Search engines and large language models (LLMs) have transformed how people discover and interact with content. While searchers’ expectations remain the same – requiring engaging, fast, solution-oriented information – the pathways to discovery have evolved toward AI-driven systems. This shift calls for a different approach to gaining visibility. Some experts call it GEO (Generative Engine Optimization), some call it AEO (Answer Engine Optimization), some prefer SXO (Search Everywhere Optimization). Regardless of the acronym, this approach merges traditional SEO principles with the modern demands of AI retrieval and synthesis.

Understanding Google’s AI mode (and other AI search systems)

Traditional search was based on keywords – a string of keywords were matched against web content in one set of search results composed of lists of webpages. Gradually search moved to being user-intent based. AI search, however, is intended to be more conversational. It relies on “query fan-out” where the AI system breaks the search query into smaller sub-intent queries. The system tries to predict in real-time the next few queries the user might input, thus allowing it to generate answers to longer, complex queries from multiple webpages/sources.

In this post, I will outline the strategies that marketers widely consider essential for improving visibility in AI-powered search results.

Step 0: Build on traditional SEO best practices

The baseline for optimizing for AI search has to have elements from traditional SEO – fast-loading and accessible webpages, uniqueness and recency of your content.

A GEO content strategy starts by building a comprehensive keyword build consisting of related queries and subtopics. For a higher education institution, this means identifying how prospective students search for programs, career prospects, financial aid or campus life.

Step 1: Writing for clarity and connection

To succeed in modern retrieval systems, content must be both machine-readable and human-centered. AI embedding models rely on clear, well-defined language to interpret meaning accurately.

Techniques for writing with synthesis and clarity include:

• Semantic chunking: Modern retrieval systems divide content into small, distinct sections. This is not too different from SEO of years past. Organize your page into concise paragraphs with descriptive headers, each covering a single, specific idea. This structure improves retrieval accuracy and helps both readers and machines find the information they need.
• Provide original and specific insights: Content that reflects institutional expertise, such as research findings, student outcomes or faculty perspectives, is more likely to be recognized as authoritative by systems. Verifiable facts, figures and quotes from experts can all contribute to your website’s “authority.”
• Avoid ambiguity: Use clear, active language. Avoid fluff or abstract tag lines. Avoid jargon or overly complex phrasing, as AI models interpret direct statements more effectively.
• Use semantic triples: This is an interesting idea put forth by SEO experts at iPullRank – sentences that follow a subject-predicate-object format make content easier to interpret. For instance, instead of writing, “There are many benefits to attending graduate school,” write, “Graduate education (subject) provides (predicate) advanced skills and professional opportunities (object) for students. 90% of CSU graduates find jobs in their industry within a year of graduating.”

Step 2: Strengthening meaning through Entities and Context

Clarity is essential in AI-driven retrieval because systems cannot serve what they cannot understand. Entities, or identifiable concepts like People, Places, Organizations and Programs, help define the context of your content.

When creating institutional content, include all relevant entities to strengthen meaning. For example, in a page about “Agricultural Sciences at CSU,” you might include:

• Geographical entities: The university’s location (Fort Collins, Colorado), nearby landmarks and regional context.
• Organizational entities: Academic departments, research centers and partner institutions.
• Conceptual entities: Topics such as history of the program, sustainability, field research and program rankings.

Mapping these relationships with good old internal linking helps AI systems understand how different parts of your content connect, increasing discoverability and relevance.

Step 3: Use structured data

Structured data can play a huge role in removing ambiguity. While Schema.org provides a foundation, building a deeper, machine-readable framework can elevate visibility and accuracy. Here is a full guide on using structured data.

In addition, meta titles, descriptions and alt texts are some elements that can help AI better parse your content.

Key takeaways for AI-ready content

To ensure your institution’s content is visible, retrievable and cited within AI-generated summaries and search results, focus on these guiding principles:

• Prioritize information gain: Publish content only your institution can create, such as original research, alumni stories or expert commentary.
• Embed verifiable data: Include clear, factual information such as, enrollment numbers, rankings and outcomes data to strengthen credibility.
• Develop topical clusters: Organize content into interconnected themes that help users and AI understand your full ecosystem and help build topical authority.
• Diversify formats: Expand beyond text by integrating video, infographics and podcasts. Multimedia content provides depth and supports a variety of discovery channels. We have observed AI synthesize answers from Google reviews, Reddit, social media posts among other channels. So, GEO requires marketers to think about building your brand’s presence on these allied channels too.

The overarching goal is building and showcasing your expertise and a process that structures content for clarity.