The secret to getting your marketing team to use AI

Marketing is built on trust — trust in brands, in agencies and in the quality of the data that guides decisions. AI now promises unprecedented targeting, optimization and efficiency. However, without transparency, it can just as easily undermine that trust, leading to costly missteps and eroded confidence.

The cautionary tale of Zillow’s iBuying collapse in 2021 is a case in point. Zillow leaned heavily on an AI-powered home-buying algorithm to identify properties to purchase, renovate and resell at a profit. But the model was a black box to many people relying on it — and it overestimated home values in a rapidly changing market. 

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Lacking guardrails, the system led Zillow to overpay for thousands of homes. Within months, the company reported hundreds of millions in losses, laid off a quarter of its workforce and shut down the program entirely. The underlying issue wasn’t just poor predictions — it was the lack of transparency, oversight and human-in-the-loop checks that could have flagged problems before they spiraled.

The most successful marketing organizations won’t be the ones with the flashiest AI tools, but the ones that design systems their teams can understand, monitor and explain.

Two pillars of trusted AI: Observability and explainability

Observability means real-time visibility into system behavior — from data inputs to decision logic. It allows marketers, analysts, compliance teams and brand safety leads to see how AI operates, catch errors and intervene when human judgment is needed.

Explainability takes it further, revealing why the AI makes specific recommendations in language business users can understand. Instead of statistical jargon, outputs should connect to marketing logic: “This segment is recommended because they engage 40% more with video content and resemble your highest-value customers.”

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Without these two pillars, AI adoption stalls, workarounds emerge and the investment underdelivers. With them, teams gain confidence to act on AI insights, test bold ideas and move faster without sacrificing governance.

The business case for transparency

Transparent AI reduces risk by making compliance easier, protecting brand safety and preventing poor decisions before they happen. It also unlocks competitive advantage: marketers who trust the technology are more willing to experiment, personalize and push creative boundaries. As regulations tighten, companies with transparency built in will adapt more easily than those relying on opaque, black-box models.

How to design AI your team will trust

• Start with transparency in mind. Retrofitting is harder. Choose platforms with built-in logging, metrics and dashboards for non-technical users.
• Make outputs interpretable. Use techniques like LIME or SHAP to explain results in plain language.
• Tailor explanations to the audience. Executives need top-line reasoning, analysts may want detail.
• Enable human feedback loops. Show marketers their input shapes future recommendations.
• Consider independent audits. Third-party validation builds stakeholder confidence.

The bottom line

AI that requires blind faith is AI your team won’t use. AI that is observable, explainable and collaborative becomes a force multiplier — amplifying human creativity and insight rather than replacing them.

The real question isn’t whether your marketing team will use AI — it’s whether they’ll trust it. Build that trust from day one and your AI investment will drive growth, innovation and long-term competitive advantage.