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MKT8875 Governance for Enterprise Generative AI in Regulated Marketing

$199.00
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What is the Governance for Enterprise Generative AI course about?

Implementation-grade governance for AI-driven marketing teams in high-compliance environments Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

What situation is the Governance for Enterprise Generative AI for?

Security and compliance leaders face growing pressure to validate AI-generated marketing content under tight regulator timelines. The recurring burden is reassembling control evidence across tools and teams, often manually, just before review cycles. This course eliminates that churn with a built-for-purpose ISO 20000 framework.

Who is the Governance for Enterprise Generative AI course for?

Senior security and compliance leaders in tech-driven marketing or AI product environments, responsible for ensuring AI deployments meet regulatory standards without slowing innovation.

What do you take away from the Governance for Enterprise Generative AI course?

Produce regulator-ready AI governance artefacts on demand Reduce audit preparation from weeks to hours Align generative AI initiatives with ISO 20000 control objectives Establish clear ownership and evidence trails across AI workflows Turn governance from a bottleneck into a competitive enabler.

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.

What does the Governance for Enterprise Generative AI cover on delivery and format?

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: 90 minutes per week for 12 weeks, or self-paced over 90 days.

How does this compare to the alternatives?

Unlike generic AI ethics guides or high-level compliance overviews, this course delivers implementation-grade ISO 20000 controls tailored to generative AI in marketing , with templates, workflows, and artefacts you can deploy immediately.

What does the Governance for Enterprise Generative AI cover on frequently asked?

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

Closely related courses: Strategic Generative AI Policy Design for Regulated, Practical Generative AI Policy Design for Regulated, Scalable Generative AI Policy Design for Regulated, Securing Generative AI in Regulated Healthcare.

More answers: what you get with every course, refund policy, all help answers.

A tailored course, built for your situation

Governance for Enterprise Generative AI in Regulated Marketing

Implementation-grade governance for AI-driven marketing teams in high-compliance environments

$199 one-time
30-day money-back guarantee Verified against latest insights, updated access provided within 24h

Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.

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.
Audit packages that require last-minute evidence stitching

The situation this course is for

Security and compliance leaders face growing pressure to validate AI-generated marketing content under tight regulator timelines. The recurring burden is reassembling control evidence across tools and teams, often manually, just before review cycles. This course eliminates that churn with a built-for-purpose ISO 20000 framework.

Who this is for

Senior security and compliance leaders in tech-driven marketing or AI product environments, responsible for ensuring AI deployments meet regulatory standards without slowing innovation.

Who this is not for

Junior compliance staff, general marketing operations without AI governance responsibility, or teams not operating under formal regulatory frameworks.

What you walk away with

  • Produce regulator-ready AI governance artefacts on demand
  • Reduce audit preparation from weeks to hours
  • Align generative AI initiatives with ISO 20000 control objectives
  • Establish clear ownership and evidence trails across AI workflows
  • Turn governance from a bottleneck into a competitive enabler

The 12 modules (with all 144 chapters)

Module 1. Foundations of ISO 20000 in AI-Driven Marketing Environments
Understand how service management standards apply to generative AI workflows in regulated sectors.
12 chapters in this module
  1. Mapping ISO 20000 principles to generative AI service lifecycles
  2. Why traditional ITIL practices fall short for AI content pipelines
  3. Key regulatory touchpoints in marketing AI under ISO 20000
  4. Defining service ownership for AI-generated customer touchpoints
  5. Aligning AI model updates with service change control processes
  6. Integrating compliance checks into the AI content approval workflow
  7. Establishing service level agreements for AI marketing outputs
  8. Documenting AI service dependencies for audit readiness
  9. Managing third-party AI vendors under ISO 20000 controls
  10. Versioning AI prompts and outputs as service deliverables
  11. Tracking AI service incidents and resolution timelines
  12. Setting up continuous improvement loops for AI marketing services
Module 2. Control Mapping for Generative AI in Regulated Campaigns
Translate high-level ISO 20000 controls into specific implementation rules for AI marketing.
12 chapters in this module
  1. Mapping ISO 20000 control 5.1 to AI campaign initiation processes
  2. Designing approval workflows for AI-generated ad copy
  3. Ensuring data provenance in AI-driven personalization engines
  4. Control objectives for AI model retraining and drift detection
  5. Version control for AI-generated creative assets
  6. Change management for AI prompt libraries
  7. Incident response planning for AI content misfires
  8. Service continuity for AI marketing platforms
  9. Capacity planning for AI inference workloads in campaigns
  10. Monitoring AI service performance against SLAs
  11. Auditing AI model inputs and outputs for compliance
  12. Documenting AI service handoffs between teams
Module 3. Evidence Packaging for Regulator-Facing Reviews
Build self-contained, reusable evidence packages that pass scrutiny on first submission.
12 chapters in this module
  1. Structuring the AI governance evidence package for ISO 20000
  2. Automating evidence collection from AI content platforms
  3. Linking AI outputs to control objectives in documentation
  4. Creating timestamped audit trails for AI decision logs
  5. Storing AI model parameters as audit evidence
  6. Documenting ethical review processes for AI marketing
  7. Capturing approval chains for AI-generated campaigns
  8. Versioning control documents alongside AI model updates
  9. Generating regulator-ready summaries from technical logs
  10. Validating evidence completeness before submission
  11. Using templates to standardize evidence across campaigns
  12. Maintaining evidence retention for AI marketing activities
Module 4. Cross-Team Alignment on AI Governance Workflows
Orchestrate collaboration between security, marketing, legal, and engineering with clear handoffs.
12 chapters in this module
  1. Defining RACI matrices for AI marketing governance
  2. Aligning security controls with marketing campaign timelines
  3. Legal review checkpoints for AI-generated messaging
  4. Engineering responsibilities for AI model monitoring
  5. Marketing team obligations in AI content validation
  6. Facilitating cross-functional AI governance meetings
  7. Documenting decisions in shared governance repositories
  8. Escalation paths for AI compliance conflicts
  9. Training non-technical teams on AI governance basics
  10. Communicating control requirements across departments
  11. Integrating feedback loops between governance and execution
  12. Measuring cross-team adherence to AI governance rules
Module 5. Vendor Management for Third-Party AI Tools
Extend ISO 20000 controls to external AI platforms used in marketing.
12 chapters in this module
  1. Assessing third-party AI vendors against ISO 20000 criteria
  2. Negotiating AI service level agreements with compliance terms
  3. Auditing external AI providers for control adherence
  4. Managing data sharing agreements for AI training inputs
  5. Ensuring AI vendor transparency in model behavior
  6. Monitoring third-party AI for unexpected changes
  7. Documenting vendor onboarding and offboarding
  8. Requiring AI vendors to provide audit evidence
  9. Handling AI service disruptions from external providers
  10. Evaluating AI vendor incident response capabilities
  11. Maintaining independence in AI tool assessments
  12. Updating vendor risk assessments for AI model updates
Module 6. Automating Control Execution in AI Workflows
Embed compliance checks directly into generative AI pipelines.
12 chapters in this module
  1. Identifying automation opportunities in AI governance
  2. Building policy checks into AI content generation
  3. Using metadata tagging for automatic evidence capture
  4. Integrating AI output validation with approval systems
  5. Deploying real-time compliance monitoring for campaigns
  6. Automating AI model version tracking and reporting
  7. Creating self-documenting AI workflows
  8. Triggering alerts for policy violations in AI outputs
  9. Logging AI decisions for future audit reconstruction
  10. Generating compliance dashboards from AI activity
  11. Scheduling recurring control tests in AI systems
  12. Validating automation logic for control accuracy
Module 7. Change Management for AI Model Iterations
Apply ISO 20000 change control rigor to evolving AI models.
12 chapters in this module
  1. Defining change thresholds for AI model updates
  2. Requiring impact assessments for AI retraining
  3. Establishing approval workflows for AI model deployment
  4. Documenting reasons for AI model changes
  5. Testing AI updates against compliance requirements
  6. Communicating AI changes to affected teams
  7. Rolling back AI models when compliance fails
  8. Maintaining change logs for AI model versions
  9. Scheduling planned AI model refreshes
  10. Handling emergency AI model fixes
  11. Verifying post-change stability of AI services
  12. Auditing change management adherence for AI
Module 8. Incident Response for AI Marketing Failures
Prepare for and respond to governance breaches in AI-generated content.
12 chapters in this module
  1. Defining AI marketing incident types and severity levels
  2. Establishing detection mechanisms for harmful AI outputs
  3. Activating response teams for AI content crises
  4. Containing AI-generated misinformation quickly
  5. Investigating root causes of AI model failures
  6. Documenting AI incident responses for regulators
  7. Communicating AI incidents to stakeholders
  8. Implementing fixes to prevent recurrence
  9. Reviewing AI incidents for process improvement
  10. Conducting post-mortems on AI governance failures
  11. Testing incident response plans for AI scenarios
  12. Updating controls based on AI incident learnings
Module 9. Service Level Agreements for AI Marketing Outputs
Define and enforce quality, timeliness, and compliance standards.
12 chapters in this module
  1. Setting measurable SLAs for AI content accuracy
  2. Defining turnaround times for AI campaign delivery
  3. Including compliance adherence in SLA metrics
  4. Monitoring AI output consistency over time
  5. Reporting SLA performance to leadership
  6. Handling SLA breaches in AI service delivery
  7. Negotiating realistic SLAs with marketing teams
  8. Aligning AI SLAs with customer expectations
  9. Automating SLA tracking for AI workflows
  10. Reviewing SLAs for changing regulatory requirements
  11. Adjusting SLAs based on AI model improvements
  12. Documenting SLA exceptions and justifications
Module 10. Capacity Planning for AI Marketing Workloads
Ensure AI systems can handle campaign demands without compromising governance.
12 chapters in this module
  1. Forecasting AI inference needs for marketing campaigns
  2. Sizing infrastructure for peak AI workloads
  3. Monitoring AI resource utilization in real time
  4. Scaling AI services during high-traffic periods
  5. Planning for seasonal marketing surges
  6. Ensuring governance checks scale with volume
  7. Budgeting for AI infrastructure and compliance
  8. Optimizing AI model efficiency for cost control
  9. Evaluating cloud vs on-prem AI deployment
  10. Managing AI service availability during updates
  11. Testing disaster recovery for AI marketing systems
  12. Reporting capacity metrics to finance and leadership
Module 11. Continual Improvement of AI Governance Practices
Refine your approach based on data, feedback, and changing standards.
12 chapters in this module
  1. Collecting feedback on AI governance from teams
  2. Analyzing audit findings to improve controls
  3. Benchmarking AI governance against industry peers
  4. Updating policies based on regulatory changes
  5. Incorporating new ISO 20000 guidance into practice
  6. Measuring the efficiency of AI compliance processes
  7. Reducing manual effort in governance workflows
  8. Identifying training needs for AI governance
  9. Adopting new tools to enhance control effectiveness
  10. Celebrating improvements in AI compliance maturity
  11. Sharing best practices across the organization
  12. Planning the next cycle of AI governance evolution
Module 12. Implementation Playbook for Enterprise Adoption
Deploy the full framework across your organization with confidence.
12 chapters in this module
  1. Assessing current AI governance maturity level
  2. Prioritizing ISO 20000 controls for quick wins
  3. Building executive sponsorship for AI governance
  4. Piloting the framework with a high-impact campaign
  5. Training teams on new AI governance procedures
  6. Integrating tools for automated evidence collection
  7. Rolling out controls across business units
  8. Monitoring adoption and adherence metrics
  9. Addressing resistance to new governance rules
  10. Celebrating successful audit outcomes
  11. Scaling the playbook to new AI use cases
  12. Maintaining momentum in AI governance improvement

How this maps to your situation

  • Audit preparation
  • Vendor oversight
  • Cross-team coordination
  • Regulatory submission

Before vs. after

Before
Spending weeks assembling evidence, chasing approvals, and reacting to audit findings for AI marketing campaigns.
After
Producing regulator-ready governance packages on demand, with automated controls and clear ownership.

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: 90 minutes per week for 12 weeks, or self-paced over 90 days.

If nothing changes
Without a structured approach, AI governance remains reactive, costly, and vulnerable to regulatory scrutiny, limiting the organization's ability to innovate confidently.

How this compares to the alternatives

Unlike generic AI ethics guides or high-level compliance overviews, this course delivers implementation-grade ISO 20000 controls tailored to generative AI in marketing , with templates, workflows, and artefacts you can deploy immediately.

Frequently asked

Is this course technical or strategic?
It's implementation-focused , designed for practitioners who need to build and maintain compliant AI systems, not just discuss principles.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this work for non-ISO 20000 environments?
Yes , the principles are transferable, though the course uses ISO 20000 as the anchor framework for clarity and compliance alignment.
$199 one-time. 90 minutes per week for 12 weeks, or self-paced over 90 days..

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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