Generative AI Governance Framework for Marketing
MMA's framework for governing generative AI in marketing: governance dimensions, an implementation guide, and a decision tree. Built for CMOs and teams putting GenAI into production with consumer protection and compliance built in.
A governance framework from MMA’s Responsible AI Innovation Lab (RAIL) for marketing organizations putting generative AI into production. The structure to move fast without skipping the controls that matter.
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 standing up generative AI with consumer protection and legal compliance designed in from the start, not bolted on after.
How to use it
Run a proposed use case through the decision tree first: purpose, ethical considerations, vendor fit, data governance, legal compliance. Any “no” routes you back to adjust the strategy before you touch 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.