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DAT9352 Mastering ISO 42001 for Principal Continuous Improvement Engineers

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Principal Continuous Improvement Engineers

Turn AI governance maturity into premium project ownership and higher-impact work

$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.
Most engineers engage with AI governance reactively, fixing audit gaps, adapting to imposed controls. You can lead from the front instead.

The situation this course is for

Without a structured approach to ISO 42001, even skilled practitioners end up as implementers, not designers. Their work stays within cost centers. The highest-value projects, those with room for innovation, visibility, and budget flexibility, go to those who speak the framework fluently from day one.

Who this is for

Principal-level engineers in regulated manufacturing who influence process design and AI integration but want greater ownership of high-margin, framework-led initiatives.

Who this is not for

Entry-level quality analysts, auditors without engineering influence, or practitioners focused solely on legacy process tweaks without governance integration.

What you walk away with

  • Lead ISO 42001-aligned AI integration projects from design to deployment
  • Position yourself for work with larger budgets tied to AI governance compliance
  • Build reusable control templates that reduce audit cycle time by 30-50%
  • Gain recognition as a first-call resource for AI/OPEX convergence planning
  • Deliver implementations that pass regulatory review without rework

The 12 modules (with all 144 chapters)

Module 1. Introduction to ISO 42001 in Medical Device Manufacturing
Understand how ISO 42001 redefines process engineering in AI-integrated medical devices, with a focus on battery safety and continuous improvement workflows.
12 chapters in this module
  1. How ISO 42001 reshapes OPEX in regulated hardware environments
  2. Key differences between ISO 27001 and ISO 42001 in practice
  3. Why AI governance is now a manufacturing floor concern
  4. Mapping AI risks in pacemaker battery production systems
  5. Regulatory drivers behind ISO 42001 adoption in MedTech
  6. The role of principal engineers in framework implementation
  7. How ISO 42001 integrates with existing quality management systems
  8. Common misconceptions about AI governance in manufacturing
  9. Real-world case: AI-driven anomaly detection in cell voltage testing
  10. Documenting AI system boundaries for compliance readiness
  11. Linking AI governance to CAPA and design control workflows
  12. First steps for initiating an ISO 42001 gap assessment
Module 2. Core Structure of the ISO 42001 Framework
Break down the mandatory clauses and control areas of ISO 42001 with a focus on engineering-led implementation.
12 chapters in this module
  1. Clause 4 context and its impact on engineering scope decisions
  2. Identifying internal and external stakeholders in AI systems
  3. Defining AI system boundaries in manufacturing environments
  4. Clause 5 leadership roles and delegation in OPEX teams
  5. Establishing AI policy ownership across functional silos
  6. Clause 6 planning for AI risk and opportunity assessment
  7. Integrating AI risk registers with existing FMEA workflows
  8. Clause 7 on resources and competence mapping for engineers
  9. How training records support ISO 42001 compliance
  10. Clause 8 implementation and control integration patterns
  11. Documenting AI system lifecycle stages for audit readiness
  12. Clause 9 performance evaluation in continuous improvement cycles
Module 3. AI Risk Assessment in Regulated Manufacturing
Apply ISO 42001 risk methodology to real AI use cases in battery testing and quality assurance.
12 chapters in this module
  1. Identifying AI system types in Medtronic’s manufacturing stack
  2. Classifying AI functions in safety-critical environments
  3. Risk criteria for AI influence on battery longevity predictions
  4. Establishing severity and likelihood scales for AI failures
  5. Mapping AI risks to patient safety impact pathways
  6. Documenting risk treatment plans for AI bias in test results
  7. Integrating AI risk logs with existing quality event systems
  8. Using FMEA to cross-walk with ISO 42001 risk assessments
  9. Managing model drift in real-time monitoring systems
  10. Risk communication templates for cross-functional teams
  11. Updating risk assessments during process upgrades
  12. Audit-ready documentation of AI risk treatment decisions
Module 4. Designing AI Controls for Quality and Compliance
Build engineering controls that satisfy ISO 42001 while improving yield and reducing variation.
12 chapters in this module
  1. Control types applicable to AI-driven test equipment
  2. Implementing data quality controls in AI training pipelines
  3. Version control strategies for AI models in production
  4. Human oversight mechanisms in autonomous systems
  5. Designing fallback procedures for AI system failures
  6. Input data monitoring for out-of-bound behavior
  7. Bias testing protocols for AI in clinical device testing
  8. Model validation frequency based on production volume
  9. Logging and traceability for AI decision pathways
  10. Control integration with existing MES and SCADA systems
  11. Defining acceptable performance thresholds for AI systems
  12. Pre-audit control checks for ISO 42001 readiness
Module 5. AI System Documentation for Audit Readiness
Create and maintain ISO 42001-compliant documentation that survives auditor scrutiny.
12 chapters in this module
  1. Required documentation list per ISO 42001 clause
  2. Creating system descriptions for AI-influenced test stations
  3. Process maps showing AI decision points in workflow
  4. Version-controlled specification templates for AI systems
  5. Maintaining records of model validation and testing
  6. Documenting data provenance and lineage for AI inputs
  7. Training records for engineers managing AI systems
  8. Change management logs for AI model updates
  9. Standard operating procedures for AI oversight
  10. Audit preparation checklists for ISO 42001 reviews
  11. Responding to auditor questions on AI explainability
  12. Maintaining documentation through team transitions
Module 6. Integrating ISO 42001 with Existing Quality Systems
Align AI governance with existing QMS, ISO 13485, and internal audit cycles.
12 chapters in this module
  1. Mapping ISO 42001 controls to ISO 13485 requirements
  2. Integrating AI governance into internal audit schedules
  3. Updating quality manuals to include AI system oversight
  4. Change control workflows for AI model updates
  5. Linking AI system performance to customer complaint data
  6. Corrective action workflows for AI-related deviations
  7. Vendor management for third-party AI components
  8. Supplier audits for AI software providers
  9. Managing legacy equipment with AI retrofitting
  10. Training legacy teams on new AI governance expectations
  11. Documenting AI’s role in design history files
  12. Aligning AI review cycles with biennial audits
Module 7. Leading AI Governance Initiatives Without Formal Authority
Position yourself as the de facto leader on AI governance even without a compliance title.
12 chapters in this module
  1. Building credibility through precise control language
  2. Identifying early AI integration opportunities in your domain
  3. Framing proposals using ISO 42001 compliance benefits
  4. Gaining buy-in from quality and regulatory teams
  5. Presenting AI improvements with risk reduction metrics
  6. Creating lightweight pilot projects to demonstrate value
  7. Documenting wins to build internal reputation
  8. Using ISO 42001 as a common language across silos
  9. Positioning for leadership in enterprise AI rollouts
  10. Negotiating ownership of AI-related workstreams
  11. Developing internal training materials as influence tools
  12. Becoming the go-to contact for AI audit prep
Module 8. Vendor and Third-Party Management Under ISO 42001
Evaluate and govern external AI tools and services with confidence.
12 chapters in this module
  1. Assessing vendor compliance with ISO 42001 requirements
  2. Procurement language for AI system contracts
  3. Reviewing third-party AI documentation for gaps
  4. Conducting due diligence on AI model training data
  5. Ensuring transparency in black-box AI systems
  6. Managing model updates from external providers
  7. Audit rights and access clauses in AI vendor agreements
  8. Tracking vendor performance on AI system stability
  9. Handling data residency and access in cloud AI tools
  10. Evaluating explainability claims from AI vendors
  11. Managing open-source AI components in regulated settings
  12. Exit strategies for underperforming AI vendors
Module 9. Internal Audit and Readiness for External Reviews
Prepare your team and systems for ISO 42001 audits with confidence.
12 chapters in this module
  1. Developing internal audit checklists for AI systems
  2. Mock audit exercises for AI governance readiness
  3. Identifying high-risk AI applications for prioritization
  4. Responding to auditor questions on model validation
  5. Demonstrating continuous improvement in AI oversight
  6. Preparing evidence packs for clause-specific reviews
  7. Simulating regulator interviews on AI safety
  8. Handling findings and non-conformities professionally
  9. Tracking closure of audit actions in quality systems
  10. Benchmarking against peer MedTech ISO 42001 adoption
  11. Using audit outcomes to justify further investment
  12. Building a culture of compliance through engineering
Module 10. Scaling AI Governance Across Product Lines
Replicate successful AI governance patterns across Medtronic platforms.
12 chapters in this module
  1. Identifying transferable control patterns across devices
  2. Creating standardized templates for AI system registration
  3. Developing a center of excellence model for AI governance
  4. Training engineers across divisions on ISO 42001 basics
  5. Managing governance consistency in global manufacturing
  6. Documenting lessons from first AI implementations
  7. Streamlining approval workflows for AI use cases
  8. Creating reusable risk assessment blocks
  9. Building dashboards for AI governance maturity metrics
  10. Aligning AI oversight with corporate risk committees
  11. Scaling AI audits without increasing headcount
  12. Measuring ROI of standardized AI governance
Module 11. Future-Proofing AI Systems for Regulatory Changes
Anticipate and adapt to evolving AI governance requirements.
12 chapters in this module
  1. Tracking emerging AI regulations in medical devices
  2. Preparing for EU AI Act alignment with ISO 42001
  3. Anticipating FDA expectations for AI in implantables
  4. Updating AI systems for changing data privacy laws
  5. Designing modular controls for regulatory flexibility
  6. Building compliance into AI system architecture
  7. Versioning strategies for regulatory submissions
  8. Maintaining audit trails through regulatory transitions
  9. Engaging with standards bodies on future revisions
  10. Participating in pilot programs for new AI rules
  11. Balancing innovation speed with governance readiness
  12. Creating adaptive governance frameworks for R&D
Module 12. Leading the Evolution of AI Governance in MedTech
Become a recognized authority on AI governance in medical device innovation.
12 chapters in this module
  1. Positioning yourself as a thought leader internally
  2. Publishing internal white papers on AI lessons learned
  3. Contributing to enterprise AI governance strategy
  4. Mentoring junior engineers on ISO 42001 practices
  5. Representing engineering in enterprise risk forums
  6. Shaping AI policy in cross-functional working groups
  7. Presenting at internal innovation councils
  8. Influencing procurement decisions on AI platforms
  9. Advocating for resources in AI governance budgets
  10. Building a personal brand around technical compliance
  11. Translating engineering work into business value narratives
  12. Creating a legacy of robust, scalable AI systems

How this maps to your situation

  • Current role: Principal Continuous Improvement Engineer
  • Domain: Medtronic battery manufacturing for Azure pacemaker
  • Key standard: ISO 42001 for AI governance
  • Strategic opportunity: Leadership in AI-integrated process improvement

Before vs. after

Before
Reactively adapting to AI governance requirements, often after deployment, with limited influence on project scope or budget.
After
Proactively designing ISO 42001-aligned systems, leading high-margin initiatives, and shaping AI strategy in regulated manufacturing.

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 4 hours per module, designed for execution-focused learning with immediate application to current projects.

If nothing changes
Engineers who wait to engage with ISO 42001 will be assigned to implement others' designs, missing the opportunity to lead high-impact, well-funded projects at the intersection of AI and medical device innovation.

How this compares to the alternatives

Generic AI compliance courses cover broad theory. This course is built for principal engineers in medical device manufacturing who need to apply ISO 42001 to real-world production systems, no abstractions, only actionable frameworks.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for someone in battery manufacturing?
Yes. The course uses medical device production, including battery and sensing systems, as primary examples throughout.
Do I need to be in a compliance role?
No. It’s designed for principal engineers influencing process design and AI integration who want to lead governance efforts.
$199 one-time. Approximately 4 hours per module, designed for execution-focused learning with immediate application to current projects..

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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