A tailored course, built for your situation
Mastering ISO 22301 for Senior AI Innovation Leaders
Build a self-reinforcing operational resilience practice that compounds across AI product cycles
$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.
Delivering AI at speed often sacrifices long-term durability, until now
The situation this course is for
AI leaders face a hidden cost: each rapid deployment erodes institutional memory if not anchored to a consistent operational backbone. Without one, teams repeat effort, miss compliance signals, and lose credibility during escalation.
Who this is for
Senior AI innovation leads who transition from project-by-project delivery to building self-reinforcing technical and governance IP
Who this is not for
Junior developers, general IT staff, or compliance novices without AI product leadership experience
What you walk away with
- Design AI delivery playbooks that strengthen with each deployment
- Turn incident responses into auditable, reusable resilience modules
- Build a cross-client portfolio of ISO 22301-aligned AI continuity frameworks
- Establish internal credibility that attracts premium engagements
- Create stakeholder-facing narratives that compound trust over time
The 12 modules (with all 144 chapters)
Module 1. Foundations of Operational Resilience in AI Systems
Establish the core linkage between ISO 22301 principles and AI lifecycle management, focusing on proactive continuity planning for model deployment and inference infrastructure.
12 chapters in this module
- Defining operational resilience in the context of generative AI
- Mapping AI failure modes to business impact scenarios
- Integrating ISO 22301 with MLOps pipelines
- Key roles in AI resilience governance
- Establishing resilience KPIs for AI products
- Documenting critical AI-dependent business functions
- Assessing third-party AI service dependencies
- Planning for data drift and concept drift events
- Building executive communication protocols for AI outages
- Creating a resilience-first AI development charter
- Benchmarking against industry AI resilience standards
- Initiating the ISO 22301 scoping process for AI
Module 2. Business Impact Analysis for AI Workloads
Learn to quantify the true cost of AI downtime and prioritize systems based on business continuity needs, ensuring compliance and strategic alignment.
12 chapters in this module
- Identifying critical AI-powered business processes
- Measuring financial impact of AI service interruption
- Assessing reputational risk from AI failures
- Calculating maximum tolerable downtime for AI models
- Determining recovery time objectives for inference APIs
- Prioritizing AI systems using business criticality tiers
- Gathering stakeholder input on AI dependencies
- Documenting AI-related legal and regulatory exposures
- Building the business impact register for AI
- Validating BIA findings with leadership
- Updating BIA after AI product changes
- Linking BIA to insurance and vendor contracts
Module 3. Risk Assessment and Control Design for AI
Develop targeted controls that address AI-specific threats while aligning with ISO 22301 requirements for risk treatment and mitigation planning.
12 chapters in this module
- Identifying threats to AI system availability
- Assessing AI model poisoning and evasion risks
- Evaluating infrastructure failure points in AI deployment
- Designing controls for AI monitoring and alerting
- Implementing failover strategies for generative AI services
- Securing AI training data pipelines
- Protecting model weights and architecture IP
- Ensuring AI explainability during incident response
- Building AI-specific risk treatment plans
- Integrating AI controls into ISO 22301 framework
- Testing control effectiveness for AI workloads
- Documenting control ownership and accountability
Module 4. Developing AI-Specific Business Continuity Plans
Create actionable plans that ensure AI services can be restored within defined timeframes, with clear roles, escalation paths, and communication protocols.
12 chapters in this module
- Structuring AI continuity plans by service tier
- Defining clear roles for AI incident response
- Establishing communication trees for AI outages
- Creating runbooks for common AI failure scenarios
- Planning for AI model retraining during disruption
- Securing access to backup AI infrastructure
- Documenting vendor escalation procedures
- Integrating AI continuity with broader IT DR
- Building crisis simulation scenarios for AI
- Validating plan readiness through tabletop exercises
- Maintaining plan currency through change control
- Reporting on AI continuity plan effectiveness
Module 5. Incident Management for Generative AI Systems
Implement structured response protocols for AI incidents, ensuring rapid containment, stakeholder communication, and regulatory compliance.
12 chapters in this module
- Detecting generative AI service anomalies
- Classifying AI incident severity levels
- Activating response teams for AI outages
- Containing model drift and hallucination events
- Preserving evidence for AI incident investigations
- Communicating with customers during AI failures
- Reporting to regulators on AI incidents
- Documenting root cause analysis for AI failures
- Implementing corrective actions from incident reviews
- Building AI incident playbooks
- Conducting post-mortems on AI outages
- Improving AI resilience from incident data
Module 6. Exercising and Testing AI Resilience Plans
Design and execute realistic tests that validate AI continuity capabilities, uncover gaps, and demonstrate compliance readiness.
12 chapters in this module
- Planning annual AI resilience test calendar
- Designing tabletop exercises for AI scenarios
- Conducting partial evacuation drills for AI teams
- Testing AI failover to backup environments
- Measuring test success against recovery objectives
- Involving stakeholders in AI resilience testing
- Documenting test findings and action items
- Tracking remediation of test findings
- Reporting test results to leadership
- Integrating lessons into AI playbooks
- Ensuring test compliance with ISO 22301
- Maintaining test records for audit
Module 7. AI Resilience Maintenance and Improvement
Establish sustainable processes for updating AI resilience documentation, incorporating lessons learned, and adapting to changing threats.
12 chapters in this module
- Scheduling regular AI resilience reviews
- Updating documentation after AI product changes
- Incorporating incident learnings into plans
- Tracking AI resilience KPIs and metrics
- Conducting management reviews of AI resilience
- Auditing AI continuity plan effectiveness
- Benchmarking against industry AI resilience
- Improving AI resilience through feedback loops
- Documenting improvement initiatives
- Ensuring continuous compliance with ISO 22301
- Managing AI resilience documentation lifecycle
- Reporting on AI resilience maturity
Module 8. Stakeholder Engagement in AI Resilience
Build trust with executives, clients, and regulators by demonstrating proactive AI resilience governance and transparent communication.
12 chapters in this module
- Identifying key stakeholders for AI resilience
- Communicating AI resilience capabilities
- Reporting on AI continuity testing results
- Engaging clients on AI service commitments
- Preparing for regulator inquiries on AI
- Building executive dashboards for AI resilience
- Training staff on AI continuity responsibilities
- Managing third-party AI resilience expectations
- Documenting stakeholder communication
- Building credibility through demonstrated readiness
- Addressing stakeholder concerns proactively
- Maintaining stakeholder trust during AI incidents
Module 9. Integrating ISO 22301 with AI Governance Frameworks
Align operational resilience with broader AI ethics, risk, and compliance programs to create a unified governance approach.
12 chapters in this module
- Mapping ISO 22301 to AI governance domains
- Integrating resilience with model risk management
- Aligning with AI ethics review boards
- Connecting with data protection compliance
- Incorporating AI audit findings into resilience
- Building cross-functional AI governance teams
- Ensuring consistency across compliance frameworks
- Documenting governance interdependencies
- Reporting integrated AI governance to leadership
- Streamlining compliance evidence collection
- Managing AI governance tool integration
- Demonstrating holistic AI risk coverage
Module 10. Scaling AI Resilience Across Product Portfolios
Extend proven resilience practices across multiple AI products and business units, creating organization-wide consistency.
12 chapters in this module
- Developing AI resilience standards
- Creating templates for AI continuity plans
- Establishing AI resilience governance body
- Training teams on AI resilience practices
- Conducting AI resilience maturity assessments
- Benchmarking AI resilience across units
- Sharing best practices across AI teams
- Standardizing AI incident response
- Implementing centralized AI monitoring
- Managing AI resilience at scale
- Ensuring consistency across AI product lines
- Reporting organization-wide AI resilience
Module 11. Demonstrating Value of AI Resilience Investment
Quantify and communicate the ROI of AI resilience programs to secure ongoing support and funding.
12 chapters in this module
- Tracking AI incident reduction metrics
- Measuring resilience program cost avoidance
- Calculating reduced downtime costs
- Assessing improved customer retention
- Documenting regulatory compliance benefits
- Quantifying improved stakeholder trust
- Building business cases for AI resilience
- Reporting ROI to leadership
- Demonstrating competitive advantage
- Linking resilience to AI product quality
- Justifying resilience tool investments
- Maintaining funding for AI resilience
Module 12. Leading AI Resilience Transformation
Drive cultural change that embeds operational resilience into AI innovation practices across the organization.
12 chapters in this module
- Building executive sponsorship for AI resilience
- Creating AI resilience champions network
- Integrating resilience into AI development lifecycle
- Rewarding resilient AI practices
- Communicating AI resilience vision
- Overcoming resistance to change
- Scaling learning from AI incidents
- Embedding resilience into AI training
- Celebrating resilience successes
- Sustaining momentum for AI resilience
- Evolution of AI resilience leadership
- Leaving a legacy of resilient AI innovation
How this maps to your situation
- After first AI model launch
- During AI product scaling phase
- Before regulatory review cycle
- Post incident review and improvement
Before vs. after
Before
Delivering AI products without a structured resilience backbone, leading to repeated effort and reactive fixes
After
Running self-reinforcing AI delivery cycles where each deployment strengthens the organization's operational resilience and trust capital
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 2.5 hours per module, designed to be completed at your pace over 6-8 weeks
If nothing changes
Without a compounding resilience strategy, each AI deployment becomes a standalone effort, fragile, hard to scale, and vulnerable to regulatory or operational setbacks.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to AI innovation leaders, blending ISO 22301 with real-world generative AI delivery challenges and compounding value creation.
Frequently asked
Is this course suitable for technical AI leads without formal compliance background?
Yes. The course is designed for AI delivery leaders who need to speak the language of resilience and compliance without becoming auditors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive practical tools I can use immediately?
Yes. Each module includes downloadable templates, checklists, and a final implementation playbook tailored to AI innovation contexts.
$199 one-time. Approximately 2.5 hours per module, designed to be completed at your pace over 6-8 weeks.
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