AI Didn’t Kill Traffic – It Filtered It

Inspired by the Fireside Chat: Stop Chasing Traffic. Start Converting It.

If you look at your analytics right now and feel a pit in your stomach, you’re not alone.

Traffic is down. Sessions are down. A lot of the metrics teams have reported on for years are trending in the wrong direction. And if you’ve spent your career learning how to grow traffic, that’s a scary place to be.

But here’s the part that gets lost in the panic:

The traffic that is showing up from AI-driven discovery – from GPT, from Google’s AI summaries – is often better traffic.

These are shoppers who arrive with context. They’ve already asked a specific question. They’re closer to a decision. And in many cases, they convert at a much higher rate than the broad, top-of-funnel traffic we spent years trying to manufacture.

AI didn’t kill traffic. It filtered it.

The metrics look worse – the business often doesn’t

One of the most consistent patterns we’re seeing across brands right now is this:

Traffic drops. Top-line performance holds.

That gap is uncomfortable, because it challenges a lot of assumptions baked into our reporting.

For years, we treated sessions and pageviews as proxies for success. If those went up, things were good. If they went down, something was broken.

AI-driven discovery breaks that mental model.

When a system answers a shopper’s question directly and sends them to one or two highly relevant destinations, you should expect fewer clicks. That’s not a failure – it’s the system doing its job.

Fewer visitors doesn’t automatically mean less demand. It often means less noise.

SEO trained us to optimize for bots, not buyers

This is the uncomfortable part.

For a long time, SEO rewarded behavior that had very little to do with helping real people.

We learned how to:

• Produce pages at scale
• Tune metadata for crawlers
• Game ranking systems
• Optimize for algorithms instead of intent

It worked – until it didn’t.

Large language models don’t behave like traditional search engines. They’re not looking for who published the most pages or who tweaked the most tags.

They’re trying to answer a question. And that means clarity, relevance, and meaning matter more than manipulation.

LLMs reward understanding, not optimization tricks.

AI is pushing us back toward storytelling – and that’s a good thing

One of the most interesting side effects of AI-driven discovery is that it’s forcing brands to relearn something we should never have forgotten.

Good storytelling converts.

When a shopper asks an AI system for advice, they’re not looking for a list of links. They’re looking for context. They want to know which option fits them and why.

That shifts the burden onto the experience itself.

Your site has to:

• Tell a clear, authentic product story
• Connect features to real-world use cases
• Reduce cognitive load instead of increasing it

This isn’t about being poetic. It’s about being understandable.

And for high-intent traffic, understanding is often the difference between bouncing and buying.

PDPs are becoming the front door

As discovery changes, so do entry points.

We’re seeing more shoppers land directly on PDPs – often from an AI-generated recommendation that already narrowed the field.

That changes what the PDP needs to do.

It’s no longer just a specification sheet. It’s the primary decision surface.

That means:

• Clear value propositions above the fold
• Obvious next steps
• Bundling and cross-sell that reduce effort
• Content that answers the questions a shopper already asked upstream

In an AI-shaped journey, your PDP is often the first (and last) chance to earn the sale.

Where Fastr fits: building for high-intent traffic, not volume

This shift toward fewer, higher-intent visitors exposes a technical reality many teams have been able to ignore until now.

Most commerce stacks were built to attract volume, not to adapt experiences based on intent, context, or how someone arrived. Insight lives in analytics. Experience changes live in backlogs. By the time something ships, behavior has already moved on.

Fastr was built for this moment.

When insight, experimentation, and execution live in the same workflow, teams can respond to high-intent traffic as it arrives – adjusting PDPs, merchandising, and storytelling without tickets, handoffs, or weeks of delay. That’s what it takes to turn filtered traffic into revenue.

The real technical challenge is adaptability

Here’s where this stops being a marketing conversation and becomes a systems problem.

If shoppers arrive with different levels of context depending on where they came from, a single static experience won’t cut it.

A visitor from GPT asking a very specific question should not see the same experience as someone browsing casually from a search result.

That requires:

• Fast iteration
• The ability to test and adjust without massive backlogs
• Systems that let teams respond to behavior, not just report on it

Waiting for perfect answers in an environment that changes daily is a losing strategy.

The teams that win are the ones that can learn, adapt, and ship continuously.

This is a better internet for buyers – if brands adapt

I get why this moment feels unsettling. A lot of the rules we learned over the last decade are breaking. Metrics are moving in ways that don’t line up neatly anymore.

From a systems perspective, this is progress.

AI-driven discovery reduces noise. It surfaces higher-intent shoppers. And it rewards brands that are clear, authentic, and useful – not just good at gaming an algorithm.

The traffic that’s left is smaller. It’s also smarter. The question isn’t whether AI traffic is good or bad.

It’s whether your site is built to serve it.