For more than two decades, digital search has operated on a simple model: search, scan, click, decide.
This worked when humans were the only ones searching on the web; But with the advent of AI agents, the primary consumer of information is no longer always humans.
This is giving rise to a new paradigm: answer engine optimization (AEO), also called generative engine optimization (GEO). Because agents look at data very differently than humans, success is no longer defined by rankings and clicks, but by whether content is understood, selected, and cited by AI systems.
The SEO model on which the web was built will no longer cut it, and enterprises need to prepare now.
How do LLMs interpret web content?
Traditional SEO is built around keywords, rankings, page-level optimization, and click-through rates. Users manually search through multiple sources and click to find what they need. Simple, but sometimes frustrating and a definite waste of time.
But AEO works on a completely different level. Agents are increasingly taking over users’ workflows: Cloud 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 like humans do. They analyze user intent based not only on phrases, but also persistent memory and context from previous sessions (instead of simple autocomplete). They need content that is concise, structured, and accurate.
Additionally, agents are moving beyond browsing to delegation, handling more downstream tasks. What started as “search, read, decide” evolves to “agent retrieval, agent summary, human decision” (and, furthermore, “agent acts → human validates”).
“In practice, AEO picks up where SEO stops,” said Dustin Engel, founder of the consultancy company Elegant Disruption. “AEO is the next layer of search,” or “zero-click search.”
In this new world where agents synthesize answers, users may never see an enterprise’s website, click-through rates decline, and attribution and siteability (rather than pure visibility, or appearing at the top of a list of blue links) become important.
“The new default quote is closer to the map: where the model is coming 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 on SEO.
It’s for “a certain class of questions,” Yang says. Any question where the user wants a synthesized answer – "What is the best way for X," "Compare these two options," "What do I need to know about Y?" – Being solved fast by AI without a single click.
Many analysts say Google’s own AI observations are already accelerating it on the consumer side. “SEO is not dead,” Yang said. “But the optimization goal has shifted from ‘rank on page 1’ to ‘cite in answers’.”
How developers are already using AI agents
Are there scenarios where regular searching/Googling is still the best option?
“Absolutely,” said analyst Wyatt Mayham of Northwest AI Consulting. In particular, for personal tasks like finding nearby restaurants or local service providers. In those cases the interface is “better” because it integrates maps, reviews, and photos. “It’s hard to beat that experience right now,” he said.
However, for work-related research, he says he is now “barely” using traditional search, and it is getting “closer to zero” every month.
“When I need to understand a company or person professionally, agents do it faster and give me more useful output than a page with blue links,” he said.
His firm uses autonomous agents “heavily”, and has built a cloud skills function that powers its sales operations. Before a discovery call with a prospect, team members can trigger a skill that pulls the contact’s LinkedIn profile, scours their company website, pulls relevant information from sources like ZoomInfo, and builds a clear picture of their revenue, team size, tech stack, and pain points.
“By the time I get the call, I have a customized research brief without having to spend 30 to 45 minutes manually Googling,” Meham said.
The big advantage, he said, is that these tools run in the background. You don’t have to sit around clicking on browser tabs: you just tell the agent what you need, it does it, and you get a structured output that’s really useful.
“Now an entire hour of preparation time for the sale has collapsed into a few minutes,” Mehmed said.
Carlos Dutra, data science manager at fintech company Trustly, said cloud code has “really changed” his daily workflow. He uses it for most of his coding work, and what surprised him was not the speed, but the fact that he didn’t need to keep browser tabs open and keep track of them. “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 the latest. “But for technical reasons? Agents have mostly taken over the search for me personally,” he said. Quora’s Yang has had a similar experience. He has been using Cloud Code daily for the past few months primarily for content strategy, knowledge management, and competitive research. A workflow that previously took him half a day now takes 30 minutes. But what is most advantageous is that he can now run research and synthesis tasks in parallel that he previously had to do sequentially. It’s also helpful that agents’ context retention during sessions is “meaningfully better” than web-based tools. When he needs to understand a concept, map the competitive landscape, or synthesize industry trends, he turns to the cloud or Perplexity before even opening a browser tab. “I’ve started considering agent search as my first stop, not Google. Traditional search is now where I verify, not where I discover.” However, the complications are real. Mehmed 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 reach, so a fallback plan is important for those who rely on agents. “Reliability is not 100% yet, and that’s probably the biggest thing hindering widespread adoption,” he said. Mehmed’s advice to other developers: Stop chasing shiny objects. A new AI tool launches “practically every day,” and many (including experienced developers) are jumping from one platform to another without going in depth with any of them. “Pick a model, dig deep, build a real workflow on it,” he emphasized. “You’ll get more value from mastery of one platform than surface-level experimentation in five.”
How enterprises can compete in an AEO-driven world
When AI agents make discoveries, the rules change. Now the question is not whether your content ranks on the first page, the question is whether the model chooses you as the source when answering.
Structure matters more than ever. Ingredients needed:
- Stay organized according to the intent of the conversation, provide direct answers, and reflect actual user questions and follow-ups;
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Be authoritative and reflect strong expertise;
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Stay fresh (and, when necessary, refresh regularly);
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Keep clear headers and established FAQ scheme.
Another requirement is to maintain a strong brand presence on the forums and platforms – Wikipedia, Reddit, LinkedIn, industry publications – on which models are trained. Enterprises may also consider investing in original data such as research.
In Meham’s experience, when a business is recommended by an LLM during a search-style query, the conversion rate is “dramatically higher” than through 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.”
“When someone is interacting with an AI and it suggests your name the signal of intent is completely different.”
Searchability within an LLM matters as much as Google rankings, “probably more,” Meham said. “This is a whole new surface for customer acquisition that most businesses aren’t even thinking about yet.”
Trustly’s Dutra agreed that the “inconvenient truth” is that most enterprise content is becoming “basically invisible” in agent-driven queries. “AEO is about whether your content survives being fragmented, embedded, and meaningfully retrieved,” he said.
He said the companies moving forward aren’t doing anything “exotic.” They have clear, declarative content that doesn’t need context to understand. Those who are still writing keyword-laden copy will be left behind because LLMs care about semantic clarity.
He gives customers a quick test: Ask the LLM the question your page should answer, without giving the URL. “If it can’t create an answer from your content, you have a problem.”
Jeff Oxford of SEO agency Visibility Labs offers valuable step-by-step advice:
- Join the conversation on Reddit, one of the most cited domains in AI searches. Enterprises should establish a positive reputation on Reddit, and engage on any relevant threads where customers are asking for recommendations.
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Build a strong presence on YouTube. According to Ahrefs, which tracks internet behavior, YouTube mentions that ChatGPT, AI Mode, and AI Overview have the “strongest correlation” with AI visibility. “This makes sense, as 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.”
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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 more and more places,” Oxford said.
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Create content aligned with AI citation patterns. Enterprises should audit the signals and topics where AI search engines are outperforming competitors, then create authoritative content on those same topics.
“The goal is to become a source that AI models consider worth citing,” he said.
Still, there may be a lot of unnecessary hype about how large-scale change enterprises need, said Shashi Bellamkonda, principal research director at consultancy firm Info-Tech Research Group. Those who follow best practices in producing content that their audiences actually need, that is written by experts and showcases expert opinions, are well-positioned to get cited in AI-powered search. He explained that Google has developed an EEAT framework (Experience, Expertise, Authority and Trust) to evaluate the quality and helpfulness of content and to help algorithms identify reliable, high-quality information. To stand out, enterprises should use structured data and schema to indicate context: is it an article, a research study, a product overview? “Original long-form content will be given importance 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 views across multiple channels, and "about us" The pages should be “strong” and include bios that highlight the expertise of thought leaders.
“Ultimately, the reputation of AI-powered search is about making sure the user finds what you think they should read,” Bellamkonda said. “So a good focus on the end user is a great way to succeed.”
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