POV

Trust must precede vision, no matter how advanced or appealing the technology. Just as consumers won’t follow a brand they don’t trust, they won't embrace AI-driven experiences that feel invasive or unexplained.

Today, CMOs and marketing executives face a critical test of trust: Do you truly understand and trust the AI that’s powering your campaigns, audience segmentation and recommendations? And just as important, can you explain why it suggests the ideas it suggests?

According to recent insights from Kyndryl, 45% of CEOs say their employees still actively resist

AI, specifically due to a lack of employee trust. The issue isn’t capability, it is clarity. AI is increasingly advanced, yet often frighteningly opaque. Leaders wouldn’t tolerate an employee who refused to explain their actions. Why would businesses tolerate AI that operates as a perpetual “black box”?

Why the Black Box Must Go

Imagine your marketing team rolls out an AI-driven campaign and sees a drop in conversions, or worse, backlash over perceived bias. Of course you want answers, but with most AI tools today, there aren’t any clear answers. Models generate decisions and predictions, but hide their rationale behind layers of algorithmic complexity.

That’s not just a technical inconvenience. It’s a brand and business liability. When an opaque AI model makes errors, those errors compound. Future campaigns trained on flawed or biased outputs only worsen performance. Like trying to “unbake” sugar from a cake, companies discover too late that undoing systemic flaws in their AI infrastructure becomes impossibly costly and complex.

Take customer segmentation, for example. If an AI model begins excluding certain consumer profiles based on flawed logic or biased inputs, you could set up a structure that indefinitely alienates valuable audience segments. Without AI transparency, it's hard to determine what caused your campaign to fall short.

Causal AI: Making Transparency the Norm

This is where causal AI enters—not as a nice-to-have, but as an essential foundation for sustainable AI investment. Causal AI explicitly models the “why” behind every decision, not just the “what.” Instead of treating data points as isolated correlations, causal AI understands and documents the relationships, influences and dependencies behind each decision.

Rather than passively trusting predictions, marketing leaders and data teams can interrogate decisions proactively by answering questions like:

• Why was this audience segment targeted over another?
• Why did this ad perform better with one demographic than another?
• Why is this product being recommended for this segment, and based on what factors?

Causal AI provides clear, audit-friendly answers, turning black boxes into transparent pathways so that leaders don’t have to dig for rationale. These aren’t just technical questions, these insights empower marketing leaders to align AI outputs with brand strategy and business objectives.

The Price of Delaying Transparency

Without causal understanding built into AI from the start, marketers risk having to tear down and rebuild their AI infrastructure later. A reliance on opaque AI models today risks catastrophic, expensive overhauls tomorrow.

We’ve seen costly data privacy scandals, algorithmic bias incidents and multimillion-dollar compliance fines. Each of these linked directly to a lack of transparency and accountability. The damage is more than financial: brand equity, consumer trust and market position all erode swiftly when trustworthiness falters. Consider Amazon, which famously scrapped an AI hiring tool after it was showing negative bias against female applicants. These real-world consequences demonstrate why a lack of transparency and explainability is no longer tolerable because it is a brand image and human ethics issue.

So, Why Now?

Companies at the forefront, like Kellanova and other innovative marketers, are investing now in explainable, transparent causal models precisely to avoid these risks. Early movers aren't simply adopting AI, they’re reshaping its role within their organization. They are ensuring the foundation of trust is laid before scale.

As David Ogilvy famously said, “It’s not creative unless it sells.” Applied to AI, we might adapt this: “It’s not innovation unless it can be explained.” Marketing leaders who proactively embed causality into their AI strategy today won't have to tear down and rebuild tomorrow. They’re not just future proofing, they are also creating a competitive advantage built on trust, clarity and accountability.

The best time to build trustworthy, causal AI was yesterday. The second-best time is right now. In five years from now, the most successful marketing campaigns won’t be the ones that used the flashiest AI tools, but instead, the ones that used the most trusted ones. For CMOs, causal AI is more than a technological feature, it’s the foundation of the future of marketing strategy.