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
| Department | The human defines... | The system produces... |
|---|---|---|
| Engineering | Architecture, constraints, tests | The implementation |
| Marketing | Strategy, positioning, hypotheses | Campaigns, variants, reports |
| Sales | Qualification logic, deal rules | Outreach, follow-ups, proposals |
| Customer Service | Escalation logic, success criteria | Responses, health scoring, actions |
| Finance | Financial models, rules | Reports, forecasts, anomaly detection |
| HR | Hiring profiles, evaluation grids | Sourcing, screening, summaries |
| Product | Problem, constraints | Specs, test cases, drafts |
| Leadership | Direction, trade-offs | Scenarios, analyses |
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.
Level 1 — 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
Benefit: 10-30% efficiency gains for those who adopt
Risk: Competitors at Level 3 get 10x leverage and make Level 1 non-viable
Level 2 — 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"
- 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
Benefit: 2-3x output with the same headcount
Risk: Half-measures create confusion; uneven adoption limits gains
Level 3 — 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
Benefit: 10x leverage, structural cost advantage, speed that competitors can't match
Risk: Requires people who are hard to find; no room for passengers
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 — Autocomplete | Human codes, AI suggests | Human | Human |
| 1 — Intern | Human assigns scoped tasks | AI | Human (everything) |
| 2 — Junior developer | Human supervises multi-file changes | AI | Human (everything) |
| 3 — Manager | Human directs, reviews at feature/PR level | AI | Human (PR) |
| 4 — Product manager | Human writes the spec, verifies results | AI | Nobody (tests verify) |
| 5 — Dark factory | 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
Prerequisites:
- Leadership is willing to eliminate roles, not just tasks
- Hiring profiles change to require Tier 2+ minimum
- Product/service is redesigned assuming AI execution
- Organizational structure flattens significantly
Timeline: 6-12 months
Leadership Tiers
The company can't exceed the tier of its leadership. Leadership is the ceiling.
| Tier | Name | Key behaviors |
|---|---|---|
| 1 | AI-Supportive | Publicly endorses AI. Uses it personally. Doesn't push adoption. |
| 2 | AI-Operational | Sets expectations by role. Asks "how did AI help?". Funds automation before hiring. |
| 3 | AI-Strategic | Redesigns the organizational structure. Rewrites roles and KPIs. Makes AI literacy a condition of leadership. |
Individual Tiers
| Tier | Name | Telltale signal |
|---|---|---|
| 1 | AI-Aware (Consumer) | "AI helps me do my job faster." |
| 2 | AI-Augmented (Operator) | "AI helps us do this task better and more systematically." |
| 3 | AI-Native (Architect) | "This role should exist differently because AI exists." |
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