AAOS Framework v1.2

The Operating System for AI-Augmented Work

AI adoption is not the success condition; decision reliability is.

The AAOS integrates three elements—Capability, Controls, and Workflow—to produce defensible, evidence-backed decisions at scale. Developed by Dr. Darren Pulsipher, author of Becoming AI-Augmented.

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Outcomes first

The framework helps you move from manual craft to systematic orchestration.

Grounding
Validation
Ownership

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Experience AAOS in Context

Select the lens that matches your role to see the specific roadmap, tools, and examples for your journey.

Professional

Individual

One person and one workflow. Build personal reliability and repeatable daily habits.

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Management

Team Leader

A shared workflow and handoff system. Ensure consistency across people and roles.

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Executive

Organization Leader

An operating model across functions. Build an architecture of trust for the entire enterprise.

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Learner

Student

A relationship to study and explanation. Use AI to deepen understanding without skipping the learning.

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Instruction

Teacher

Classroom practice and feedback. Augment lesson design and student support while maintaining rigor.

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Institution

Education Admin

Schools and education organizations. Align policy, strategy, and student outcomes across the system.

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Operating Cycle

The 6 AAOS Stages

The AAOS stages describe the required sequence of AI-augmented work. They govern how work moves from initial draft to evidence-backed scale.

Stage 1

Diagnose

Classify the workflow posture before intervention. Establish the current state of work, evidence, and risk.

Stage 2

Activate

Target capability development at the weakest pillar (AI, SME, Core, or Meta skills) to improve reliability.

Stage 3

Controls

Apply grounding, validation, and workflow integration to make capability trustworthy and defensible.

Stage 4

Execute

Run the six-stage loop (Draft → Ground → Validate → Approve → Handoff → Learn) for all work.

Stage 5

Measure

Verify reliability using the operating dashboard. Track correction load and handoff return rates.

Stage 6

Scale

Make governed expansion decisions based on control stability evidence, not just adoption volume.

Core Model

The 6 Maturity Levels

The AAOS maturity ladder describes how a person, team, or organization becomes more capable of using AI well over time. Based on research into High-Reliability Organizations (HRO) and Augmentation Maturity.

Level	General Meaning	Diagnostic Signal
Aware	Need for AI is visible, but work is mostly manual.	Can describe the gap, but not yet close it consistently.
Exploring	AI tried in limited cases; learning what helps/fails.	Curiosity and first attempts, but no repeatable control.
Experimenting	Useful patterns begin to repeat; more deliberate use.	Early standardization, but reliability varies by context.
Integrating	AI is part of workflow; grounding/validation is routine.	Work moves through normal operations without reinvention.
Leading	Practice is strong enough to teach or standardize.	System can absorb variation while preserving quality.
Augmenting	Human expertise and AI are a durable partnership.	Reliable enough to improve, transfer, and scale safely.

Framework Version: 1.2.4 | Last Major Update: July 2026 | Authority: Dr. Darren Pulsipher, Becoming AI-Augmented.