Transforming Mazda Through AI
Building the AI Dojo to Drive $40M+ in Annual Value
The Imperative for Change
The Cost of Waiting
Mazda North American Operations faces a critical inflection point. We've identified $78.6M in annual benefits locked within AI use cases across our enterprise. Yet our current approach to AI development is constrained by legacy processes designed for traditional IT projects.
Every week of delay costs us approximately $1.49 million in unrealized value. The dealer lead processing opportunity alone represents $22M annually—money left on the table while competitors accelerate their digital capabilities.
$78.6M
Total AI Portfolio Value
Annual benefits identified across use cases
$1.49M
Weekly Cost of Delay
Lost value per week of inaction
The Current State Challenge
Infrastructure Bottlenecks
Today, every AI initiative requires extensive coordination with Enterprise Infrastructure & Security (EIS). While EIS provides critical governance and security, the current model creates significant friction:
  • 8-12 week lead times for basic cloud resource provisioning
  • No self-service development environments for rapid prototyping
  • Manual deployment processes requiring multiple approval layers
  • Limited Cloud expertise within traditional IT operations
Organizational Constraints
Our teams are eager to innovate, but the path forward is unclear:
  • Business units identify AI opportunities but lack technical resources or Platform Capabilities
  • Data & AI Teams struggle to productionize solutions without control over Platform Engineering and Flexibility
  • IT processes designed for traditional Waterfall Methodology
  • Knowledge and capabilities remain siloed across departments
The Builder/Owner Model Vision
The AI Dojo represents a fundamental shift in how Mazda develops and deploys AI solutions. Rather than treating each AI initiative as a one-off IT project, we're establishing a dedicated capability that can rapidly deliver and iterate on AI solutions while maintaining rigorous governance.
Builder
End-to-end ownership of AI solution development, from ideation through deployment and support
Owner
Full responsibility for platform operations, security compliance, and continuous improvement
Governed
Robust controls and oversight aligned with enterprise standards and industry frameworks
Strategic Rationale: Why Now?
The convergence of several factors makes this the optimal moment to launch the AI Dojo:
  1. Proven Use Cases: We have a validated backlog of high-ROI opportunities with clear business sponsors
  1. Technology Maturity: Generative AI and MLOps platforms have reached enterprise readiness
  1. Competitive Pressure: Automotive industry leaders are rapidly deploying AI across operations
  1. Vision 2030 Alignment: Mazda's commitment to doubling productivity requires digital transformation
  1. Cost of Delay: The financial impact of postponing these initiatives is quantifiable and severe
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 while EIS manages 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—Foundations & Quick Wins
Duration: Months 0-6
Primary Objectives
  • Establish core processes and governance framework
  • Deliver 1-2 low-complexity pilot solutions
  • Build credibility through transparent progress reporting
  • Document platform requirements for Phase 2
  • Learn from EIS dependency constraints
Operating Model
In this phase, EIS manages all cloud infrastructure changes. The Dojo focuses on analysis, design, configuration in non-production environments, and coordinating with EIS for implementation.
This conservative approach allows us to establish trust and processes while demonstrating value with minimal risk.
Phase 1: Team Structure
Dojo Lead
Leader provides overall vision, secures executive buy-in, and removes organizational roadblocks. Acts as champion for AI initiatives across the enterprise.
Program RTE (Contract)
Manages governance, risk, and coordination with EIS. Handles all infrastructure requests and ensures policy compliance for each project.
Flow & Value Lead
Combines Product Owner and Scrum Master responsibilities. Interfaces with business stakeholders to define requirements and manage project backlog.
AI Developers (Contract)
1-2 contracted specialists to accelerate pilot delivery. Focus on NLP/chatbot development and AI integration for quick wins.
Total team size: ~5 core members (2 FTE, 3 contractors)
Phase 1: Pilot Use Cases
1
HR Policy & Handbook Agent
Value: $3.0M annually
A Teams-based AI assistant that answers employee HR policy questions instantly, with citations from the Employee Handbook. Saves HR staff time on repetitive inquiries and reduces employee search time.
Why Phase 1: Static content, minimal integration required, leverages Azure QnA Maker or AI Model with document references.
2
HR Goal-Setting Coach
Value: $295K annually
SLM-based assistant helping employees draft better annual goals. Takes initial drafts and suggests improvements based on HR guidance.
Why Phase 1: Self-contained use case using internal documents. Simple interface through M365 Apps.
Phase 1: Delivery Timeline
1
Month 1-2
Foundation Building
  • Finalize team and operating model
  • Submit EIS requests for required services
  • Begin HR use case discovery
  • Codify governance charter
2
Month 3-4
Development Sprint
  • Build and test HR Policy Agent
  • Develop Goal-Setting Coach
  • Iterate with HR stakeholders
  • Document platform requirements
3
Month 5-6
Deployment & Learning
  • Deploy pilots to production
  • Measure usage and impact
  • Conduct Phase 1 retrospective
  • Planning for Phase 2
Phase 1: Managing EIS Dependencies
Key Strategies
  • Early Specification: Submit all infrastructure requests on Day 1 to account for lead times
  • Frequent Follow-ups: Regular status checks and management escalation when needed
  • Delay Documentation: Log all bottlenecks and their impact to justify Phase 2 platform investment
  • Embedded Support: Secure 20% capacity from an EIS cloud engineer to assist
Operating Level Agreement
We'll establish a formal OLA with EIS setting expectations:
  • Standard requests fulfilled in 5 business days
  • Priority escalation path for blockers
  • Weekly sync meetings for active initiatives
  • Clear handoff procedures for deployments
Phase 1: Expected Outcomes
$3.3M
Annual Benefit
From two pilot solutions delivered
$350K
Phase Investment
Fully loaded cost including contractors
9.4x
Return on Investment
Benefit-to-cost ratio in first year
Key Deliverables by Month 6:
  • ✓ AI Dojo Charter & Standard Operating Procedures documented
  • ✓ Two pilot AI solutions deployed to production
  • ✓ Backlog prioritization framework using WSJF methodology
  • ✓ Phase 2 platform requirements proposal for EIS leadership
  • ✓ Governance processes validated through pilot execution
Phase 1: Risk Mitigation
Risk: Delivery Delays from EIS Dependencies
Mitigation: Choose simple use cases with minimal infrastructure needs. Build buffer into timeline. Maintain flexible scope—better to fully deliver one solution than half-deliver two.
Risk: Low User Adoption
Mitigation: HR department formally introduces tools and encourages usage. Collect feedback continuously and iterate. Communications emphasize these are helpers, not mandatory systems.
Risk: Team Bandwidth Constraints
Mitigation: Strict scope management. Capture new requests in backlog but say no to Phase 1 additions. Prioritize completing HR Policy Agent if resources become stretched.
Risk: Data Security or Accuracy Issues
Mitigation: Thorough testing with HR review of knowledge base. Clear disclaimers that bot is advisory, not authoritative. Keep systems internal within tenant to minimize exposure.
Transitioning to Phase 2
The success of Phase 1 creates the foundation and justification for significant capability expansion. By demonstrating that the Dojo can deliver value while maintaining governance, we earn the trust needed to build our own platform infrastructure.
Phase 1 Learnings Drive Phase 2
  • Documentation of EIS bottlenecks justifies platform investment
  • Proven governance processes enable greater autonomy
  • Early ROI validates budget for team expansion
  • Stakeholder relationships position next wave of projects
The Business Case Strengthens
With $3.3M in demonstrated value and a pipeline of opportunities worth $75M+ more, the case for scaling becomes irrefutable. The cost of maintaining status quo far exceeds the investment in the Dojo's growth.
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: The Platform Foundation
Azure Landing Zone
Working with EIS and Microsoft FastTrack, we establish an enterprise-approved environment that enables the Dojo to self-serve development infrastructure:
  • Dedicated subscription with inherited corporate security policies
  • Network connectivity to on-premises and cloud data sources
  • Identity and access management for team permissions
  • Infrastructure-as-Code templates for consistent deployments

This platform aligns with Microsoft's Cloud Adoption Framework, achieving the critical "Ready" milestone while maintaining enterprise governance and security standards.
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 managment .
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
$22M
Mid-Case Value
At 1% 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. EIS 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: The Autonomy Model
What Full Autonomy Means
At Phase 3 launch, a formal decision grants the AI Dojo production deployment authority:
  • Direct Cloud Access: Platform Engineer has admin privileges for AI subscription production environment
  • Self-Service Pipeline: Promote code to production upon internal approval, no external CAB required
  • EIS Monitoring: Infrastructure team monitors compliance but doesn't gate every deployment
  • Pre-Authorized Operations: Whitelisted activities proceed without separate approval

This is not carte blanche—it's earned autonomy within guardrails. Azure Policy enforces enterprise standards automatically. Any unusual requests outside the whitelist still engage EIS. But for standard AI development and deployment, the Dojo moves at the speed of business.
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: 8,400 additional sales × $2,408 profit per vehicle = $20.2M incremental profit, plus $1.75M in labor savings from automated follow-up, totaling $21.9M net annual benefit.

This single initiative has the potential to deliver returns exceeding the entire two-year AI Dojo investment by a factor of 5x or more.
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: Mazda Sales Promise AI Agent
The Challenge
The Mazda Sales Promise is a $50M program touching Retail Operations, Legal, and Dealer Training. Managing compliance, tracking progress, and ensuring consistent execution across dealers is complex and time-intensive.
The AI Solution
Intelligent coordination system that:
  • Tracks project tasks and dealer compliance automatically
  • Provides daily status summaries to program managers
  • Flags dealers behind on required activities
  • Ensures consistent follow-ups and documentation
  • Integrates multiple data sources into unified view
Expected Annual Value: $50M through improved program outcomes, faster dealer onboarding, and higher compliance rates leading to better sales performance.
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
  • 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, Mazda 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
$40-50M
Annual Portfolio Value
Cumulative benefit by Year 2 end
12x
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

Option B (Balanced) delivers the optimal risk-adjusted return: strong benefits at each stage while building sustainable capabilities. Option A leaves significant value unrealized. Option C accelerates benefits but requires higher organizational readiness.
Two-Year Financial Summary: Option B
Total Investment
7%
Phase 1
$350K - Foundation & pilots
19%
Phase 2
$900K - Platform & expansion
74%
Phase 3
$3.5M - Full operation (annual)
Cumulative Two-Year Investment: $4.75M
Value Creation
  • Phase 1 Benefit: $3.3M/year
  • Phase 2 Incremental: +$5-7M/year
  • Phase 3 Incremental: +$30-40M/year
Year 2 Run-Rate: $40-50M annual benefit
9-12x ROI
Benefit-to-cost ratio over two-year period
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.
$78.6M
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
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) across all three phases as the optimal path forward:
  • Proven ROI at each stage justifies next-phase investment
  • Sustainable growth of capabilities without overextension
  • Risk-managed approach builds confidence across stakeholders
  • Achieves transformative impact by Year 2 end
Total Two-Year Investment: $4.75M
Expected Year 2 Annual Benefit: $40-50M
Decision Points
We seek executive approval for:
  1. Phase 1 Launch (Immediate): $350K budget, team formation, EIS OLA
  1. Platform Investment (Month 6): $900K Phase 2 budget contingent on Phase 1 success
  1. Full Autonomy (Month 12): Authorization for production deployment rights and $3.5M annual operating budget
  1. Strategic Alignment: Prioritization of Dealer Lead AI as flagship initiative
The Time to Act is Now
$40-80M
Annual value within reach in 24 months
The AI Dojo represents more than a technology initiative—it's a strategic transformation of how Mazda operates in an AI-driven world. The opportunity is clear. The path is proven. The time is now.
Every week of delay costs us $1.49 million. Every month without the Dealer Lead AI costs us $1.8 million in unrealized profit. The question is not whether to invest in AI capabilities, but whether we can afford not to.
We have the vision. We have the plan. We have the team ready to execute.
Let's transform Mazda together through the power of AI.