How to Build Customer Trust in the Age of AI Marketing

Christina Garnett's Customer Trust Equation breaks down where brands lose trust, and why AI is making the gaps harder to hide.

You post something you'd normally expect a wave of comments on. Today it's quiet. Not dead, just... thinner than it used to be. The reply that comes in feels shorter than the ones from a few months ago, the kind of short that isn't rudeness, just distance.

You'd think nothing of it on its own. But whoever runs your social has seen this pattern before, and they know what it usually means before anyone else in the building does.

None of it shows up in a retention report for months. By the time the churn numbers catch up, the erosion has usually been building for a quarter or more, buried in the small daily signals nobody was tracking because nobody thought to.

Christina Garnett has spent her career studying that gap. As Chief Community Officer at HeyOrca and founder of Customer Trust Infrastructure, she's built a framework for something most brands treat as a mood rather than a metric: the specific, trackable ways trust breaks down between a company and the people who used to believe in it. Her term for the moment brands are in right now is blunt. A trust recession. And she thinks AI is making it worse, faster, in ways most marketing teams haven't fully clocked yet. She laid the framework out in a recent SNMA webinar, free to watch for anyone who wants the full talk.

Part 1 of this series looked at why Canadians trust a static billboard more than a targeted ad. This piece is about what happens after that trust is gone, and the mechanics of getting it back.

What Trust Actually Breaks Down Into

Garnett doesn't treat trust as a feeling. She treats it as an equation, and one with a subtraction sign built in.

The model comes down to four things a brand builds and one thing that undoes them: Consistency, Response, Connection, and Value, minus Friction. Show up the same way over time, answer when someone reaches out, make people feel like more than a transaction, give them something worth the exchange, and take out whatever makes any of that harder than it needs to be. Miss on any one factor and the math doesn't balance, no matter how strong the others are.

Response is where most brands fail without realizing it. Not because they ignore complaints, most companies have gotten decent at damage control, but because they ignore the good stuff. A glowing comment gets a heart react at best. A five-star review gets nothing. That kind of one-sided attention trains an audience to expect that engagement only flows in one direction: theirs.

Then there's the pattern Garnett is sharper about than almost anything else in her framework. She calls it trust cosplay: the apology post, the “we hear you," the badge or banner that signals accountability, without any of the follow-through that would actually make it true.

Why Trust Is Becoming the Only Thing Left to Compete On

Features used to buy a company time. A useful tool, a clever bit of UX, a new capability, these things used to take competitors months or years to catch up to. That window is closing fast. A rival can now replicate a feature almost overnight, built with AI tools rather than a quarter of engineering time.

When the functional gap between brands collapses that quickly, Garnett's argument is that trust becomes the only differentiator left standing over the product itself, or the price tag attached to it.

That's also where she thinks AI is making the trust recession worse rather than better. The same tools that let a brand respond faster, personalize more, and produce more content are the tools most likely to trip the exact wires this framework is built to catch: generic responses that fail the Connection factor, automated outreach that reads as friction rather than removing it, and content produced at a volume that outpaces any brand's actual capacity for consistency.

Getting Leadership to Care Before the Numbers Force Them To

The hardest part of any of this isn't diagnosing where trust is breaking. It's getting a budget approved to fix it before the damage shows up somewhere leadership can't ignore.

Garnett's advice here is practical rather than aspirational: stop presenting trust as a values statement and start presenting it as a leading indicator. The response gap on positive comments, the drop in reply depth, the rising friction in a support flow, these are the same kind of early signals a finance team would take seriously if they showed up in a cash flow forecast instead of a comments section. Framed that way, trust stops being a nice-to-have for the brand team and starts being something a CFO can see coming.

It's a shift in language more than in substance. The work underneath, actually closing the gaps in Consistency, Response, Connection, and Value, is the same either way. But how it gets pitched upstairs is often the difference between a framework that gets funded and one that gets nodded at in a meeting and forgotten by Friday.

A few places to start:

• Reply to the good stuff, not just the bad. A five-star review or a glowing comment deserves the same attention as a complaint. Ignoring it reads as indifference even when that's not the intent.

• Audit your friction points, not just your funnel. Every extra click, unclear policy, or slow response time chips away at the Value side of the equation, even when the core product is solid.

• Match your response speed to your promise. If a brand claims to be responsive, the comment section and DMs need to actually prove it, consistently, not just when someone's upset.

• Don't let AI-generated responses replace the Connection factor. Faster isn't the same as more human. If a reply reads like it could have gone to anyone, it isn't building trust, it's spending it.

• Show consistency over time, not just in a single campaign. Trust compounds slowly and erodes fast. One good gesture doesn't offset months of silence or inconsistency.

• Treat trust as a metric leadership can see, not a mood the brand team feels. Track response rates and sentiment shifts the same way you'd track any other early-warning KPI.

There's a version of this that goes even further than fixing a brand's own trust gap. It asks who else, or what else, might end up vouching for a company instead. Part 3 picks up there, with a framework built around a different kind of endorsement altogether: the kind that comes from an AI agent instead of a person.