Certified AI Executive: Chief Marketing Officer

What You Will Be Able to Do

• Explain what AI can and cannot do, test a team's or vendor's claims, and count the true cost of using it
• Choose where AI creates value, build a business case on measured baselines, and move pilots to scale or stop them
• Set accountability, risk controls and compliance for AI using recognized frameworks and regulation
• Lead the organization through AI: operating model, skills, workforce commitments, change and adoption
• Use AI for segmentation, personalization and next-best-action within clear limits, and plan for discovery through AI answer engines
• Run generative content operations that protect brand voice, quality and safety, disclose synthetic content and respect rights in content and likenesses
• Keep AI-driven marketing within the law on truthful advertising, consent, reviews and endorsements, and children's data in each market
• Measure marketing's incremental effect, manage agencies and platforms, and shape the team and budget for AI

Modules

1. Part 1: Executive Core

   Part 1 is the same in every executive certification.

2. 1AI Essentials for Executives

   Leaders do not need to build AI, but they do need to judge it. This module explains the main kinds of AI system, what models can and cannot do, why data sets the limits, how AI costs behave, and the vocabulary and tests a leader needs to question a team or a vendor.

   • Rule-based systems, machine learning, generative AI and AI agents
   • What models do and where they fail: errors, bias, drift and explainability
   • Data, economics and the vocabulary for questioning teams and vendors
   • Spotting hype and testing vendor claims on your own data

   Exercise. List the AI tools and features already in use in your area, including those built into software you buy. For each, note whether it is rule-based, machine learning, generative or an agent, what data it uses, and who reviews its outputs before they are acted on.

3. 2AI Strategy, Value and the Business Case

   AI is worth funding only where it changes a measure that matters. This module covers where AI creates value, how to build and rank a portfolio of use cases, when to build, buy or partner, how to write a business case that counts the full cost and tracks realized benefit, and how to move from proof of concept to scale or stop.

   • Where AI creates value and how to measure it
   • Use-case portfolios: value, feasibility and risk; build, buy or partner
   • The business case: baselines, total cost of ownership, payback and ROI
   • Proof of concept, pilot and scale; why pilots stall; stage gates

   Exercise. Take one AI proposal now under discussion in your organization. Write down its baseline measure, list every cost it will carry over three years (including data work, integration, change, monitoring and review effort), name the owner of each benefit, and set the criteria for the next stage gate.

4. 3AI Governance, Risk and Regulation for Leaders

   AI needs clear owners, a view of what is in use and controls that match the risk. This module covers accountability structures, risk tiering, the main frameworks and standards, how AI and privacy law apply in the US, Canada and the EU/UK, and the confidentiality, security, vendor and incident risks that leaders must manage.

   • Accountability, the AI governance committee, RACI and the three lines
   • AI inventory, risk tiering and risk appetite
   • NIST AI RMF, ISO/IEC 42001, and AI and privacy law across jurisdictions
   • Confidentiality, AI security, vendor risk and incident response

   Exercise. List the AI systems and tools used in your area, including AI features inside software you already buy. For each one, name the business owner, assign a risk tier using the questions in this module, and note one control that is missing.

5. 4Leading People and the Organization Through AI

   AI value depends on how people and work are organized around it. This module covers operating models and leadership roles, AI literacy, workforce impact and honest commitments, change management, shadow AI and acceptable use, and how to measure real adoption and lead by example.

   • Operating models and AI leadership roles
   • AI literacy and workforce impact: tasks, redesign and reskilling
   • Leading change: Kotter's eight steps, involvement and resistance
   • Shadow AI, acceptable use, real adoption and communication

   Exercise. Pick one team that has access to an AI tool. Find out how many people used it in the last week, which tasks they used it for and what stops the others, then agree two changes with the team and measure use again a month later.

6. Part 2: Chief Marketing Officer

   Written for chief marketing officers.

7. 5AI in Marketing Strategy and Customer Insight

   AI changes how marketers understand customers, decide what to say to each one and how customers find a brand in the first place. This module looks at those shifts from the CMO's seat: segmentation and insight, personalization and its limits, next-best-action decisioning, and the move from search results to AI answer engines, together with the first-party data that all of them depend on.

   • Segmentation, prediction and customer insight
   • Personalization and where it stops helping
   • Next-best-action decisioning and contact policy
   • AI answer engines and the future of discovery

   Exercise. Ask three widely used AI assistants the five questions a new customer is most likely to ask about your category, such as which brands to consider and what each one costs. Record whether your brand appears, what is said about it, which sources are cited and what is wrong. Name one owner and one action for the most serious error.

8. 6Generative Content and the Brand

   Generative AI lets a marketing team produce more content, in more versions, faster than ever. This module covers running content operations at that scale, keeping brand voice and quality under control, protecting brand safety, telling audiences when content or people are synthetic, and managing rights in generated content, likenesses and voices.

   • Content operations and the human review model
   • Brand voice, quality control and brand safety
   • Disclosure of AI-generated content and synthetic people
   • Intellectual property, likeness and voice rights

   Exercise. Pick one campaign produced in the last quarter with generative AI. Trace each asset back through who prompted it, who reviewed it, what claims it makes and what evidence supports them, which tools and source material were used, and whether any real person's likeness or voice appears. Note each gap and who should close it.

9. 7Marketing Law and Consumer Trust

   Most marketing law was written before AI, and all of it still applies. This module teaches the principles that hold in most markets and how AI tests them: truthful advertising and claims about AI, consent and privacy in targeting, email and messaging rules, fake reviews and endorsements, and the extra protection owed to children, with examples from the US, Canada, the UK and the EU.

   • Truthful advertising, substantiation and claims about AI
   • Consent and privacy in targeting and tracking
   • Email, text and messaging rules in the US, Canada, the UK and the EU
   • Reviews, endorsements and marketing to children

   Exercise. Take your five most prominent current marketing claims, including any claim about AI. For each, write down the evidence held before the claim was made and the general impression an ordinary customer would take from it. Then pick one email list and confirm, for a sample of 20 contacts, what consent you hold and where it is recorded.

10. 8Measurement and the Marketing Organization

    AI makes marketing easier to automate and harder to measure, as tracking weakens and platforms grade their own results. This module covers the measurement methods a CMO can defend to the CFO, from attribution to incrementality tests and marketing mix modeling, and then the organization around them: agency and platform contracts, skills and team design, and how budgets should shift.

    • The limits of attribution and why incrementality matters
    • Incrementality testing and marketing mix modeling
    • Agency and platform relationships and contracts
    • Skills, team design and budget shifts

    Exercise. Choose your largest digital channel and compare the return it reports in its own dashboard with the best independent evidence you have, such as a holdout test or your mix model. If you have no independent evidence, design a test: the regions or customers to hold out, its length, the result that would change your budget and who will decide.