at Average Robot, and the spine of our book, The Elephant in the Algorithm. It was designed from the AI adoption failure patterns themselves, not adapted from generic change work.
People asks whether your teams are aligned and equipped to use AI with intent. Process asks whether your ways of working have adapted, or whether AI is bolted onto old workflows. Platform asks whether your tool and model choices serve your business strategy and your customers. The People layer is where most AI programs quietly break, and it is where this framework puts its weight.
Where each one wins.
Pick ADKAR when
- You need a standardized, certifiable change methodology.
- The priority is moving individuals through a transition.
- You want internal change leads trained and credentialed.
Pick QuantumBlack when
- You are building AI systems at enterprise scale.
- The work is heavy on data science and engineering.
- You have the budget for a large delivery team.
Pick People, Process, Platform when
- AI is already in the building but the return is flat.
- Adoption stalled and it looks like a people problem.
- You need AI strategy aligned with your teams and customers.
Using them together.
These frameworks are not mutually exclusive. A common and effective pattern is ADKAR for the human transition, a QuantumBlack-style model for technical delivery at scale, and People, Process, Platform as the AI-specific read that tells you what actually needs to change for AI to land. The diagnostic comes first. It tells you which gaps are real, so you do not spend transition effort in the wrong place.
Questions people ask.
What is the best AI change management framework?
There is no single best framework. Prosci ADKAR is strongest for the individual human transition, McKinsey's QuantumBlack approach is strongest for large-scale technical delivery, and the People, Process, Platform model is built specifically to diagnose why AI adoption stalls after the tools are in place. Most organizations combine a human-transition model with an AI-specific read.
Is ADKAR enough for AI change management?
ADKAR moves people through awareness, desire, knowledge, ability, and reinforcement. It handles the human side well, but it pre-dates generative AI and does not name AI-native failure modes like shadow tools, prompt hygiene, or value-versus-activity confusion. Teams usually pair it with an AI-first diagnostic to surface those gaps.
How is the People, Process, Platform framework different?
Most frameworks were designed for generic organizational change and then pointed at AI. People, Process, Platform starts from the AI adoption failure patterns themselves. It asks whether your people are aligned, whether your processes have adapted, and whether your platform choices serve strategy, then names the specific gaps to fix.
Can these frameworks work together?
Yes. A common pattern is ADKAR for the human transition, a QuantumBlack-style delivery model for technical scale, and People, Process, Platform as the AI-specific read that tells you what actually needs to change for AI to land.