The SEO your business needs to appear correctly in AI

Try an uncomfortable experiment. Ask ChatGPT, Gemini, Copilot, or Perplexity what your company does. Then ask a second question: Why should I hire your company instead of your competitors?

Don’t check just yet to see if it comes up first. Look at something more important: what the AI understood about your business.

It may describe the company correctly but omit the initiative that currently represents your biggest growth opportunity. It might place you in a category where you no longer compete. It might mention services that ceased to be a priority long ago, ignore the markets where you actually operate, or compare you to companies that your sales team would never consider direct competitors. Something even more troubling could happen: the answer might be technically correct but strategically wrong.

That’s where the new SEO challenge begins.

The digital battle is no longer fought solely to appear among the top search results. More and more decisions begin a few screens earlier, within a conversation in which artificial intelligence interprets a need, consults sources, rules out alternatives, and constructs an initial version of the market for the user. By the time a prospect finally reaches a company’s website —if they ever do—they may have already received an explanation of who that company is, what it does, and why they should or shouldn’t consider it.

This shift alters something that marketing and sales used to take almost for granted: the company no longer necessarily controls the first explanation of its own value proposition.

Previously, an executive looking for a supplier might type a few words into Google, open five tabs, and form their own criteria by reading directly what each company said about itself. Now they can formulate a much more sophisticated query: “Which consulting firms can help a mid-sized insurance company reduce churn and improve customer profitability?” or “Which pricing platform would be suitable for a company with thousands of SKUs, multiple channels, and a discount structure that’s difficult to manage?”

The difference is not merely cosmetic. Google explains that its generative search experiences can use query fan-out techniques: starting from a single complex query, the system generates multiple related searches to explore subtopics and find information that allows for a more comprehensive answer. (Google Developers)

Users no longer always receive a list of items to research. They may receive part of the research already done for them.

And that means a click no longer necessarily marks the beginning of the commercial relationship. Sometimes it comes after a significant portion of the user’s perception has already been formed.

Evidence is beginning to show the magnitude of this change. An analysis by the Pew Research Center on the behavior of 900 U.S. adults found that when Google displayed an AI-generated summary, users clicked on a traditional result in 8% of visits, compared to 15% when that summary did not appear. Links cited directly within the AI response received clicks in just 1% of those visits. (Pew Research Center)

This doesn’t mean that websites have ceased to matter. It means something more challenging: part of a website’s value is now captured before a visitor even gets to see it.

A corporate webpage no longer serves solely to convince those who visit it. It also helps feed the systems that might speak on behalf of that company when no sales representative is present.

That’s the point at which SEO ceases to be merely a discipline of traffic and begins to become a discipline of interpretation as well.

Long Live GEO: How to Get AIs to Recommend Your Website

Appearing on Google and appearing correctly in an AI are two different challenges

Traditional SEO was built around a fairly straightforward mechanism. There was a search query, a set of results, and competition to grab attention within those results. The higher a page appeared for a relevant search, the greater the chance it would receive a click.

It was a battle for position.

AI-powered search introduces something different: a battle for meaning.

Imagine a CFO trying to solve a pricing problem. On Google, they might type “pricing consulting firms,” review the top results, open a few sites, and start comparing providers.

In a conversation with an AI, they might say:

“We’re a manufacturing company with $40 million in sales; we have 3,000 SKUs; we sell directly and through distributors; and we suspect our discounts are eroding our margin. What should we do, and what kind of provider could help us?”

The difference between these two searches is enormous.

In the first, the company is trying to match a keyword.

In the second, it needs to be considered relevant within a business problem involving multiple variables.

An AI can connect manufacturing, discounts, B2B pricing, profitability, channel management, margin optimization, and pricing software without the user having literally typed any of those terms.

This shift forces us to rethink what it means to “be well-positioned.”

None of this renders traditional SEO obsolete. In fact, Google has been explicit on this point: the technical foundations of SEO remain essential for participating in its generative experiences. The page must be crawlable, indexable, and eligible for Search; Google also does not require a special Schema or secret technique to appear in AI Overviews or AI Mode. (Google Developers)

OpenAI approaches this from a similar angle: any public site can appear in ChatGPT Search, but it recommends allowing access to the OAI-SearchBot to make it easier for content to be discovered, displayed, and cited. It also does not guarantee a specific ranking. (OpenAI Help Center)

The SEO infrastructure is still in place.

What has changed is the purpose for which it is intended.

Before, it was enough for the search engine to understand a page well enough to decide where to display it. Now the bar is higher: the system may need to understand it well enough to explain it to another person.

And that’s where the interesting problems begin.

Hybrid SEO: Integrating the Classic and the New

The real risk isn’t that AI doesn’t know your company

One of the first things an organization usually does when it starts worrying about its visibility in artificial intelligence is to type its own name into various platforms.

“What do you know about our company?”

If a detailed response appears, someone breathes a sigh of relief.

They shouldn’t.

The fact that an AI knows a company’s name is a pretty poor indicator of its standing. What really matters is discovering what connection it makes between that company and the problems it wants to address.

A firm may be perfectly well-known yet commercially misunderstood.

It may come across as an agency when it wants to compete as a consulting firm.

It may be remembered primarily for implementing technology when its current offering is focused on strategy.

It may be associated with small businesses when its new market is enterprise organizations.

It may appear correctly when asked by name but disappear entirely when a prospect asks who can solve the problem that accounts for 30% of its revenue.

This phenomenon can be understood as a digital interpretation gap.

The company has an identity it wishes to project. The internet contains a collection of evidence about it. AI constructs a third version using that evidence.

And those three versions do not always match.

This is particularly dangerous because the company tends to assess its positioning by looking at its own assets from the inside.

Read the homepage knowing what it meant.

Read the service names with an understanding of the methodology.

Understand the differences between practices because you helped create them.

An AI doesn’t have that internal context. Neither does a new customer.

There is an interesting precedent for what happens when an organization’s digital layers begin to contradict each other. In the well-known Air Canada case, the airline’s chatbot provided a passenger with incorrect information about a bereavement fare policy that conflicted with other terms published by the company itself. The case ended up before the Civil Resolution Tribunal of British Columbia, and Air Canada was held liable for the information communicated through the chatbot.

It wasn’t an SEO case, but it offers a useful lesson: when a machine must interpret inconsistent business information, the contradiction ceases to be a content issue and can become a business problem.

Now apply that principle to business positioning.

What happens if your sales team claims that the company specializes in strategic transformation, but two hundred old web pages suggest that it focuses primarily on implementation?

What happens if your proposal for the United States says one thing, but external mentions continue to associate the brand exclusively with Latin America?

What happens if marketing changes the positioning but never updates the evidence that supports it?

Artificial intelligence doesn’t have a meeting with the CEO to clarify this.

It does the best it can with what it finds.

The Future of SEO When Searches Don’t Happen on Google

Your homepage no longer has the monopoly on telling people who you are

Companies devote an extraordinary amount of energy to their homepage.

Every word of the HERO is debated. The order of services is discussed. The corporate message goes through marketing, management, and design. They try to condense into a single screen who the organization is and why it should matter.

It makes sense.

What no longer makes as much sense is believing that this page holds absolute authority over the digital interpretation of the company.

AI doesn’t receive a carefully crafted corporate brochure before answering a question. It can retrieve information scattered across service pages, articles, case studies, biographies, FAQs, social media profiles, third-party mentions, technical documentation, directories, and other available sources.

A company’s digital identity no longer resides on a single page.

It lives in a network.

If AI gets it wrong, perhaps the problem lies within your company

It’s tempting to get upset when an AI misrepresents an organization.

“ChatGPT is wrong.”

It might be.

But stopping there wastes valuable information.

The interesting question isn’t just why the model produced an incorrect answer. The question is: What clues did it find that made that interpretation reasonable?

Suppose a consulting firm specializing in strategy asks several systems how they would describe it, and three of them respond, “digital agency.”

The team could try to figure out how to “correct ChatGPT.”

Or it could review what the firm has published over the past few years.

Perhaps half of its website discusses web design, campaigns, CRM, and implementations. Its external profiles use the category “digital agency.” Some directories inherited that description a decade ago. The success stories showcase technological deliverables but rarely explain the strategic decision behind them.

So the AI didn’t invent the problem.

It found it.

Something similar can happen when a company switches markets, launches a new practice, or discontinues a service. The organizational chart can change in a matter of weeks. The public narrative takes much longer.

The internet accumulates layers.

And models can capture those layers.

That’s why it’s helpful to classify misinterpretations into three types.

The first is omission: the company wants to be recognized for something for which there simply isn’t enough public information.

The second is contradiction: different sources offer incompatible explanations.

The third is ambiguity: content exists, but it’s written in such a generic way that it could describe hundreds of companies.

The third category is particularly common in corporate language.

“We drive results.”

“We transform organizations.”

“We create memorable experiences.”

“We drive business growth through innovation.”

They could come from a consulting firm, an agency, a software company, a bank, or an architecture firm.

To a creative director, they might sound sophisticated.

But for a system trying to determine which company has experience solving a specific problem, they offer very little distinguishing information.

What’s really interesting is that AI can become an unwitting auditor of that vagueness.

When multiple models misinterpret a company in the same way, they may not be revealing just a technological limitation.

They may be pointing to a positioning gap that had been hidden for years.

 After the ranking comes the answer

Here’s another shift that traditional SEO dashboards don’t yet fully capture.

A company can know exactly what position it holds for a keyword, how many impressions a URL receives, and what its CTR was over the last thirty days.

But ask it something different:

How many generative responses related to its core services did it appear in this week?

The answer will likely be less precise.

Then ask:

How was it described?

That’s harder.

Which competitors did it appear alongside?

Even harder.

What sources did the AI use to justify that recommendation?

In many organizations, no one is watching.

That lack of visibility is starting to change.

In February 2026, Microsoft introduced the AI Performance view in Bing Webmaster Tools, which allows you to see how a site’s content is cited in Microsoft Copilot responses, Bing generative summaries, and some related integrations. The tool includes metrics on citations, cited pages, and queries used by the systems to retrieve information. (Bing Blogs)

The very existence of this dashboard speaks volumes.

Being cited in an AI response is already starting to become a metric.

But this should only be the beginning.

A company needs to build a second layer of measurement to complement traditional SEO.

Note that this table does not ask, “Are we number one?”

It shouldn’t.

Generative responses are dynamic, contextual, and sensitive to the wording of the query. Attempting to directly apply the old concept of ranking to this environment would be like forcing outdated logic onto a new system.

The unit of analysis is no longer just the keyword.

It’s the decision-making scenario.

A pricing software manufacturer should test how it appears when someone searches for “software.” But also when they search for “discounts,” “elasticity,” “margin erosion,” “dynamic pricing,” “pricing architecture,” “retailers,” “manufacturing,” or “thousands of SKUs.”

A customer experience consulting firm should monitor searches related to CX. But it should also monitor searches for churn, NPS, customer journey, complaints, digital drop-off, retention, and Voice of the Customer.

That’s much closer to how a real buyer thinks.

And it’s also much closer to how a company sells.

AI-Friendly Web Design: The Evolution of Search Engine Optimization

Audit the version of your company that exists within AI

The solution isn’t to gather the team for an afternoon, ask ChatGPT twenty questions, and collect screenshots. That exercise may yield interesting observations, but it rarely generates actionable insights.

A serious audit must be based on conversations that could actually take place before a sale. Consider, for example, a company that offers customer experience consulting services to the banking sector. A superficial test would be to ask, “What is Company X?” However, a question much closer to a real business situation would be: “We’re a regional bank, and our NPS is high, but complaints and churn are also on the rise. What kind of consulting firm could help us understand what’s happening?” It’s in these types of scenarios that truly useful information begins to emerge.

The methodology we propose can be understood as an AI Visibility & Accuracy Audit and unfolds through six consecutive steps: ask, observe, compare, diagnose, intervene, and remeasure.

The process begins with scenario building. The goal is not to generate hundreds of questions, but to define just enough to adequately represent the brand, its services, the problems it solves, the industries in which it operates, and the different stages of a potential customer’s consideration process.

From there, these questions are run through different systems at various times. The goal is not merely to determine whether the company appears in the results, but to understand how it appears, what attributes are assigned to it, which competitors it is associated with, and what sources or evidence support that interpretation.

The next step is to compare that perception with the positioning the organization actually wants to build. It is at this point that the most relevant findings typically emerge. A company may discover that it has excellent brand recognition but a very weak association with the service that generates the highest profitability. Another company may appear frequently, though linked to a market segment that does not represent its strategic priority. It may also be the case that competitors dominate certain responses not because they have a technically superior website, but because they have spent years building a greater body of specific evidence around the problems users are seeking solutions for.

However, identifying these differences is only the beginning. The truly valuable part lies in tracing their causes. If artificial intelligence systems fail to correctly recognize a service, it is necessary to analyze whether there is a sufficiently robust, clear, and in-depth page explaining it. If they confuse two business practices, it may be a sign that the site’s architecture itself does not adequately differentiate between them. If the company isn’t associated with a particular industry, it’s worth reviewing how many success stories, articles, web pages, and other evidence explicitly link the organization to that sector.

The same applies when systems use outdated information. In that case, it will be necessary to identify where the outdated information is still published and which sources continue to reinforce it. And if information from external sources contradicts what the company communicates on its own website, the problem ceases to be exclusively an SEO issue and begins to involve aspects of reputation, public relations, and brand governance.

For this reason, corrective actions can take very different forms. In some cases, they will be technical; in others, editorial; and in many, eminently strategic. Precisely for this reason, reducing this entire phenomenon to the concept of “GEO”may fall short. We’re not simply talking about optimizing pages for a new type of search engine, but rather about managing the company’s digital identity in the face of systems that collect, interpret, and synthesize information on their own.

The final stage consists of measuring again. It’s not a matter of expecting a change published on a Tuesday to transform all responses by the following Wednesday. Each system operates with different mechanisms for crawling, indexing, retrieving, and updating information. The key is to establish a clear baseline and track progress over time, so the organization can verify whether the interpretation generated by these systems is gradually aligning with the ranking it actually aims to achieve.

When Sales Has to Correct the AI

AI Is Already Seated at the First Sales Meeting

Marketing often thinks of artificial intelligence as a tool for producing content, while sales tends to view it as a resource for prospecting, researching accounts, or streamlining certain sales tasks. Both approaches are valid, but they may fall short, because artificial intelligence isn’t solely on the seller’s side. It’s also on the buyer’s side, and that can end up becoming one of its most important business functions.

Imagine, for example, a CFO who is evaluating whether to adopt a pricing platform. Before requesting a demo, she can ask an AI what types of platforms are available, which ones work best for her industry, what features she should compare, how long implementation typically takes, what risks she should consider, and who the most well-known vendors are. From there, she can request a comparison chart, delve deeper into three specific companies, and even ask for talking points or critical questions she should use during a sales demo.

When she finally fills out a contact form, the salesperson may think the conversation is just beginning. However, the buyer has likely already spent half an hour conversing, researching, and forming an opinion. This significantly changes the meaning of the lead, because the person reaching out to the sales team may do so with a significantly higher level of information, context, and preparation than before.

Adobe has identified interesting signs of this behavior in retail. In published data on referral traffic from generative assistants, visitors coming from AI tools showed higher levels of engagement, and during the 2025 holiday shopping season, those referrals converted 31% more than other aggregate traffic sources. This does not constitute sufficient evidence to automatically extrapolate the same behavior to the B2B environment, but it does raise a relevant hypothesis: a person who arrives after interacting with an AI might do so with a different intent and a higher level of preparation. (Adobe for Business)

In a high-value consultative sale, the implications may be even greater. Traditionally, the initial meeting allowed the company to work with the prospect to define the problem. Now that prospect may arrive with a preliminary definition already in place. In the past, the company would explain how the market was structured; today, the potential customer may arrive with an initial map of categories and solutions. The salesperson used to introduce the main competitors; now the buyer may bring them organized in a comparison chart highlighting strengths, weaknesses, and perceived differences.

And the artificial intelligence that prepared all that context worked primarily with information available on the internet.

It is at this point that SEO, marketing, and sales cease to function as three separate disciplines and begin to form part of a single cycle. Marketing defines how the organization wants to compete and what position it aims to occupy. SEO translates that definition into a digital architecture that can be found, interpreted, and correctly applied to specific problems. Content provides evidence to support that position. External sources can reinforce or contradict it. Artificial intelligence processes all these signals, constructs an interpretation, and, ultimately, the sales team receives a prospect who may already have formed an opinion.

That’s why the new business challenge isn’t just about teaching sales teams how to use artificial intelligence. It’s also about preventing AI from beating the salesperson to the punch by incorrectly telling the company’s story.

Conclusion

There’s a scenario that will likely become increasingly common in the coming years. An executive needs to solve a problem, but doesn’t know your company, has never seen your ads, doesn’t follow your consultants on LinkedIn, hasn’t downloaded any of your e-books, and hasn’t participated in any of your webinars. They simply open an AI tool and explain what they need.

At that moment, a sales competition begins in which none of the companies under consideration necessarily knows they’re participating. There isn’t yet a sales proposal, a meeting, a contact form, or a salesperson involved. There’s only digital evidence.

Based on that evidence, the artificial intelligence must determine how it understands the problem at hand, which sources it considers relevant, which companies seem related to that need, which ones have enough information to be considered, and how it could explain their differences.

This is one of the new frontiers of SEO. It does not replace the traditional search engine; rather, it significantly expands its reach. A page still needs to rank, a website still needs traffic, and keywords remain important. Links, content, indexing, information architecture, and authority continue to form part of the fundamentals of digital ranking. The difference is that there is now an additional layer between the company and the market: a system that interprets the organization before the customer necessarily visits its website.

And that system is under no obligation to adhere to the corporate narrative the company wishes to project. It knows nothing about the latest internal SEO workshop, the business priorities set for the year, or the decisions made at an executive meeting. Nor does it know that a certain service is no longer considered strategic, that another has become the main growth driver, or that the company intends to shift from being perceived as an implementer to competing as a consultant.

Artificial intelligence has only signals to work with.

This is where one of the real challenges of this new era of SEO comes into play. Many organizations may discover that the problem isn’t simply that artificial intelligence systems are misunderstanding them. The problem may be that the internet has been misrepresenting them for years, and no one had ever had such a clear way of seeing it.

AI is simply laying all those contradictions out on the table.

That’s why ranking correctly requires much more than optimizing a page with a generative response in mind. It requires reviewing the overall consistency of an organization’s digital presence: what the company claims about itself, what it can demonstrate, what other sources say, how it organizes and explains its capabilities, what topics it is actually associated with, and what evidence exists to support each of its claims.

It also forces us to question one of digital marketing’s long-standing obsessions: the assumption that every form of visibility must immediately translate into traffic to a website.

The Pew Research Center study already shows that generative responses can reduce click-through behavior in certain
searches. (Pew Research Center) The initial reaction might be to interpret this phenomenon solely as a loss of traffic. However, there is another possible interpretation.

If an AI correctly explains what your company does, connects it to the right problem, uses reliable evidence to describe it, and includes it among the set of alternatives a buyer should consider, something commercially valuable has already happened even before a session is recorded in Google Analytics.

The brand has entered the conversation.

That will likely be one of the major measurement challenges of this new era: learning to distinguish traffic from influence. Microsoft has already begun offering metrics specifically related to citations within generative responses. (Bing Blogs) OpenAI allows websites to make themselves more discoverable within ChatGPT Search through its search crawler. (OpenAI Help Center) Google, for its part, maintains that fundamental SEO practices remain the foundation for competing within its generative experiences as well. (Google Developers)

The various pieces are starting to come together. What’s still missing in many organizations is a shift in the question they use to evaluate their digital presence. It’s no longer enough to ask , “Where do we rank?” It will also be necessary to ask , “What version of our company is appearing?”

The difference between these two questions is enormous.

An organization can be found and yet not understood. It can be understood but not considered. It can be considered for a problem it doesn’t want to solve or recommended to a segment that isn’t part of its strategy. It may appear alongside competitors with whom it shouldn’t be compared. Or it can achieve something far more valuable: building a digital presence so clear, consistent, and demonstrable that, when artificial intelligence needs to explain who can solve a particular problem, linking the company to that need becomes a natural conclusion.