A tailored course, built for your situation
Become the Go-To Practitioner for ISO 42001 Implementation
The tailored course for performance marketing leaders stepping into AI governance with authority and visibility.
The situation this course is for
Many practitioners absorb governance tasks reactively, leading to confusion, rework, and missed influence. The real challenge isn't understanding standards, it's being trusted as the definitive point of clarity.
Who this is for
Senior project managers in tech-driven marketing environments who are expected to deliver fast results while meeting rising compliance expectations
Who this is not for
Those satisfied with executing isolated tasks without shaping outcomes or visibility
What you walk away with
- Confidently structure AI system documentation that satisfies auditors and aligns with marketing velocity
- Host cross-functional reviews where your approach becomes the default framework
- Produce clear SoA sections specific to AI use cases in customer acquisition and retention
- Answer peer challenges with cited control mappings from ISO 42001
- Build a personal library of reusable templates tied directly to ISO 42001 clauses
The 12 modules (with all 144 chapters)
- Defining AI systems in customer journey analysis
- Mapping automation touchpoints to clause A4
- Differentiating AI from ML models in reporting
- How ISO 42001 complements SOC 2 efforts
- Key overlaps with ISO 27001 controls
- Why ISO 42001 is not just IT’s responsibility
- Identifying AI-influenced campaign decisions
- Documenting vendor AI tools in scope
- Tracking generative content in ad copy
- Auditor expectations for transparency
- Common misapplications in digital teams
- First steps in boundary definition
- Translating controls into marketing impact
- Running effective kickoff workshops
- Building trust without technical overreach
- Documenting assumptions across teams
- Managing conflicting priorities
- Securing early buy-in from leaders
- Creating alignment checklists
- Facilitating joint control mapping
- Avoiding siloed interpretations
- Using common examples in discussions
- Tracking decisions in shared logs
- Closing alignment gaps pre-audit
- Identifying triggers for system inclusion
- Drawing clean lines around AI components
- Excluding non-AI automation fairly
- Capturing data flows visually
- Listing third-party dependencies
- Justifying scope exclusions
- Versioning system diagrams
- Linking to campaign performance data
- Handling A/B testing platforms
- Clarifying human-in-the-loop roles
- Updating boundaries after changes
- Preparing boundary statements for audit
- Framing AI-specific risks correctly
- Avoiding generic risk libraries
- Sourcing real incident examples
- Assessing bias in audience targeting
- Evaluating transparency in personalization
- Measuring explainability gaps
- Rating model drift exposure
- Documenting residual risk decisions
- Involving legal and ethics reviewers
- Linking risks to control objectives
- Updating assessments quarterly
- Presenting findings to cross teams
- Populating the base SoA template
- Justifying inclusion for each control
- Writing clear implementation statements
- Excluding controls with valid rationale
- Linking controls to existing processes
- Using marketing-specific examples
- Maintaining version history
- Reviewing with internal stakeholders
- Preparing for external validation
- Formatting for readability
- Updating after system changes
- Archiving superseded versions
- Integrating control checks into sprints
- Automating documentation triggers
- Assigning control owners clearly
- Tracking control effectiveness monthly
- Flagging deviations early
- Using tools like Jira for traceability
- Aligning with sprint retrospectives
- Reporting control status to leads
- Auditing control logs remotely
- Adjusting controls after incidents
- Scaling across multiple campaigns
- Reducing manual overhead
- Identifying vendor AI components
- Requiring ISO 42001 alignment clauses
- Reviewing vendor SOC 2 reports
- Auditing model transparency commitments
- Tracking model update frequency
- Validating bias testing claims
- Assessing vendor change controls
- Documenting due diligence steps
- Managing multi-vendor environments
- Handling fallback procedures
- Reporting vendor risks centrally
- Terminating non-compliant tools
- Scheduling audit cycles by quarter
- Selecting sample campaigns
- Creating auditor checklists
- Conducting walkthroughs with teams
- Capturing findings in standard format
- Prioritizing remediation actions
- Tracking closure rates
- Sharing summaries across functions
- Involving neutral facilitators
- Improving processes post-audit
- Building institutional memory
- Reducing repeat findings
- Identifying key concerns for execs
- Summarizing compliance posture
- Highlighting risk reduction wins
- Reporting control coverage rates
- Showing audit readiness status
- Explaining AI-specific risks
- Using dashboards effectively
- Avoiding technical jargon
- Linking to business outcomes
- Answering escalation questions
- Preparing monthly updates
- Embedding into broader reports
- Tracking changes in AI tools
- Updating documentation proactively
- Running quarterly control reviews
- Soliciting team feedback
- Benchmarking against peers
- Adjusting risk registers
- Revising SoA annually
- Incorporating audit findings
- Celebrating compliance wins
- Sharing lessons across teams
- Updating training materials
- Measuring maturity growth
- Curating a reference collection
- Building a response library
- Developing go-to workshop flow
- Creating speaker-ready materials
- Answering tough questions confidently
- Hosting office hours for teams
- Publishing internal guides
- Mentoring junior practitioners
- Tracking influence growth
- Documenting key wins
- Sharing templates widely
- Becoming the default reference
- Shaping new project kickoffs
- Being invited to strategy talks
- Advising on AI tool selection
- Setting internal standards
- Influencing budget decisions
- Mentoring future leads
- Contributing to org knowledge
- Speaking at internal forums
- Guiding cross-functional teams
- Earning recognition formally
- Scaling your playbook
- Leaving durable systems behind
How this maps to your situation
- Starting an AI governance initiative
- Responding to internal audit requests
- Onboarding new AI tools in marketing
- Preparing for external assessment
Before vs. after
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 to fit around delivery commitments. Most practitioners complete the course in 6-8 weeks.
How this compares to the alternatives
Generic compliance courses teach abstract frameworks. This course gives you sourced, reusable methods tailored to marketing AI systems and the ISO 42001 standard.
Frequently asked
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.