What It Takes for Brands to Show Up in AI Search
AI search visibility requires a cross-functional marketing effort across messaging, technical structure, and third-party credibility
Whether you call it answer engine optimization (AEO), generative engine optimization (GEO), or AI search readiness, the underlying issue is the same: buyers and stakeholders are increasingly using AI tools to understand markets, compare companies, and form early impressions.
That makes AI search readiness both a strategic and tactical marketing issue.
Publishing SEO-informed content still matters. But AI search adds another layer: brands also need to understand how their expertise is being summarized, surfaced, and cited by AI tools — and whether their content is clear, credible, and structured enough to show up in those answers.
The data points to a shift, but not a wholesale replacement of traditional search. Gartner predicted that traditional search engine volume would drop 25% by 2026 as AI chatbots and virtual agents take share from search marketing. More recently, Gartner also found that one-third of consumers say generative AI rivals search engines.
The takeaway is that marketers need to optimize for both traditional search and AI-driven discovery.
AEO is becoming marketing’s new team sport
AI tools are pulling from a mix of public signals: website content, product pages, third-party articles, analyst mentions, reviews, and other sources that shape how a company is understood. That is why AEO should not sit off to the side as a novelty project. It is becoming part of how marketing teams shape content, technical structure, and market credibility.
It is also not as simple as buying a tool or publishing keyword-heavy FAQ pages. Those tactics may play a role, but AI search readiness depends on a broader set of inputs and practices—from owned and earned messaging to technical website structure, cross-functional coordination, and ongoing monitoring.
That is why many marketing leaders are treating AEO as a team sport. Existing SEO and content specialists are upskilling, while working more closely with product marketing, PR, web, and communications teams. As one marketing leader noted in a Gartner Peer Community discussion: “Over time, I think AEO becomes part of how SEO, content, and growth teams operate.”
What is an AEO marketing strategy?
An AEO (answer engine optimization) marketing strategy is a cross-functional approach to improving how a company appears in AI-generated answers and search experiences. It combines messaging, content, SEO, technical structure, and third-party credibility so AI systems can better understand, surface, and cite the company’s expertise.
Why AEO matters for complex B2B categories
In complex AI and technology categories—from cybersecurity and AI infrastructure to data management and cloud services—buyers are not usually making simple impulse decisions. They are trying to understand risk, compare approaches, evaluate credibility, and determine which companies belong on a shortlist.
AI tools are increasingly part of that discovery process.
That does not mean every buyer is making decisions based on an AI-generated answer. But AI can shape the first layer of understanding: which companies appear relevant, which problems they are associated with, what language is used to describe the category, and which sources are treated as credible.
For marketers and communications leaders, the challenge is to strengthen the signals AI tools are likely to interpret. That means making sure the company’s positioning is clear enough to be understood, the website explains what the company does in plain language, and proof points are easy to find and connect.
These aren’t new marketing challenges. But AI search makes the gaps more visible.
Five ways to strengthen AI search visibility
AI search readiness does not need to start with a massive new program. A practical first step is to look at the signals your company is already sending — through your website, product pages, content, PR, analyst relations, reviews, and other public-facing materials — and identify where those signals are clear, credible, or inconsistent.
Clarify your positioning and add context.
Align your category language, customer problems, differentiators, and proof points across all public-facing content. Explain the audience, problem, market relevance, and why the topic matters.Answer real buyer questions.
Shape content around questions your customers are already asking, including the comparisons, use cases, risks, and category questions that come up in sales conversations, analyst discussions, and executive meetings.Strengthen website content structure and technical signals.
Pair clear content structure with schema, crawlability, internal linking, and sound site architecture.Build authority beyond your site.
Use media coverage, analyst validation, and external reviews to strengthen the third-party signals AI tools may surface and cite.Monitor answers and sources.
Review AI answers for priority prompts, competitor comparisons, citations, and visibility gaps, then adjust strategy and content accordingly.
AI search is becoming another way markets are interpreted. B2B companies need to understand what it is saying, where those answers are coming from, and how to build the kind of clear, credible market presence that helps both people and AI systems understand the value they bring.
For CMOs and corporate communications leaders, this is the practical opportunity. AI search readiness is a cross-functional visibility and reputation challenge — one that sits at the intersection of marketing strategy, content, PR, analyst relations, technical structure, and market credibility.
Lorraine Hamby serves as an interim Chief Marketing Officer and Chief Communications Officer, guiding technology companies through growth and transformation.
