Accuracy is not the same as trust

1. When everything is accurate, everything starts to sound the same.

A lot of AI-generated content is, frankly, pretty good. It’s coherent, well-structured, and usually does the job.

In many cases, it’s more accurate than what teams were producing even a few years ago.

That shift makes a lot of content seem interchangeable. When most content clears the same bar, it becomes harder to tell one perspective from another.

Spend time with enough of it, and the similarities become hard to ignore – the structure, the points being made, even the language being used to make them.

Accuracy still matters. Buyers will notice when something is wrong. But once everything is right, correctness stops helping them choose.

At that point, being “credible” isn’t much of a differentiator. It’s expected. What starts to matter instead is whether something feels distinct, whether it reflects a point of view, real experience, or a perspective that holds up across interactions.

2. Buyers build conviction over time, not from a single interaction

One pattern keeps coming up consistently: No single piece of content is doing the job on its own.

People move across sources and formats without much friction. They read something, then look at the product, then listen to an executive, then check who else is using it. Each of those interactions adds context.

What matters most is how those interactions stack. Buyers are looking for alignment between what a company says, what others say about it, and what they can see for themselves.

CMO of Kooth Digital Health Kate Biehl put it this way: “Consistency matters more than flash.”

3. Irrelevance is still the fastest way to lose someone.

For all the focus on AI and scale, the most consistent issue is still relevance.

If something doesn’t connect to what a buyer is dealing with, it gets ignored. Not because it’s poorly written, but because it doesn’t feel useful in that moment.

That reaction tends to be immediate. People don’t work through content to find value; they decide very quickly whether it applies.

This is where a lot of content still breaks down. Teams are getting better at producing more, but not necessarily better at making it matter.

One team described the gap as “technically right, practically useless.”

4. A lot of teams are scaling before they have a point of view

Much of the current conversation around AI is centered on production: more content, more formats, more distribution, more personalization.

But the biggest problem going unaddressed is that many teams haven’t clearly defined what makes their perspective distinct. They’re working from similar inputs, similar narratives, and similar assumptions about what matters, and the output reflects that.

As one content lead put it, “We got faster at saying the same thing.” AI didn’t create that dynamic, but it has made it easier to see.