Colaborix Cognitive Production System (CCPS)

Inspired by Toyota Kata for AI Adoption

The operating system for human work in the AI era.

Executive Summary

Colaborix Cognitive Production System (CCPS) is a continuous improvement learning method designed to develop stable, embodied AI capabilities in individuals and teams.

Instead of focusing on tools or one-off skills, the method systematically transforms how people learn, work, reflect, and adapt in the presence of AI.

The framework integrates:

• Toyota Production System (TPS) — PDCA, standard work, gemba, kaizen

• Kolb’s Experiential Learning Cycle — theory, action, experience, reflection

• Double-loop learning — transforming not only actions, but underlying assumptions

• Adult learning science — deliberate practice, self-directed learning, habit formation

The outcome is not “AI literacy”, but AI-enabled operational capability that becomes part of daily work.

Core Principle

No improvement without standardization.No standardization without learning.No learning without real work.

AI capability is developed the same way Toyota develops production capability:through small experiments, real practice, disciplined reflection, and stabilized habits.

The Process Flow (High-Level)

Each participant or team runs repeated PDCA learning cycles focused on one real AI-related bottleneck at a time.

After several cycles, learning becomes cumulative and structural, not incremental.

Over time, this produces:

• New working patterns

• New cognitive habits

• New mental models of work itself

Colaborix Cognitive Production System (CCPS): Step-by-Step Manual

Step 0 — Define True North (Direction)

Purpose: Establish meaningful orientation.

Define the True North for AI-enabled work:What does “better work” mean with AI in this role or team?

This is not about tools.It is about outcomes and identity, for example:

• Faster sense-making

• Better decision quality

• Reduced cognitive load

• Higher creative leverage

This corresponds to Toyota’s True North and provides motivational coherence.

Step 1 — Define Current Standard (Baseline)

Purpose: Make the invisible visible.

Describe how work is actually done today without idealization.

This creates:

• A behavioral baseline

• A reference for improvement

• A concrete learning anchor

Without a standard, improvement cannot be measured.

Step 2 — Identify a Performance Gap (Problem Finding)

Purpose: Frame the right problem.

Identify one specific performance gap between current work and desired AI-enabled work.

Important:This is problem finding, not problem solving.

Typical gaps:

• “I use AI, but still think manually.”

• “I prompt, but don’t integrate into workflow.”

• “We generate outputs, but don’t act on them.”

This mirrors Toyota’s gap-based problem framing.

Step 3 — Root Cause Exploration (Sense-Making)

Purpose: Avoid surface solutions.

Explore why this gap exists:cognitive habits, workflow structure, assumptions, fears, identity, incentives.

This step introduces double-loop potential:we investigate not just actions, but thinking patterns.

Step 4 — PDCA Learning Cycle (Core Engine)

Now the actual learning loop starts.

This is where PDCA and Kolb fully overlap.

PLAN — Theorize (Abstract Conceptualization)

Form a working hypothesis:“If I change X in how I use AI, Y will improve.”

This is:

• Mental model building

• Assumption making

• Learning intent formulation

DO — Operationalize (Active Experimentation)

Translate theory into concrete steps or micro-procedures.

Not vague:

• Exact prompts

• Workflow changes

• Decision rules

• New rituals

This is standard-in-the-making.

CHECK — Go to Gemba (Concrete Experience)

Apply the steps in real work situations.

This is not simulation.This is:

• Actual tasks

• Actual decisions

• Actual cognitive load

This is your AI gemba.

ACT — Reflect and Reconfigure (Reflective Observation)

Structured reflection with four key questions:

1. What worked?

2. What failed?

3. What assumption was invalidated?

4. What should change next cycle?

Question 3 is the double-loop trigger.

This is where:

• Identity shifts happen

• Mental models update

• Real learning occurs

Step 5 — Stabilize via Standard Work (SOP / Habit)

Purpose: Convert learning into capability.

If the new pattern works reliably:formalize it as Standard Operating Procedure or daily habit.

Run the standard for minimum two weeks.

This is Toyota’s core doctrine:

Without standardization, learning evaporates.

Step 6 — Meta-Reflection (Every 2–3 Cycles)

Purpose: Enable triple-loop learning.

Reflect on how you are learning:resistance patterns, cognitive shifts, emotional reactions, identity changes.

This creates:

• Learning about learning

• Not just learning about AI

This is extremely rare in corporate training.

Step 7 — Repeat with Next Bottleneck

Identify next performance gap.Restart the cycle.

After 6–7 cycles:

• AI is no longer “a tool”

• It becomes part of how thinking itself works

Why This Works (Theoretical Justification)

1. Toyota Production System

This method directly implements:

• PDCA

• Gemba

• Standard work

• Kaizen

• True North

• Learning organization logic

But applied to knowledge work and cognition, not factories.

This is essentially:

TPS for the human mind in the AI age.

2. Kolb’s Experiential Learning

Every cycle enacts:

• Theory → Action → Experience → Reflection

But in operational reality, not classroom abstraction.

This avoids:

• Slideware learning

• Tool fetishism

• Passive consumption

3. Double-Loop Learning

Each cycle challenges:

• Assumptions

• Mental models

• Identity-level beliefs about work

This is not just:

“How do I use AI?”

But:

“What does work even mean now?”

4. Adult Learning & Skill Formation

The method satisfies:

• Self-directed learning

• Deliberate practice

• Real feedback

• Habit formation

• Identity transformation

Colaborix offer is capability engineering, not training.