Why this path
CMOs are on the front line of generative AI adoption in their teams. This path covers practical applications alongside the governance, brand, and IP risks that come with them.
10 concepts
Generative AI produces new content—text, images, code, summaries, audio—on demand, based on patterns learned from vast amounts of existing data.
Why it matters for CMOs: The capability driving most marketing AI today
The AI models behind most generative tools today — capable of remarkable language tasks, and unreliable about facts they were never trained on.
Why it matters for CMOs: What powers AI writing, summarization, and chat
Prompt engineering is the practice of writing clear instructions for an AI system, specifying the task, context, format, and constraints, so it produces more useful, consistent output.
Why it matters for CMOs: How to get consistent, useful outputs from AI tools
AI applied where customers directly feel it — which makes it both the highest-visibility opportunity and the fastest way to damage trust.
Why it matters for CMOs: AI in the customer journey: support, personalization, self-service
AI that generates images from text descriptions — genuinely useful for creative work, and carrying unsettled intellectual property and brand governance questions most organizations haven't resolved.
Why it matters for CMOs: AI-generated imagery: use cases and IP risks
Using historical data to estimate what's likely to happen next — not a crystal ball, but a way to act on probability rather than intuition.
Why it matters for CMOs: Forecasting customer behavior and campaign outcomes
AI creates more ways for personal data to move, be retained, and end up somewhere it shouldn't than most organizations have mapped.
Why it matters for CMOs: Customer data obligations in AI-driven marketing
Turning data into decisions — from understanding what happened to figuring out what to do about it.
Why it matters for CMOs: Measuring what AI is actually doing for the business
Hallucinations are AI outputs that are confidently stated but factually wrong. The model isn't lying or guessing, it's generating plausible-sounding language that happens to be false.
Why it matters for CMOs: Why AI content needs editorial review before publication