Generative AI Governance Framework for Marketing
MMA's framework for governing generative AI in marketing: governance dimensions, a five-step implementation guide, and a decision tree for approving use cases. For CMOs and teams taking GenAI to production.
A governance framework from MMA's Responsible AI Innovation Lab (RAIL) for marketing organizations putting generative AI into production. It covers the controls to set up before launch and the test each new use case has to pass before anyone builds it.
What's inside
- Governance dimensions: the decisions every GenAI program has to make
- Implementation guide: assessment, policy development, training, monitoring, and feedback loops
- Decision tree: a route from a proposed use case to an approval decision
Who it's for
CMOs, marketing teams, and agencies that want consumer protection and legal compliance designed into a generative AI program before it launches.
How to use it
Run a proposed use case through the decision tree first: purpose, ethical considerations, vendor fit, data governance, legal compliance. A "no" at any step sends you back to adjust the strategy before you start the implementation guide's five steps (assessment, policy development, training, monitoring, and feedback). Pair it with the Marketing AI Risk Evaluation Framework to quantify what the decision tree flags as risky.
Published by the Marketing + Media Alliance's AI Leadership Think Tank and Responsible AI Innovation Lab. Free to download.