📋 90-Day AI Implementation Checklist
Complete step-by-step implementation guide with milestones, deliverables, and success metrics. Track your progress and ensure nothing gets missed.
📊 Implementation Progress
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Phase 1: Planning & Assessment
Foundation and strategy development
Days 1-14
Week 1: Initial Assessment & Planning
Establish project foundation and gather requirements
Project Kickoff Meeting
Assemble project team and define roles and responsibilities
Duration: 2 hours | Participants: Leadership team, key stakeholders
Business Requirements Analysis
Document current processes and identify improvement opportunities
Duration: 3-5 days | Deliverable: Requirements document
Technology Infrastructure Audit
Assess current systems, APIs, and integration capabilities
Duration: 2-3 days | Deliverable: Technical assessment report
Data Quality Assessment
Evaluate data sources, quality, and accessibility for AI training
Duration: 2-3 days | Deliverable: Data quality report
Success Metrics Definition
Define KPIs and measurement criteria for project success
Duration: 1 day | Deliverable: Metrics framework
🎯 Milestone: Assessment Complete
All assessments completed, requirements documented, and project scope finalized
Week 2: Solution Design & Planning
Design AI solution architecture and create implementation plan
AI Solution Architecture Design
Design technical architecture and integration points
Duration: 3-4 days | Deliverable: Architecture diagram
Implementation Roadmap Creation
Create detailed timeline with milestones and dependencies
Duration: 2 days | Deliverable: Project roadmap
Risk Assessment & Mitigation Plan
Identify potential risks and develop mitigation strategies
Duration: 1-2 days | Deliverable: Risk register
Resource Allocation & Budget Approval
Finalize budget and secure necessary resources
Duration: 1-2 days | Deliverable: Approved budget
Stakeholder Communication Plan
Define communication strategy and update schedule
Duration: 1 day | Deliverable: Communication plan
Phase 2: Development & Integration
Build and integrate AI solutions
Days 15-56
Weeks 3-4: Infrastructure Setup
Prepare technical infrastructure and data pipelines
Cloud Infrastructure Setup
Configure cloud services and computing resources
Duration: 3-5 days | Platform: AWS/Azure/GCP
Data Pipeline Development
Build automated data collection and preprocessing pipelines
Duration: 5-7 days | Deliverable: Data pipeline
Security & Compliance Configuration
Implement security measures and compliance requirements
Duration: 2-3 days | Compliance: Industry standards
API Integration Framework
Establish connections with existing business systems
Duration: 3-4 days | Integration: ERP, CRM, etc.
Weeks 5-6: AI Model Development
Develop and train AI models for specific use cases
Data Preprocessing & Feature Engineering
Clean and prepare data for AI model training
Duration: 4-5 days | Quality: 95%+ accuracy target
AI Model Training & Validation
Train models and validate performance against requirements
Duration: 5-7 days | Target: >90% accuracy
Model Optimization & Tuning
Optimize model performance and resource usage
Duration: 2-3 days | Focus: Speed and accuracy
Model Testing & Quality Assurance
Comprehensive testing with edge cases and error handling
Duration: 2-3 days | Testing: 100+ scenarios
🤖 Milestone: AI Models Ready
AI models developed, tested, and validated for production deployment
Weeks 7-8: Application Development
Build user interfaces and business logic
User Interface Development
Create intuitive interfaces for AI system interaction
Duration: 5-7 days | Focus: User experience
Business Logic Implementation
Implement workflow automation and business rules
Duration: 4-5 days | Integration: Existing processes
Monitoring & Alerting System
Implement real-time monitoring and notification systems
Duration: 2-3 days | Coverage: 24/7 monitoring
Integration Testing
Test end-to-end integration with existing systems
Duration: 2-3 days | Coverage: All integrations
Phase 3: Deployment & Training
Deploy to production and train users
Days 57-77
Week 9: Production Deployment
Deploy AI system to production environment
Production Environment Setup
Configure production infrastructure and security
Duration: 2-3 days | Environment: Production-ready
Staged Deployment
Deploy in phases to minimize risk and enable rollback
Duration: 2-3 days | Strategy: Blue-green deployment
Performance Testing
Test system performance under production load
Duration: 1-2 days | Load: Expected peak usage
Backup & Recovery Testing
Verify backup systems and disaster recovery procedures
Duration: 1 day | RTO: <4 hours, RPO: <1 hour
🎉 Milestone: Production Live
AI system successfully deployed and running in production environment
Weeks 10-11: User Training & Change Management
Train users and manage organizational change
Training Material Development
Create user manuals, videos, and training guides
Duration: 3-4 days | Materials: Complete training package
User Training Sessions
Conduct comprehensive training for all user groups
Duration: 5-7 days | Coverage: 100% of users
Super User Certification
Train and certify internal champions and support staff
Duration: 2-3 days | Certification: Internal experts
Change Management Activities
Communicate benefits and address user concerns
Duration: Ongoing | Focus: User adoption
Support Desk Setup
Establish help desk and support procedures
Duration: 2 days | Availability: Business hours
Phase 4: Optimization & Scaling
Optimize performance and plan for scale
Days 78-90
Week 12: Performance Optimization
Monitor performance and optimize system efficiency
Performance Metrics Analysis
Analyze system performance against baseline metrics
Duration: 2-3 days | Analysis: Comprehensive review
Model Retraining & Optimization
Retrain models with production data for improved accuracy
Duration: 2-3 days | Target: Improved performance
System Optimization
Optimize system configuration and resource allocation
Duration: 2 days | Focus: Speed and efficiency
User Feedback Integration
Collect and integrate user feedback for improvements
Duration: 1-2 days | Feedback: All user groups
Week 13: Scaling & Future Planning
Plan for scaling and identify next opportunities
Scaling Strategy Development
Plan for increased usage and additional use cases
Duration: 2-3 days | Strategy: Growth planning
ROI Assessment & Business Case
Measure actual ROI and build case for expansion
Duration: 2 days | Target: Quantified benefits
Next Phase Planning
Identify and prioritize next AI implementation opportunities
Duration: 2 days | Output: Future roadmap
Documentation & Knowledge Transfer
Complete documentation and transfer knowledge to internal team
Duration: 1-2 days | Deliverable: Complete documentation
Project Closure & Celebration
Formal project closure and team celebration
Duration: 1 day | Milestone: Project completion
🏆 Milestone: Implementation Complete
AI system fully optimized, scaled, and ready for ongoing operations
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