GEO Is Bigger Than SEO, and Most Companies Aren’t Organized for It

Search strategy used to be straightforward. A customer had a question, typed it into a search engine, and brands competed to rank highest on the results page. But this model is quickly changing.

A growing share of discovery now happens inside AI systems that interpret information, synthesize sources and provide an answer before a customer ever reaches a brand’s website. Generative Engine Optimization (GEO) has emerged as one response, but companies risk thinking too narrowly about what that shift actually requires.

In the September update to IAB’s 2026 Outlook Study, 44% of U.S. ad buyers named adapting to changing consumer behavior, including AI-driven search, as their top media investment challenge. And 76% are increasing their focus on optimizing content for AI-generated answers, more than any other area.

The mistake is treating GEO as simply the next iteration of SEO. AI-driven discovery is becoming an enterprise visibility challenge that cuts across marketing, data, technology, content, communications and customer experience. Companies that adapt well will not simply optimize for a new search interface. They will rethink how their digital presence, organizational knowledge and external authority work together.

The Search Result Is Becoming an Answer

Traditional SEO was built around rankings with a clear objective and a clear way to measure progress. GEO introduces a different kind of visibility. Instead of asking whether a page ranks for a particular keyword, companies increasingly need to understand whether their brand appears in the answers generated for the questions their customers are asking, how the brand is being described and which sources are influencing that description.

The shift is already affecting companies. Reach, the UK publisher behind the Mirror and the Express, reported a 46% year-over-year decline in traffic from Google, which it attributed to features such as AI Overviews and AI Mode that reduce the need for readers to click through to websites. Declining traditional search traffic doesn’t always mean people are losing interest in a category or a brand. It can also signal that discovery is fragmenting across different interfaces and increasingly happening before a website visit ever occurs.

For brands, that changes the question. Visibility is no longer only about where you rank. It is also about whether AI systems have the right information and context to understand why your brand is relevant to the question being asked.

GEO Starts With the Foundations of SEO

It is tempting to treat GEO as separate from SEO, but much of the groundwork carries over. AI systems still need to find, interpret and evaluate information, so technical accessibility, site structure and clear content all matter.

This year, Cloudflare’s Radar data showed bots generating more than half of requests for web pages across its network. Not all of those bots are AI agents, but a growing share of any website’s audience is now software, and it needs enough content and context to understand what it finds. That may mean going back to basics many companies have let slide.

If important information is buried inside a complicated interface, inconsistently presented across pages or difficult for a crawler to access, an AI system has less to work with, and so do human visitors. Common culprits are easy to spot: product details that only appear after JavaScript runs, which not every AI crawler executes, and pricing or policy information that reads differently from one page to the next.

But technical accessibility is only the starting point. Every brand is competing inside the same handful of AI systems, which means the systems themselves are not the differentiator. Context is. For brands, that context comes from their data, expertise, content, customer knowledge and the broader information ecosystem in which they appear.

Content Quality Matters More When AI Is Doing the Reading

There is a lot of discussion about how to write content that AI systems will surface. Some of it has focused on formatting tactics or attempts to reverse engineer individual models. Most of those tactics have a short shelf life, and I would not build a strategy on them.

Models, retrieval systems and sources will continue to change. A tactic that appears to be effective today may produce very different results several months from now. A more sustainable approach is to create useful, authoritative content that clearly answers the questions customers are actually asking and demonstrates subject matter expertise. Companies should start by examining what they have already published. Does it answer meaningful customer questions? Is expertise clearly demonstrated? Is important information consistent across the digital ecosystem?

AI visibility is influenced by a broader set of signals, including content quality, technical foundations, structured data, authority and third-party references. Muck Rack’s latest research on AI citations found that 84% of the links cited in ChatGPT, Claude and Gemini answers came from earned media, including journalism, academic research and other third-party sources. That is why GEO extends well beyond what a brand publishes on its own site, and why communications teams belong in the conversation.

The Workflow Problem Is Bigger Than the Technology Challenge

In my experience, the technology is rarely the hardest part of adapting to AI-driven discovery. The bigger challenge is how teams work together once AI becomes part of the search and discovery process. Historically, search strategy has been divided across different responsibilities, with SEO teams monitoring rankings, content teams producing pages, communications teams managing press coverage and developers managing the technical side of the website. AI discovery blurs those boundaries because what shows up in an AI answer can be influenced by all of those areas at once.

McKinsey’s 2026 State of AI survey makes the broader point: nearly three-quarters of the AI high performers it identified have fundamentally redesigned workflows because of AI, compared with a quarter of other respondents. AI visibility works the same way. If a content team discovers that an AI engine is describing the brand incorrectly, the fix may sit in the website, the product data, a third-party listing or the press coverage the model is drawing from.

Addressing it requires the teams responsible for those areas to work together rather than treating GEO as another task owned by one department.

That raises a question leadership teams increasingly need to answer: Who owns AI visibility?

The work will span several teams, but accountability should sit with one leader who owns the scorecard, the review cadence and the response when an AI system gets the brand wrong. From there, marketing, communications, technology and data leaders need a shared understanding of how the brand is represented across AI systems, what influences those answers and how the organization responds when gaps emerge.

Measure Visibility as a Rate, Not a Rank

One of the biggest changes ahead is how companies measure search visibility. Search rankings have traditionally given marketers a relatively straightforward metric to track, but AI-generated answers are much more fluid. There is no single position that guarantees a brand will be seen, and just appearing in an answer won’t tell the full story. A brand might be mentioned frequently but described inaccurately, included without a citation, or cited in one market and left out entirely in another.

That means GEO is shifting the focus from where a brand ranks to how often it appears, how it is described and which sources are influencing the answer. Measurement practice is still catching up. In the same IAB study, 86% of buyers said they are changing, or expect to change, how they measure media performance because of conversational AI tools and AI agents, and 45% named comparing AI-driven and traditional customer journeys as their top measurement challenge. In practice, that makes GEO as much a measurement and workflow challenge as a search one.

A practical place to start is with the questions customers are already asking. Build a representative set of those questions, run them across the major AI platforms on a regular schedule and record whether the brand appears, how it is described, which competitors show up alongside it and which sources are cited. Run each question more than once. The same prompt can return different answers from one run to the next, so visibility should be reported as a rate, not a single screenshot. Over time, that visibility can be connected to downstream signals, including AI referral traffic, engagement and conversion.

There are already signs of how quickly this can change. Adobe’s holiday shopping data showed that traffic from generative AI tools to U.S. retail sites rose 693.4% year over year during the 2025 holiday season, and that those AI-referred visitors converted at a higher rate than other traffic. That growth started from a small base, and not every company will see the same curve. But it shows that AI visibility can be measured and connected to real business outcomes today.

Treat GEO as an Operating Capability, Not a Campaign

AI-driven search does not mean throwing out everything companies already know about SEO. It means looking beyond rankings and recognizing that customers increasingly have another layer between the questions they ask and the information they receive.

But companies should resist turning AI visibility into another isolated marketing tactic or a one-time audit. Models and sources keep changing, so the first audit is not the finish line. The larger opportunity is to use those insights to determine where content, data, technology, communications and customer experience need to work differently together. Search is becoming more conversational and increasingly agentic, with AI systems starting to research and act on a customer’s behalf. When that happens, visibility depends on what those systems can find, verify and repeat about your brand.