The Reference Framework
This document consolidates the maturity model, the operating principle, and the two scales that structure the AI transformation.
The Universal Translation Rule
The operating principle of the entire transformation fits in one sentence:
Replace "the human produces the artifact" with "the human defines the spec → the system produces the artifact."
What This Means by Department
The Litmus Test
If this person disappeared, could a system execute 80% of their tasks?
- If no → the role is still execution-based
- If yes → the role is AI-native
This isn't "AI adoption." It's the shift from a labor-based company to a systems-based company.
Organizational Scale — Levels 1 to 3
This scale applies across the entire group — engineering, marketing, sales, finance, customer service.
AI-Assisted
What it looks like:
- AI is a tool that individuals choose to use
- Same structures, same processes, same roles
- If AI disappeared tomorrow, nothing structural would change
Typical behaviors:
- Using ChatGPT/Claude like Google or a spell checker
- Isolated prompts, no iteration
- AI outputs manually pasted into work
- No shared prompts, no documentation
- Adoption is uneven and optional
The gap is measurable: in technical roles, AI has 94% theoretical task coverage but only 33% actual usage. Level 1 organizations leave most of AI's capability untouched.
AI-Integrated
What it looks like:
- AI is integrated into workflows and systems
- Some processes redesigned around AI capabilities
- Roles start shifting from "doing" to "directing" (see role evolution patterns)
- If AI disappeared tomorrow, some workflows would break
Typical behaviors:
- Saved prompts, templates, prompt libraries
- AI used across multiple steps of a task, not just one
- Tools like Copilot, Notion AI, Zapier, n8n in active use
- Prompts and workflows shared among colleagues
- AI usage is expected, not optional
AI-Native
What it looks like:
- Organizational design assumes AI as a first-class resource
- Roles are defined by judgment and direction, not execution
- Headcount is a fraction of a traditional company at the same output
- If AI disappeared tomorrow, the company couldn't function
Typical behaviors:
- The starting question is: "What part should be automated?"
- Agents, pipelines, and decision systems built (code or no-code)
- Processes designed so humans handle judgment, AI handles execution
- AI impact is measured (time saved, costs reduced, quality improved)
- AI literacy is a condition of employment
Engineering Scale — Rungs 0 to 5
Engineering needs finer granularity. Based on Dan Shapiro's framework, this scale describes the progression of software development. The AI Lab details it and how it operates.
| Rung | Human's role | Who writes the code | Who reviews the code |
|---|---|---|---|
| 0 — Assisted coding | Human codes, AI suggests | Human | Human |
| 1 — Scoped delegation | Human assigns scoped tasks | AI | Human (everything) |
| 2 — Supervised generation | Human supervises multi-file changes | AI | Human (everything) |
| 3 — Directed development | Human directs, reviews at feature/PR level | AI | Human (PR) |
| 4 — Spec-driven development | Human writes the spec, verifies results | AI | Nobody (tests verify) |
| 5 — Autonomous production | Spec goes in, software comes out | AI | Nobody (scenarios verify) |
Mapping
| Organizational scale | Engineering scale |
|---|---|
| Level 1 — AI-Assisted | Rungs 0-1 |
| Level 2 — AI-Integrated | Rungs 2-3 |
| Level 3 — AI-Native | Rungs 4-5 |
Diagnostic Questions
For the organization
"If AI disappeared tomorrow, what would change?"
- Nothing structural → Level 1
- Some workflows break → Level 2
- The company can't function → Level 3
For leaders
"What would you remove from the org chart if AI were fully utilized?"
- Can't answer → Tier 1
- Mentions tasks → Tier 2
- Mentions roles or processes → Tier 3
For individuals
"Show me something you've built or changed because AI exists."
- Talks about prompts used → Tier 1
- Shows workflows or templates → Tier 2
- Shows systems or process changes → Tier 3
Acceptance Criteria
Level 2 — Achieved when ALL these criteria are met:
- AI usage is a documented expectation for every role, not optional
- Every department maintains a structured context file loaded before AI tasks
- Shared prompt libraries or workflow templates exist and are in use
- At least 1 workflow per department has been redesigned around AI (before/after documented)
- KPIs include AI output metrics (not just activity)
- "How did AI help?" is asked in reviews and retrospectives
- If AI disappeared tomorrow, at least some workflows would break
Level 3 — Achieved when ALL these criteria are met:
- Roles are defined by judgment and direction, not execution
- Agents, pipelines, or decision systems are in production (not prototypes)
- Non-trivial tasks have written specifications conforming to the execution standards
- Every AI system in production has an assigned Spec Owner, Context Owner, and Evaluation Owner
- AI impact is measured by department (time saved, costs reduced, quality improved)
- Hiring profiles require Tier 2+ minimum
- If AI disappeared tomorrow, the department couldn't function
The Transformation Path
Level 1 → Level 2
Prerequisites:
- Leadership commits to AI as an operational standard, not optional
- Investment in shared AI infrastructure (tools, templates, training)
- Processes audited and redesigned for AI integration
- KPIs updated to measure AI output
- "How did AI help?" becomes a standard question
Timeline: 3-6 months with committed leadership
Level 2 → Level 3
Level 2 is the operational floor: every department must reach it. Level 3 is the organizational target. Non-engineering departments aim for Level 2 as their first milestone; engineering aims directly for Level 3 via the AI Lab.
Prerequisites:
- Leadership is willing to eliminate roles, not just tasks (see the Role Decision Matrix)
- Hiring profiles change to require Tier 2+ minimum
- Product/service is redesigned assuming AI execution
- Organizational structure flattens significantly
Timeline: 6-12 months
For engineering, the AI Lab lifecycle defines the specific phase sequence from Rung 3 to Rung 5.
Leadership Tiers
The company can't exceed the tier of its leadership. Leadership is the ceiling.
Publicly endorses AI. Uses it personally. Doesn't push adoption.
Sets expectations by role. Asks "how did AI help?". Funds automation before hiring.
Redesigns the organizational structure. Rewrites roles and KPIs. Makes AI literacy a condition of leadership.
Individual Tiers
"AI helps me do my job faster."
"AI helps us do this task better and more systematically."
"This role should exist differently because AI exists."
The difference between tiers is operational, not attitudinal. A Tier 1 person uses AI tools but has no current sense of where the human-agent boundary sits for their domain — they calibrated once (or never) and haven't updated. A Tier 2 person designs clean handoffs between human and agent work, maintains an accurate model of how agents fail for their specific tasks, and restructures workflows as capabilities shift. A Tier 3 person does all of this plus forecasts where the boundary will move next and allocates their attention where it creates the most value — treating human attention as the scarcest resource in an agent-rich environment.
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