Project Delivery Leaders Shift-Left Checklist

Purpose: Transform from ticket coordinators → AI-powered solution architects by producing executable specifications and working prototypes before development starts.

Pre-Sprint Planning (Both Legacy & AI Apps)

System Understanding

• Generate comprehensive system documentation using AI analysis of existing codebases
• Create architectural diagrams with current data flows and dependencies
• Identify technical debt hotspots with AI-generated priority rankings and remediation costs
• Map external system dependencies with API specifications and failure scenarios
• Document performance baselines and capacity constraints for existing systems

Specification Creation

• Write acceptance criteria as executable prompts (include API payloads, UI states, error handling)
• Generate working prototypes for core functionality using AI tools
• Create integration specifications with sample requests, responses, and error codes
• Design data migration scripts and validation procedures (for legacy integrations)
• Establish monitoring and observability requirements with specific metrics and alerts

AI-Specific Pre-Sprint Planning

Model & Prompt Management

• Create prompt libraries with versioned templates for core AI functionality
• Define model performance benchmarks (accuracy, latency, cost per operation)
• Generate comprehensive test datasets covering edge cases and bias scenarios
• Establish safety guardrails (content filtering, privacy protection, hallucination detection)
• Design A/B testing frameworks for prompt optimization and model comparison

AI Integration Architecture

• Generate end-to-end workflow simulations using multi-agent scenarios
• Create cost estimation models based on token usage and processing volume
• Design fallback procedures for AI service failures or performance degradation
• Establish compliance frameworks (GDPR, HIPAA, bias auditing) with automated checks
• Plan continuous learning pipelines for model improvement and retraining triggers

Legacy-Specific Pre-Sprint Planning

Legacy System Analysis

• Perform automated code analysis to identify refactoring opportunities and security vulnerabilities
• Generate data quality assessments with cleansing scripts and migration procedures
• Create integration compatibility matrices showing system interaction patterns and constraints
• Design strangler fig migration strategies with incremental replacement timelines
• Establish rollback procedures and compatibility testing for each legacy integration

Modernization Planning

• Generate API specifications for legacy system modernization with backward compatibility
• Create performance optimization recommendations based on current system bottlenecks
• Design monitoring dashboards for legacy system health and integration status
• Plan incremental deployment strategies with feature flags and traffic routing
• Document knowledge transfer procedures for maintaining AI-generated documentation

During Sprint Execution

Continuous Validation

• Monitor prototype performance against established benchmarks and adjust specifications
• Update living documentation as system changes are implemented
• Validate integration points using AI-generated test scenarios and edge case coverage
• Track technical debt metrics and prioritize remediation based on business impact
• Review AI model performance and optimize prompts based on real-world usage patterns

Team Support

• Provide just-in-time specifications when developers encounter edge cases or ambiguities
• Generate troubleshooting guides for common integration and deployment issues
• Create onboarding materials for new team members using AI-generated system summaries
• Facilitate knowledge sharing through searchable prompt libraries and prototype repositories
• Coordinate stakeholder demonstrations using working prototypes rather than static presentations

Post-Sprint Review & Optimization

Performance Analysis

• Analyze delivery metrics (velocity, defect rates, rework cycles) and identify improvement opportunities
• Review AI model performance in production and update benchmarks based on real usage
• Assess technical debt evolution and update prioritization based on system changes
• Evaluate integration stability and refine monitoring alerts and escalation procedures
• Document lessons learned and update prompt libraries and specification templates

Continuous Improvement

• Refine AI prompts and templates based on development team feedback and outcomes
• Update architectural documentation to reflect system changes and new integration patterns
• Optimize prototype generation processes to reduce time from concept to working demo
• Enhance team training materials with new AI tools and delivery techniques
• Plan knowledge transfer sessions to share successful patterns across delivery teams

Critical Success Indicators

Delivery Acceleration

• Development teams receive executable specifications rather than abstract requirements
• Zero ambiguity tickets: Every story includes working examples and clear success criteria
• Prototype-first planning: Major features begin with AI-generated working demos
• Living documentation: System specifications update automatically as code changes
• Predictive issue identification: Problems surface during planning, not during development

Quality Assurance

• Comprehensive test coverage: AI-generated test scenarios cover functional and edge cases
• Performance validation: System capacity and response time requirements verified before coding
• Security assessment: Vulnerability scanning and compliance checks integrated into specifications
• Integration testing: Cross-system interactions validated through AI-generated scenarios
• Rollback readiness: Every deployment includes tested procedures for safe reversion

Emergency Protocols

When AI Tools Fail

• Maintain manual fallback procedures for critical specification generation tasks
• Keep template libraries for common patterns when AI generation is unavailable
• Have alternative AI providers configured for essential prototype generation workflows
• Document manual override processes for time-sensitive delivery commitments

When Prototypes Mislead

• Establish validation checkpoints to verify prototype accuracy against real system constraints
• Create feedback loops between development teams and prototype generation processes
• Maintain version control for all generated specifications and prototypes
• Design quick pivot procedures when prototypes reveal incorrect assumptions

Reminder: If delivery leaders hand off tickets without executable prompts, working prototypes, and comprehensive specifications, the story isn't ready for development. Shift left = no development without AI-generated validation and concrete examples.