CODER Framework
Apply Brian Balfour's CODER Framework to systematically drive AI adoption across your organization.
Entry Point
When this skill is invoked, start with:
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CODER FRAMEWORK
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Drive AI adoption across your organization.
- Constraints: Make new behavior easier than old
- Ownership: Assign clear responsibility
- Directives: Create specific, actionable instructions
- Expectations: Set measurable goals
- Rewards: Tie to career progression
What do you want to do?
1. Diagnose adoption barriers
2. Create full CODER plan
3. Focus on specific team
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What This Does
Guides you through creating an AI adoption plan using the CODER framework:
- Constraints - Make new behavior easier than old behavior
- Ownership - Assign clear responsibility
- Directives - Create specific, actionable instructions
- Expectations - Set measurable goals
- Rewards - Tie to career progression
Usage
/coder
Optional parameters:
/coder --diagnose- Identify your primary adoption barrier/coder --team [team-name]- Create plan for specific team/coder --export- Generate implementation doc
What Happens
-
Diagnoses your situation:
- Company size and structure
- Current AI adoption level
- Primary barriers (political, retrofitting, procurement, knowledge, permission)
- Team composition (catalysts 15-20%, converts 60-70%, anchors 15-20%)
-
Guides through CODER framework:
- Designs 1-2 constraints for your context
- Assigns ownership (CEO/functional leaders)
- Creates 2-3 directives per function
- Sets specific, universal, measurable expectations
- Defines how to tie to rewards (performance reviews, leveling)
-
Generates implementation plan:
- Immediate actions
- 30/60/90 day milestones
- Metrics to track
- Common pitfalls to avoid
The Framework Components
C - Constraints
Make AI behavior easier than old behavior:
- Time constraints (hackathons, dedicated AI days)
- Process constraints ("I only review work demonstrating AI augmentation")
- Tool constraints (Copilot required, AI in workflows)
O - Ownership
Assign clear responsibility:
- CEO: Overall cultural shift
- Functional leaders: Team-specific directives
- Clear escalation paths for blockers
D - Directives
Create specific, actionable instructions (2-3 per team):
- Product: "All features must include AI prototype before design review"
- Engineering: "All code reviews must use GitHub Copilot"
- Design: "Design critiques include AI generation process"
E - Expectations
Set specific, universal, measurable goals:
- ✓ "100% of PMs prototype features with AI" (not "use AI effectively")
- ✓ "90%+ of code commits show Copilot usage"
- ✓ AI fluency levels: Aware→Exploratory→Proficient→Advanced→Expert
R - Rewards
Tie to career progression:
- Performance review criteria
- Leveling guide updates
- Promotion requirements
Common Adoption Barriers
| Barrier | Symptom | Solution Focus | |---------|---------|----------------| | Political | Leaders disagree on value | Ownership, CEO mandate | | Retrofitting | "We can't use AI for X" | Directives, examples | | Procurement | Tool access blocked | Constraints, budgets | | Knowledge | Don't know how to use tools | Directives, training | | Permission | Fear of doing wrong | Expectations, psychological safety |
Learn More
See the full CODER framework at:
frameworks/ai-era-practices/organizational-ai-adoption.md
Framework: Brian Balfour (Reforge, 2025) Best for: Driving organizational change, AI adoption, behavioral transformation Key insight: Move from value capture (10-15% efficiency) to value creation (new capabilities)