LLM-referred traffic converts at 30-40% — and most enterprises aren't optimizing for it

For more than two decades, digital discovery has operated on a simple model: search, scan, click, decide.

That worked when humans were the ones doing the web searching; but with the advent of AI agents, the primary consumer of information is no longer always human.

This is giving rise to a new paradigm: Answer engine optimization (AEO), also referred to as generative engine optimization (GEO). Because agents look at data much differently than humans do, success is no longer defined by rankings and clicks, but whether content is understood, selected, and cited by AI systems.

The SEO model that the web was built on simply isn’t going to cut it anymore, and enterprises need to prepare now.

How LLMs interpret web content

Traditional SEO is built around keywords, rankings, page-level optimization, and click-through rates. Users manually search across multiple sources and click around to get what they need. Simple, but sometimes frustrating and a definite time suck.

But AEO operates on a whole different level. Agents are increasingly taking over users’ workflows: Claude Code, OpenClaw, CrewAI, Microsoft Copilot, AutoGen, LangChain, Agent Bricks, Agentforce, Google Vertex, Perplexity’s web interface, and whatever else comes along.

These agents do not “browse” the web the way humans do. They analyze user intent based not just on phrasing, but persistent memory and context from past sessions (rather than simple autocomplete). They require materials that are concise, structured, and to the point.

What’s more, agents are moving beyond browsing to delegation, handling more downstream work. What started as “search, read, decide,” evolves to “agent retrieves, agent summarizes, human decides” (and, beyond that, “agent acts → human validates”).

“In practice, AEO begins where SEO stops,” said Dustin Engel, founder of consultancy company Elegant Disruption. “AEO is the next layer of discovery,” or “zero-click discovery.”

In this new world where agents synthesize answers, users may never even see an enterprise’s website, click-through rates decline, and attribution and citability (rather than pure visibility, or showing up at the top of a list of blue links) become critical.

“The new default is closer to a citation map: Where the model is pulling from, how often you show up, and how you are described,” Engel said.

Some, like Adam Yang of Q&A platform Quora, argue that AEO is already becoming the default over SEO.

This is for “a certain class of queries,” Yang notes. Any question where the user wants a synthesized answer — "what's the best approach to X," "compare these two options," "what do I need to know about Y" — is increasingly resolved by an AI without a click.

Google's own AI Overviews are already accelerating this on the consumer side, many analysts note. “SEO isn't dead,” Yang said. “But the optimization target has shifted from ‘rank on page 1’ to ‘get cited in the answer.’”

How devs are already using AI agents

Are there scenarios where regular search/Googling is still the best option?

“Absolutely,” said analyst Wyatt Mayham of Northwest AI Consulting. Notably, for personal tasks like finding nearby restaurants or local service providers. The interface is “just better” in those cases because it integrates maps, reviews, and photos. “That experience is hard to beat right now,” he said.

For work-related research, though, he says he’s “barely” using traditional search anymore, and it’s getting “closer to zero” every month.

“When I need to understand a company or a person professionally, agents do it faster and give me a more useful output than a page of blue links ever did,” he said.

His firm uses autonomous agents “heavily,” and built a Claude Skills function that powers its sales operation. Before a discovery call with a prospect, team members can trigger a skill that pulls the contact’s LinkedIn profile, scrapes their company website, grabs relevant info from sources like ZoomInfo, and crafts a clear picture of their revenue, team size, tech stack, and pain points.

“By the time I get on a call, I have a tailored research brief ready to go without spending 30 to 45 minutes manually Googling around,” Mayham said.

The big advantage is that these tools run in the background, he noted. You don’t have to sit clicking through browser tabs: You just tell the agent what you need, it does it, and you get a structured output that’s actually useful.

“It's collapsed what used to be a full hour of sales prep into a few minutes,” Mayham said.

Carlos Dutra, data science manager at fintech company Trustly, said Claude Code has “genuinely changed” his daily workflow. He uses it for most of his coding work, and what surprised him wasn't the speed, but the fact that he didn’t need to open and keep track of browser tabs. “Not because I'm lazy, but because the answers are better,” he said. He still uses Google for some tasks: Pricing pages, recent news, anything that needs to be current. “But for technical reasoning? Agents have mostly replaced search for me personally,” he said. Quora’s Yang has had a similar experience. He’s been using Claude Code daily for the past few months, primarily for content strategy, knowledge management, and competitive research. Workflows that used to take him half a day now take 30 minutes. But what’s been most advantageous is that he can now run research and synthesis tasks in parallel that he previously had to do sequentially. Also helpful is that agents’ context retention across sessions is “meaningfully better” than web-based tools. When he needs to understand a concept, map a competitive landscape, or synthesize industry trends, Claude or Perplexity are the go-to before opening a browser tab. “I've started treating agent search as my first stop, not Google. Traditional search is now where I verify, not where I discover.” The kinks are real, though. Mayham pointed out that LinkedIn, in particular, is “aggressive” about blocking automated access, and many other sites have (or are implementing) similar protections. Users will hit walls when agents can't get through, so a fallback plan is important for those relying on agents. “The reliability isn't 100% yet, and that's probably the biggest thing holding broader adoption back,” he said. Mayham’s advice for other devs: Stop chasing shiny objects. A new AI tool launches “practically every day,” and many (experienced devs included) are jumping from platform to platform without ever going deep with any of them. “Pick a model, go deep, build real workflows on it,” he emphasized. “You'll get more value from mastery of one platform than surface-level experimentation across five.”

How enterprises can compete in an AEO-driven world

When AI agents do the searching, the rules change. The question is no longer whether your content ranks on the first page, it's whether the model selects you as the source when generating an answer.

Structure matters much more than it used to. Content should:

• Be organized around conversational intent, provide direct answers, and mirror real user questions and follow-ups;

• Be authoritative and reflect strong expertise;

• Be fresh (and, when necessary, regularly refreshed);

• Have clear headers and established FAQ schema.

Another must is maintaining a strong brand presence across the forums and platforms — Wikipedia, Reddit, LinkedIn, industry publications — that models are trained on. Enterprises might also consider investing in original data, like research.

In Mayham’s experience, when a business gets recommended by an LLM during a search-style query, the conversion rate is “dramatically higher” than traditional channels. For his company, LLM-referred traffic is converting at 30 to 40%, which “blows away what we see from SEO or paid social.”

“The intent signal is just different when someone is having a conversation with an AI and it recommends you by name.”

Discoverability inside LLMs will matter as much as Google rankings, “maybe more,” Mayham said. “It's a whole new surface for customer acquisition that most businesses aren't even thinking about yet.”

Trustly’s Dutra agreed that the “uncomfortable truth” is that most enterprise content is becoming “basically invisible” in agent-driven queries. “AEO is about whether your content survives being chunked, embedded, and semantically retrieved,” he said.

The companies getting ahead aren’t doing anything “exotic,” he noted. They have clean, declarative content that doesn’t require context to understand. Those still writing copy stuffed with keywords are going to fall behind because LLMs care about semantic clarity.

A quick test he gives clients: Ask an LLM a question your page is supposed to answer, without giving it the URL. “If it can't construct the answer from your content, you have a problem.”

Jeff Oxford of SEO agency Visibility Labs offers valuable step-by-step advice:

1. Engage in conversations on Reddit, which is one of the most-cited domains in AI search. Enterprises should establish a positive reputation on Reddit, and engage on any relevant threads where customers are asking for recommendations.

2. Build a strong YouTube presence. According to Ahrefs, which tracks internet behavior, YouTube mentions have the “strongest correlation” with AI visibility across ChatGPT, AI Mode, and AI Overviews. “This makes sense, since both Google and OpenAI have trained their models on YouTube transcripts,” Oxford said, “and YouTube is the most-cited domain in Google's AI products.”

3. Invest in digital PR and brand mentions; the latter is the second-highest correlated factor with AI visibility. “Brands need to improve their digital presence by being in as many places as possible,” Oxford said.

4. Create content aligned with AI citation patterns. Enterprises should audit the prompts and topics where AI search engines are surfacing competitors, then create authoritative content on those same topics.

“The goal is to become a source that AI models consider worth citing,” he noted.

Still, there may be a lot of unnecessary hype around how drastically enterprises need to change, said Shashi Bellamkonda, principal research director at consultancy firm Info-Tech Research Group. Those following best practices of producing content that their audience actually needs, written by experts and showcasing expert opinion, are in a good position to be cited in AI-powered search. He pointed out that Google developed an EEAT framework (experience, expertise, authority, and trust) to evaluate content quality and helpfulness and help algorithms identify reliable, high-quality information. To stand out, enterprises should use structured data and schema to signal the context: Is this an article, a research study, a product overview? “Original long-form content will be valued by AI-powered answer engines,” Bellamkonda said. “Copycat strategies or trying to game the system are taboo in this era.” Experts should also share their thoughts across several channels, and "About Us" pages must be “robust” and include bios highlighting thought leaders’ expertise.

“Ultimately, the reputation of AI-powered search is in making sure the user likes the search rather than what you think they should read,” Bellamkonda said. “So a good focus on the end user is a great way to succeed.”