AI Audit

Marketing teams increasingly rely on AI to make high-stakes decisions: who to target with ads, what price to show, which customers to prioritize for retention. But unlike a human marketer, you can't just ask an AI why it made a choice. An AI audit is the process of opening that black box and verifying that the system is working correctly, fairly, and legally.

The problem this solves is accountability. If your AI-powered ad system shows different prices to different demographic groups, or if your predictive model systematically excludes certain customer segments, you could face discrimination lawsuits, regulatory fines, or brand damage. An audit catches these issues before they become public crises.

Here's a marketing analogy: imagine you hired a sales team and gave them a commission structure, but you never checked whether they were actually following it or if they were unconsciously favoring certain customer types. An AI audit is that quality control process. It examines the training data (did it include biased examples?), the model's outputs (does it treat all customer segments fairly?), and the decision logic (can we explain why it made this recommendation?).

In practice, an audit might reveal that your email personalization AI was trained primarily on data from high-income customers, so it performs poorly for mid-market segments. Or it might show that your lookalike audience model is inadvertently excluding women from certain product categories. A vendor conducting an audit would document these findings, quantify the impact, and recommend fixes.

For CMOs, this means you need audit capabilities built into your AI vendor contracts and your internal governance. It's not a one-time checkbox—it's an ongoing process, especially as you feed new data into your models or expand AI use to new channels.