What to Do When Google Isn’t Your #1 Traffic Source Anymore

If there’s one thing marketers have learned over the years, it’s this: never get too comfortable with Google.

Every few years, an algorithm update drops like a surprise pop quiz. Panda punished thin content, Penguin cracked down on spammy links, and Hummingbird made everyone suddenly care about “semantic search.” Each time, SEO pros scrambled to decode what it meant, rewrite their playbooks, and calm their clients.

But this time, it’s different.

The rise of AI-driven search and shopping isn’t just another algorithm tweak; it’s a full-blown revolution. Artificial intelligence is changing how people search, where they search, what they click (or don’t click), and how they buy. AI Overviews, large language models (LLMs), and new AI-driven shopping tools are reshaping the organic landscape faster than you can say “page rank.”

In short: Google’s updates used to shake up your keyword rankings.
Now, AI innovation is shaking up the entire idea of organic traffic.

The question every marketer is now asking: what happens when Google stops being your biggest traffic driver?

The Decline of Traditional Click-Through Experiences

If your organic traffic graph lately looks like a ski slope, you’re not alone.

According to SEMrush, AI-driven search could overtake traditional engines as the preferred search method by 2028. And we’re already seeing the ripple effects of this. Google’s AI Overviews, launched in 2024 as part of its Search Generative Experience (SGE), now appear in roughly 13% of queries, more than double what we saw from January to March 2025.

The result? Nearly 60% of all searches now end without a single click. Some industries are seeing traffic declines as steep as 15% to 64% since AI Overviews rolled out. Publishers, eCommerce sites, and SaaS companies are feeling it the hardest.
That means even when you’ve done everything “right”, optimized your content, targeted the perfect keywords, and snagged a top position, users might get their answer straight from Google’s shiny AI summary instead of visiting your site, and you’ll never reap the rewards.

In short: you’re still the brain behind the answer, but the robot gets the credit.

The Rise of LLM Search

If that all sounds a little bleak, here’s the silver lining: overall there are some signs suggesting that AI-referral traffic may be higher quality than traditional.

Visitors coming from LLMs like ChatGPT, Claude, or Gemini are proving to be high-intent power users. Data from SEMRush shows that LLM-driven visitors are 4.4 times more likely to convert than those arriving from traditional search.

Adobe’s 2024 holiday retail analysis backs this up: visitors from AI-driven search stayed 8% longer, viewed 12% more pages, and bounced 23% less than their Google-born counterparts.

And the growth? Astronomical. Between July 2024 and February 2025, AI-driven traffic to retail websites jumped 12x, outperforming traditional sources in engagement across the board.

Think of it this way: traditional SEO was about ranking. LLM search is about relevance. The AI doesn’t care about backlinks—it cares about useful answers. And if your brand delivers those, you’re in the game.

AI As a Sales Channel

Until recently, AI-powered interactions were all about discovery: product recommendations, personalized search results, and maybe a chatbot helping users find the right pair of sneakers. But now, artificial intelligence isn’t just influencing shopping decisions, it’s completing them.

Nearly a third of consumers have already used some kind of AI interface to make a purchase. Whether it’s asking a digital assistant for “the best noise-canceling headphones under $200,” or letting a smart mirror recommend a shade of lipstick, shoppers are starting to trust machines with their buying decisions. And the more intuitive these experiences get, the more that trust turns into transactions.

The biggest shake-up so far? ChatGPT’s instant checkout feature.
It’s still new, and the data’s early, but its potential is enormous. Imagine a shopper typing, “What’s a good moisturizer for sensitive skin?” and ChatGPT not only recommends your product, but lets them hit ‘Buy Now’ right there in the chat. No tabs, no scrolls, no bouncing between apps. Just a single, seamless conversation from curiosity to conversion.

For marketers, that’s both thrilling and a little terrifying. Traditional eCommerce funnels, the carefully crafted journeys from awareness to decision, are collapsing into a single AI-powered moment. Discovery, evaluation, and purchase now happen in one interface, guided by algorithms trained on millions of data points and conversations.

This shift builds an entirely new sales ecosystem, where the “storefront” might not even belong to you. The future of selling isn’t just about ranking high on Google or optimizing your PDPs. It’s about being present, trusted, and purchasable wherever the conversation happens, whether that’s on ChatGPT, Perplexity, Gemini, or whatever AI assistant your next customer decides to befriend.

How to Adapt to the Future of AI Commerce

If all this talk about AI eating your traffic and rewriting the sales funnel has you sweating—don’t. This isn’t the end of organic discovery; it’s just the start of a smarter, more dynamic version of it.

The brands that thrive in this new era won’t be the ones clinging to keyword rankings or chasing every algorithm tweak. They’ll be the ones structuring their data, refining their product stories, and meeting customers (and machines) exactly where they are.

Here’s how to future-proof your visibility and make sure your brand stays discoverable even when the next click never comes.

1. Understand how LLMs currently understand your business

Before you can optimize your visibility in AI-driven search, you first need to know what these models think you do. Large language models like ChatGPT, Gemini, and Claude are essentially well-read students; they’ve consumed massive amounts of information about your industry, your products, and your competitors. But whether they actually understand your brand correctly? That’s another story.

This is where tools like Akeneo’s AI Discovery Optimization feature come in, which helps businesses enrich, structure, and present product information in ways that align with how LLMs interpret and retrieve data. Instead of chasing the right keyword density or backlinks, the goal now is to make your content machine-comprehensible. The better an AI model understands your catalog, your values, and your differentiators, the more likely it is to include you in responses—or even recommend you outright.

Think of it as SEO for the AI age. Clarity, structure, and semantic richness replace keyword stuffing and meta tag tinkering. Your job isn’t to trick algorithms anymore—it’s to teach them who you are.

2. Incorporate customer feedback & insights into your product language

If you want to know how AI describes your brand, don’t look at your website – look at your customers. A recent study revealed that Reddit, Quora, and LinkedIn are among the most-cited sources in Google’s AI Overviews. Translation? AI is literally learning how to talk about your products from what real people are saying about them online.

The best way to stay relevant is to listen to those customer conversations and incorporate them into your product language. Use the same words, phrases, and problem statements your customers do. Reflect their tone and intent in your product descriptions, FAQs, and educational content. When your content mirrors authentic customer language, AI systems are more likely to cite you as a trusted, contextually relevant source.

3. Leverage structured and enriched data to increase AI discoverability

AI thrives on structured data. It’s how it decides what to trust. Use schema markup, comprehensive metadata, and enriched product attributes to make your content “machine-friendly.” The more context you give, the more likely AI systems are to understand (and cite) you accurately. When your product information is consistent, complete, and machine-readable, it’s far more likely to surface in AI Overviews, be cited in conversational answers, and even be recommended in emerging AI shopping tools.

4. Start tracking LLM referral traffic & other new KPIs to stay ahead

 As large language models like ChatGPT, Perplexity, Gemini, and Copilot start shaping more customer journeys, they’re quietly becoming meaningful sources of referral traffic. The catch? Most brands aren’t even tracking it yet. Start by looking for patterns in your analytics that hint at AI origins; unusual referral URLs, sudden spikes in direct traffic after trending topics, or visits that seem to appear out of thin air (spoiler: they probably came from an AI assistant). Some analytics platforms are already experimenting with dedicated tracking for AI-based referrals, and those who adopt early will gain a serious head start.

Beyond traffic, it’s time to expand your KPIs. Measure AI citation volume (how often your content is mentioned or quoted by AI tools), engagement depth (how long users stay after arriving from AI-powered sources), and AI-assisted conversions (purchases or sign-ups that began with an AI interaction).

Embracing the Future of Search & Shopping

Google may have been the sun around which your marketing universe once revolved, but that solar system is expanding fast. Now, discovery happens across an entire constellation of AI-driven platforms, assistants, and interfaces, all illuminating new pathways between curiosity and conversion.

Yes, that means the organic landscape is more complex than ever. But it’s also more exciting. For the first time, brands aren’t confined to search results. They can exist in conversations, recommendations, and even purchases that happen without a browser tab. The marketers who embrace this shift, who enrich their product data, listen to customer language, and understand how AI perceives their business, will be the ones who stay visible in the age of machine-mediated discovery.