The AI Adaptability Framework

Adaptability Level: Rigid

The starting point. The person may know AI exists, have attended a session, or even have access to the tool. But their capability falls into one of two patterns:

Pattern #1: Aware but not acting.

They know AI is important. They may have attended an awareness session or read about it. But nothing changes in practice.

• “I know AI can help.”
• “I attended the session.”
• “I should probably use it more.”

Pattern #2: Acting without understanding.

They are using the tool, but only through memorised routines, copied prompts, or rigid steps. They know which button to click. They can repeat a workflow. But they get stuck the moment the tool changes, the interface changes, or the context changes.

This is one of the most important distinctions in the framework: a person can still be at Foundation even if they are producing some results, if those results come from rote repetition rather than genuine comprehension.

Think of it like fitness. Many people know they should exercise. Many people know they should eat better. That does not mean they are actually living it.

The same applies to AI. Knowing is not the same as doing, and doing without understanding is not the same as mastery.

Adaptability Level: Routine

The person knows what the tool can do. They can name features, describe use cases, and navigate a familiar interface.

They know ChatGPT can summarise, rewrite, brainstorm, and analyse. They know Copilot, Gemini, or Claude each have certain built-in functions.

But they are still bound to one tool, one set of prompts, or one known workflow.

The limitation: they know the tool’s features, but cannot yet flex them across contexts.

Signs someone is still at Features:

• They work well only in one tool.
• They struggle when switching platforms.
• They depend on familiar prompts or known buttons.
• They can explain the feature, but not redesign a workflow around it.

This is real progress, but it is still routine.

Their capability holds up only in known environments with known interfaces.

Adaptability Level: Adaptive

This is where practical fluency begins. The person connects AI to their actual role, adapts known features to solve real problems, and exercises judgment in execution.

The shift is clear: from “What can the tool do?” to “How do I use this in my actual work?”

At this level:

• A manager uses AI not just to summarise reports, but to structure a weekly decision memo.
• A customer service lead uses AI not just to analyse tickets, but to identify likely spike categories and prep team actions.
• A key account manager uses AI not just to draft an email, but to prep negotiation angles based on meeting notes and account behaviour.

The key distinction from Features: a Tool User knows what the tool can do. A Practitioner knows how to use it for work.

They can adjust within familiar contexts, apply judgment, and connect AI use to real job outcomes.

Adaptability Level: Fluid

The most strategic level. The person thinks beyond current tasks and reimagines what workflows, decisions, and outcomes could look like. They use AI as a lever for adaptability, not just efficiency.

Instead of asking “How can AI help me do this faster?”, they ask:

• “How can AI help us forecast issues before they happen?”
• “How can AI surface patterns that change how we allocate resources?”
• “What should this workflow become now that AI exists?”

This is where transferability becomes the defining signal. A Game-Changer’s capability survives tool changes, interface changes, and context changes.

They can move from ChatGPT to Claude, from one company’s setup to another, and still preserve the logic of the workflow. Their success is not bound to any single environment.

The distinction between Adaptive and Fluid matters here. A Practitioner adjusts within familiar contexts. A Game-Changer transfers and evolves across unfamiliar ones. That is the difference between competence and true adaptability.