Transforming Mazda Through AI
Building the AI Dojo to Drive $60M+ in Annual Value
Building on Proven Success
Seizing the Azure Opportunity
We have secured critical CIO approval to establish and own our dedicated Azure Cloud Infrastructure for Data & AI Platforms. We've already proven we can execute: our Data Intelligence Platform (Azure Databricks, Microsoft Fabric, Unity Catalog, Microsoft Purview) is fully operational—delivered in just 7 weeks through agile collaboration between our Enterprise Data Architect, Cloud Solution Architect, and an Azure Cloud Infrastructure Engineer from EIS.
Building on Proven Success
This momentum positions us perfectly for the next challenge: the Mazda Sales Promise Agent must be operational within 3 months to support enterprise-wide MSP execution and dealer enablement.
The Immediate Need
  • MSP launch timing requires AI-powered support for project execution, dealer enablement, and compliance monitoring
  • 500+ users across dealer network and internal teams need intelligent assistance NOW
  • Current manual coordination creates bottlenecks and inconsistent dealer experience
  • We've proven rapid delivery capability—now we apply it to AI
The Opportunity
  • Data Intelligence Platform already operational provides the foundation
  • Copilot Studio enables rapid deployment using out-of-the-box capabilities
  • Atlassian Confluence/Jira and SharePoint integration provides immediate knowledge access
  • $8-12M annual value from improved MSP execution and dealer support
The Risk of Delay
We have the approval, the proven delivery model, the operational platform, and the business case. Every week we delay is a week without intelligent dealer support and competitive advantage.
$85M
AI Portfolio Potential
Annual benefits now being captured and tracked, but stalled due to lack of foundational capabilities
$1.61M
Weekly Value Unlock
Accelerating towards realizing this value each week
The Strategic Shift: From Dependency to Ownership
Previous State: EIS Dependency
AI initiatives faced extensive coordination and delays due to Enterprise Infrastructure Services (EIS) dependencies, creating bottlenecks and slowing innovation.
Current State: Proven Execution
We've successfully transitioned to infrastructure ownership with a major achievement: Data Intelligence Platform fully operational in 7 weeks.
What We've Already Built:
  • Azure Databricks: Data engineering and ML workloads operational
  • Microsoft Fabric: Unified analytics platform deployed
  • Unity Catalog: Data governance framework active
  • Microsoft Purview: Compliance and discovery capabilities live
How We Did It:
Cross-functional agile team working in 2-week sprints:
  • Enterprise Data Architect (leadership and design)
  • 2 Cloud Data Infrastructure Engineers (platform implementation)
  • 1 Azure Cloud Infrastructure Engineer from EIS (infrastructure support)
  • Delivered ahead of schedule through focused execution and collaboration
Key Success Factors:
  • Removed silos between teams for adequate access
  • Executive support enabled plan execution without outside intervention
  • Omotenashi mindset: built for sustainment from day one
  • Sharp focus: team stayed dedicated to the objective
Next Phase: AI Enablement
With the data foundation operational, we now build AI capabilities on top of this proven platform. The same agile, focused approach that delivered the Data Intelligence Platform in 7 weeks will deliver the Mazda Sales Promise Agent in 3 months.
This isn't a proposal—it's continuing proven momentum.
The Builder/Owner Model Vision
Already Achieved: Data Intelligence Platform
We've proven the builder/owner model works. In just 7 weeks, our cross-functional team delivered:
  • Azure Databricks, Microsoft Fabric, Unity Catalog, and Microsoft Purview
  • Fully operational and supporting data workloads today
  • Delivered ahead of schedule through agile 2-week sprints
  • Team: Enterprise Data Architect + 2 Cloud Data Infrastructure Engineers + 1 Azure Cloud Infrastructure Engineer (EIS)
The Success Formula
Based on our Project ELMO:
  • Collective Opportunity: Cross-functional collaboration without silos
  • Building Capabilities: Focus on sustainable, long-term solutions
  • What's Possible: Prioritize and measure continuously
  • Enablement: Never enough time, but always enough focus
  • Continuous Learning: Fail fast, learn faster
Expanding the Model: AI Dojo
With CIO approval and proven delivery capability, we now expand the builder/owner model to AI:
  • Foundation Ready: Data Intelligence Platform operational provides the base
  • AI Infrastructure Next: WAF/CAF-aligned Azure AI Foundry and Azure AI Services
  • Power Platform Architecture: Enterprise-wide Copilot Studio enablement
  • Same Agile Approach: 2-week sprints, cross-functional teams, sharp focus
Continuing the Momentum
We're not starting from zero. We have:
  • ✓ Operational data platform
  • ✓ Proven delivery team and methodology
  • ✓ Executive support and CIO approval
  • ✓ Track record of ahead-of-schedule delivery
The AI Dojo builds on this foundation with the same empowerment and enablement approach that made Project ELMO successful.
Strategic Rationale: Why This Approach?
CIO Approval Unlocks New Possibilities:
Our recent approval to own Azure Cloud Infrastructure for Data & AI Platforms fundamentally changes what's possible:
Key Strategic Advantages:
  • Speed: Direct infrastructure control eliminates dependency bottlenecks
  • Integration: Unified Data Intelligence Platform (Databricks, Fabric, Unity Catalog, Purview) enables seamless data-to-AI workflows
  • Governance: We control security, compliance, and operational standards aligned to WAF/CAF
  • Innovation: Direct access to Azure AI Foundry and AI Services enables rapid experimentation
  • Enterprise Enablement: Power Platform architecture scales Copilot Studio across the organization
  • Cost Control: FinOps practices and direct Azure management optimize spending
  • Microsoft Partnership: FastTrack and direct support accelerate capability building
Market Timing:
  • AI technology maturity makes this the right time to build production-grade infrastructure
  • Microsoft's investment in Azure AI creates partnership opportunity
  • Competitive pressure requires faster AI deployment than dependency models allow
This isn't just about AI solutions - it's about building the platform capability Mazda needs for the next decade.
A Three-Phase Journey
We propose a measured, risk-managed approach that progressively builds capability while delivering tangible value at each stage. This phased rollout allows us to prove the model, learn from experience, and scale with confidence.
Crawl (0-6 Months)
Establish foundations with quick wins with joint development of base infrastructure
Walk (6-12 Months)
Build platform capabilities and expand delivery with partial autonomy
Run (13-24 Months)
Achieve full autonomy and deliver highest-impact strategic initiatives
Phase 1: Crawl—AI Enablement on Proven Foundation
Duration: Months 1-3 (accelerated timeline)
Primary Objective
Build AI capabilities on our operational Data Intelligence Platform while delivering immediate business value through the Mazda Sales Promise Agent
Key Deliverables
Immediate Priority - Mazda Sales Promise Agent (Months 1-3)
  • Deploy using out-of-the-box Copilot Studio functionality
  • Integrate two primary knowledge sources: Atlassian Confluence/Jira and Microsoft SharePoint
  • Support strategic intent, business case, project execution, dealer enablement, risk & compliance monitoring
  • Enable agentic mesh architecture with child agents for specialized functions
  • Deliver enterprise-wide support for MSP execution
AI Infrastructure Build (Parallel Track)
  • Establish Power Platform architecture to support Copilot Studio at scale
  • Implement WAF/CAF-aligned infrastructure for Azure AI Foundry and Azure AI Services
  • Set up governance framework for agent deployment and monitoring
  • Integrate AI capabilities with existing Data Intelligence Platform (already operational)
Leveraging Existing Foundation
Our Data Intelligence Platform is already operational (delivered in 8 weeks):
  • ✓ Azure Databricks - ready for ML workloads
  • ✓ Microsoft Fabric - unified analytics available
  • ✓ Unity Catalog - governance framework active
  • ✓ Microsoft Purview - compliance monitoring live
This phase builds AI capabilities on top of proven infrastructure using the same agile delivery approach that made Project ELMO successful.
Phase 1: Team Structure
Our Data Intelligence Platform team has already demonstrated rapid delivery capability (8 weeks, ahead of schedule). We expand this proven team for AI enablement:
Existing Data Team (Continuing)
  • Enterprise Data Architect: Platform design, architecture governance, cross-team coordination
  • Cloud Data Infrastructure Engineers (2): Data platform operations, integration, performance optimization
  • Azure Cloud Infrastructure Engineer (EIS): Infrastructure support, Azure resource management
New AI-Focused Roles (Phase 1 Additions)
  • Dojo Lead: Overall AI vision, CIO alignment, Microsoft partnership management, business case ownership
  • Platform Architect (AI Focus): Designs WAF/CAF-aligned infrastructure for Azure AI Foundry, Azure AI Services, and Power Platform
  • AI Engineer: Builds Mazda Sales Promise Agent and agentic mesh architecture in Copilot Studio
  • Integration Specialist: Connects Copilot Studio with Atlassian Confluence/Jira and SharePoint knowledge sources
Team Size: ~7-8 members (4 existing + 3-4 new)
Proven Delivery Model
  • Agile 2-week sprints (same approach that delivered Data Intelligence Platform)
  • Cross-functional collaboration without silos
  • Sharp focus on objectives with executive support
  • Omotenashi mindset: build for sustainment from day one
This isn't a new team—it's expanding a proven winner.
Phase 1: Mazda Sales Promise Agent - Flagship Delivery
Business Opportunity
Supporting MSP (Mazda Sales Promise) execution and dealer enablement across the enterprise. Eliminates friction, personalizes experience, and unifies customer engagement.
Agent Architecture - Agentic Mesh
A multi-agent system leveraging:
  • Base Copilot Studio Agent: M365 Generative AI Chat Based orchestrator using M365 Apps
  • Child Agents:
  • Risk & Compliance Agent
  • Dealer Support Agent
  • Project Execution Agent
  • Data Insights Agent
Knowledge Sources
  • Atlassian Confluence/Jira (project execution, tracking, requirements)
  • Microsoft SharePoint (documentation, policies, dealer resources)
  • Public websites (as needed)
  • Dataverse and Files
Tools & Integrations
  • Connectors (Atlassian integration)
  • Flows (automations)
  • Prompts (pre-defined and business-defined)
  • MCP (Fabric Data Agent integration)
Delivery Timeline
3 months from kickoff
Mazda Sales Promise Agent: Implementation Steps
This is a rapid deployment approach leveraging out-of-box Copilot Studio capabilities to deliver value within 3 months while foundational infrastructure is being established in parallel.
Create POC Environment
Begin testing the AI Agent as required by the MSP team.
Grant Environment Permissions
Ensure Environment Makers have permissions for build out and connector configurations.
Review Business Requirements
Confirm requirements and timing for MSP launch.
Prepare Production Environment
Ensure the Production environment is ready to support the business timing.
Infrastructure Coordination
Infrastructure is contacting TechM to build the environments.
Phase 1: Delivery Timeline
1
Month 1: Foundation & POC
  • Week 1-2: POC environment setup, permissions configuration
  • Week 3-4: Atlassian and SharePoint connector integration
  • Parallel: Begin Azure infrastructure planning and Microsoft gap assessment and validations using support hours
2
Month 2: Build & Test
  • Week 1-2: Base Copilot Studio agent development with knowledge sources
  • Week 3-4: Child agent development (Risk & Compliance, Dealer Support, Project Execution, Data Insights)
  • Parallel: Power Platform architecture deployment, initial governance framework
3
Month 3: Deploy & Scale
  • Week 1-2: Production environment deployment, user acceptance testing
  • Week 3-4: Enterprise rollout, user training, adoption support
  • Parallel: Data Intelligence Platform initial deployment (Databricks, Fabric foundations)
Success Criteria:
  • Mazda Sales Promise Agent operational with sign off from the business
  • Agentic mesh architecture proven with 4 child agents
  • Platform foundation established for Phase 2 expansion
  • $8-12M annual value delivery path validated
This accelerated timeline proves rapid delivery capability while building infrastructure foundation.
Phase 1: AI Infrastructure & Microsoft Partnership
Leveraging Operational Foundation:
Our Data Intelligence Platform is already fully deployed and operational (delivered in 7 weeks):
  • ✓ Azure Databricks: Data engineering and ML workloads running
  • ✓ Microsoft Fabric: Unified analytics platform active
  • ✓ Unity Catalog: Data governance enforced
  • ✓ Microsoft Purview: Compliance and discovery operational
Phase 1 Focus: AI Layer on Top:
Building AI capabilities on this proven foundation:
Microsoft Partnership Expansion:
  • Partner Assessments & Validation: Leverage Microsoft Support Hours for Azure Foundations
  • AI-Specific Guidance: Copilot, Azure AI Foundry, and Azure AI Services validation support
  • Power Platform Expertise: Copilot Studio enterprise architecture and best practices
New Infrastructure Deployment:
  • WAF/CAF Implementation for AI: Extend Well-Architected Framework and Cloud Adoption Framework to AI services
  • Azure AI Foundry: Model development and deployment infrastructure
  • Azure AI Services: Pre-built AI capabilities (document intelligence, speech, vision)
  • Power Platform Architecture: Enterprise-grade Copilot Studio deployment supporting agentic mesh
Integration with Existing Platform:
  • Connect Copilot Studio agents to Fabric data sources via MCP (Model Context Protocol)
  • Leverage Unity Catalog for AI model governance
  • Use Purview for AI compliance monitoring and data lineage
  • Databricks for custom model training when needed
Governance & Security:
Extend existing Data Intelligence Platform governance to AI workloads with enterprise-grade controls we own and manage.
We're not starting from scratch—we're building the AI layer on operational infrastructure.
Phase 1: Expected Outcomes
Immediate Business Impact
  • Mazda Sales Promise Agent Operational: Enterprise-wide deployment supporting MSP execution, dealer enablement, project tracking, risk & compliance
  • Agentic Mesh Architecture Proven: Base orchestrator with 4 specialized child agents (Risk & Compliance, Dealer Support, Project Execution, Data Insights)
  • Knowledge Integration Complete: Atlassian Confluence/Jira and SharePoint fully integrated as primary knowledge sources
  • User Adoption Metrics: 500+ users across dealer network and internal teams within first 3 months
AI Infrastructure Established
  • Power Platform architecture operational and supporting Copilot Studio at scale
  • Azure AI Foundry and Azure AI Services infrastructure deployed with WAF/CAF compliance
  • Governance framework operational for agent deployment and monitoring
  • AI capabilities integrated with existing Data Intelligence Platform
Leveraging Operational Foundation
  • Data Intelligence Platform (Databricks, Fabric, Unity Catalog, Purview) already supporting AI workloads
  • Proven agile delivery model (2-week sprints) applied to AI development
  • Cross-functional team collaboration continuing from Project ELMO success
  • Integration between AI agents and data platform operational
Organizational Capability
  • Second major delivery in 3 months (following 7-week Data Intelligence Platform success)
  • Proven builder/owner model delivering both infrastructure and solutions
  • Patterns Documented with Plan - Do - Check - Act (PDCA) leveraging Microsoft & EEARB as validation mechanisms
  • Team capability demonstrated across data and AI domains
  • Executive confidence in rapid delivery model reinforced
The Mazda Sales Promise Agent demonstrates we can deliver AI solutions as fast as we delivered the data platform.
Phase 1: Risk Mitigation
Risk: Platform Deployment Complexity
Mitigation: Microsoft Support partnership, phased rollout, experienced platform architects
Risk: Skills Gap in Azure Infrastructure
Mitigation: Strategic hiring, Microsoft training programs, external consulting for knowledge transfer
Risk: Governance & Security Gaps
Mitigation: WAF/CAF frameworks from day one, security reviews at each milestone, automated compliance monitoring
Risk: Balancing Infrastructure Build with Solution Delivery
Mitigation: Parallel workstreams, choose pilot use cases that can run on partially-complete platform, incremental platform maturity
Risk: Integration Complexity Across Data Intelligence Platform
Mitigation: Unity Catalog as central governance layer, clear data architecture, integration testing at each phase
Phase 1 Success Builds on Proven Foundation
Foundation Already Operational
  • Data Intelligence Platform delivered in 7 weeks (Azure Databricks, Fabric, Unity Catalog, Purview)
  • Proven agile delivery model with 2-week sprints
  • Cross-functional team collaboration working effectively
  • Executive support and empowerment validated
Phase 1 Achievements Add AI Layer
  • Mazda Sales Promise Agent operational in 3 months
  • Azure AI infrastructure deployed with WAF/CAF compliance
  • Power Platform architecture supporting Copilot Studio at scale
  • Agentic mesh architecture proven with 4 child agents
Business Validation
  • $8-12M annual value from Mazda Sales Promise Agent
  • Two consecutive rapid deliveries (8 weeks for data, 3 months for AI)
  • Platform reliability and performance demonstrated
  • Team capability proven across data and AI domains
Phase 2 Readiness
  • Deploy multiple concurrent AI projects on mature platform foundation
  • Build advanced MLOps pipeline leveraging Databricks and AI Foundry
  • Scale team with confidence in proven delivery model
  • Expand agentic mesh to additional use cases
Momentum Multiplier: Each success builds confidence and capability. Data Intelligence Platform → Mazda Sales Promise Agent → Enterprise AI at scale.
We're not building from zero—we're accelerating from proven success.
Phase 2: Walk—Building Platform & Expanding Delivery
Duration: Months 6-12
Phase 2 represents the critical transition from dependency to capability. We establish the technical infrastructure and team capacity needed to operate at scale, while simultaneously expanding our delivery of high-value AI solutions.
Platform Establishment
Build dedicated Azure Landing Zone with dev/test environments, CI/CD pipelines, and governance guardrails
Team Expansion
Grow to ~12 core members with critical platform engineering and additional delivery capabilities
Parallel Delivery
Tackle 3-5 projects simultaneously, demonstrating increased throughput and value
Governance Maturity
Operationalize Definition of Ready/Done, RAI checklists, and automated compliance checks
Phase 2: Platform Maturity & Expansion
Building on Operational Foundation:
With Data Intelligence Platform operational since Phase 0 (delivered in 7 weeks) and AI infrastructure established in Phase 1, Phase 2 focuses on maturity and advanced capabilities:
Data Platform Enhancements
Our operational Data Intelligence Platform (Databricks, Fabric, Unity Catalog, Purview) gets advanced capabilities:
  • Advanced ML workloads and model training in Databricks
  • Real-time data streaming and processing
  • Enhanced Unity Catalog governance for AI models and sensitive data
  • Expanded Purview integration for AI compliance monitoring
AI Platform Expansion
  • Advanced Azure AI Foundry capabilities for complex model deployment
  • Expanded Azure AI Services integration (document intelligence, speech, vision)
  • Enterprise-scale Copilot Studio deployment across multiple use cases
  • MLOps pipeline connecting Databricks, AI Foundry, and production deployment
Infrastructure Scaling
  • Multi-environment strategy (dev, test, prod) fully operational across data and AI platforms
  • Disaster recovery and business continuity capabilities
  • Advanced monitoring and observability across all platforms
  • Cost optimization and FinOps practices
Microsoft Partnership Deepening
  • Continued Microsoft Support for advanced scenarios
  • Early access to preview features and roadmap alignment
  • Joint innovation initiatives
The foundation is operational—Phase 2 adds sophistication and scale.
Phase 2: MLOps Pipeline
A cornerstone of Phase 2 is establishing the machine learning operations capability that enables rapid, reliable model deployment:
01
Development
Data scientists experiment and train models in Azure ML workspace with full tracking and versioning
02
Testing
Automated pipeline runs model validation, bias checks, security scans, and performance benchmarks
03
Staging
Successful builds deploy to test environment for user acceptance and integration testing
04
Production
Approved models deploy to production with monitoring, logging, and automated alerting enabled
05
Monitor
Continuous tracking of model performance, data drift, and business metrics with feedback loop
Phase 2: Expanded Team Structure
Growing from 5 to ~12 members requires strategic hiring across multiple disciplines:
Cloud Platform Engineer
Sets up Landing Zone, implements Infrastructure-as-Code, manages cloud resources. Initially contractor, convert to FTE.
MLOps Engineer
Builds CI/CD pipelines, implements model registry and versioning, ensures DevSecOps practices. FTE by end of phase.
Additional Data Scientists
Specialized in NLP, ML modeling, and data analytics. Mix of FTE hires and contractors for surge capacity.
Flow & Value Leads
Add 1-2 more FVLs to manage parallel workstreams. Domain expertise in sales, aftersales, and supply chain management
Phase 2: Priority Use Cases
With expanded capability, we target medium-complexity, high-value opportunities requiring data integration and ML expertise:
Customs Audit & Payment Reconciliation AI
Annual Value: $1.86M
Automates cross-checking of import duty documents, flagging discrepancies and identifying refund opportunities. Requires integration with customs databases and SAP.
Warranty & Technical Support Agent
Annual Value: $750K
AI assistant for warranty team to instantly answer dealer inquiries by referencing warranty policies and technical service bulletins. Scales Q&A capability to broader audience.
Mazda Mexico Internal Support Agent
Annual Value: $972K
Bilingual (Spanish/English) AI support for Mexico operations. Tests localization capability and cross-regional collaboration.
Phase 2: Customs Audit AI Deep Dive
The Challenge
Mazda imports thousands of vehicles and parts annually, each subject to customs duties. Manual reconciliation of declarations against invoices and payments is time-consuming and error-prone, leading to:
  • Unclaimed duty refunds
  • Compliance risks from documentation gaps
  • Significant auditor time on repetitive checks
The AI Solution
Machine learning model trained on historical audit data identifies patterns and anomalies:
  • Ingests data from customs systems and SAP
  • Flags discrepancies for auditor review
  • Learns from auditor decisions to improve
  • Prioritizes highest-value opportunities
This project validates our ability to handle complex data integration and deploy ML models requiring ongoing learning—essential capabilities for future initiatives.
Phase 2: Dealer Lead Agent Pilot
While full deployment is reserved for Phase 3, we begin groundwork on what will become our highest-impact initiative:
Phase 2 Pilot Scope
  • Analyze historical lead data to understand patterns and outcomes
  • Build proof-of-concept AI agent on test data
  • Develop response generation using GPT models fine-tuned on vehicle information
  • Validate technical feasibility with 1-2 pilot dealers
  • Identify integration points with dealer CRM systems
This de-risks the Phase 3 full rollout, ensuring we understand the technical and operational requirements before scaling to all 540 dealers.
70K
Monthly Leads
Volume requiring follow-up
23%
Baseline Close Rate
Current 90-day conversion
$33.5M
Mid-Case Value
At 1.5% improvement
Phase 2: Delivery Timeline
Month 7
Platform build begins. Team onboarding. Project discovery for Audit AI and Warranty Agent.
Month 8
Platform MVP ready. Development sprints in full swing. First pipeline deployment to non-prod.
Month 9
End-to-end testing complete. Warranty Agent ready for deployment.
Month 10
Warranty Agent goes live. Audit AI in final testing. Platform refinements based on learnings.
Month 11
Audit AI deployed. Mexico Agent in development. Dealer Lead pilot begins.
Month 12
Phase 2 review. Updated EIS OLA. Phase 3 planning and stakeholder alignment.
Phase 2: Process Maturity Evolution
With multiple concurrent projects, we institute formal coordination mechanisms and quality gates:
Definition of Ready
Before entering development: data sources identified and access approved, success metrics defined, RAI considerations documented, stakeholders aligned on scope
Definition of Done
Before production: all tests pass, CI/CD pipeline executes successfully, security scans clean, documentation complete, training materials ready, business owner sign-off obtained
Scrum of Scrums
Bi-weekly synchronization across workstreams to manage dependencies and shared resources. Led by Delivery RTE.
Steering Committee
Monthly executive review of progress, interim results, and escalations. Ensures leadership alignment and rapid decision-making.
Phase 2: Expected Outcomes
3-4
Solutions Delivered
Live in production
50%
Lead Time Reduction
Vs. Phase 1 delivery
70%
Self-Sufficiency
Tasks handled internally
Value Delivered
Phase 2 Incremental Benefit: $5-7M annually
Cumulative Portfolio Value: $8-10M annually
Phase 2 Investment: ~$900K
The platform investment yields immediate dividends through faster delivery cycles, with projects moving from 4-5 months to 2-3 months from kickoff to production.

By end of Phase 2, the AI Dojo has proven it can operate with significantly increased autonomy while maintaining rigorous governance—setting the stage for full Builder/Owner mode.
Phase 2: Risk Management
1
Risk: Platform Setup Delays
Impact: Could slow project development
Mitigation: Expert contractor for rapid build. EA/CISO/EEARB involvement in design for buy-in. Phased approach starting with basic dev environment before full capabilities.
2
Risk: New Staff Onboarding
Impact: Potential knowledge gaps or delayed productivity
Mitigation: Begin recruiting in Phase 1. Use contractors as interim. Pair new hires with Phase 1 team members. Allocate time for Mazda context training.
3
Risk: Resource Contention
Impact: Multiple projects competing for shared resources
Mitigation: Stagger project start dates. Clear prioritization framework. RTEs and FVLs coordinate frequently. Maintain backlog flexibility to adjust if needed.
4
Risk: Value Realization Gaps
Impact: Solutions built but not properly utilized
Mitigation: Half-time Adoption Lead ensures each solution has business owner. Training sessions and quick reference guides. Monitor usage metrics and address barriers.
Preparing for Full Autonomy
Phase 2's success creates the conditions for Phase 3's transformative impact. By demonstrating responsible operation of our own platform while accelerating delivery, we earn the trust needed for full Builder/Owner autonomy.
Evidence Building
  • Zero security incidents despite increased velocity
  • All projects passed privacy and compliance reviews
  • EIS change requests reduced from 10 to 3 per phase
  • Average turnaround improved from 4 weeks to 1 week
  • Documented adherence to governance frameworks
Updated Operating Agreement
Based on Phase 2 performance, we negotiate an updated OLA with EIS that further reduces touchpoints:
  • Pre-approved operations whitelist for Dojo
  • Production deployment authority within guardrails
  • EIS monitoring role vs. gatekeeper role
  • Exception-based engagement model
Phase 3: Run—Full Autonomy & Scaled Impact
Duration: Months 13-24
Phase 3 represents the full realization of the Builder/Owner model. The AI Dojo now operates as an AI factory—a high-velocity, highly-governed capability that continuously delivers solutions driving revenue, cost savings, and competitive advantage.
End-to-End Ownership
Complete authority to design, build, test, deploy, and maintain AI solutions with only high-level oversight
Strategic Bets
Tackle the highest-impact, most complex initiatives including dealer-facing and revenue-generating solutions
Continuous Delivery
Fast flow mode with frequent releases, quick iterations, and ability to respond to new demands in weeks not months
Sustained Excellence
Robust governance scales with velocity—policy-as-code, automated checks, and mature risk management
Phase 3: Operational Excellence & Enterprise Scale
What Full Maturity Means
Building on infrastructure ownership established in Phase 1, Phase 3 represents operational excellence:
Platform Maturity
  • Self-service capabilities for AI teams to provision resources within governance guardrails
  • Automated compliance monitoring and remediation
  • Advanced FinOps with cost allocation and optimization
  • Platform-as-a-service model for internal customers
Operational Excellence
  • 99.9% platform uptime with automated failover
  • Continuous platform updates with zero-downtime deployments
  • Advanced security posture with threat detection and response
  • Comprehensive observability and performance optimization
Enterprise Scale
  • Supporting 5-8 concurrent AI solution deliveries per quarter
  • Power Platform/Copilot Studio deployed across multiple business units
  • Data Intelligence Platform serving analytics and AI workloads enterprise-wide
  • Center of Excellence model with reusable patterns and accelerators

This phase leverages the infrastructure ownership gained in Phase 1 to drive operational excellence and enterprise-wide scalability. Automated policies and robust governance ensure all solutions meet compliance standards, enabling the AI Dojo to deliver high-impact results rapidly and consistently across the organization.
Phase 3: Flagship Initiative—Dealer Lead Processing AI
The crown jewel of the AI Dojo's portfolio: an intelligent system that transforms how Mazda dealers engage with potential customers.
The Opportunity
  • 70,000 leads per month across 540 dealers
  • Current 23% baseline close rate within 90 days
  • Speed of response strongly correlates with conversion
  • BDC staff struggle to respond promptly and consistently
  • Each 1% improvement = 8,400 additional sales annually
The Solution
  • AI agent ingests leads via email or CRM API integration
  • Generates personalized responses using GPT fine-tuned on vehicle data
  • Incorporates dealer-specific inventory and pricing
  • Provides draft to salesperson or auto-responds based on dealer preference
  • Continuous learning from successful conversions
Dealer Lead AI: Financial Impact
Mid-case scenario detail: 12,600 additional sales × $2,408 profit per vehicle = $30.3M incremental profit, plus $3.2M in labor savings from automated follow-up, totaling $33.5M net annual benefit.

This single initiative has the potential to deliver returns exceeding the entire two-year AI Dojo investment by a factor of 7x or more, representing the majority of the $60-85M total annual value target.
Dealer Lead AI: Implementation Approach
Technical Foundation (Months 13-14)
Build production-grade agent infrastructure. Integrate with dealer CRM systems. Establish vehicle data feeds. Create response templates and quality guardrails.
Pilot Expansion (Months 15-16)
Expand beyond Phase 2 test to 10-15 dealers. Measure conversion impact. Refine based on dealer feedback. Train model on successful interactions.
Dealer Engagement (Months 17-18)
Present results to dealer advisory councils. Develop training materials and support resources. Coordinate with Sales Operations on rollout plan.
National Rollout (Months 19-21)
Phased deployment to all 540 dealers in waves. Provide white-glove support for early adopters. Monitor performance and address issues rapidly.
Optimization (Months 22-24)
Continuous improvement based on conversion data. Expand features based on dealer requests. Measure ROI and document success stories.
Phase 3: Advanced Agentic Mesh Expansion
Building on the Mazda Sales Promise Agent success from Phase 1, Phase 3 expands the agentic mesh architecture enterprise-wide:
Expanded Agent Ecosystem
  • Manufacturing Operations Agent (production planning, quality monitoring)
  • Supply Chain Intelligence Agent (logistics optimization, supplier coordination)
  • Customer Experience Agent (post-sale support, warranty processing)
  • Financial Planning Agent (budget analysis, forecasting support)
Advanced Capabilities
  • Cross-agent orchestration for complex multi-domain workflows
  • Proactive insights and recommendations based on pattern detection
  • Integration with Azure AI Foundry for custom model deployment
  • Real-time data integration via Fabric Data Agents
Enterprise Scale
  • 2,000+ active users across all business functions
  • 10+ specialized child agents supporting various domains
  • Automated agent deployment and lifecycle management
  • Comprehensive analytics on agent performance and business impact
Expected Value: $15-20M annually from operational efficiency and decision support across the enterprise
Phase 3: Enterprise Knowledge Ecosystem
Building on earlier successes, we scale and integrate our AI assistants into a comprehensive knowledge platform:
HR Agent
Phase 1 success expanded with enhanced capabilities and integration
Warranty Agent
Phase 2 deployment supporting dealer technical inquiries
IT Support Agent
Level-1 helpdesk automation for password resets and software requests
Multilingual Support
Spanish for Mexico, French for Canada expanding accessibility
Unified Analytics
Cross-agent insights on common questions and knowledge gaps
Phase 3: Advanced Analytics Integration
Productionizing Data Science Assets
Phase 3 brings existing analytics work into reliable production deployment:
  • Owner Retention Models: Predict which customers are at risk of switching brands and trigger proactive engagement
  • Marketing Propensity: Target campaigns to highest-probability prospects
  • Inventory Optimization: ML-driven predictions for optimal dealer stock levels
  • Service Demand Forecasting: Anticipate parts needs and service center capacity
The MLOps infrastructure built in Phase 2 enables these models to run reliably with automated retraining, monitoring, and integration into operational systems—transforming proof-of-concepts into business value.
Phase 3: Team at Full Capacity
Phase 3 team reaches ~18-20 members organized into specialized squads with platform support:
Dealer Experience Squad
5-6 members dedicated to dealer-facing AI including Lead Agent and Sales Promise. Includes FVL, developers, dealer relationship manager.
Corporate Operations Squad
5-6 members focusing on internal process AI for HR, Finance, Audit, and IT. Includes FVL, data scientists, integration specialists.
Platform & Governance Team
5-6 members providing shared services: platform engineering, MLOps, architecture, RAI oversight, change management.
Innovation Reserve
15-20% of capacity allocated to exploring emerging AI capabilities, technical debt reduction, and continuous learning.
Phase 3: Operating at Scale
Continuous Delivery Rhythm
  • Quarterly PI Planning: All teams align on objectives for next 10-12 weeks
  • Two-Week Sprints: Regular delivery cadence with demos and retrospectives
  • Daily Standups: Within squads for coordination and blocker removal
  • Weekly Sync: Cross-squad dependencies and resource allocation
  • Monthly Steering: Executive review of progress and strategic decisions
You Build It, You Run It
Squads own their solutions in production:
  • On-call rotations for critical systems
  • Monitoring dashboards for each application
  • Incident response procedures
  • Support ticket triage and resolution
  • Continuous optimization based on usage patterns
This DevOps approach ensures solutions remain healthy and high-performing long after initial deployment.
Phase 3: Governance at Velocity
Speed without control is recklessness. Our governance model scales with our velocity through automation and clear accountability:
Policy-as-Code
Azure Policy and automated scans enforce security and compliance standards. No sensitive data in external AI prompts detected automatically.
Automated Checks
Pipeline includes bias testing, security scanning, performance validation. Issues block deployment until resolved.
Continuous Monitoring
All solutions instrumented with telemetry. Dashboards track usage, performance, errors, and business outcomes.
Clear Accountability
RASCI matrices define who's Responsible, Accountable, Supportive, Consulted, Informed for every process and decision.
Continuous Improvement
Quarterly governance reviews identify improvement opportunities. Retrospectives after incidents lead to process updates.
Phase 3: Framework Alignment Achievement
NIST AI Risk Management Framework
  • Govern: AI Steering Committee, policies, and risk register operational
  • Map: Risk assessment standard for every use case
  • Measure: Defined metrics for accuracy, fairness, performance tracked continuously
  • Manage: Incident response procedures and model retraining triggers established
ISO/IEC 42001:2023
  • Leadership commitment via Steering Committee (EEARB)
  • Comprehensive planning and risk management
  • Training and competency programs
  • Operational controls from intake to deployment
  • KPI tracking and quarterly management reviews
  • Feedback loops for continuous improvement
By Phase 3 end, MNAO has the option to pursue external certification of our AI management system—demonstrating leadership in responsible AI governance to stakeholders, regulators, and customers.
Phase 3: Expected Outcomes
5-8
Solutions Per Quarter
New AI capabilities delivered continuously
<8 Weeks
Avg Lead Time
Idea to production
for moderate projects
$60-85M
Annual Portfolio Value
Cumulative benefit by Year 2 end
15-18x
Return on Investment
Benefit vs. two-year total investment
Value Delivered
  • Dealer Lead AI: $21.9M (mid-case)
  • Sales Promise AI: $50M program optimization
  • Phase 1-2 Solutions: $8-10M maintained and enhanced
  • Additional Projects: $10-15M
Capabilities Established
  • Permanent AI development function
  • Platform for continuous innovation
  • Proven governance model
  • Cross-trained team with deep Mazda knowledge
Phase 3: Risk Management at Scale
Major AI Failure in Production
Risk: Incorrect AI output causes customer or business harm
Mitigation: Human-in-loop for critical communications initially. AI constrained to known topics. Regular quality reviews. Phased rollout with opt-in approach. Comprehensive testing and validation.
Change Resistance
Risk: Users resist AI adoption fearing job replacement or impersonal service
Mitigation: Position AI as assistant not replacement. Show data on success improvements. Celebrate early adopters. Emphasize how AI makes jobs more interesting by handling repetitive work.
Talent Retention
Risk: Skilled team members recruited away by external offers
Mitigation: Competitive compensation. Positive team culture. Career growth opportunities. Highlight meaningful impact of work. Exposure to cutting-edge technology and problems.
Oversight Gaps
Risk: Autonomy leads to unchecked problems
Mitigation: Monthly EEARB reviews. Open invite for Internal Audit. Transparent reporting. Maintain high visibility even as we move fast.
Strategic Options: Choosing Your Path
At each phase, leadership can adjust investment and velocity based on appetite for speed versus risk. We've designed three option tracks:
Option A: Conservative
Philosophy: Minimize cost and risk with lean teams and sequential delivery
Best For: Risk-averse culture or budget constraints. Proves concept before scaling.
Trade-off: Slower value capture, may lose competitive ground
Option B: Balanced (Recommended)
Philosophy: Optimize for sustainable growth with proven ROI at each stage
Best For: Most organizations balancing speed and prudence
Trade-off: None—best risk-adjusted return
Option C: Aggressive
Philosophy: Maximize speed with heavy upfront investment
Best For: Urgent competitive pressure or executive mandate for rapid transformation
Trade-off: Higher risk of inefficiency if processes aren't mature
Options by Phase: Investment & Outcomes

Phase 1's rapid ROI (36x in the first year for Option B) provides crucial funding for subsequent phases. Option B (Balanced) consistently delivers strong benefits while building sustainable capabilities, reaching $60-85M in annual value by Phase 3.
Two-Year Financial Summary: Option B
Total Investment
7%
Phase 1
$350K - Mazda Sales Promise Agent & infrastructure foundation (3 months)
19%
Phase 2
$900K - Platform maturity & expansion
74%
Phase 3
$3.5M - Full operation (annual)
Cumulative Two-Year Investment: $4.75M
Value Creation
  • Phase 1 Benefit: $10-15M/year from Mazda Sales Promise Agent
  • MSP execution efficiency and dealer enablement
  • Reduced project coordination overhead
  • Faster dealer onboarding and support
  • Improved compliance monitoring
  • Phase 2 Incremental: +$15-20M/year (Customs Audit AI, expanded agents)
  • Phase 3 Incremental: +$35-50M/year (Dealer Lead AI, enterprise agent mesh)
Year 2 Run-Rate: $60-85M annual benefit
15-18x ROI
Benefit-to-cost ratio over two-year period
The Mazda Sales Promise Agent delivers immediate, substantial ROI that funds subsequent phases, with total annual benefits reaching $60-85M.
The Cost of Inaction
Every week we delay this initiative costs Mazda $1.49 million in unrealized value from our identified use case backlog.
Consider just the Dealer Lead AI opportunity: at current volumes and conversion rates, we're missing ~700 additional sales per month that this solution could enable. That's $1.8 million in lost profit monthly, or $21.6 million annually.
Meanwhile, competitors are rapidly deploying AI across their operations. The gap between early movers and followers grows wider each quarter. This isn't about keeping pace—it's about competitive survival.
$85M
Value at Risk
If we don't act
700
Sales Lost Monthly
From lead AI alone
Success Metrics & KPIs
We will track progress through clear, measurable indicators at each phase:
Delivery Metrics
  • Throughput: Number of solutions delivered per quarter
  • Lead Time: Average days from kickoff to production
  • Cycle Time: Average days in active development
  • Deployment Frequency: Releases per month
Value Metrics
  • Benefits Realized: Actual savings/revenue vs. projected
  • Adoption Rate: % of target users actively using each solution
  • User Satisfaction: NPS or CSAT scores for AI tools
  • ROI: Cumulative benefit-to-cost ratio
Quality Metrics
  • Incidents: Production issues per solution per month
  • Model Performance: Accuracy, precision, recall tracked continuously
  • Compliance: % of projects passing all governance gates
  • Technical Debt: Outstanding issues and velocity impact
Capability Metrics
  • Team Satisfaction: Engagement and retention rates
  • Knowledge Growth: Skills acquired and certifications achieved
  • Platform Uptime: Availability of dev/prod environments
  • Autonomy Index: % of work completed without external dependencies
Quarterly business reviews will track these metrics against targets, with executive dashboards providing real-time visibility into Dojo performance.
Governance Structure
The AI Dojo would operate within a robust governance framework ensuring alignment with enterprise strategy and risk management:
1
2
3
4
5
1
Executive Committee (UECM)
Quarterly strategy reviews, major investment decisions, cross-functional alignment
2
AI Governance Council (EEARB)
Monthly oversight of portfolio, risk reviews, policy updates, phase gate approvals
3
Responsible AI Board (Governance Council)
Reviews use cases for ethical implications, bias testing, fairness assessments
4
Technical Architecture Review (CISO/EA/MS)
Validates solution designs, ensures standards compliance, approves exceptions
5
Dojo Operations Team
Day-to-day execution, sprint planning, delivery, continuous improvement
Change Management & Adoption Strategy
Building Organizational Readiness
Technology alone doesn't deliver value—adoption does. Our change management approach ensures each AI solution achieves its potential:
  • Stakeholder Engagement: Early involvement of business owners and end users in design
  • Communication Plans: Clear, consistent messaging about what's changing and why
  • Training Programs: Role-specific education on using new AI tools
  • Champion Networks: Identify and empower enthusiasts to drive peer adoption
By Phase 3, we have a full-time Change & Adoption Lead ensuring every solution has a path to realizing its projected benefits through effective organizational change.
Long-Term Vision: AI Center of Excellence
Beyond the two-year roadmap, the AI Dojo evolves into Mazda's permanent AI Center of Excellence:
Innovation Engine
Continuously exploring emerging AI capabilities and pilot testing new applications
Training Hub
Educating employees across Mazda on AI literacy and hands-on skills development
Best Practices Repository
Documenting lessons learned, reusable components, and proven patterns for AI development
Global Collaboration
Partnering with Mazda Japan and other regions to share capabilities and insights
External Partnerships
Maintaining relationships with AI vendors, academic institutions, and industry consortia
Recommendations & Next Steps
Our Recommendation
We recommend Option B (Balanced) with immediate Phase 1 execution as the optimal path forward:
Why Option B:
  • Mazda Sales Promise Agent delivers $8-12M annual value within 3 months
  • Proven ROI (23-34x first year) funds subsequent phases without additional justification
  • Balances rapid delivery with sustainable infrastructure foundation
  • De-risks future phases through early capability demonstration
  • Positions us for Phase 2 expansion with operational platform
Immediate Next Steps (Week 1-2)
  1. Secure Phase 1 Funding: $350K approval for 3-month delivery
  1. Establish POC Environment: Copilot Studio environment with proper permissions
  1. Initiate Microsoft Partnership: FastTrack engagement for accelerated deployment
  1. Confirm Integration Access: Atlassian Confluence/Jira and SharePoint connector permissions
  1. Assemble Core Team: Dojo Lead, Platform Architect, AI Engineer, Cloud Infrastructure Engineer
  1. Align with MSP Timeline: Confirm business requirements and launch coordination
Decision Required:
Approve Phase 1 funding and team allocation to begin delivery within 2 weeks
The Mazda Sales Promise Agent is not just a pilot—it's immediate business value that proves our builder/owner model.
The Time to Act is Now
$60-85M
Annual value within reach in 24 months—starting with $8-12M in the first 3 months
The AI Dojo represents more than a technology initiative—it's a strategic transformation of how Mazda operates in an AI-driven world.
We have everything we need to start:
  • CIO approval to own our Azure infrastructure ✓
  • Proven technology in Copilot Studio ✓
  • Clear business need with MSP launch timing ✓
  • Knowledge sources ready (Atlassian, SharePoint) ✓
  • $8-12M annual value opportunity in first 3 months ✓
The Mazda Sales Promise Agent is our proof point. In 90 days, we can demonstrate:
  • Rapid delivery capability (3 months from approval to production)
  • Substantial ROI (23-34x in first year)
  • Infrastructure ownership model working
  • Foundation for enterprise-wide AI transformation
Every week we delay is a week without intelligent dealer support, efficient MSP execution, and competitive advantage. We have the approval. We have the plan. We have the technology.
The only question is: when do we start?
Our recommendation: Approve Phase 1 funding this week and begin delivery immediately.