Marketing AI Risk Evaluation Framework
A repeatable way to score the risk of an AI marketing initiative. Applies a three-factor LIME method (Likelihood, Impact, Mitigation effort) across privacy, accuracy, ethics, and compliance, so teams can evaluate before and during deployment.
A generative-AI pilot and a fully automated ad-bidding system don’t carry the same risk, but most marketing orgs size them up with the same shrug. This framework from MMA’s Responsible AI Innovation Lab (RAIL) replaces the shrug with a number: a repeatable way to rate any AI marketing initiative before it launches and again as it runs.
What’s inside
- Four risk categories: privacy (data handling and consumer exposure), accuracy (hallucination and output reliability), ethics (bias, fairness, and workforce impact), and compliance (regulatory and contractual exposure).
- The LIME rating: Likelihood (0 to 1), Impact (1 to 5), and Mitigation effort in person-months, combined into a Risk Score (Likelihood × Impact) you can rank across projects.
- Worked examples across ten marketing use cases: ad-creative testing, dynamic pricing, audience segmentation, lead scoring, and more, each scored end to end so you can calibrate your own ratings against a reference point.
- Risk mitigation and incident response: three mitigation strategies (avoidance, transfer, mitigation) and a four-stage incident protocol covering severity classification, notification, investigation, and remediation.
Who it’s for
Anyone who has to say yes or no to an AI marketing initiative (a governance committee, a risk or legal lead, a marketing ops manager) and needs the same yardstick for a chatbot pilot and a full ad-automation rollout. It complements the Marketing AI Implementation Checklist, whose risk-management section points back to this framework, and the Generative AI Usage Policy for turning a high-risk score into an actual rule.
How to use it
Score likelihood and impact for the initiative in front of you, multiply them for the risk score, then weigh that against the mitigation effort: a high score with a light fix gets done first, a high score with a heavy fix gets a plan, and a low score with a heavy fix probably waits. Revisit the score as the initiative moves from pilot to production, since both the likelihood and the impact usually change.
Published by the Marketing + Media Alliance's AI Leadership Think Tank and Responsible AI Innovation Lab. Free to download.