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BCM5761 Mastering ISO 22301 for GenAI Product Leaders in High-Pressure Tech Environments

$199.00
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A tailored course, built for your situation

Mastering ISO 22301 for GenAI Product Leaders in High-Pressure Tech Environments

Build a self-reinforcing cycle of delivery credibility and stakeholder trust

$199 one-time
24-hour access provisioning 30-day money-back guarantee Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Last-minute compliance delays that erode launch momentum

The situation this course is for

AI product teams are increasingly held accountable for delivery resilience, but final-cycle validation bottlenecks, especially around business continuity planning and incident response alignment, still delay launches and dilute stakeholder trust. The cost isn't just time; it's credibility erosion on repeat.

Who this is for

Senior AI product leader at a high-growth tech firm, accountable for on-time, compliant GenAI releases under tight scrutiny and cross-functional dependencies

Who this is not for

Junior PMs, non-product roles in AI, or teams focused solely on model development without delivery ownership

What you walk away with

  • A reusable, auditable launch validation package for GenAI releases
  • Faster cross-functional alignment on continuity requirements
  • Fewer last-minute changes during final review windows
  • Increased trust from engineering and compliance partners
  • A growing library of delivery artifacts that accelerate future cycles

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 22301 in the Context of GenAI Systems
Lays the foundation by mapping ISO 22301 clauses to real GenAI delivery risks, focusing on continuity of training pipelines, inference availability, and incident response.
12 chapters in this module
  1. How ISO 22301 applies to machine learning infrastructure
  2. Key differences between traditional IT continuity and AI system resilience
  3. Mapping GenAI failure modes to business impact scenarios
  4. The role of product management in continuity planning
  5. Integrating ISO 22301 requirements into AI product specs
  6. Common gaps in AI teams’ current continuity posture
  7. Why AI systems fail differently under stress
  8. Case study: GenAI outage at a major platform
  9. Stakeholder expectations during AI service disruption
  10. Aligning incident response with model versioning
  11. Documenting recovery time objectives for AI features
  12. Translating compliance needs into engineering tasks
Module 2. Building a Business Impact Analysis for GenAI Workloads
Guides the creation of a credible BIA tailored to generative AI use cases, prioritizing services by user impact and downstream dependencies.
12 chapters in this module
  1. Defining criticality for GenAI features versus traditional services
  2. Identifying dependent teams and systems in the AI stack
  3. Quantifying user impact of GenAI downtime
  4. Setting realistic recovery time and point objectives
  5. Involving legal and trust teams in BIA scoping
  6. Documenting assumptions for AI model reinitialization
  7. Prioritizing workloads by revenue, engagement, and compliance
  8. Validating BIA inputs with engineering leads
  9. Handling dynamic workloads with variable inference demand
  10. Updating BIA for new model releases
  11. Linking BIA outcomes to continuity strategy
  12. Common pitfalls in AI-specific BIA development
Module 3. Designing Continuity Strategies for AI Infrastructure
Covers practical strategies for ensuring GenAI systems remain available or recover quickly, including fallback mechanisms and data pipeline resilience.
12 chapters in this module
  1. Architectural options for GenAI continuity
  2. Fallback strategies for model serving layers
  3. Data pipeline redundancy for training and inference
  4. Model checkpointing and warm restart protocols
  5. Cross-region deployment considerations
  6. Caching strategies during partial outages
  7. Human-in-the-loop escalation paths
  8. Version rollback playbooks for AI models
  9. Monitoring continuity readiness in production
  10. Integrating continuity into CI/CD pipelines
  11. Cost-benefit analysis of redundancy levels
  12. Documenting strategy decisions for audit
Module 4. Developing Incident Response Plans for GenAI Disruptions
Provides a framework for creating actionable, role-specific response plans tailored to AI system failures.
12 chapters in this module
  1. Defining incident severity levels for AI services
  2. Role assignments during AI outages
  3. Communication protocols with internal teams
  4. External messaging strategy during AI downtime
  5. Escalation paths for model drift incidents
  6. Playbooks for data poisoning or prompt injection
  7. Coordinating with security and legal teams
  8. Post-incident review processes
  9. Documenting response actions for compliance
  10. Simulating AI incident scenarios
  11. Integrating IR plans with existing NOC workflows
  12. Maintaining plan currency across model updates
Module 5. Integrating ISO 22301 into GenAI Product Roadmaps
Shows how to embed continuity requirements into product planning without slowing innovation.
12 chapters in this module
  1. Mapping ISO 22301 clauses to product milestones
  2. Balancing speed and resilience in sprint planning
  3. Incorporating continuity into feature definition
  4. Working with engineering to scope recovery needs
  5. Prioritizing technical debt related to continuity
  6. Tracking compliance readiness in Jira
  7. Reporting continuity status to leadership
  8. Handling roadmap changes mid-cycle
  9. Aligning with platform-wide resilience goals
  10. Documenting decisions for audit trails
  11. Measuring progress on continuity deliverables
  12. Reducing last-minute compliance work
Module 6. Validating Continuity Plans Through Realistic Testing
Covers effective testing methods for GenAI continuity plans, avoiding token exercises in favor of meaningful validation.
12 chapters in this module
  1. Choosing appropriate test scenarios for AI systems
  2. Tabletop exercises for model degradation events
  3. Technical failover tests for inference pipelines
  4. Involving cross-functional teams in testing
  5. Measuring test effectiveness with clear metrics
  6. Documenting test results for auditors
  7. Addressing gaps identified in tests
  8. Scheduling regular test cycles
  9. Scaling test complexity over time
  10. Avoiding disruptive testing in production
  11. Integrating test feedback into product updates
  12. Building stakeholder confidence through testing
Module 7. Documenting ISO 22301 Compliance for GenAI Systems
Provides templates and best practices for creating audit-ready documentation that reflects actual AI system operations.
12 chapters in this module
  1. Required documents under ISO 22301 for AI teams
  2. Writing policies that reflect real GenAI practices
  3. Creating evidence of continuity planning
  4. Maintaining version control for compliance docs
  5. Linking documentation to product artifacts
  6. Demonstrating leadership commitment
  7. Showing continuous improvement in documentation
  8. Preparing for internal and external audits
  9. Using automation to maintain documentation
  10. Avoiding boilerplate compliance language
  11. Tailoring documentation to AI use cases
  12. Common documentation gaps in AI teams
Module 8. Managing Third-Party Risks in GenAI Continuity
Addresses continuity risks introduced by external vendors, APIs, and cloud providers in the AI stack.
12 chapters in this module
  1. Mapping vendor dependencies in GenAI pipelines
  2. Assessing vendor continuity capabilities
  3. Contractual requirements for vendor resilience
  4. Monitoring vendor performance and uptime
  5. Fallback strategies for third-party API failures
  6. Managing model dependencies on external providers
  7. Incident coordination with vendors
  8. Auditing vendor compliance claims
  9. Reducing single points of failure
  10. Diversifying vendor relationships
  11. Documenting vendor risk decisions
  12. Communicating vendor risks to stakeholders
Module 9. Training Teams on GenAI Continuity Practices
Covers how to effectively train engineers, PMs, and support staff on their roles in maintaining system resilience.
12 chapters in this module
  1. Identifying training needs by role
  2. Developing role-specific continuity training
  3. Onboarding new team members
  4. Conducting regular refresher sessions
  5. Using real incidents as training material
  6. Measuring training effectiveness
  7. Documenting training completion
  8. Integrating training into on-call rotations
  9. Creating accessible training resources
  10. Updating training for new system changes
  11. Engaging leadership in training
  12. Avoiding training fatigue in fast-moving teams
Module 10. Measuring and Reporting on GenAI Continuity Performance
Establishes metrics and reporting practices that demonstrate ongoing compliance and improvement.
12 chapters in this module
  1. Key metrics for GenAI system resilience
  2. Tracking recovery time and success rates
  3. Measuring test participation and results
  4. Reporting on continuity posture to leadership
  5. Benchmarking against industry standards
  6. Using dashboards for real-time visibility
  7. Identifying trends in incident data
  8. Reporting to compliance and audit teams
  9. Demonstrating continuous improvement
  10. Avoiding vanity metrics
  11. Aligning metrics with business goals
  12. Documenting performance for auditors
Module 11. Maintaining ISO 22301 Compliance Through GenAI Evolution
Addresses the challenge of maintaining compliance as GenAI systems rapidly evolve through model updates and feature changes.
12 chapters in this module
  1. Change management for continuity plans
  2. Assessing impact of new models on continuity
  3. Updating documentation for model changes
  4. Revalidating plans after major updates
  5. Managing technical debt in continuity
  6. Scaling compliance practices with team growth
  7. Handling mergers or reorganizations
  8. Responding to audit findings
  9. Integrating lessons from incidents
  10. Staying current with ISO 22301 updates
  11. Continuous improvement cycles
  12. Documenting compliance evolution
Module 12. Scaling GenAI Continuity Across Product Portfolios
Provides guidance for extending proven continuity practices across multiple GenAI products and teams.
12 chapters in this module
  1. Identifying common patterns across GenAI products
  2. Creating reusable continuity templates
  3. Standardizing documentation formats
  4. Sharing lessons across teams
  5. Establishing center of excellence practices
  6. Coordinating cross-product incident response
  7. Managing dependencies between AI systems
  8. Scaling testing programs
  9. Developing organization-wide metrics
  10. Maintaining consistency with autonomy
  11. Reducing duplication of effort
  12. Documenting portfolio-level continuity

How this maps to your situation

  • Q3 GenAI release cycle under compliance scrutiny
  • Cross-functional alignment on continuity standards
  • Efficiency pressure to reduce last-minute validation
  • Growing stakeholder expectations for AI reliability

Before vs. after

Before
Last-minute compliance validations delaying GenAI releases and consuming disproportionate team bandwidth.
After
A repeatable, audit-ready validation package that ships with every release, building stakeholder trust over time.

What's included with your purchase

  • 12 modules with 12 chapters each (144 chapters)
  • Downloadable templates and worked examples for every module
  • Hand-built implementation playbook delivered alongside course access
  • 30-day money-back guarantee

Delivery and format

  • Course and learning environment access provisioned within 24 hours of purchase
  • Hand-built implementation playbook delivered alongside course access

Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.

Time investment: Approximately 90 minutes per week over six weeks, designed to fit around product delivery cycles.

If nothing changes
Without structured continuity practices, GenAI teams risk repeated launch delays, eroded stakeholder trust, and growing technical debt that compounds with each release cycle.

How this compares to the alternatives

Unlike generic ISO 22301 courses, this program is tailored to GenAI product leaders, focusing on real-world delivery challenges rather than theoretical compliance. It provides actionable templates and examples specific to AI infrastructure, not generic IT continuity frameworks.

Frequently asked

Is this course suitable for non-technical product managers?
Yes, it's designed for product leaders who need to understand and manage continuity requirements without needing deep engineering expertise.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with internal audit cycles?
Yes, the course includes templates and strategies specifically designed to produce audit-ready evidence for ISO 22301 compliance.
$199 one-time. Approximately 90 minutes per week over six weeks, designed to fit around product delivery cycles..

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

30-day money-back guarantee· 144 chapters· Hand-built playbook included· Account access within 24 hours
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