Skill Progression Map
What Level 1, 2, and 3 look like for your role — concretely.
How to Use This Page
The Reference Framework defines three maturity levels. This page shows what those levels mean in practice for specific role families — not in theory, but in the work you do day to day.
For each role family:
- Level 1 (AI-Assisted): You use AI as a tool. Same workflows, faster in spots.
- Level 2 (AI-Integrated): AI is embedded in your workflow. Some of your work has been redesigned around what AI can do.
- Level 3 (AI-Native): You define specifications and judge results. AI handles execution.
Find your role family below. Identify where you are. Then use Transforming Your Role for the transition process and the Recognizing Your Pattern section to understand which structural forces are acting on your role.
Engineering
Dominant pattern: Elevation — from writing code to specifying what the code should do.
Level 1 — AI-Assisted
You use AI for code completion and quick lookups. Copilot or ChatGPT suggests lines; you accept or reject.
What this looks like:
- AI autocompletes code as you type
- You paste code into ChatGPT to debug or explain
- AI outputs require significant manual review and editing
- No shared configurations or prompt templates across the team
- Your workflow is fundamentally the same as before AI
The data: 84% of developers use or plan to use AI tools, 51% daily. But trust has fallen to 29%, and 66% report spending more time fixing AI-generated code than they save (Stack Overflow, 2025). This is the Level 1 experience: AI helps in spots, but the net gain is uncertain because the workflow hasn't been redesigned.
Level 2 — AI-Integrated
AI is part of the development workflow, not just a helper. You direct multi-file changes, review AI-generated code at the PR level, and maintain shared context files.
What this looks like:
- AI generates code from descriptions; you review and iterate
- Shared prompt templates and context files exist for the codebase
- AI handles tests, documentation, and boilerplate systematically
- You spend more time on architecture and review, less on typing
- Removing AI would break your development velocity
The shift: At Level 2, you accept ~30% of AI suggestions but retain 88% of generated characters (GitHub/Accenture, 2024). The skill is knowing what to accept, what to reject, and how to direct the generation.
Level 3 — AI-Native
You write specifications. AI writes the code. Tests and scenarios verify the result. This corresponds to Rungs 4–5 on the engineering scale.
What this looks like:
- You define the spec: constraints, acceptance criteria, test cases
- AI produces the implementation end to end
- You don't read every line of code — you verify through automated tests and scenarios
- Your accountability is in the spec and the verification system, not in the code itself
- See the AI Lab for the full operating model
The critical warning: Level 1 without progression actively degrades quality. Analysis of 211 million lines of code shows that AI-assisted development without skill progression caused refactoring to drop from 25% to under 10% of changes, while code churn nearly doubled (GitClear, 2025). The tools make it easy to produce code and hard to produce good code. Level 2 and 3 skills — review judgment, specification quality, test design — are what prevent this.
Self-assessment
| Question | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| How do you start a new feature? | Open editor, start coding, use AI for completion | Describe the feature to AI, review the output, iterate | Write a specification with constraints and test cases, let AI implement |
| What happens when AI code is wrong? | Fix it line by line | Improve the prompt/context and regenerate | Improve the spec or scenarios and re-run |
| What do you share with teammates? | Nothing AI-specific | Prompt templates, context files | Specification patterns, scenario libraries |
Marketing
Dominant pattern: Specialization — shedding content production, deepening strategic judgment.
Level 1 — AI-Assisted
You use AI for first drafts and idea generation. Every output gets manually edited.
What this looks like:
- AI generates blog post drafts, email copy, or social media posts
- You edit 80%+ of AI output before publishing
- No systematic workflow — AI is used ad hoc
- Each team member uses AI differently (or not at all)
- Campaigns are still planned and executed the traditional way
The data: 91% of marketing leaders say their teams use AI, with content creation (43%) as the top use case. But 86% edit AI-generated content before publishing (HubSpot, 2025). And 68% receive no formal AI training (Marketing AI Institute, 2025).
Level 2 — AI-Integrated
Campaign workflows are redesigned around AI. AI doesn't just draft — it generates variants, handles research, and produces analysis as a systematic step.
What this looks like:
- Shared prompt libraries encode brand voice and positioning
- AI generates campaign variants; you select and refine
- Research, competitive analysis, and reporting are AI-first workflows
- New team members are onboarded into AI-integrated processes
- The team produces more with fewer people
The shift: You stop writing content and start directing content systems. Your value moves from production speed to strategic judgment: which angle, which audience, which positioning.
Level 3 — AI-Native
You define strategy, positioning, and constraints. Systems produce campaigns, variants, and reports.
What this looks like:
- You specify the campaign: target, positioning, constraints, success metrics
- AI produces the creative, copy, and distribution plan
- You review, select, and adjust — not produce
- The marketing team is significantly smaller but produces significantly more
- Your role is strategy and taste, not execution
External validation: The Marketing AI Institute's own maturity survey maps almost directly to these levels: 40% of marketing teams are at Experimentation (Level 1), 26% at Integration (Level 2), 17% at Transformation (Level 3) (Marketing AI Institute, 2025).
Self-assessment
| Question | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| How do you create a campaign? | Plan it, then use AI for some drafts | Define the brief, AI generates variants, you curate | Define the strategy and constraints, AI produces the campaign |
| What's your bottleneck? | Writing and production | Review and strategic decisions | Defining the right problem to solve |
| How much do you edit AI output? | 80%+ | 30-50% | 10-20% (selecting, not rewriting) |
Customer Service
Dominant pattern: Elevation shifting to Convergence — from answering tickets to designing service systems.
Level 1 — AI-Assisted
AI suggests responses. Agents copy, paste, and edit. The workflow is the same, slightly faster.
What this looks like:
- AI drafts reply suggestions for agents
- Agents handle the same volume and types of interactions
- AI handles only the simplest, most scripted inquiries
- No role changes — everyone still does the same job
- Quality depends on individual agents, not systems
Level 2 — AI-Integrated
AI handles routine inquiries autonomously. Agents shift from answering to training, reviewing, and handling complex cases. New roles emerge.
What this looks like:
- AI resolves the majority of routine tickets without human involvement
- Agents spend more time training AI systems than doing traditional support
- New roles emerge: conversation analysts, knowledge managers, AI operations leads
- Escalation logic is designed and documented, not improvised
- The team handles significantly more volume with stable or reduced headcount
The data: 82% of support teams feel positive about AI collaboration. 60% say roles are evolving. 40% of teams report agents spend more time training AI systems than doing traditional support (Intercom, 2025). This is Level 2 in action.
Level 3 — AI-Native
Humans define service strategy, escalation logic, and quality standards. AI executes the vast majority of interactions.
What this looks like:
- You define: what constitutes good service, when to escalate, what quality looks like
- AI handles 80%+ of interactions
- Human agents exist for judgment calls, relationship moments, and cases the system can't handle
- The team is a fraction of its previous size, but service quality is equal or better
- Your role is system design and quality ownership, not ticket resolution
Customer service is often the first function to reach Level 3. It shows the highest actual AI task coverage (Anthropic, 2026) and generates the largest share of AI value (38%, per BCG, 2025).
Self-assessment
| Question | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| What do you do most of the day? | Answer tickets | Train AI, handle escalations, review quality | Design service strategy and escalation rules |
| What happens when AI gives a bad answer? | Fix it and move on | Update the training data or knowledge base | Redesign the escalation logic or quality criteria |
| How is your performance measured? | Tickets resolved, response time | AI deflection rate, escalation quality | Service quality metrics, system design effectiveness |
Sales
Dominant pattern: Specialization — shedding administrative overhead, deepening relationship and deal judgment.
Level 1 — AI-Assisted
AI helps with email drafts and basic research. Sellers still spend most of their time on non-selling tasks.
What this looks like:
- AI drafts cold emails and follow-ups
- Research is semi-manual with AI assistance
- CRM is updated by humans
- 70% of time goes to non-selling tasks (Salesforce, 2024)
- The sales process hasn't changed, just individual tasks
Level 2 — AI-Integrated
AI automates research, outreach sequencing, and CRM enrichment. Sellers focus on relationships and complex deal strategy.
What this looks like:
- AI handles prospecting research, outreach sequences, and follow-up timing
- CRM is enriched automatically with AI-gathered data
- Sellers focus on high-value conversations: qualification, negotiation, closing
- AI users are 2.4× less likely to feel overworked
- The non-selling overhead drops significantly
The shift: The value moves from activity volume (calls made, emails sent) to deal quality (pipeline accuracy, win rate, deal size). Level 2 sellers don't work harder — they work on the right things.
Level 3 — AI-Native
Humans define qualification logic, deal rules, and escalation thresholds. AI produces outreach, proposals, and pipeline analysis.
What this looks like:
- You define: ideal customer profile, qualification criteria, pricing rules, escalation conditions
- AI produces: outreach, follow-ups, proposals, competitive analysis
- Your time goes to relationship building, strategic accounts, and judgment calls
- By 2027, 95% of seller research workflows are predicted to begin with AI (Gartner, 2025)
- Sellers who partner with AI are 3.7× more likely to meet quota
Self-assessment
| Question | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| How much time do you spend on admin? | 70%+ | 30-40% | Under 15% |
| How do you research a prospect? | Manually, with some AI help | AI produces the research brief, you review | AI identifies and qualifies prospects, you handle relationships |
| What's your competitive advantage? | Activity volume | Deal judgment | Specification of what "good" looks like |
Design
Dominant pattern: Elevation — from pixel production to system direction.
Level 1 — AI-Assisted
AI generates mood boards, initial concepts, or copy. You refine everything manually.
What this looks like:
- AI produces inspiration: mood boards, concept variations, style explorations
- All production work (layouts, components, assets) is done manually
- AI is a starting point, not a workflow participant
- The design process is unchanged — AI adds a brainstorming step
Level 2 — AI-Integrated
AI handles production work. You shift to system thinking, brand direction, and quality judgment.
What this looks like:
- AI generates layouts, asset variations, and responsive adaptations
- You define design systems and brand constraints; AI operates within them
- Production time drops dramatically; review and direction time increases
- Entry-level production roles contract as AI absorbs that work
- 71% of UX professionals believe AI will shape the future of UX (UX Design Institute, 2025)
The shift: Your value moves from craft execution to taste and system design. You're not less of a designer — you're more of an architect.
Level 3 — AI-Native
You define systems, constraints, and brand rules. AI produces the artifacts.
What this looks like:
- You specify: design system, brand parameters, constraints, quality criteria
- AI produces: mockups, components, responsive layouts, asset libraries
- You review, curate, and refine — not draw
- Outcome-oriented design replaces pixel-level work
- "Systems Architects" and "AI Directors" emerge as the high-value design roles (NN/g, 2025)
By Q3 2025, "manual pixel-pushing had effectively ended for commercial production" (UX Design Institute, 2025). The progression from Level 1 to Level 3 is happening faster in design than in most other functions.
Self-assessment
| Question | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| What do you produce? | Pixel-perfect deliverables | Design systems and direction, AI produces deliverables | Specifications and quality criteria |
| What's your bottleneck? | Production time | Making the right design decisions | Defining the right constraints |
| What skills are growing? | Tool mastery | System thinking, brand judgment | Specification engineering, taste at scale |
Cross-Cutting: The Skills That Matter at Every Level
Regardless of your role family, certain skills compound across the progression:
Level 1 → Level 2: The critical skill is recognizing which parts of your work are legacy patterns — repeatable execution that AI can absorb. The transition is about seeing the opportunity, not just using the tool.
Level 2 → Level 3: The critical skill is specification engineering — writing clear enough instructions that AI can execute without real-time supervision. This is the Universal Translation Rule applied to your individual work.
At every level: The five irreplaceable functions — Direction, Judgment, Taste, Relationship, Accountability — define what stays human. Your progression isn't about doing less. It's about concentrating on what only you can do.
Workers in AI-exposed roles earn up to 30% salary premiums (PwC, 2025). The market is already pricing the progression.
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