# Resource Planning Framework

## Overview
This document establishes comprehensive resource planning strategies for AI-assisted development projects, ensuring optimal allocation of human talent, AI tools, and infrastructure resources.

## Resource Categories

### Human Resources

#### Core Development Team
- **Senior AI-Assisted Developers**: 2-3 FTE
  - Proficient in Claude Code and AI pair programming
  - Experience in prompt engineering and AI workflow optimization
  - Strong foundation in traditional development practices
  
- **AI Integration Specialists**: 1-2 FTE
  - Focus on AI tool integration and optimization
  - Responsible for prompt template development
  - Monitor AI tool performance and effectiveness

- **Quality Assurance Engineers**: 1-2 FTE
  - Specialized in testing AI-assisted development outputs
  - Develop testing strategies for AI-generated code
  - Maintain quality standards across AI-enhanced workflows

#### Supporting Roles
- **Technical Product Manager**: 0.5 FTE
  - AI-aware product planning and roadmap management
  - Stakeholder communication on AI capabilities
  - ROI tracking for AI tool investments

- **DevOps Engineer**: 0.5 FTE
  - CI/CD pipeline integration with AI tools
  - Infrastructure scaling for AI workloads
  - Performance monitoring and optimization

### AI Tool Resources

#### Primary AI Tools
```yaml
AI_Tools:
  Claude_Code:
    Subscription: Enterprise
    Users: 5
    Monthly_Cost: $500
    Usage_Limits: Unlimited
    
  GitHub_Copilot:
    Subscription: Business
    Users: 5
    Monthly_Cost: $195
    Usage_Limits: Standard
    
  Additional_LLM_APIs:
    OpenAI_GPT4: $200/month estimated
    Anthropic_Claude: $150/month estimated
    Local_Models: Infrastructure cost
```

#### Tool Specialization Matrix
| Tool | Code Generation | Documentation | Testing | Architecture | Debugging |
|------|----------------|---------------|---------|-------------|-----------|
| Claude Code | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐ |
| GitHub Copilot | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐ | ⭐⭐⭐ |
| GPT-4 | ⭐⭐⭐ | ⭐⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ | ⭐⭐⭐ |

### Infrastructure Resources

#### Development Environment
- **Workstations**: High-performance development machines
  - 32GB RAM minimum for AI tool performance
  - NVMe SSD storage for fast file operations
  - Multiple monitor setup for AI-assisted workflows

- **Cloud Infrastructure**:
  - AWS/GCP instances for AI model hosting
  - Dedicated GPU instances for local model inference
  - High-bandwidth connections for AI API calls

#### Storage and Compute
- **Project Storage**: 1TB cloud storage per project
- **AI Model Storage**: 500GB for local models
- **Compute Budget**: $2000/month for cloud AI services
- **Backup Systems**: Automated backup for all AI-generated artifacts

## Resource Allocation Strategy

### Sprint-Based Allocation

#### Sprint Planning Phase (Week 0)
```yaml
Resource_Allocation:
  Senior_Developers: 
    - Story analysis and breakdown: 40%
    - AI prompt preparation: 30% 
    - Architecture planning: 30%
  
  AI_Tools:
    - Claude Code: Requirements analysis
    - GPT-4: User story refinement
    - Local Models: Technical feasibility assessment
```

#### Development Phase (Weeks 1-2)
```yaml
Resource_Allocation:
  Senior_Developers:
    - AI-assisted coding: 60%
    - Code review and refinement: 25%
    - Documentation: 15%
  
  QA_Engineers:
    - Test strategy development: 40%
    - AI-generated test review: 35%
    - Manual testing: 25%
  
  AI_Tools:
    - Claude Code: Primary development assistant
    - GitHub Copilot: Code completion and suggestions
    - Testing AI: Automated test generation
```

#### Review and Integration Phase (Week 3)
```yaml
Resource_Allocation:
  Team_Focus:
    - Integration testing: 40%
    - Performance optimization: 30%
    - Documentation finalization: 20%
    - Sprint review preparation: 10%
```

### Project Phase Resource Planning

#### Discovery Phase (Months 1-2)
- **Human Resources**: 70% allocation
- **AI Tools**: 30% utilization
- **Focus**: Requirements gathering, feasibility analysis
- **Deliverables**: Project specifications, technical architecture

#### Development Phase (Months 3-8)
- **Human Resources**: 90% allocation
- **AI Tools**: 80% utilization
- **Focus**: Feature development, testing, integration
- **Deliverables**: Working software, comprehensive tests

#### Optimization Phase (Months 9-10)
- **Human Resources**: 60% allocation
- **AI Tools**: 90% utilization
- **Focus**: Performance tuning, bug fixes, polish
- **Deliverables**: Production-ready software

## Capacity Planning

### Team Capacity Metrics
```yaml
Weekly_Capacity:
  Senior_Developer: 32 hours productive time
  AI_Integration_Specialist: 35 hours productive time
  QA_Engineer: 30 hours productive time
  
AI_Productivity_Multipliers:
  Code_Generation: 2.5x faster
  Documentation: 3x faster
  Test_Creation: 2x faster
  Bug_Investigation: 1.8x faster
```

### Capacity Constraints
- **AI API Rate Limits**: Monitor daily usage patterns
- **Human Context Switching**: Limit concurrent AI interactions
- **Tool Learning Curve**: Account for 2-week ramp-up time
- **Quality Assurance**: 25% overhead for AI-generated code review

## Cost Management

### Budget Allocation
```yaml
Monthly_Budget: $15000
  Salaries: $12000 (80%)
  AI_Tools: $1045 (7%)
  Infrastructure: $1500 (10%)
  Training: $455 (3%)

Annual_Projections:
  Year_1: $180000
  Year_2: $195000 (8% growth)
  Year_3: $210000 (8% growth)
```

### Cost Optimization Strategies
1. **AI Tool Utilization Monitoring**
   - Track usage patterns and optimize subscriptions
   - Identify underutilized tools for cost reduction
   - Negotiate volume discounts with AI providers

2. **Infrastructure Right-Sizing**
   - Monitor compute usage and scale appropriately
   - Use spot instances for non-critical AI workloads
   - Implement automatic scaling policies

3. **Training Investment ROI**
   - Measure productivity gains from AI training
   - Calculate time-to-competency for new tools
   - Focus training on highest-impact areas

## Risk Assessment and Mitigation

### Resource Risks
| Risk | Impact | Probability | Mitigation Strategy |
|------|--------|-------------|-------------------|
| AI tool service disruption | High | Low | Multiple tool redundancy |
| Key developer unavailability | High | Medium | Cross-training, documentation |
| Budget overrun on AI costs | Medium | Medium | Usage monitoring, alerts |
| AI tool performance degradation | Medium | Low | Performance baselines, alternatives |

### Mitigation Plans
1. **Tool Redundancy**: Maintain 2+ AI tools for critical functions
2. **Documentation**: Comprehensive knowledge base for all AI workflows
3. **Backup Plans**: Manual development procedures for AI outages
4. **Cost Controls**: Automated spending alerts and usage caps

## Performance Monitoring

### Key Performance Indicators
- **Development Velocity**: Story points per sprint with AI assistance
- **AI Tool Utilization**: Hours of productive AI assistance per week
- **Quality Metrics**: Defect rates in AI-assisted vs manual code
- **Cost Efficiency**: Development cost per feature with AI tools

### Reporting Schedule
- **Daily**: AI tool usage and availability status
- **Weekly**: Sprint progress and resource utilization
- **Monthly**: Cost analysis and budget variance
- **Quarterly**: ROI assessment and tool effectiveness review

## Scaling Strategies

### Team Scaling
```yaml
Phase_1: 5 team members (Current)
  - Establish AI workflows
  - Build competency
  - Refine processes

Phase_2: 8 team members (+3)
  - Add specialized AI roles
  - Scale successful patterns
  - Expand AI tool usage

Phase_3: 12 team members (+4)
  - Multiple AI-assisted teams
  - Advanced AI integration
  - Tool optimization specialists
```

### Tool Scaling
- **Horizontal Scaling**: Add more AI tool subscriptions
- **Vertical Scaling**: Upgrade to higher-tier AI services
- **Integration Scaling**: Custom AI tool integrations
- **Optimization Scaling**: Fine-tuned models for specific tasks

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*This resource planning framework ensures efficient allocation and utilization of all resources while maximizing the benefits of AI-assisted development practices.*