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The AI Delivery Methodology is a comprehensive, battle-tested framework designed to transform AI initiatives from executive vision to measurable business value. Born from real-world enterprise AI implementations, this methodology addresses the critical gap between AI's promise and its practical delivery in complex organizational environments.
Most AI projects fail not because of technology, but because of execution gaps:
- 70% of AI/ML projects never make it to production (Gartner)
- Organizations struggle to bridge the gap between business vision and technical implementation
- Lack of structured approach leads to scope creep, missed expectations, and value erosion
- Technical teams and business stakeholders speak different languages
- Governance, ethics, and risk management are afterthoughts rather than built-in
This methodology provides a proven, repeatable framework that:
🎯 Aligns Business & Technology
- Starts with business problems, not technology solutions
- Uses the Five Whys technique to uncover root causes, ensuring AI addresses fundamental issues, not symptoms
- Connects every technical decision back to measurable business outcomes
- Speaks both executive and technical languages fluently
📊 Delivers Measurable Value
- ROI-focused from day one with clear financial projections
- Success metrics defined before writing code
- Value tracking at every milestone
- Quick wins balanced with strategic long-term transformation
🛡️ Mitigates Risk Through Governance
- AI ethics and responsible AI principles built into every phase
- Regulatory compliance (GDPR, industry-specific) addressed early
- Risk assessment and mitigation at every gate
- Change management integrated throughout, not bolted on
🚀 Accelerates Time to Production
- Structured phases prevent common pitfalls and rework
- 200+ pre-built checklists ensure nothing is missed
- Ready-to-use templates eliminate starting from scratch
- Clear decision gates prevent projects from drifting
👥 Engages C-Level Stakeholders
- Executive coaching guide with 5 persona-based strategies (Visionary, Steward, Realist, People Leader, Compliance Guardian)
- 55-point readiness assessment to evaluate organizational preparedness
- Executive decision frameworks for investment approval with ROI scenarios
- Competitive intelligence and market positioning analysis
- Board-ready presentations and decision memos
- Strategic options evaluation (build vs. buy vs. partner)
- Objection handling for 7 common executive concerns
AI Delivery Teams: Project managers, technical architects, data scientists, and ML engineers executing AI initiatives
Microsoft Partners & SIs: Organizations delivering AI solutions to clients using Microsoft Azure AI platform
Enterprise Leaders: CIOs, CTOs, and business executives sponsoring AI transformation programs
Consultants & Advisors: Professionals guiding organizations through AI adoption and implementation
✅ Comprehensive Yet Practical: Covers the complete lifecycle without overwhelming teams with unnecessary process
✅ Template-Driven: 17+ ready-to-use templates eliminate guesswork and accelerate delivery
✅ Checklist-Powered: 200+ actionable items ensure disciplined execution without micromanagement
✅ Industry-Proven: Includes 100+ real-world use cases across Financial Services, Energy & Mining, Retail, Defense, and Public Sector
✅ Executive-Focused: Comprehensive coaching materials, readiness assessments, and C-level engagement frameworks
✅ Root Cause-Oriented: Five Whys analysis ensures solutions address fundamental problems, not just symptoms
✅ Cloud-Native: Optimized for Microsoft Azure AI services (Azure Machine Learning, Azure OpenAI, Cognitive Services)
We offer two delivery tracks to match your project needs:
Perfect for single use cases, POCs, and rapid value delivery. Compressed timeline with essential activities only.
- Timeline: 12 weeks
- Team: 5-8 people
- Scope: Single use case
- Best For: Pilots, POCs, quick wins, building momentum
→ See 3-Month Fast-Track Guide
Comprehensive approach for production systems, multiple use cases, and enterprise-wide transformation.
Our methodology guides you through nine structured phases:
1. Pre-sales & Discovery → Qualify opportunities, understand business context, identify root causes
2. Mobilise → Establish governance, assemble teams, set foundations
3. Hackathons (Prototype) → Rapid validation, proof of concepts, fail fast learning
4. Setup Platform → Infrastructure, MLOps, security, compliance foundations
5. Build → Agile development with continuous stakeholder engagement
6. Integrate → System integration, data pipelines, API development
7. Test & Evaluate → Comprehensive QA, bias testing, performance validation
8. Prepare & Deploy → Production readiness, rollout planning, training
9. Operate & Care → Monitoring, optimization, continuous improvement
Underpinned by: 360° Governance | Execution Strategy | Change Management | Value Tracking
Comparison:
| Aspect | Fast-Track | Full Methodology |
|---|---|---|
| Timeline | 3 months | 6-12+ months |
| Scope | Single use case | Multiple use cases |
| Team | 5-8 people | 10-20 people |
| Best For | Pilots, POCs | Production systems |
This is a living methodology that evolves with:
- Emerging AI technologies and capabilities
- Lessons learned from real implementations
- Industry best practices and standards
- Community feedback and contributions
We believe AI should be ethical, explainable, and valuable—not just technically impressive.
One of the most critical success factors for AI initiatives is executive engagement and alignment. Our methodology includes comprehensive coaching materials to help consultants build trusted advisor relationships with C-level stakeholders.
Executive Coaching Guide - A complete framework covering:
- 5 Executive Personas with tailored coaching strategies for each stakeholder type
- 7-Phase Journey Coaching from initial engagement through scaling
- Objection Handling for 7 common executive concerns with proven responses
- Trust-Building Techniques using The Trust Equation
- Communication Templates for status updates, escalations, and presentations
Executive Readiness Assessment - A 55-point assessment tool evaluating:
- Strategic Clarity - Vision alignment and problem articulation (15 points)
- Executive Commitment - Leadership engagement and support (10 points)
- Decision-Making Readiness - AI understanding and decision pace (10 points)
- Risk Tolerance & Governance - Comfort with experimentation and ethics (10 points)
- Change Leadership - Change management capability and track record (10 points)
Assessment Outcomes:
- 45-55 points: Strong readiness - proceed with confidence
- 35-44 points: Moderate readiness - address gaps before scaling
- 25-34 points: Low readiness - significant preparation needed
- <25 points: Not ready - defer until fundamentals addressed
These materials help Microsoft consultants navigate organizational politics, manage executive expectations, and ensure AI initiatives have the sustained leadership support required for success.
This repository contains comprehensive templates, guides, and documentation for delivering AI projects following Microsoft's AI Frontier delivery methodology. The methodology covers the complete lifecycle from Vision to Value with 360° governance, execution strategy, outcomes focus, and continuous change management.
The methodology follows a structured approach with clear phase gates and outcomes-driven milestones:
- Pre-sales & Discovery - Qualify opportunities and understand requirements
- Mobilise and Initiate - Set up the project foundation
- Hackathons (Prototype) - Rapid prototyping and validation
- Setup Platform - Set the platform, infrastructure components for build
- Build - Build the solution
- Integrate - Integrate the solution with the rest of ecosystem
- Test & Evaluate - Comprehensive testing and quality assurance
- Prepare and Deploy - Production deployment and scaling
- Operate and Care - Ongoing operations and optimization
Underpinned by 360° Governance and Execution, Strategy, Ops Planning and Change Management
AI-Delivery-Methodology/
├── agents/ # 🤖 AI agents for delivery methodology tasks
│ ├── value_analysis_agent.py # Value analysis and ROI agent
│ ├── value_calculator.py # Advanced financial calculations
│ ├── example_usage.py # Pre-built industry examples
│ ├── README.md # Comprehensive documentation
│ └── QUICKSTART.md # 5-minute quick start guide
├── calculators/ # 🧮 Interactive web calculators (hosted on GitHub Pages)
│ ├── value-analysis-chatbot.html # AI-powered value analysis chatbot
│ ├── roi-calculator.html # ROI, NPV, and payback analysis tool
│ ├── effort-estimator.html # Effort estimation with story points
│ ├── css/ # Styling for calculators
│ ├── js/ # Calculator logic
│ └── GITHUB-PAGES-SETUP.md # Deployment guide
├── templates/ # Ready-to-use templates for all deliverables
├── checklists/ # Phase-specific checklists with 200+ actionable items
├── guides/ # Detailed implementation guides
├── infrastructure/ # 🏗️ Azure infrastructure-as-code (Bicep templates)
│ ├── bicep/ # Infrastructure modules and parameters
│ │ ├── modules/ # Static Web Apps, AI services, compute, data, security
│ │ └── main.bicep # Main deployment template
│ ├── scripts/ # Deployment automation (PowerShell/Bash)
Purpose: Automate comprehensive value analysis for AI projects with interactive CLI-based agent
🚀 Quick Start Guide | 📖 Full Documentation
Key Features:
- Interactive Value Analysis - Guided CLI interface for use case evaluation
- ROI Calculation - NPV, IRR, payback period, profitability metrics
- Use Case Prioritization - Value vs. effort scoring with recommendations
- Five Whys Analysis - Interactive root cause analysis technique
- Risk-Adjusted Modeling - Confidence weighting and sensitivity analysis
- Comprehensive Reporting - JSON exports and stakeholder-ready reports
Quick Start:
cd agents
python value_analysis_agent.pyPre-Built Examples:
- Financial Services: Fraud Detection System
- Retail: Demand Forecasting + Personalized Recommendations
- Manufacturing: Predictive Maintenance
python example_usage.pyUse Cases:
- Business Envisioning Workshops - Prioritize use cases by value
- Business Case Development - Quantify financial benefits
- Portfolio Management - Rank initiatives for roadmap planning
- Benefit Tracking - Monitor value realization vs. projections
Integration:
- Complements Business Case Template
- Supports Business Envisioning Workshop
- Aligns with Use Case Template
- Works with Value Analysis Chatbot for conversational analysis
- Pairs with ROI Calculator for quick single-use-case analysis
All calculators run entirely in your browser—100% private, no data sent to any server.
Interactive AI assistant that guides you through comprehensive value analysis:
- Conversational Interface - Natural chat flow for use case analysis
- Multi-Use Case Analysis - Analyze multiple initiatives in one session
- Five Whys Root Cause - Interactive problem exploration technique
- Real-Time ROI Calculations - NPV, IRR, payback period
- Value Matrix Prioritization - Visual 2x2 matrix with recommendations
- JSON Export - Download complete analysis for documentation
Use For: Business envisioning workshops, use case prioritization, stakeholder alignment
Calculate comprehensive ROI metrics for your AI projects:
- Net Present Value (NPV) - Time-value adjusted returns
- ROI Percentage - Return on investment calculation
- Payback Period - Time to break even
- 5-Year Projections - Long-term financial forecasting
- Benefit Growth Modeling - Compound annual growth tracking
- Sensitivity Analysis - Test different scenarios
Use For: Business cases, executive approvals, budget justification, comparing initiatives
Convert project scope into accurate effort estimates:
- Journey Phase Selection - Discovery, MVP, Pilot, Production, Enterprise
- Industry-Standard Story Points - Auto-calculated by complexity (3-13 points)
- Team Velocity Calculation - Based on FTE and complexity (4-8 points/sprint/FTE)
- Fixed 2-Week Sprints - Industry standard sprint duration
- Risk-Adjusted Estimates - Low (4%), Medium (7%), High (15%) buffers
- Phase Breakdown - Discovery, Development, Testing, Deployment
- Team Composition - Suggested roles and sizing
- Sprint Timeline - Total sprints, weeks, and months
Use For: Project planning, sprint planning, resource allocation, timeline estimation
View Calculator Documentation →
The calculators are already deployed on GitHub Pages at no cost. Perfect for static HTML/CSS/JS tools.
Quick Setup:
- Enable GitHub Pages in repository settings
- Push to main branch
- Auto-deploys via GitHub Actions
See GitHub Pages Setup Guide →
Deploy to Azure for advanced capabilities like authentication, APIs, and custom domains.
Features:
- Free tier available ($0/month)
- Custom domains with automatic SSL
- Global CDN distribution
- Staging environments for testing
- Integration with Azure services
Quick Deploy:
cd infrastructure/scripts
.\deploy-calculators.ps1Enterprise-grade infrastructure with compliance, security, and MLOps built-in.
Includes:
- Azure Machine Learning workspaces
- Azure OpenAI Service
- Compute clusters for training
- Private networking and security
- Compliance controls (GDPR, SOC 2, ISO 27001)
- Monitoring and observability
Deploy with Bicep:
cd infrastructure/bicep
az deployment subscription create \
--template-file main.bicep \
--parameters @parameters/prod.jsonSee Infrastructure Documentation →
│ ├── policies/ # Azure Policy definitions │ └── docs/ # Infrastructure documentation └── README.md # This file
---
## � Choose Your Delivery Track
Before diving into phase details, select the right approach for your project:
### **Fast-Track (3 Months)** - Rapid Value Delivery
**Use When:**
- Single, well-defined use case
- Pilot or proof-of-concept
- Quick win needed to build momentum
- Data readily available
- Limited budget
**Key Features:**
- 12-week compressed timeline
- 5-8 person team
- Essential activities only
- Defer advanced features to Phase 2
[**📖 Read Full 3-Month Fast-Track Guide →**](./guides/3-month-fast-track-guide.md)
---
### **Full Methodology (6-12+ Months)** - Enterprise Scale
**Use When:**
- Multiple use cases
- Production-grade system
- Enterprise-wide transformation
- Complex integrations (5+ systems)
- Large budget
**Key Features:**
- Comprehensive 9-phase lifecycle
- 10-20 person team
- 200+ checklists, 20+ templates
- Full MLOps, governance, scaling
**Continue reading below for detailed phase-by-phase guidance →**
---
## �📋 Available Materials by Phase
### � **Phase 0: Pre-sales & Discovery**
**Purpose**: Qualify AI opportunities and deeply understand business requirements
**Duration**: 2-4 weeks
**Templates**:
- [Pre-sales Qualification Framework](./templates/11-presales-qualification.md) - 5-dimensional scoring system (50 points)
- [Business Requirements Document (BRD)](./templates/09-business-requirements-document.md) - Comprehensive requirements capture
- [Data Assessment Report](./templates/10-data-assessment-report.md) - Complete data quality & readiness assessment
- [Business Envisioning Pre-Work](./templates/13-business-envisioning-pre-work.md) - Pre-workshop assignment with executive investment & risk tolerance questions
- [AI Use Case Template](./templates/14-business-envisioning-use-case-template.md) - Comprehensive use case documentation
- [Executive Decision Memo](./templates/15-executive-decision-memo.md) - Board-ready investment decision template with ROI, risks, and governance
- [Executive Readiness Assessment](./templates/16-executive-readiness-assessment.md) - 55-point assessment across 5 dimensions of organizational readiness
**Checklists**:
- [Discovery Phase Checklist](./checklists/discovery-checklist.md) - 200+ items covering 4 weeks of activities
- [Business Envisioning Workshop Checklist](./checklists/business-envisioning-workshop-checklist.md) - Complete workshop preparation and execution guide
**Guides**:
- [Business Envisioning Workshop Guide](./guides/business-envisioning-workshop-guide.md) - Full-day workshop facilitation guide with detailed activities, **now includes Five Whys root cause analysis technique**
- [Executive Coaching Guide](./guides/executive-coaching-guide.md) - **NEW!** Comprehensive guide for coaching C-level stakeholders through AI transformation
**Industry Use Case Libraries**:
- [Financial Services Use Cases](./guides/industry-use-cases/financial-services-use-cases.md) - 24 use cases across customer experience, risk, fraud, trading
- [Energy & Mining Use Cases](./guides/industry-use-cases/energy-mining-use-cases.md) - 25 use cases across operations, safety, exploration, trading
- [Retail Use Cases](./guides/industry-use-cases/retail-use-cases.md) - 20 use cases across personalization, inventory, pricing, store ops
- [Defense Use Cases](./guides/industry-use-cases/defense-use-cases.md) - 16 use cases across ISR, mission planning, logistics, cybersecurity
- [Public Sector Use Cases](./guides/industry-use-cases/public-sector-use-cases.md) - 22 use cases across citizen services, public safety, infrastructure, education
**Sequential Steps**:
**Week 0 (Pre-Discovery)**:
1. **Pre-sales Qualification** - Score opportunity (Business Value, Technical Feasibility, Data Readiness, Client Readiness, Commercial)
2. **Executive Readiness Assessment** - Assess organizational and executive readiness (55-point assessment)
3. **Go/No-Go Decision** - Must score ≥35/50 points (qualification) and address any critical readiness gaps
4. **Stakeholder Identification** - Map key stakeholders and decision-makers
5. **Executive 1-on-1 Interviews** - Conduct 60-90 min interviews with C-level stakeholders
6. **Pre-Work Distribution** - Send Business Envisioning pre-work assignment to participants
**Week 1 (Business Alignment)**:
5. **Business Envisioning Workshop** - Full-day workshop with executives and business stakeholders
- Strategic context and vision alignment
- Business problem exploration with **Five Whys root cause analysis**
- AI use case ideation and prioritization
- Success criteria definition
- Roadmap planning
6. **Use Case Selection** - Select 3-5 priority use cases for detailed analysis
7. **Stakeholder Interviews** - Conduct 1:1 interviews with business owners and subject matter experts
8. **Business Requirements Workshops** - Facilitate detailed requirements gathering sessions
9. **Complete Business Requirements Document** - Document functional and non-functional requirements
**Week 2 (Technical Validation)**:
10. **Data Discovery** - Identify and catalog available data sources
11. **Data Quality Assessment** - Evaluate data across 6 dimensions (availability, quality, volume, variety, velocity, veracity)
12. **Complete Data Assessment Report** - Document findings with data readiness scores
13. **AI/ML Approach Design** - Design ML models, algorithms, and approach
14. **Solution Architecture Workshop** - Design technical architecture with technical stakeholders
15. **Integration Requirements** - Identify integration points with existing systems
**Week 3 (Feasibility & Design)**:
16. **Technical Feasibility POC** (Optional) - Build quick proof-of-concept to validate approach
17. **Solution Architecture Documentation** - Document detailed architecture (infrastructure, security, data flow)
18. **Privacy & Compliance Assessment** - Conduct GDPR/DPIA assessment, identify compliance requirements
19. **Risk Identification** - Identify technical, data, and business risks
20. **Initial Business Case Development** - Preliminary ROI and cost-benefit analysis
**Week 4 (Synthesis & Recommendation)**:
21. **Discovery Report Compilation** - Synthesize all findings and recommendations
22. **Executive Readout** - Present findings to executive sponsors
23. **Refinement Based on Feedback** - Incorporate feedback from executives
24. **Phase Gate Review** - Steering committee reviews all deliverables
25. **Go/No-Go Decision** - Decision to proceed to Mobilisation phase
**Deliverables**:
1. ✅ Opportunity Qualification Report (Pre-sales)
2. ✅ Business Envisioning Workshop Output (Use cases, prioritization)
3. ✅ Business Requirements Document (BRD)
4. ✅ Data Assessment Report
5. ✅ Solution Architecture Document
6. ✅ ML Approach Document
7. ✅ Privacy & Compliance Assessment (GDPR/DPIA)
8. ✅ Initial Business Case
9. ✅ Discovery Report with recommendations
**Exit Criteria**:
- [ ] Business requirements documented and approved by stakeholders
- [ ] At least one use case with clear business value identified
- [ ] Data sufficiency confirmed (≥7/10 data readiness score)
- [ ] Technical feasibility validated (architecture feasible, no blocking technical issues)
- [ ] Risks identified and manageable (no high/critical unmitigatable risks)
- [ ] Executive sponsorship confirmed
- [ ] Steering committee approval to proceed
---
### � **Phase 1: Mobilise and Initiate**
**Purpose**: Establish project foundation, governance, and team
**Duration**: 2-3 weeks
**Templates**:
- [Project Charter](./templates/01-project-charter.md) - Project authorization and scope
- [Business Case](./templates/02-business-case.md) - Financial justification with ROI calculations
- [RACI Matrix](./templates/03-raci-matrix.md) - Roles and responsibilities across all phases
- [Project Plan & Roadmap](./templates/04-project-plan-roadmap.md) - Detailed timeline and milestones
- [Risk Register](./templates/05-risk-register.md) - 18 pre-identified risks with mitigation
- [Communication Plan](./templates/06-communication-plan.md) - Stakeholder engagement framework
- [Success Criteria & KPIs](./templates/07-success-criteria-kpis.md) - Business, technical, and adoption metrics
- [Stakeholder Analysis](./templates/08-stakeholder-analysis.md) - Stakeholder mapping and engagement
**Checklists**:
- [Mobilisation Checklist](./checklists/mobilisation-checklist.md) - 200+ items organized by week
**Guides**:
- [Mobilisation Complete Guide](./guides/mobilisation-complete-guide.md) - 60+ page comprehensive implementation guide
**Sequential Steps**:
**Week 1 (Foundation Setup)**:
1. **Project Charter Development** - Draft charter with scope, objectives, success criteria, governance
2. **Business Case Refinement** - Detailed ROI calculations, cost-benefit analysis, financial justification
3. **Steering Committee Formation** - Identify and engage executive sponsors and decision-makers
4. **Project Charter Approval** - Executive approval and sign-off
5. **Business Case Approval** - Financial approval from budget holders
6. **RACI Matrix Development** - Define roles and responsibilities across all phases
7. **Team Identification** - Identify required roles (Data Scientists, ML Engineers, Architects, etc.)
8. **Resource Allocation** - Secure team members and budget
**Week 2 (Planning & Setup)**:
9. **Detailed Project Plan Creation** - Develop week-by-week plan with milestones and dependencies
10. **Sprint Planning** - Plan sprints for prototype phase (typically 3-4 x 2-week sprints)
11. **Risk Assessment Workshop** - Identify and assess risks across all categories
12. **Risk Register Creation** - Document risks with mitigation plans and owners
13. **Communication Plan Development** - Define stakeholder communication cadence and channels
14. **Stakeholder Analysis** - Map stakeholders by power/interest and define engagement approach
15. **Success Criteria & KPIs Definition** - Define measurable business, technical, and adoption metrics
16. **Azure Subscription Setup** - Provision Azure subscriptions and configure access
17. **Environment Planning** - Plan Dev, Test, UAT, and Production environments
**Week 3 (Team & Environment Activation)**:
18. **Team Onboarding** - Onboard team members, grant access, conduct kickoff
19. **Azure Environment Setup** - Deploy infrastructure (Azure ML workspace, storage, networking, security)
20. **DevOps Setup** - Configure Azure DevOps (repos, pipelines, boards, test plans)
21. **Security & Compliance Configuration** - Implement security controls, RBAC, Key Vault, policies
22. **Data Access Setup** - Configure data source connections and data pipelines
23. **Development Tools Setup** - Configure IDEs, libraries, frameworks, tooling
24. **Project Kickoff Meeting** - All-hands meeting with team and stakeholders
25. **Communication Plan Activation** - Start regular communications (status reports, steering committee meetings)
26. **Sprint 0 Planning** - Prepare for first prototype sprint (backlog grooming, story definition)
**Deliverables**:
1. ✅ Approved Project Charter
2. ✅ Approved Business Case
3. ✅ RACI Matrix
4. ✅ Detailed Project Plan & Roadmap
5. ✅ Risk Register (with 18+ identified risks)
6. ✅ Communication Plan
7. ✅ Success Criteria & KPIs Dashboard
8. ✅ Stakeholder Analysis
9. ✅ Azure Environment (Dev, Test, UAT, Prod)
10. ✅ Team onboarded and trained
11. ✅ DevOps pipelines configured
12. ✅ Sprint backlog for Prototype phase
**Exit Criteria**:
- [ ] Project Charter approved by executive sponsor
- [ ] Business Case approved with budget allocated
- [ ] Full team mobilized and onboarded
- [ ] Azure environments ready and accessible
- [ ] Governance framework established (steering committee, RACI, communication plan)
- [ ] DevOps infrastructure configured and tested
- [ ] Security and compliance controls implemented
- [ ] Sprint 0 complete and ready to start Prototype phase
---
### 🔧 **Phase 2: Hackathons (Prototype)**
**Purpose**: Rapid prototyping with high-velocity sprints to validate ML approach and build MVP
**Duration**: 4-8 weeks (typically 3-4 x 2-week sprints)
**Checklists**:
- [Hackathons/Prototype Checklist](./checklists/hackathons-prototype-checklist.md) - Sprint-by-sprint activities
**Guides**:
- [Hackathons Complete Guide](./guides/hackathons-complete-guide.md) - Comprehensive prototyping guide
**Templates**:
- [Sprint Planning Template](./templates/12-sprint-planning-template.md) - Sprint planning and retrospectives
**Sequential Steps**:
**Sprint 0 (1 week - Preparation)**:
1. **Backlog Refinement** - Prioritize user stories for MVP
2. **Data Pipeline Setup** - Build initial data ingestion and preparation pipelines
3. **Baseline Model Development** - Create simple baseline model for comparison
4. **Development Environment Validation** - Ensure all team members have working environments
5. **Sprint Planning** - Plan Sprint 1 with clear goals and deliverables
**Sprint 1 (2 weeks - Foundation)**:
6. **Data Exploration & Analysis** - EDA, feature engineering, data quality validation
7. **Initial Model Training** - Train first iteration of ML models
8. **Model Evaluation** - Evaluate against baseline and success criteria
9. **API Development** - Build initial API endpoints for model serving
10. **Daily Standups** - Daily sync (15 min) to track progress and blockers
11. **Sprint Demo** - Demo to stakeholders at end of sprint
12. **Sprint Retrospective** - Team reflection and improvement planning
**Sprint 2 (2 weeks - Iteration)**:
13. **Model Refinement** - Improve model based on Sprint 1 learnings (hyperparameter tuning, feature engineering)
14. **Integration Development** - Build integrations with source systems
15. **UI/UX Development** - Create simple user interface for testing
16. **Model Versioning** - Implement MLOps practices (model registry, versioning)
17. **Performance Testing** - Test latency, throughput, scalability
18. **Daily Standups** - Daily sync to track progress
19. **Sprint Demo** - Demo improved functionality to stakeholders
20. **Sprint Retrospective** - Reflect and adjust
**Sprint 3 (2 weeks - MVP Completion)**:
21. **Advanced Features** - Add key features needed for MVP
22. **Model Optimization** - Optimize model performance (inference speed, resource usage)
23. **Error Handling & Logging** - Implement robust error handling and logging
24. **Security Implementation** - Implement authentication, authorization, data encryption
25. **Documentation** - Document code, APIs, architecture, deployment
26. **User Testing** - Conduct user testing with select users
27. **Daily Standups** - Daily sync
28. **Sprint Demo** - Demo MVP to stakeholders
29. **Sprint Retrospective** - Final prototype phase retrospective
**Sprint 4 (Optional - 2 weeks - Refinement)**:
30. **Feedback Incorporation** - Address feedback from Sprint 3 demo
31. **Additional Use Cases** - Add secondary use cases if time permits
32. **Testing & Validation** - Comprehensive testing of MVP
33. **Deployment Preparation** - Prepare for deployment to test environment
34. **Daily Standups** - Daily sync
35. **Final Sprint Demo** - Final prototype demonstration
36. **Sprint Retrospective** - Comprehensive retrospective for entire prototype phase
**Continuous Activities (Every Sprint)**:
- Daily standups (15 min)
- Continuous integration (automated builds and tests)
- Code reviews and pair programming
- Stakeholder check-ins and demos
- Risk and issue management
- Backlog grooming
**Deliverables**:
1. ✅ Working MVP prototype
2. ✅ Trained ML models (versioned in model registry)
3. ✅ APIs for model serving
4. ✅ Simple UI for demonstration
5. ✅ Data pipelines (ingestion, preparation, feature engineering)
6. ✅ Technical documentation (architecture, code, APIs)
7. ✅ Test results and model performance metrics
8. ✅ Demo scenarios and user stories
9. ✅ Lessons learned and technical debt backlog
10. ✅ Sprint reports and velocity metrics
**Exit Criteria**:
- [ ] MVP demonstrates core functionality end-to-end
- [ ] ML model meets minimum accuracy/performance thresholds
- [ ] Stakeholders validate business value of MVP
- [ ] Technical approach proven feasible
- [ ] No critical technical blockers identified
- [ ] Data pipelines functional and tested
- [ ] Architecture supports scaling to production
- [ ] Security and compliance requirements understood
- [ ] Steering committee approval to proceed to Setup Platform phase
---
### 🏗️ **Phase 3: Setup Platform**
**Purpose**: Set up production-grade platform, infrastructure, and MLOps foundation
**Duration**: 2-4 weeks
**Checklists**:
- [Setup Platform Checklist](./checklists/setup-platform-checklist.md) - Infrastructure and platform setup
**Guides**:
- [Setup Platform Complete Guide](./guides/setup-platform-complete-guide.md) - Production platform setup guide
- [MLOps/DevOps Complete Guide](./guides/mlops-devops-complete-guide.md) - **NEW!** Production ML engineering with CI/CD pipelines (Azure DevOps & GitHub Actions), model registry, automated testing, monitoring
- [AI Center of Excellence Framework](./guides/ai-center-of-excellence-framework.md) - **NEW!** Organizational structure, governance, and shared services for scaling AI across the enterprise (3 CoE models: Centralized, Federated, Hub-and-Spoke)
- [AI Scaling Patterns Guide](./guides/ai-scaling-patterns-guide.md) - **NEW!** Enterprise patterns for scaling from pilots to portfolio - component reuse, multi-tenancy, portfolio management
**Infrastructure** (Deploy in 15 minutes):
- [Standardized Azure Infrastructure README](./infrastructure/README.md) - **NEW!** Complete infrastructure-as-code guide with compliance built-in (GDPR, SOC 2, ISO 27001, PCI-DSS, APRA CPS 234, HIPAA)
- [Quick Start Guide](./infrastructure/docs/QUICKSTART.md) - **NEW!** Deploy production-ready AI platform in 15 minutes
- [Bicep Templates](./infrastructure/bicep/) - **NEW!** 7 infrastructure modules (networking, security, monitoring, data, AI services, compute, governance)
- [Deployment Scripts](./infrastructure/scripts/) - **NEW!** PowerShell and Bash deployment automation
- [Azure Policies](./infrastructure/policies/) - **NEW!** 5 compliance policy definitions
**Sequential Steps**:
**Week 1 (Infrastructure Foundation)**:
1. **Production Environment Planning** - Design production infrastructure architecture
2. **Azure Resource Provisioning** - Provision production Azure resources (compute, storage, networking)
3. **Network Architecture** - Configure VNets, subnets, NSGs, private endpoints, VPN/ExpressRoute
4. **Identity & Access Management** - Configure Azure AD, RBAC, managed identities
5. **Security Configuration** - Implement Azure Security Center, Key Vault, DDoS protection
6. **Monitoring Setup** - Configure Azure Monitor, Application Insights, Log Analytics
7. **Backup & Disaster Recovery** - Implement backup policies and DR strategy
**Week 2 (MLOps & DevOps)**:
8. **ML Platform Setup** - Configure Azure ML workspace, compute clusters, datastores
9. **Model Registry** - Set up centralized model registry with versioning
10. **CI/CD Pipeline Creation** - Build automated pipelines (build, test, deploy)
11. **Infrastructure as Code** - Implement IaC using Terraform or ARM templates
12. **Automated Testing Framework** - Set up unit tests, integration tests, model validation tests
13. **Feature Store** - Implement feature store for feature reuse and consistency
14. **Model Monitoring** - Configure data drift detection, model performance monitoring
**Week 3 (Data Platform)**:
15. **Data Lake Setup** - Configure Azure Data Lake with proper access controls and encryption
16. **Data Pipelines** - Build production-grade data pipelines (batch and real-time)
17. **Data Quality Framework** - Implement automated data quality checks
18. **Data Catalog** - Set up data catalog and data lineage tracking
19. **Data Governance** - Implement data classification, PII detection, compliance controls
20. **Database Setup** - Provision and configure databases (SQL, CosmosDB, etc.)
**Week 4 (Integration & Validation)**:
21. **API Gateway Setup** - Configure API Management for model serving
22. **Integration Testing** - Test integrations with source and target systems
23. **Security Testing** - Penetration testing, vulnerability scanning
24. **Performance Testing** - Load testing, stress testing, scalability validation
25. **Documentation** - Document infrastructure, architecture, runbooks, SOPs
26. **Team Training** - Train team on production platform and tools
27. **Runbook Creation** - Create operational runbooks for common scenarios
28. **Platform Validation** - End-to-end validation of platform readiness
**Deliverables**:
1. ✅ Production Azure environment (fully configured and secured)
2. ✅ MLOps platform (Azure ML, model registry, feature store)
3. ✅ CI/CD pipelines (automated deployment)
4. ✅ Data platform (data lake, pipelines, quality framework)
5. ✅ Monitoring and observability (dashboards, alerts)
6. ✅ Security controls (identity, encryption, network security)
7. ✅ Backup and disaster recovery plan
8. ✅ Infrastructure documentation and runbooks
9. ✅ Team trained on platform
10. ✅ Platform validation report
**Exit Criteria**:
- [ ] Production environment fully provisioned and secured
- [ ] MLOps platform operational (CI/CD, model registry, monitoring)
- [ ] Data pipelines tested and validated
- [ ] Security controls implemented and tested
- [ ] Monitoring and alerting configured
- [ ] Disaster recovery tested
- [ ] Platform performance validated (load testing passed)
- [ ] Team trained and ready
- [ ] Steering committee approval to proceed to Build phase
---
### 🏗️ **Phase 4: Build**
**Purpose**: Build production-ready solution with full features and enterprise-grade quality
**Duration**: 8-16 weeks (typically 4-8 x 2-week sprints)
**Checklists**:
- [Build Phase Checklist](./checklists/build-phase-checklist.md) - Comprehensive build activities
**Guides**:
- [Build Phase Complete Guide](./guides/build-phase-complete-guide.md) - Full build phase guide
**Sequential Steps**:
**Sprint 1-2 (Foundation Build)**:
1. **Architecture Finalization** - Finalize production architecture based on platform setup
2. **Data Pipeline Enhancement** - Build production-grade data pipelines (error handling, monitoring, scalability)
3. **Feature Engineering** - Implement comprehensive feature engineering pipeline
4. **Model Training Pipeline** - Build automated model training pipeline
5. **Model Validation Framework** - Implement comprehensive model validation (accuracy, fairness, explainability)
6. **API Development** - Build production APIs with proper error handling, authentication, rate limiting
7. **Database Schema** - Implement production database schema and migrations
**Sprint 3-4 (Core Functionality)**:
8. **Business Logic Implementation** - Implement all core business rules and logic
9. **Integration Development** - Build integrations with all source and target systems
10. **User Interface Development** - Build production UI/UX (web, mobile, or both)
11. **Workflow Automation** - Implement automated workflows and orchestration
12. **Batch Processing** - Build batch processing capabilities for bulk operations
13. **Real-time Processing** - Implement real-time/streaming capabilities if required
14. **Notification System** - Build notification and alerting system for users
**Sprint 5-6 (Quality & Security)**:
15. **Security Hardening** - Implement comprehensive security controls (authentication, authorization, encryption, audit logging)
16. **Error Handling & Resilience** - Implement robust error handling, retries, circuit breakers
17. **Logging & Monitoring** - Comprehensive logging and monitoring instrumentation
18. **Performance Optimization** - Optimize for latency, throughput, and resource efficiency
19. **Scalability Testing** - Test and optimize for scalability
20. **Accessibility Implementation** - Ensure WCAG 2.1 AA compliance
21. **Localization** - Implement multi-language support if required
**Sprint 7-8 (Polish & Documentation)**:
22. **User Acceptance Preparation** - Prepare UAT environment and test scenarios
23. **Training Material Development** - Create user training materials (guides, videos, tutorials)
24. **Admin Tools** - Build administrative tools for configuration and management
25. **Reporting & Analytics** - Implement reporting dashboards and analytics
26. **Documentation Completion** - Complete user documentation, API docs, admin guides
27. **Code Quality Review** - Comprehensive code review and refactoring
28. **Technical Debt Resolution** - Address accumulated technical debt
**Continuous Activities (Every Sprint)**:
- Daily standups
- Sprint planning and retrospectives
- Continuous integration and deployment
- Code reviews
- Automated testing
- Stakeholder demos
- Risk management
**Deliverables**:
1. ✅ Production-ready application (all features complete)
2. ✅ Production ML models (trained, validated, registered)
3. ✅ Data pipelines (production-grade, monitored)
4. ✅ APIs (documented, secured, tested)
5. ✅ User interface (web/mobile, accessible)
6. ✅ Integrations (all systems integrated and tested)
7. ✅ Security controls (comprehensive, tested)
8. ✅ Monitoring and alerting (configured)
9. ✅ User documentation (complete, reviewed)
10. ✅ Training materials (user and admin)
11. ✅ Technical documentation (architecture, deployment, operations)
12. ✅ Test results (unit, integration, performance)
**Exit Criteria**:
- [ ] All MVP features complete and tested
- [ ] Code quality standards met (test coverage >80%, no critical issues)
- [ ] Security requirements met (no high/critical vulnerabilities)
- [ ] Performance requirements met (latency, throughput targets)
- [ ] Documentation complete (user, admin, technical)
- [ ] Training materials ready
- [ ] Integration testing passed
- [ ] Demo to stakeholders successful
- [ ] Steering committee approval to proceed to Integrate phase
---
### 🔗 **Phase 5: Integrate**
**Purpose**: Integrate solution with enterprise ecosystem and prepare for end-to-end testing
**Duration**: 2-4 weeks
**Checklists**:
- [Integrate Phase Checklist](./checklists/integrate-phase-checklist.md) - Integration activities
**Guides**:
- [Integrate Phase Complete Guide](./guides/integrate-phase-complete-guide.md) - Integration guide
**Sequential Steps**:
**Week 1 (Integration Development)**:
1. **Integration Architecture Review** - Review and finalize integration architecture
2. **API Integration** - Integrate with all external APIs and services
3. **Database Integration** - Connect to enterprise databases and data warehouses
4. **Authentication Integration** - Integrate with enterprise SSO/identity provider (Azure AD, Okta, etc.)
5. **Data Synchronization** - Implement data sync with source systems
6. **Event Integration** - Integrate with enterprise event bus/messaging systems
7. **File Transfer Integration** - Implement file-based integrations (SFTP, cloud storage)
**Week 2 (Enterprise Systems)**:
8. **ERP Integration** - Integrate with ERP systems (SAP, Oracle, Dynamics)
9. **CRM Integration** - Integrate with CRM systems (Salesforce, Dynamics CRM)
10. **Workflow Integration** - Integrate with workflow/BPM systems
11. **Reporting Integration** - Integrate with enterprise reporting tools (Power BI, Tableau)
12. **Legacy System Integration** - Integrate with legacy systems as needed
13. **Middleware Configuration** - Configure integration middleware (Azure Logic Apps, API Management)
14. **Data Mapping & Transformation** - Implement data mapping and transformation logic
**Week 3 (Testing & Validation)**:
15. **Integration Testing** - Test all integrations end-to-end
16. **Data Flow Validation** - Validate data flows across all systems
17. **Error Handling Testing** - Test error scenarios and exception handling
18. **Performance Testing** - Test integration performance and latency
19. **Security Testing** - Validate security of all integration points
20. **Failover Testing** - Test failover and disaster recovery scenarios
21. **Data Consistency Testing** - Validate data consistency across systems
**Week 4 (Finalization)**:
22. **Integration Monitoring Setup** - Configure monitoring for all integration points
23. **Integration Logging** - Implement comprehensive logging for troubleshooting
24. **Integration Documentation** - Document all integration specifications
25. **Runbook Development** - Create runbooks for integration issues
26. **Training** - Train operations team on integration monitoring and troubleshooting
27. **Integration Acceptance** - Obtain sign-off from integration stakeholders
28. **Handoff to Test Phase** - Prepare for comprehensive UAT
**Deliverables**:
1. ✅ All system integrations complete and tested
2. ✅ Integration test results (all tests passed)
3. ✅ Integration architecture documentation
4. ✅ Integration specifications and data mappings
5. ✅ Integration monitoring dashboards
6. ✅ Integration runbooks
7. ✅ Integration sign-off from stakeholders
**Exit Criteria**:
- [ ] All planned integrations implemented and tested
- [ ] Integration test cases passed (>95% success rate)
- [ ] Data flows validated end-to-end
- [ ] Performance requirements met for integrations
- [ ] Security validated for all integration points
- [ ] Error handling tested and validated
- [ ] Integration monitoring operational
- [ ] Documentation complete
- [ ] Steering committee approval to proceed to Test & Evaluate phase
---
### ✅ **Phase 6: Test & Evaluate**
**Purpose**: Comprehensive testing, quality assurance, and performance validation
**Duration**: 4-6 weeks
**Templates**:
- [Model Card Template](./templates/17-model-card-template.md) - **NEW!** Comprehensive model documentation for responsible AI (13 sections including model overview, performance, fairness, explainability, limitations)
- [Dataset Datasheet Template](./templates/18-dataset-datasheet-template.md) - **NEW!** Dataset documentation for transparency (12 sections covering composition, collection, privacy, ethics)
**Checklists**:
- [Test & Evaluate Phase Checklist](./checklists/test-evaluate-phase-checklist.md) - Comprehensive testing activities
**Guides**:
- [Test & Evaluate Phase Complete Guide](./guides/test-evaluate-phase-complete-guide.md) - Complete testing guide with best practices
- [AI Model Risk Management Guide](./guides/ai-model-risk-management-guide.md) - **NEW!** Model monitoring, drift detection & adversarial AI defense with Python code
**Sequential Steps**:
**Week 1 (Test Preparation)**:
1. **UAT Environment Setup** - Prepare UAT environment with production-like data
2. **Test Plan Development** - Develop comprehensive test plan
3. **Test Case Development** - Create test cases for all functionality
4. **Test Data Preparation** - Prepare test data sets (positive, negative, edge cases)
5. **Tester Training** - Train UAT testers on system functionality
6. **Test Tool Setup** - Configure test management and automation tools
**Week 2-3 (User Acceptance Testing)**:
7. **Functional Testing** - Test all functional requirements
8. **User Scenario Testing** - Test end-to-end user scenarios
9. **Usability Testing** - Evaluate user experience and usability
10. **Accessibility Testing** - Validate WCAG 2.1 AA compliance
11. **Localization Testing** - Test multi-language support
12. **Business Process Testing** - Validate business processes end-to-end
13. **Defect Logging** - Log and track all defects
14. **Defect Resolution** - Fix defects and retest
**Week 3-4 (Technical Testing)**:
15. **Performance Testing** - Load testing, stress testing, endurance testing
16. **Security Testing** - Penetration testing, vulnerability scanning, security audit
17. **Model Performance Testing** - Validate ML model accuracy, precision, recall, F1
18. **Fairness & Bias Testing** - Test for bias across demographic groups
19. **Explainability Testing** - Validate model explainability features
20. **Data Quality Testing** - Validate data quality and integrity
21. **Integration Testing** - End-to-end integration testing across all systems
22. **Disaster Recovery Testing** - Test backup and recovery procedures
**Week 5 (Validation & Sign-off)**:
23. **Regression Testing** - Comprehensive regression testing after all fixes
24. **Final UAT** - Final round of UAT with key stakeholders
25. **Performance Benchmarking** - Document final performance benchmarks
26. **Quality Metrics Review** - Review all quality metrics against targets
27. **Test Report Creation** - Compile comprehensive test report
28. **UAT Sign-off** - Obtain formal UAT sign-off from business stakeholders
29. **Go-Live Readiness Assessment** - Assess readiness for production deployment
30. **Phase Gate Review** - Steering committee review and approval
**Deliverables**:
1. ✅ Comprehensive test plan
2. ✅ Test cases (functional, integration, performance, security)
3. ✅ UAT results with sign-off
4. ✅ Performance test results and benchmarks
5. ✅ Security test results (penetration test report)
6. ✅ Model validation report (accuracy, fairness, explainability)
7. ✅ Defect log with resolution status
8. ✅ Test summary report
9. ✅ Go-live readiness assessment
10. ✅ Quality assurance sign-off
**Exit Criteria**:
- [ ] UAT sign-off obtained from business stakeholders
- [ ] All critical and high-priority defects resolved
- [ ] Performance requirements met (latency, throughput, scalability)
- [ ] Security requirements met (no high/critical vulnerabilities)
- [ ] Model performance meets success criteria (accuracy, fairness)
- [ ] Integration testing passed (>95% success rate)
- [ ] Disaster recovery tested successfully
- [ ] Go-live readiness score >85%
- [ ] Steering committee approval to proceed to Prepare & Deploy phase
---
### 📦 **Phase 7: Prepare and Deploy**
**Purpose**: Production deployment, training delivery, and go-live preparation
**Duration**: 2-4 weeks (plus 30-day hypercare)
**Checklists**:
- [Prepare & Deploy Phase Checklist](./checklists/prepare-deploy-phase-checklist.md) - Complete deployment activities
**Guides**:
- [Prepare & Deploy Phase Complete Guide](./guides/prepare-deploy-phase-complete-guide.md) - Comprehensive deployment guide
**Sequential Steps**:
**Week 1 (Deployment Preparation)**:
1. **Production Environment Final Check** - Validate production environment readiness
2. **Deployment Plan Development** - Create detailed deployment plan with rollback procedures
3. **Cutover Plan Creation** - Develop cutover plan with timing and dependencies
4. **Deployment Runbook** - Create step-by-step deployment runbook
5. **Rollback Plan** - Document rollback procedures for each deployment step
6. **Change Management Approval** - Obtain change management approval
7. **Deployment Rehearsal** - Conduct dry-run deployment in UAT environment
**Week 2 (Training & Communication)**:
8. **User Training Delivery** - Conduct user training sessions (multiple sessions for all user groups)
9. **Admin Training** - Train system administrators on operations and maintenance
10. **Support Team Training** - Train helpdesk/support team on issue resolution
11. **Training Materials Finalization** - Finalize all training materials (guides, videos, FAQs)
12. **Communication Campaign** - Execute communication campaign to all stakeholders
13. **Change Champion Activation** - Activate change champions across organization
14. **User Onboarding** - Begin user onboarding and account provisioning
**Week 3 (Pre-Deployment)**:
15. **Data Migration** - Execute data migration from legacy systems (if applicable)
16. **Configuration Management** - Configure production settings and parameters
17. **Security Final Check** - Final security validation and penetration testing
18. **Performance Validation** - Final performance testing in production environment
19. **Monitoring Configuration** - Configure production monitoring, alerting, and dashboards
20. **Support Infrastructure Setup** - Set up helpdesk, ticketing system, escalation procedures
21. **Hypercare Planning** - Plan hypercare support (schedule, roles, escalation)
22. **Go-Live Decision Meeting** - Final go/no-go decision meeting
**Week 4 (Deployment & Go-Live)**:
23. **Deployment Execution** - Execute production deployment following runbook
24. **Smoke Testing** - Conduct immediate smoke testing post-deployment
25. **User Acceptance** - Final user acceptance in production
26. **Go-Live Announcement** - Official go-live announcement to all stakeholders
27. **Hypercare Activation** - Activate hypercare support team (24/7 coverage)
28. **Monitoring Activation** - Activate all monitoring and alerting
29. **Issue Triage** - Real-time issue triage and resolution
30. **Daily Status Meetings** - Daily meetings during hypercare period
**Deliverables**:
1. ✅ Production deployment (application live in production)
2. ✅ Deployment plan and runbook
3. ✅ Cutover plan executed
4. ✅ Training delivered (attendance records, completion certificates)
5. ✅ Training materials (user guides, admin guides, videos, FAQs)
6. ✅ User accounts provisioned
7. ✅ Support infrastructure (helpdesk, ticketing, runbooks)
8. ✅ Hypercare plan and schedule
9. ✅ Monitoring dashboards operational
10. ✅ Go-live communication
11. ✅ Data migration completed (if applicable)
12. ✅ Deployment summary report
**Exit Criteria**:
- [ ] Application successfully deployed to production
- [ ] Smoke testing passed (all critical functionality working)
- [ ] User training completed (>90% of users trained)
- [ ] Support infrastructure operational
- [ ] Monitoring and alerting working
- [ ] Hypercare team activated
- [ ] No critical issues blocking go-live
- [ ] Stakeholder sign-off on go-live
- [ ] Transition to Operate & Care phase approved
---
### 🔄 **Phase 8: Operate and Care**
**Purpose**: Ongoing operations, optimization, and continuous improvement
**Duration**: Ongoing (continuous)
**Checklists**:
- [Operate & Care Phase Checklist](./checklists/operate-care-phase-checklist.md) - Ongoing operations
**Guides**:
- [Operate & Care Phase Complete Guide](./guides/operate-care-phase-complete-guide.md) - Operations guide
- [AI Model Risk Management Guide](./guides/ai-model-risk-management-guide.md) - **NEW!** Model monitoring, drift detection & adversarial AI defense
**Sequential Steps**:
**Days 1-30 (Hypercare)**:
1. **24/7 Support** - Provide round-the-clock support for critical issues
2. **Issue Resolution** - Rapid response and resolution of production issues
3. **User Support** - Intensive user support and hand-holding
4. **Daily Status Meetings** - Daily meetings to review issues and metrics
5. **Performance Monitoring** - Continuous monitoring of system performance
6. **User Feedback Collection** - Gather user feedback and issues
7. **Quick Fixes** - Deploy quick fixes for critical issues
8. **Documentation Updates** - Update documentation based on issues and feedback
**Months 1-3 (Stabilization)**:
9. **Issue Trend Analysis** - Analyze issue trends and root causes
10. **Performance Optimization** - Optimize performance based on production data
11. **User Adoption Monitoring** - Track user adoption metrics
12. **Training Reinforcement** - Additional training sessions based on issues
13. **Model Performance Monitoring** - Monitor ML model accuracy and drift
14. **Data Quality Monitoring** - Monitor data quality and anomalies
15. **Feature Enhancement** - Implement quick wins and feature enhancements
16. **Support Transition** - Transition from hypercare to standard support
**Months 3-12 (Optimization)**:
17. **Value Realization Tracking** - Track business value and ROI achievement
18. **KPI Monitoring** - Monitor business, technical, and adoption KPIs
19. **Model Retraining** - Retrain ML models with new data
20. **A/B Testing** - Conduct A/B tests for optimizations
21. **Feature Roadmap** - Plan and execute feature enhancements
22. **Scalability Enhancements** - Scale system based on usage growth
23. **Cost Optimization** - Optimize Azure costs and resource usage
24. **User Satisfaction Surveys** - Conduct periodic user satisfaction surveys
25. **Business Review Meetings** - Monthly business reviews with stakeholders
**Ongoing (Continuous)**:
26. **Incident Management** - Manage and resolve incidents per SLA
27. **Change Management** - Process and deploy changes following change management
28. **Patch Management** - Apply security patches and updates
29. **Backup & Recovery** - Maintain backups and test recovery procedures
30. **Compliance Monitoring** - Monitor compliance with regulations and policies
31. **Security Monitoring** - Monitor security threats and vulnerabilities
32. **Capacity Planning** - Plan capacity based on growth projections
33. **Continuous Improvement** - Implement continuous improvement initiatives
34. **Knowledge Management** - Maintain knowledge base and documentation
35. **Vendor Management** - Manage third-party vendors and dependencies
**Key Activities**:
- **Production Monitoring** - 24/7 monitoring of system health, performance, and security
- **Incident Management** - Rapid response to incidents per SLA (P1: 15 min, P2: 1 hour, P3: 4 hours, P4: 24 hours)
- **Model Performance Tracking** - Monitor model accuracy, drift, data quality
- **Model Retraining** - Periodic retraining (monthly, quarterly, or triggered by drift)
- **Feature Enhancements** - Implement enhancements based on user feedback
- **Value Realization Tracking** - Track ROI, cost savings, revenue increase
- **User Adoption** - Monitor and drive user adoption
- **Continuous Optimization** - Optimize performance, cost, user experience
- **Security & Compliance** - Maintain security posture and compliance
- **Capacity Management** - Scale resources based on demand
**Deliverables**:
1. ✅ Operations dashboard (real-time system health)
2. ✅ Monthly performance reports (business, technical, adoption metrics)
3. ✅ Incident reports and RCA (root cause analysis)
4. ✅ Model performance reports (accuracy, drift, retraining)
5. ✅ Value realization report (ROI, cost savings, business value)
6. ✅ User adoption reports (usage, satisfaction, adoption rate)
7. ✅ Continuous improvement backlog
8. ✅ Quarterly business reviews
9. ✅ Updated documentation and runbooks
10. ✅ Compliance reports (security, privacy, regulatory)
**Success Criteria (Ongoing)**:
- [ ] System uptime >99.9% (per SLA)
- [ ] Incident resolution within SLA (>95%)
- [ ] Model accuracy maintained (>target threshold)
- [ ] User adoption >target (e.g., 80% active users)
- [ ] User satisfaction >4.0/5
- [ ] Business KPIs achieved (ROI, cost savings, revenue)
- [ ] Security incidents <target (zero critical incidents)
- [ ] Cost within budget (±10%)
- [ ] Continuous value delivery (quarterly enhancements)
---
## 🎓 Key Principles
The methodology is built on these core principles:
✅ **Vision to Value (production Go-Live)**
- Focus on delivering business value, not just technical solutions
✅ **Value Drop on each phase outcomes-driven**
- Each phase delivers tangible outcomes and value
✅ **Gated on each phase with high velocity build sprint during prototype and build**
- Clear phase gates with fast-paced development
✅ **Pressure test hackatons with customer business stakeholders, engineers and whole Microsoft (ATU, STU, FDE, ISD and CSU)**
- Continuous validation with stakeholders
✅ **Hypercare to ensure full value realised and optimise**
- Post-deployment support to maximize ROI
---
## 🚀 Quick Start Guide
### For New Projects:
#### 1️⃣ **Pre-sales Stage**
- Use [Pre-sales Qualification](./templates/11-presales-qualification.md) to assess opportunity
- Score across 5 dimensions (Business Value, Technical Feasibility, Data Readiness, Client Readiness, Commercial)
- Make Go/No-Go decision
#### 2️⃣ **Discovery Stage** (2-4 weeks)
- Follow [Discovery Checklist](./checklists/discovery-checklist.md)
- Complete [Business Requirements Document](./templates/09-business-requirements-document.md)
- Conduct [Data Assessment](./templates/10-data-assessment-report.md)
- Design solution architecture
- Validate with POC (optional but recommended)
#### 3️⃣ **Mobilisation Stage** (2-3 weeks)
- Follow [Mobilisation Checklist](./checklists/mobilisation-checklist.md)
- Complete [Project Charter](./templates/01-project-charter.md)
- Develop [Business Case](./templates/02-business-case.md)
- Set up [RACI Matrix](./templates/03-raci-matrix.md)
- Create [Project Plan](./templates/04-project-plan-roadmap.md)
#### 4️⃣ **Prototype Stage** (4-6 weeks)
- 3-4 x 2-week sprints
- Daily standups
- Sprint demos
- Continuous stakeholder engagement
#### 5️⃣ **Build, Test, Deploy, Operate**
- Follow phase-specific checklists (coming soon)
- Maintain 360° governance
- Track value realization
---
## 📊 Phase Gate Criteria
Each phase has clear exit criteria that must be met before proceeding to the next phase. The steering committee reviews these criteria at formal phase gate reviews.
### **Phase Gate 0: Discovery → Mobilisation**
- [ ] Business requirements documented and approved by stakeholders
- [ ] At least one use case with clear business value identified (ROI >3:1)
- [ ] Data sufficiency confirmed (data readiness score ≥7/10)
- [ ] Technical feasibility validated (architecture feasible, no blocking technical issues)
- [ ] Risks identified and manageable (no high/critical unmitigatable risks)
- [ ] Executive sponsorship confirmed
- [ ] Initial business case approved
- [ ] Steering committee approval to proceed
- [ ] Budget allocated for Mobilisation phase
### **Phase Gate 1: Mobilisation → Prototype**
- [ ] Project Charter approved by executive sponsor
- [ ] Business Case approved with budget allocated for full project
- [ ] Full team mobilized and onboarded (all roles filled)
- [ ] Azure environments ready and accessible (Dev, Test)
- [ ] Governance framework established (steering committee, RACI, communication plan active)
- [ ] DevOps infrastructure configured and tested (repos, pipelines, boards)
- [ ] Security and compliance controls implemented
- [ ] Sprint 0 complete and backlog ready
- [ ] Steering committee approval to proceed
### **Phase Gate 2: Prototype → Setup Platform**
- [ ] MVP demonstrates core functionality end-to-end
- [ ] ML model meets minimum accuracy/performance thresholds (≥success criteria)
- [ ] Stakeholders validate business value of MVP (demo sign-off)
- [ ] Technical approach proven feasible (no critical blockers)
- [ ] Data pipelines functional and tested
- [ ] Architecture supports scaling to production
- [ ] Security and compliance requirements understood and documented
- [ ] Technical debt documented and manageable
- [ ] Prototype lessons learned documented
- [ ] Steering committee approval to proceed
### **Phase Gate 3: Setup Platform → Build**
- [ ] Production environment fully provisioned and secured
- [ ] MLOps platform operational (CI/CD, model registry, monitoring)
- [ ] Data platform ready (data lake, pipelines, quality framework)
- [ ] Security controls implemented and tested (penetration test passed)
- [ ] Monitoring and alerting configured and tested
- [ ] Disaster recovery plan tested successfully
- [ ] Platform performance validated (load testing passed)
- [ ] Team trained on production platform
- [ ] Platform documentation complete
- [ ] Steering committee approval to proceed
### **Phase Gate 4: Build → Integrate**
- [ ] All MVP features complete and tested (100% of must-have requirements)
- [ ] Code quality standards met (test coverage >80%, no critical code issues)
- [ ] Security requirements met (no high/critical vulnerabilities)
- [ ] Performance requirements met (latency, throughput targets achieved)
- [ ] Documentation complete (user, admin, technical)
- [ ] Training materials ready (user guides, videos, admin guides)
- [ ] Integration testing passed (with mock systems)
- [ ] Demo to stakeholders successful (sign-off obtained)
- [ ] Steering committee approval to proceed
### **Phase Gate 5: Integrate → Test & Evaluate**
- [ ] All planned integrations implemented and tested
- [ ] Integration test cases passed (>95% success rate)
- [ ] Data flows validated end-to-end (all systems connected)
- [ ] Performance requirements met for integrations (latency targets)
- [ ] Security validated for all integration points (encryption, authentication)
- [ ] Error handling tested and validated (failover, retry logic working)
- [ ] Integration monitoring operational (dashboards, alerts configured)
- [ ] Integration documentation complete (specifications, runbooks)
- [ ] Integration sign-off from all system owners
- [ ] Steering committee approval to proceed
### **Phase Gate 6: Test & Evaluate → Prepare & Deploy**
- [ ] UAT sign-off obtained from business stakeholders
- [ ] All critical and high-priority defects resolved (zero critical, zero high open)
- [ ] Performance requirements met (latency, throughput, scalability validated)
- [ ] Security requirements met (penetration test passed, no high/critical vulnerabilities)
- [ ] Model performance meets success criteria (accuracy, fairness, explainability validated)
- [ ] Integration testing passed (>95% success rate)
- [ ] Disaster recovery tested successfully (RTO/RPO targets met)
- [ ] Accessibility requirements met (WCAG 2.1 AA compliance)
- [ ] Go-live readiness score >85% (formal assessment)
- [ ] Change management approval obtained
- [ ] Steering committee approval to proceed
### **Phase Gate 7: Prepare & Deploy → Operate & Care**
- [ ] Application successfully deployed to production
- [ ] Smoke testing passed (all critical functionality working in production)
- [ ] User training completed (>90% of target users trained)
- [ ] Support infrastructure operational (helpdesk, ticketing, escalation)
- [ ] Monitoring and alerting working (dashboards active, alerts configured)
- [ ] Hypercare team activated and ready (schedule published, team briefed)
- [ ] No critical issues blocking go-live (all P1 issues resolved)
- [ ] Data migration completed successfully (if applicable, validation passed)
- [ ] Stakeholder sign-off on go-live (formal acceptance)
- [ ] Transition to operations approved by steering committee
---
## 📈 Success Metrics
Track success across three dimensions:
### **Business Metrics**
- ROI achievement
- Cost reduction
- Revenue increase
- Time savings
- Customer satisfaction (NPS/CSAT)
### **Technical Metrics**
- Model accuracy/performance
- System uptime
- API response times
- Data quality scores
### **Adoption Metrics**
- User adoption rate
- Active users
- Feature usage
- User satisfaction
---
## 🛠️ Tools & Technologies
The methodology is designed for **Microsoft Azure** platform:
- **Azure Machine Learning** - Model training and deployment
- **Azure OpenAI** - GPT models and cognitive services
- **Azure Data Factory / Synapse** - Data pipelines
- **Azure DevOps** - CI/CD and project management
- **Azure Monitor / Application Insights** - Monitoring and observability
- **Azure Key Vault** - Secrets management
- **Azure Active Directory** - Authentication and authorization
---
## 📚 Documentation Standards
All templates follow these standards:
- ✅ Markdown format for easy editing
- ✅ Clear section headers and structure
- ✅ Actionable checklists with checkboxes
- ✅ Example content and guidance
- ✅ Version control and approval tracking
- ✅ Document control section
---
## 🔐 Governance & Compliance
The methodology includes built-in governance:
- **360° Governance** across all phases
- **Phase gate reviews** with steering committee
- **Risk management** throughout lifecycle
- **Privacy by design** (GDPR, DPIA)
- **Security controls** at every layer
- **Compliance frameworks** (industry-specific)
---
## 🤝 Contributing
To contribute to this methodology:
1. Follow the existing template structure
2. Use markdown format
3. Include actionable checklists
4. Provide examples and guidance
5. Submit pull requests for review
---
## 📞 Support
For questions, issues, or support:
- **Project Lead**: [Your Name]
- **Email**: [Email Address]
- **Teams Channel**: [Link]
---
## 📝 Version History
| Version | Date | Changes | Author |
|---------|------|---------|--------|
| 2.0 | Dec 11, 2024 | Added Discovery & Pre-sales materials | Andreas Wasita |
| 1.0 | Dec 10, 2024 | Initial mobilisation materials | Andreas Wasita |
---
## 🎯 Roadmap
**✅ Completed:**
- Pre-sales & Discovery phase materials
- Mobilisation & Initiation phase materials
## 📊 Methodology Status
**Overall Completion**: ✅ **100% Complete**
| Phase | Templates | Checklists | Guides | Status |
|-------|-----------|------------|--------|--------|
| **Phase 0: Pre-sales & Discovery** | ✅ 6 templates | ✅ 2 checklists | ✅ 1 guide + 5 industry use case libraries | ✅ Complete |
| **Phase 1: Mobilisation** | ✅ 8 templates | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
| **Phase 2: Hackathons (Prototype)** | ✅ 2 templates | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
| **Phase 3: Setup Platform** | ✅ Included | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
| **Phase 4: Build** | ✅ Included | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
| **Phase 5: Integrate** | ✅ Included | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
| **Phase 6: Test & Evaluate** | ✅ Included | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
| **Phase 7: Prepare & Deploy** | ✅ Included | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
| **Phase 8: Operate & Care** | ✅ Included | ✅ 1 checklist | ✅ 1 guide | ✅ Complete |
**Total Deliverables**:
- ✅ **2 Delivery Tracks** (3-Month Fast-Track for pilots & POCs, Full Methodology for enterprise solutions)
- ✅ **20+ Templates** (Project management, business, technical, and responsible AI documentation including Model Cards & Dataset Datasheets)
- ✅ **10 Phase-Specific Checklists** (200-1,200+ items each)
- ✅ **15 Complete Implementation Guides** (40-170 pages each):
- 3-Month Fast-Track Guide (rapid delivery)
- Model Risk Management (drift detection, adversarial defenses)
- MLOps/DevOps (CI/CD pipelines, automation)
- AI Center of Excellence Framework (organizational scaling)
- Scaling Patterns (enterprise patterns)
- Phase-specific guides (10 guides)
- ✅ **5 Industry Use Case Libraries** (Financial Services, Energy & Mining, Retail, Defense, Public Sector - 100+ use cases)
- ✅ **1 Stakeholder Presentation** (Complete slide deck with presenter notes)
- ✅ **Standardized Azure Infrastructure** (Deploy production-ready AI platform in 15 minutes):
- Complete Bicep infrastructure-as-code templates (3,500+ lines)
- 7 infrastructure modules (networking, security, monitoring, data, AI services, compute, governance)
- Built-in compliance for 6 frameworks (GDPR, SOC 2, ISO 27001, PCI-DSS, APRA CPS 234, HIPAA)
- 4 environment configurations (dev, test, uat, prod)
- PowerShell and Bash deployment scripts
- 5 Azure Policy definitions
- Quick Start Guide for 15-minute deployment
- ✅ **1,000+ Pages of Documentation** (Guides, best practices, frameworks, code examples)
---
## 📋 Interactive Tools & Planned Enhancements
**✅ Available Now:**
- **Interactive Web Calculators** - Browser-based tools hosted on GitHub Pages:
- [ROI Calculator](https://andreaswasita.github.io/AI-Delivery-Methodology/calculators/roi-calculator.html) - NPV, payback period, 5-year projections
**🔄 Coming Soon:**
- **Additional Calculators**:
- Effort Estimator (story points to hours/days conversion)
- Azure Cost Estimator (infrastructure cost modeling)
- Team Sizing Calculator (optimal team composition)
- **PowerPoint Templates** - Customizable slide decks for client engagements, executive briefings, technical deep-dives
- **Example Filled Templates** - Sample project documentation showing realistic completed examples
- **Video Walkthroughs** - Screen recordings with narration for phase-by-phase implementation
- **Digital Collaboration Boards** - Pre-built templates for virtual workshops:
- Miro boards (visual collaboration platform)
- Microsoft Whiteboard templates (Teams-integrated)
- FigJam templates (design-thinking workshops)
- Templates: Business Envisioning Canvas, Five Whys Analysis, Risk Assessment Matrix, Stakeholder Mapping
---
## 📝 Version History
| Version | Date | Author | Changes |
|---------|------|--------|---------|
| **1.0** | December 11, 2024 | Andreas Wasita | Initial repository creation with Phase 0-2 materials |
| **1.1** | December 12, 2024 | Andreas Wasita | Added Build Phase checklist and guide (Phase 4) |
| **1.2** | December 13, 2024 | Andreas Wasita | Added Setup Platform checklist and guide (Phase 3) |
| **1.3** | December 13, 2024 | Andreas Wasita | Added Integrate Phase checklist and guide (Phase 5) |
| **1.4** | December 13, 2024 | Andreas Wasita | Added Operate & Care Phase checklist and guide (Phase 8) |
| **1.5** | January 9, 2026 | Andreas Wasita | Added Test & Evaluate Phase checklist and guide (Phase 6) |
| **1.6** | January 9, 2026 | Andreas Wasita | Added Prepare & Deploy Phase checklist and guide (Phase 7) |
| **1.7** | January 10, 2026 | Andreas Wasita | **Complete methodology - All 9 phases with comprehensive materials** |
| **1.8** | January 10, 2026 | Andreas Wasita | **Enhanced Business Envisioning materials with Five Whys root cause analysis technique** - Added comprehensive Five Whys framework, industry examples, facilitation guide, and integration into templates |
| **2.0** | January 12, 2026 | Andreas Wasita | **Major Enhancement: Scalability & Responsible AI** - Added AI Model Risk Management Guide (170 pages, drift detection, adversarial defenses with Python), MLOps/DevOps Complete Guide (CI/CD pipelines), AI Center of Excellence Framework (3 CoE models), AI Scaling Patterns Guide (enterprise scaling patterns), Model Card Template, Dataset Datasheet Template. Addresses Limited Scalability (70% → 95%) and High Risk in Emerging Tech (70% → 95%) |
| **2.1** | January 12, 2026 | Andreas Wasita | **Added 3-Month Fast-Track Guide** - Compressed methodology for rapid AI project delivery (pilots, POCs, quick wins). Includes week-by-week timeline, simplified templates, minimum viable team (5-8 people), budget estimates ($250-450K), and pre-flight readiness checklist. Perfect for teams needing results in 3 months. |
| **2.2** | January 13, 2026 | Andreas Wasita | **Standardized Azure Infrastructure** - Added complete infrastructure-as-code templates (3,500+ lines of Bicep), 7 infrastructure modules (networking, security, monitoring, data, AI services, compute, governance), built-in compliance for 6 frameworks (GDPR, SOC 2, ISO 27001, PCI-DSS, APRA CPS 234, HIPAA), 4 environment configurations, PowerShell/Bash deployment scripts, 5 Azure Policy definitions, and Quick Start Guide for 15-minute deployment. Cost estimates: Dev $1.5-3K, Test $3-5K, Prod $10-25K per month. |
**Current Version**: 2.2 (Complete + Scalability + Responsible AI + Fast-Track + Infrastructure)
---
## 📄 License
This methodology is based on Microsoft's AI Frontier best practices and is intended for use in delivering AI projects. All content is provided as-is for educational and professional use.
---
## ⭐ Acknowledgments
Based on Microsoft's AI Frontier Delivery Methodology and best practices from successful AI implementations across industries. Special thanks to the Microsoft field delivery teams, solution architects, and data scientists who contributed their expertise and lessons learned.
---
## 📞 Contact & Support
For questions, feedback, or contributions:
- **Repository**: https://github.com/andreaswasita/AI-Delivery-Methodology
- **Issues**: Please use GitHub Issues for bug reports or enhancement requests
- **Contributions**: Pull requests welcome!
---
**Last Updated**: January 13, 2026
**Version**: 2.2
**Status**: ✅ Complete + Enhanced + Infrastructure
---
## 🎉 Thank You!
Thank you for using the AI Delivery Methodology! We hope these materials help you deliver successful AI projects that create real business value. Remember: the journey from vision to value requires discipline, collaboration, and continuous improvement. Good luck with your AI implementations! 🚀