A tailored course, built for your situation
Mastering AI Act for Product Practitioners in High-Growth Tech
Build compliant, defensible AI systems from the first design sprint
$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.
Spending cycles revising AI product documentation to meet emerging regulatory expectations?
The situation this course is for
Even high-performing product teams face rework when governance expectations shift. The AI Act raises the bar for evidence, traceability, and risk documentation, often leading to last-minute revisions, delayed launches, and weakened stakeholder trust when outputs lack defensibility.
Who this is for
Product leaders in AI-native and AI-integrated tech companies who own end-to-end delivery and must balance innovation velocity with regulatory readiness
Who this is not for
Legal counsel focused on liability review, auditors running formal assessments, or developers implementing model monitoring scripts
What you walk away with
- Produce AI product documentation that passes initial regulatory scrutiny without revision
- Classify system risk levels accurately under AI Act Title III criteria
- Generate traceable conformity assessments aligned with notified body expectations
- Integrate evidence collection into sprint cycles instead of retrofitting late
- Command consistent justification for design choices across stakeholder reviews
The 12 modules (with all 144 chapters)
Module 1. Understanding the AI Act’s Scope and Key Obligations
Break down the regulation’s applicability to AI systems, focusing on high-risk classifications and prohibited practices under Title I and II.
12 chapters in this module
- What the AI Act regulates
- Prohibited AI practices overview
- General purpose AI rules
- High-risk system criteria
- Classification thresholds
- Market placement rules
- Obligations for providers
- Role of deployers
- Extraterritorial reach
- Enforcement bodies
- Penalties overview
- Timeline for compliance
Module 2. AI Risk Classification Framework
Apply a structured methodology to categorize AI systems under Annex III, with practical examples from real product architectures.
12 chapters in this module
- Mapping use cases to risk tiers
- Safety component integration
- Biometric identification risks
- Remote biometric monitoring
- Emotion recognition limits
- Critical infrastructure exposure
- Education scoring systems
- Workplace evaluation tools
- Law enforcement access
- Public assistance algorithms
- Vulnerable group exposure
- Dynamic reclassification triggers
Module 3. Technical Documentation Requirements
Build complete, defensible technical files that satisfy Article 13 and Annex IV expectations.
12 chapters in this module
- System overview documentation
- Intended purpose clarity
- Input data specifications
- Model architecture diagrams
- Training data provenance
- Validation metrics set
- Performance benchmarks
- Uncertainty estimation
- Version tracking method
- Update and patching plans
- Failure mode analysis
- Human oversight design
Module 4. Conformity Assessment Pathways
Navigate module-based and full-quality-assurance routes per Article 43, tailored to product type and risk level.
12 chapters in this module
- Self-declaration process
- Notified body selection
- Internal audit steps
- External review prep
- Quality management system
- Risk management process
- Data governance checks
- Transparency alignment
- Post-market monitoring
- Incident reporting logs
- Complaint handling flow
- Certificate maintenance
Module 5. Data Governance for Training and Operation
Ensure compliance with data quality, provenance, and bias mitigation expectations in Annex III and Article 10.
12 chapters in this module
- Training data lineage
- Data cleaning standards
- Bias detection methods
- Representativeness checks
- Annotation quality control
- Synthetic data use
- Data retention policy
- Privacy-preserving techniques
- Downstream use tracking
- Data subject rights
- Third-party data sourcing
- Audit trail generation
Module 6. Transparency and User Information
Craft clear, accessible user-facing communications that meet Article 13 and Annex I requirements.
12 chapters in this module
- User instructions drafting
- Limitation disclosures
- AI use indication
- Human override notice
- Contact point setup
- Terms of use updates
- Change notification process
- Multilingual requirements
- Accessibility standards
- Support channel design
- Performance expectations
- Expected lifespan disclosure
Module 7. Human Oversight Mechanisms
Design meaningful human-in-the-loop systems that satisfy Article 14 and Annex III criteria.
12 chapters in this module
- Oversight role definition
- Decision override paths
- Monitoring interface design
- Escalation triggers
- Training for human reviewers
- Fail-safe protocols
- Responsibility clarity
- Alerting mechanisms
- Response time standards
- Audit logging
- Bias intervention plans
- System degradation response
Module 8. Robustness, Accuracy, and Cybersecurity
Implement technical safeguards that meet Annex III’s expectations for system resilience and reliability.
12 chapters in this module
- Model stress testing
- Adversarial attack resistance
- Input validation rules
- Output consistency checks
- Performance drift monitoring
- Failure recovery procedures
- Cybersecurity baseline
- Supply chain risks
- Model integrity verification
- Update validation process
- Secure deployment pipeline
- Incident response plan
Module 9. Post-Market Monitoring and Incident Reporting
Establish feedback loops and logging systems to meet ongoing compliance under Article 61.
12 chapters in this module
- Performance tracking metrics
- User feedback channels
- Error logging design
- Anomaly detection
- Incident escalation process
- Field incident reports
- Root cause analysis
- Corrective action workflow
- Product recall criteria
- Notified body reporting
- Public disclosure rules
- Regulatory audit readiness
Module 10. Quality Management System Integration
Embed AI Act requirements into existing product development lifecycles and QA workflows.
12 chapters in this module
- Sprint planning integration
- Definition of done updates
- Backlog refinement steps
- Stakeholder review gates
- Cross-functional alignment
- Compliance tracking
- Version control strategy
- Change management process
- Release sign-off criteria
- Internal audit schedule
- Training program rollout
- Continuous improvement cycle
Module 11. Vendor and Third-Party Risk Management
Assess and manage obligations when using external AI components or services.
12 chapters in this module
- Subcontractor obligations
- Due diligence checklist
- Contractual terms
- Model card review
- API risk assessment
- Dependency mapping
- Supply chain transparency
- Open source compliance
- Model monitoring continuity
- Incident response coordination
- Audit rights negotiation
- Exit strategy planning
Module 12. Preparing for Regulatory Engagement
Anticipate examiner questions and build responsive documentation workflows.
12 chapters in this module
- Common audit findings
- Documentation access setup
- Interview preparation
- Evidence trail mapping
- Cross-border coordination
- Language requirements
- Executive summary drafting
- Regulatory change monitoring
- Stakeholder communication
- Internal escalation paths
- Public affairs alignment
- Lessons from early adopters
How this maps to your situation
- When launching a new AI feature
- Before a regulatory audit cycle
- During vendor onboarding
- After a model performance incident
Before vs. after
Before
Delivering AI product documentation that requires multiple revision cycles to meet regulatory expectations.
After
Shipping polished, defensible outputs the first time, clearly aligned to AI Act requirements and internal stakeholder needs.
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 3 hours per module, designed for integration into real product cycles.
If nothing changes
Without structured alignment to the AI Act, even mature product teams face delayed launches, regulatory scrutiny, and reputational exposure when documentation lacks defensibility or traceability.
How this compares to the alternatives
Generic AI ethics courses offer broad principles but lack actionable steps for AI Act compliance. This course delivers precise, regulation-aligned artefacts and checklists used by early-adopter product teams in regulated sectors.
Frequently asked
Is this course focused on legal compliance or product execution?
It's designed for product execution, how to build and document AI systems that inherently meet AI Act standards without slowing innovation.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Are templates customizable for my product team?
Yes, all templates are provided in editable format and include guidance for adapting to specific architectures and risk profiles.
$199 one-time. Approximately 3 hours per module, designed for integration into real product 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