The Trust Stack in Action: Four Case Studies
What trust gaps look like in practice and how leaders responded
Last week, I shared the Trust Stack—four layers that replace hidden coping mechanisms with operational clarity. Today, I want to show you what those layers look like when they’re tested.
Here are four cases for each layer. Three are public and well-documented. One is a composite drawn from governance patterns that I consistently observe across regulated B2B environments. Each one reveals a different kind of trust failure and what it actually costs when the system isn’t in place.
Layer 1: Verification
Coca-Cola: when brand standards aren’t enough
Coca-Cola integrated generative AI into high-visibility creative campaigns, including its holiday advertising and the Coca-Cola Create platform. Coverage in Reuters, AdAge, and Forbes documented both the company’s formal AI adoption and the public reaction that followed.
The response was mixed and revealing.
Audiences described some of the AI-generated creative as inauthentic, wrapped in“AI-sheen,” and inconsistent with the brand’s visual legacy. Critics called out the gap between what audiences expected from Coca-Cola’s creative and what the outputs delivered.
For a brand built on emotional resonance, that gap was visible almost immediately.
What this reveals about Verification: Coca-Cola has some of the most recognized brand standards in the world. But even extensive brand guidelines don’t automatically translate into AI quality thresholds. When a team knows what the brand looks like but doesn’t define what AI-generated work for that brand must look like—what constitutes “draft complete” versus “publish ready”—quality drift is the predictable result.
You can’t blame AI for the rework and the backlash. But you can blame absence of explicit verification standards for AI-generated creative.
The question Verification answers: What does “good enough” look like for this output type, at this risk level, for this audience?
When that’s defined—and aligned— teams catch quality gaps before the work goes public.
