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AI-referred traffic to brand websites increased 632% in about 10 months, according to data from Contentsquare, an experience analytics platform. The shift away from organic traffic is happening faster than the mobile revolution of a decade ago, and most brand websites weren’t designed for it.
But here’s the problem. A drop in SEOs does not necessarily mean a decrease in sales. A Contentsquare client lost 40% of their organic search traffic to a single financial services page. Yet the conversion on this page actually increased. People from AI chatbots are more informed and more intentional. The problem is that brands don’t know what traffic is really important.
“What we thought of as SEO success is changing. It’s no longer just about viewability. It’s about the accuracy of how your brand is represented in the AI summary,” said Andrew Frank, distinguished vice president analyst at Gartner.
Here’s what brand marketers need to know about this shift.
The empty shell problem
The most immediate problem isn’t content strategy. This is how websites are built.
“The browser gets an empty shell, and then you render,” said Jane Austin, senior vice president of design at Contentsquare. AI bots “perform simple fetching of raw HTML. They don’t wait for the page to build. They don’t have to read anything.”
Many brand websites use client-side rendering. The page loads an empty HTML shell and JavaScript fills in the content after the fact. Human visitors never notice this because their browser is running the JavaScript. But most AI bots don’t run JavaScript. They grab the raw HTML, see nothing, and move on. Google’s crawler is the exception — it uses a headless Chrome service that can render JavaScript — but the AI crawlers that power ChatGPT Search, Claude, and Perplexity all retrieve only raw HTML. A site can rank well in Google and still be invisible to all AI response engines.
Machine readable is not enough
The fix is server-side rendering, where all content is assembled on the server and sent as a complete package. Brands that want their product pages, comparison tables, and category pages to be visible to AI agents should ensure they are rendered server-side by working with the engineering team.
However, this is only part of the solution. Server-side rendering faces a second problem. Content that works for humans – rich images, videos, interactive modules – is almost invisible to AI agents.
Frank calls this the “dual-mode media” challenge. Each asset requires two layers: one for humans, which is the visual experience, and one for machines, consisting of transcriptions, chapter titles, alt text, and structured metadata. AI agents don’t watch a product video. Instead, they read his transcript, thereby missing the visual elements. But there may be ways to create transcripts that somewhat compensate for this, Frank suggests.
“AI is likely to pick up on nuances of semantic presentation that are perhaps invisible to people,” Frank said. “People generally don’t read transcripts of a video. If the transcript contains descriptions that aren’t in the video, it’s possible to replace some of that lost information.”
B2B brands are accidentally ahead of the curve on this. Their content is naturally structured with comparison tables, technical sheets, FAQ sections and pricing pages. AI agents easily analyze this type of content. B2C brands that rely on rich visuals and minimal text are effectively invisible to AI agents.
Austin noted that, according to Forrester, 51% of software buyers now begin their research in an AI chatbot rather than a search engine, up from 29% the year before. For B2B brands, the urgency is already there.
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The robot identity crisis
A distinct challenge runs through all of this. Brands cannot know who the robots on their site work for.
Cloudflare and other security tools can classify traffic as bot or human. They can distinguish between crawlers, real agents, and scraping bots. But they can’t connect a robot to the specific person who ran it.
“This human binding agent is ultimately invisible,” Austin said. “The website treats this agent as a person, but there is no solution to this identity gap.”
Automated traffic now accounts for 53% of all web traffic, according to the Imperva Bad Bot report, and 40% of that traffic is malicious. The “empty Internet” theory – that most web traffic is bots talking to bots – may already be here.
For marketers, this creates a personalization problem. If a bot comes in representing a high-value prospect who has been researching a product for weeks, the brand has no way of knowing. And if a human finally arrives after the robot has performed the search, the connection between the two visits is lost.
Frank pointed out that AI agents can consolidate the entire customer journey into a single chatbot session.
“If you can do the whole journey with a chatbot that ends with a transaction, the role of the website is greatly diminished,” he said. This raises uncomfortable questions about how to allocate marketing budgets.
Gartner predicts that $15 trillion in B2B spending will flow through AI agent exchanges. The business model for brands to access the context of these AI-mediated conversations is still being negotiated.
What brands should do now
Both Austin and Frank converged on a set of practical steps brand marketers can take today.
- Audit your rendering. Check if your most important pages (product detail pages, pricing pages, comparison pages) are rendered server-side. Use a tool like curl or a bot simulator. If the page returns empty HTML, AI agents can’t see your content.
- Add machine layers to each asset. Every video needs a transcription. Every image needs alt text that describes what matters from a brand perspective, not just SEO keywords. Chapter titles and structured metadata transform visual content into machine-readable content.
- Create structured content. FAQs, comparison tables, datasheets, and pricing pages are gold for AI agents because they are easy to analyze. B2B brands are already doing this. B2C brands need to catch up.
- Measure before moving. Establish a baseline between bot traffic and human traffic. Track conversion rates separately for each. The old KPI was SEO traffic volume. The new KPI could be the conversion rate of AI-mediated visits.
- Do small experiments. Test two versions of a high-traffic page. Find out what converts AI-referred traffic versus direct traffic. Learn what works for a specific category before making big investments.
- Partner with your technical team. This is not a problem that marketing can solve alone. Server-side rendering, API exposure, and MCP server architecture require engineering. Austin recommends treating this as a cross-functional conversation starting now.
Don’t lose sight of the human
For all their talk about agents, Frank and Austin both emphasized that the human experience still matters. Austin described searching for a specific pair of gold hoop earrings with sapphires through an AI interface. She got results, but she couldn’t tell which brands were high quality.
“Brand signals and trust signals are still necessary,” she said. “You need to make sure your brand looks good, feels good, and keeps the shopping experience enjoyable.”
The brands that will win in this new environment, she said, are those that start measuring now and keep the human experience at the center while adapting their technical base to the machines that will arrive first.
“You don’t need to panic,” Austin said. “But you have to start.”
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