How to build SEO and AEO strategies that actually work

AI-optimized content requires dual readiness for traditional search engines and AI models

We’re entering a new stage in how digital content gets found and trusted. It’s no longer enough to write for Google’s algorithms alone. Today, large language models, like ChatGPT, Claude, Perplexity, and Google’s AI Overviews, decide which brands are worth citing when users ask high-value questions. To stay visible, content must perform across both traditional search engines and these AI-driven systems.

Search engines still rely on familiar ranking signals, keywords, backlinks, freshness, and authority. But AI systems operate differently. They assess context, clarity, and trustworthiness. When your content is structured well, transparent about authorship, and written with precision, AI models interpret it as a credible source worth referencing. The shift means that every piece of content must do more than rank, it must communicate expertise and reliability that language models can understand and reuse accurately.

For executives, this dual readiness changes how we measure performance. Traditional SEO metrics like organic traffic and ranking positions still matter, but they’re no longer the full story. Businesses now need to track brand mentions and citation frequency across AI tools. Investing in authorship transparency, data accuracy, and topic depth is the new baseline for discoverability.

This strategy was spearheaded by the marketing leaders at Liferay, who questioned whether their existing content, already strong in SEO, was recognizable by AI models. Their answer led to a playbook for writing content that’s not only optimized for search but ready for machine interpretation. The takeaway is simple but profound: if AI can’t clearly read and cite your brand, your visibility will gradually fade from the digital conversation.

On-page structure is the foundation of AI and SEO performance

Strong on-page structure remains the foundation of all digital performance. Clean page titles, consistent heading hierarchies, and logical content flow are essential for both human readers and machine understanding. Titles should be under 60 characters, start with a primary keyword, and clearly describe what the page covers. Search engines treat this as a confidence signal. AI systems, which analyze semantic clarity, interpret it as intent and authority.

A structured hierarchy, one H1 per page, supported by H2s and H3s where needed, helps AI models parse relationships between sections. When used consistently, this structure enhances site accessibility for screen readers while giving search and AI engines a clear map of your content. It’s not just about aesthetics or metadata, it’s about visibility. Well-structured information is easier for AI to extract, summarize, and associate with your brand when users ask relevant questions.

Meta descriptions still have value, even if Google often rewrites them. The key is writing them from a user-value perspective: start with what the reader gains. This style signals clarity of purpose. It also improves how users, and now, AI models, assess your content’s relevance.

Teams often overlook structure because it seems “technical,” but its impact compounds over time. A clear, logical structure doesn’t just help machines understand your brand; it increases trust and engagement with your human audience. Executives who invest in teaching this discipline across departments, marketing, product, and communications, create a consistent voice that both search engines and AI systems recognize and favor.

According to the Liferay content and SEO team, standardizing structure across all digital outputs unlocked greater consistency in both rankings and AI visibility. Their approach confirms a simple principle: clarity, precision, and accessibility are the architecture of future-ready content.

URLs and content length act as crucial but often overlooked optimization signals

Many teams still underestimate how much a clean URL and the right content length matter. A well-built URL communicates hierarchy and purpose before anyone even opens the page. Use lowercase letters, separate words with hyphens, and make the structure descriptive. If the topic is about “AI content strategy,” that phrase should appear in the URL. This simple detail helps users, search indexes, and AI models interpret relevance and topic alignment instantly.

At Liferay, the team treats URLs as lasting assets. They avoid changing live links unless absolutely necessary, and when they do, permanent 301 redirects preserve link equity and avoid breaking authority signals. Crawlers, both traditional and AI-based, depend on consistent URL structures to interpret a website’s stability and trustworthiness. When content is shuffled without care, search visibility and AI retrieval accuracy drop noticeably.

Content length is evolving as well. The focus has moved from hitting arbitrary word counts to achieving semantic completeness. Using tools such as SurferSEO, writers can identify natural language terms that belong in a given topic. The objective isn’t to reach a number but to answer a question thoroughly. The Liferay marketing team has learned this firsthand, content that’s structurally complete and written with authority performs better in AI-generated summaries than long, padded articles created to meet word targets.

For executives, the nuance lies in data quality and intent. Approving content budgets based on quantity is outdated. The right approach is giving teams the time and data tools they need to write precisely and with authority. In a world where AI models scan, retrieve, and evaluate content independently, every word, URL, and tag represent a piece of structured data that contributes to brand recognition and digital equity.

Schema and structured data increase visibility and AI citation likelihood

Structured data, or “schema markup,” gives search engines and AI models explicit signals about what your content is and who created it. While humans can infer meaning, AI relies on context markers. Schema provides that, defining whether a page is an article, a service description, or a video with an available transcript. The more precise and clean the markup, the easier it is for systems to categorize and trust the source.

The Liferay marketing and technical teams emphasize validating schema through Google’s Rich Results Test before publishing. Incorrect schema can cause indexing errors that take time to clean up. When applied correctly, it helps search systems and AI models retrieve brand content confidently, often improving visibility in featured snippets and AI Overviews.

There’s now numerical evidence supporting this strategy. Research from Digital Applied, which reviewed 1,000 AI Overviews, found that pages using schema markup were cited 2.3 times more often than unstructured pages. This data shows that structured content doesn’t just assist with traditional search, it directly influences whether AI systems choose your content as a reference.

For business leaders, the nuance is in prioritization. Adding schema is one of the most efficient technical improvements available. It carries little ongoing cost but can meaningfully expand digital visibility. Executives who support schema adoption across every content type, articles, services, FAQs, videos, strengthen their brand’s connection to trusted AI systems. It’s a direct way to ensure that when emerging technologies summarize knowledge, your brand isn’t excluded from the conversation.

Schema implementation should match page purpose

One-size-fits-all schema implementation doesn’t work. Each page type carries a different purpose, and the data markup needs to reflect that. Organization-wide, there should be universal schema elements such as Organization and BreadcrumbList to help search engines and AI systems understand brand identity and content hierarchy. Beyond that, the details must adapt to the page’s function.

Editorial pieces should use Article schema, including the author’s name, publication date, and last modification date. This information helps AI systems recognize the source’s credibility and freshness. Product and service pages should feature Product or Service schema, which define core details about features, pricing, and value. FAQ sections require FAQPage schema to properly list structured Q&A pairs, information that both Google and AI models often surface for direct answers. Video and webinar pages benefit from VideoObject schema, which should include transcripts and metadata. Transcripts in particular enhance machine readability since systems like Google Gemini and ChatGPT index both textual and multimedia information.

At Liferay, technical and content teams collaborate to ensure every new asset passes Google’s Rich Results Test before it goes live. This validation step is standard procedure. It prevents data errors that would otherwise reduce search visibility or create trust issues for AI-generated responses.

For executives, the nuance is operational integration. Schema implementation shouldn’t depend on individual initiative, it should be an embedded workflow. Leaders who enforce schema standards build structural consistency that pays long-term dividends in digital visibility. It ensures that content isn’t just published but continuously understood by machines, keeping the organization discoverable in human and AI-driven search systems alike.

Internal linking strengthens authority flow and AI comprehension

Internal linking determines how value and authority circulate through your digital ecosystem. It helps search and AI systems identify which pages matter most and how each section of a site contributes to overall subject coverage. The Liferay marketing team developed a disciplined internal linking model that prioritizes both structure and intent: 70% of links point to internal pages, and 30% to credible external references.

The 70/30 split achieves two outcomes. Internal links consolidate authority, guiding crawlers toward business-critical pages. External links, when directed to authoritative, relevant sources, validate that the content operates within a wider, credible information network. This ratio signals balance, self-referential enough for coherent branding but broad enough to reflect domain awareness.

The team also created a priority URL matrix mapping relevant target categories, core landing pages, and preferred anchor text. Every new article links back to at least one high-priority page using descriptive, keyword-rich phrasing. This consistency reduces ambiguity, enhances semantic relevance, and improves how AI models connect queries to brand material.

For business leaders, internal linking represents a low-cost, high-impact refinement. It improves how both search engines and AI assess a site’s depth, coherence, and authority. Decision-makers should see it as part of an integrated content governance framework. When implemented consistently, internal linking transforms scattered assets into a cohesive, machine-intelligible knowledge system, one that communicates brand expertise clearly to search and AI systems alike.

AEO readiness requires holistic measurement, governance, and accessibility practices

Traditional SEO alone cannot sustain visibility in an environment increasingly shaped by AI Overviews and language models. Sustained relevance now depends on Answer Engine Optimization (AEO)—a framework that ensures your content, data, and technical systems are optimized for both human-led searches and AI-driven discovery. The Liferay team formalized this through an internal checklist that measures readiness across several domains: content structure, schema implementation, AI accessibility, technical SEO, linking practices, and governance.

Comprehensive alignment across these areas determines how well your brand performs in emerging digital ecosystems. Pages must feature clean title tags, proper heading hierarchies, and complete schema. Technical SEO requires updated XML sitemaps, accurate redirects, and crawl permissions that explicitly allow AI bots such as GPTBot, ClaudeBot, OAI-SearchBot, and those from Perplexity and Anthropic. Without this access, language models may fail to include your content in their training or retrieval processes. Measurement also needs to evolve, tracking not just rankings and organic traffic, but also AI referrals, brand mentions within LLMs, and comparative “share of model” metrics that indicate how often your brand appears relative to competitors.

Governance ensures that these efforts scale and endure. The most effective organizations document an AEO strategy that unites content, SEO, and PR teams under a single operational standard. It includes structured training on writing for AI models, regular audits of citation performance, and accountability frameworks to sustain continuous improvement. In practice, this prevents fragmentation, ensuring that as marketing channels evolve, all departments speak the same technical and strategic language.

For executives, the nuance lies in treating AEO as both an operational discipline and a competitive advantage. Leaders should evaluate it not only as a marketing initiative but as part of broader digital governance. Prioritizing accessibility for AI crawlers, enforcing documentation across teams, and regularly measuring AI visibility all strengthen brand defensibility. This approach creates resilience against platform shifts, ensuring the brand remains present wherever users, and now machines, seek authoritative answers.

The bottom line

The shift toward AI-driven discovery isn’t a prediction anymore, it’s a current reality. Traditional SEO remains a foundation, but it now operates alongside a new layer of evaluation driven by language models. Executives who understand this dual environment will lead the next phase of digital visibility.

The key is alignment. Governance, structure, schema, and measurement are not isolated strategies, they form a unified system that determines how machines and humans perceive your brand. When these elements are integrated, every article, product page, and insight your teams publish reinforces credibility and authority at scale.

This isn’t about chasing algorithms; it’s about designing operational clarity. Companies that embed AEO and structured SEO into their daily content process will gain a measurable edge, more citations from AI systems, stronger rankings, and higher trust across digital ecosystems.

For leadership teams, the challenge is commitment. These practices require coordination between marketing, product, and technology groups, supported by long-term governance. The organizations that approach content as a structured, data-informed asset will define how intelligence systems, and their audiences, understand the market leaders of tomorrow.