A tailored course, built for your situation
Mastering AI Act for Pre-Sales Practitioners in High-Growth Tech
Build defensible AI governance positioning into every customer conversation
$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.
Who this is for
Senior pre-sales engineer or solutions consultant in data and AI platforms, operating in regulated sectors with growing compliance scrutiny
Who this is not for
This course is not for junior onboarding, generic compliance training, or technical implementation engineers focused solely on code deployment. It’s designed for individual contributors who shape go-to-market narratives and must defend architectural choices under peer review.
What you walk away with
- Walk through the AI Act with confidence, citing specific articles and regulatory interpretations
- Anchor customer-facing claims in verifiable sources, not vague assertions
- Deflect technical skepticism with precedent from EBA, ENISA, and national competent authorities
- Build customer trust by demonstrating compliance-aware solution design
- Turn governance questions into strategic differentiators during procurement cycles
The 12 modules (with all 144 chapters)
Module 1. AI Act Foundations in Pre-Sales Context
Understand the structure and scope of the AI Act as it applies to enterprise sales cycles, focusing on Articles 3, 5, and 6.
12 chapters in this module
- What qualifies as high-risk AI under the AI Act
- Understanding Annex III use cases
- Prohibited practices in customer deployments
- Obligations for deployers vs providers
- How Article 4 defines market boundaries
- Risk-based classification in practice
- Role of technical documentation in sales
- Transparency requirements for customer use
- Conformity assessment implications
- Market surveillance mechanisms
- Interaction with national regulators
- Timeline for enforcement phases
Module 2. Articulating Compliance Without Overreach
Frame compliance confidently without overpromising, using precise language from the regulation.
12 chapters in this module
- Distinguishing 'compliant with' vs 'aligned to'
- Using 'demonstrable effort' as a shield
- Avoiding binding commitments unnecessarily
- Mapping product features to Article 10
- When to involve legal teams
- Documenting compliance posture
- Handling third-party audits
- Clarifying vendor responsibilities
- Managing customer expectations early
- Balancing innovation and obligation
- Communicating limitations honestly
- Escalation paths for edge cases
Module 3. AI Act and Data Provenance in Customer Workflows
Connect data lineage practices to AI Act transparency requirements.
12 chapters in this module
- Linking data sources to Article 13
- Demonstrating traceability in model inputs
- Logging decisions for audit readiness
- Establishing data quality benchmarks
- Customer responsibilities in data supply
- Handling synthetic data disclosures
- Versioning training data sets
- Attribution for public data use
- Bias mitigation documentation
- Record retention expectations
- Cross-border data flow implications
- Tools for automated provenance tracking
Module 4. Risk Classification in Customer Scenarios
Apply AI Act risk tiers to real-world customer implementations.
12 chapters in this module
- Mapping use cases to Annex III
- Financial services classification examples
- Healthcare model deployment nuances
- HR and recruitment tool boundaries
- Public sector AI procurement rules
- Education sector limitations
- Law enforcement exceptions
- Unintended high-risk design patterns
- Customer self-declaration pitfalls
- Dual-use technology concerns
- When to flag for internal review
- Building risk-tier checklists
Module 5. Transparency and Explainability Demands
Meet Article 13 obligations with clarity and precision.
12 chapters in this module
- Defining 'meaningful information'
- User-facing explanations vs developer docs
- Model behavior summaries for non-experts
- Timing of disclosures in deployment
- Right to be informed under AI Act
- Documentation for affected parties
- Explainability techniques that scale
- Logging model drift events
- Version control for model updates
- Handling model retraining
- Human oversight triggers
- Fallback mechanisms for failure modes
Module 6. Conformity Assessments and Vendor Claims
Navigate conformity without overstating readiness.
12 chapters in this module
- Understanding internal vs third-party assessments
- When CE marking applies
- Role of notified bodies
- Technical documentation depth
- Self-declaration risks
- Customer expectations vs reality
- Handling partial compliance
- Exemption scenarios
- Post-market monitoring duties
- Incident reporting thresholds
- Corrective action planning
- Vendor coordination strategies
Module 7. Governance in Multi-Tenant AI Platforms
Apply AI Act principles in shared environments.
12 chapters in this module
- Isolation of high-risk workloads
- Tenant-specific compliance boundaries
- Shared responsibility models
- Audit access rights
- Logging across tenant boundaries
- Data leakage prevention
- Model access controls
- Tenant configuration locks
- Cross-tenant bias assessment
- Incident containment procedures
- Compliance reporting per tenant
- Service-level agreements for governance
Module 8. Benchmarking Against ISO 42001
Understand where AI Act and ISO 42001 overlap and diverge.
12 chapters in this module
- AI Act vs ISO 42001 scope
- Common controls in both frameworks
- Certification vs regulation
- Internal audit expectations
- Documentation alignment
- Risk management integration
- Stakeholder communication standards
- Continuous monitoring overlap
- Training and awareness parallels
- Resource allocation differences
- Enforcement mechanisms compared
- How to reference both credibly
Module 9. NIST AI RMF and AI Act Alignment
Use NIST guidance to strengthen AI Act readiness.
12 chapters in this module
- Mapping NIST functions to AI Act articles
- Govern and Map from NIST RMF
- Measure and Govern from AI Act
- Using NIST profiles for scoping
- Risk tolerance definitions
- Performance metrics for compliance
- Traceability in model development
- Validation against NIST baselines
- Bias testing protocols
- Incident response planning
- Adaptation to regulatory feedback
- Cross-framework consistency
Module 10. Customer Engagement Playbook
Embed AI Act reasoning into discovery and scoping.
12 chapters in this module
- Early-stage compliance questioning
- Identifying red-flag use cases
- Positioning governance as enablement
- Differentiating on verifiable standards
- Handling competitive comparisons
- Using precedent in negotiations
- Compliance as a trust accelerator
- Avoiding fear-based selling
- Framing limitations positively
- Building credibility through precision
- Leveraging regulatory language
- Closing with confidence
Module 11. Pre-Sales Artifacts That Defend Themselves
Create documentation that stands up to scrutiny.
12 chapters in this module
- Architecture diagrams with citations
- Compliance appendices for proposals
- Standard responses to RFPs
- Glossary of regulated terms
- Version-controlled policy statements
- Customer onboarding checklists
- Risk disclosure templates
- Model cards with AI Act alignment
- Audit trail readiness summaries
- Compliance dashboards for review
- Internal alignment documents
- Post-engagement review reports
Module 12. Maintaining Defensibility at Scale
Keep arguments strong as use cases grow.
12 chapters in this module
- Automated compliance tracking
- Centralized knowledge base setup
- Training new team members
- Updating playbooks with enforcement updates
- Monitoring regulatory changes
- Engaging with standards bodies
- Contributing to best practices
- Feedback loops from customers
- Handling enforcement actions
- Scaling documentation workflows
- Cross-team governance coordination
- Long-term defensibility planning
How this maps to your situation
- When a customer asks if your solution complies with AI Act
- When internal teams question the compliance posture of a deployment
- When preparing for a procurement review involving AI governance
- When comparing against competitive offerings on regulatory maturity
Before vs. after
Before
Relying on general assurances and high-level claims when discussing AI compliance.
After
Walking through the AI Act with specific examples, sources, and reasoning that hold up under scrutiny.
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 45 minutes per module, designed for completion in two weeks with consistent pacing.
How this compares to the alternatives
Unlike generic AI ethics training or broad compliance overviews, this course focuses exclusively on enforceable AI Act provisions and how to apply them in pre-sales contexts with precision and defensibility.
Frequently asked
Is this course about Databricks or competing platforms?
No. This course is platform-agnostic and avoids referencing specific vendor products. It focuses on regulatory requirements and defensible reasoning applicable across AI and data platforms.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course include certifications or exams?
No. It builds deep, referenceable knowledge of the AI Act and how to apply it in practice, but does not lead to a formal certification.
$199 one-time. Approximately 45 minutes per module, designed for completion in two weeks with consistent pacing..
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