As a global leader in SEO, content marketing, and data analytics, Anastasia Braitsik has dedicated her career to understanding the intricate dance between content and algorithms. Fresh off a groundbreaking study that analyzed over 300,000 AI-cited URLs, she’s here to demystify the emerging world of AI search. We’ll delve into the specific, text-based qualities that cause platforms like ChatGPT and Perplexity to favor one piece of content over another. Our conversation will explore the surprising power of structured summaries, the nuanced signals of expertise that AIs look for, and why a purely informational tone might be holding your content back from this new wave of digital discovery.
Leading content with a clear, structured summary has been shown to improve AI citations by over 30%. What specific elements should this summary contain, and how can writers balance making it useful for both AI models and human readers? Please share a step-by-step approach.
That 32.83% correlation was one of the most significant findings in our research, and it really speaks to the AI’s need for efficiency. The key is to think of the summary not as a teaser, but as a micro-version of the article itself. It needs to provide the primary answer or key takeaway immediately, without forcing the user—or the AI—to dig for it. For a human reader, this builds instant trust and shows you respect their time. For an AI, it’s a beautifully clean, parsable data point that directly answers a potential query.
My step-by-step approach is straightforward. First, identify the single most critical piece of information your article delivers and state it in a clear, declarative sentence. Second, support that statement with a few bullet points that highlight the core findings or steps, using concrete data where possible. Finally, place this entire block right at the top of the page, before the formal introduction. It’s about front-loading the value, which serves both audiences perfectly because what makes content easy for a person to scan also makes it easy for a machine to interpret.
E-E-A-T signals show a strong positive correlation with AI citations. Beyond an author bio, what are the most effective, yet often overlooked, ways to demonstrate expertise and trustworthiness directly within the text for an AI to parse? Please provide concrete examples.
This is a fantastic question because so many people think E-E-A-T—Experience, Expertise, Authoritativeness, and Trustworthiness—is just about an author box at the bottom of the page. Our data, which showed a 30.64% positive correlation, points to something much deeper that’s woven into the fabric of the content itself. One of the most powerful, and often missed, techniques is embedding attribution directly within your sentences. Instead of saying, “Studies show that structure is important,” you should write, “As our senior editor, Cecilia Meis, notes, ‘Clarity and structure… make information easier for both people and AI systems to interpret.'”
Another critical element is the strategic use of outbound links to primary sources or highly authoritative domains. This creates a verifiable trail of evidence for your claims. It’s like showing your work on a math problem; you’re not just giving the answer, you’re demonstrating how you arrived at it. These in-text signals are tangible, parsable proof of credibility that an AI can easily recognize and weigh far more heavily than a simple, isolated bio.
Formats like Q&A and clear section headings seem to resonate well with AI models. From a technical perspective, why are these structures so effective for AI interpretation, and what is your process for converting a standard article into this more segmented, AI-friendly format?
From a technical standpoint, LLMs are fundamentally designed for information retrieval based on patterns. A Q&A format, which we saw had a +25.45% correlation, is the purest form of this pattern: a distinct problem followed by a self-contained solution. Similarly, well-defined section headings, which correlated at +22.91%, act as logical signposts. They effectively chunk a large, complex document into smaller, topically-focused segments. This makes it incredibly efficient for a model to parse the text and isolate the exact snippet of information needed to answer a specific query without having to interpret the context of the entire article.
When I convert a standard article, my process begins with reverse-engineering the user’s intent. I ask myself, “What specific questions would someone type into a search bar to find this content?” I then transform my existing subheadings into those very questions. Next, I scan for dense paragraphs that are explaining a complex idea and break them down, either into a dedicated FAQ section or a simple bulleted list. The goal isn’t just to add formatting, but to re-architect the flow of information to be as direct and segmented as possible.
Research indicates a surprising negative correlation for non-promotional tone, suggesting that professionally written, persuasive content may perform better. How should brands navigate this? What is the right balance between being purely informational and using a persuasive tone to improve AI visibility?
That -26.19% correlation for a non-promotional tone certainly raised some eyebrows, and it’s crucial to interpret it correctly. We don’t believe that AI models are actively seeking out “salesy” language. Instead, as our data scientist pointed out, this is likely a secondary correlation. Content written by professional copywriters—which is often designed to persuade, attract traffic, or offer a service—also happens to be exceptionally well-structured, clearly written, and properly optimized. The AI isn’t rewarding the promotional aspect; it’s rewarding the high-quality attributes that frequently accompany it.
The right balance for brands is to shift from a dry, encyclopedic tone to one of confident authority. Don’t be afraid to have a clear point of view and guide the reader toward a logical conclusion or a recommended next step. The key is to be compelling and useful, not just informational. Write with a purpose. That sense of professional polish, structure, and persuasive clarity is what’s being picked up by the models, not the promotional language itself.
A common issue is a page ranking well on Google but getting ignored by AI search. What is your step-by-step process for auditing such a page? Please detail the specific text-based elements you would analyze first to diagnose why it isn’t being cited.
This is a scenario we’re seeing more and more, and it highlights the growing divergence between traditional SEO and AI optimization. My audit process for this is entirely text-focused, stripping away factors like backlinks that Google weighs heavily. The very first thing I look at is the introduction. Is the core answer buried three paragraphs deep, or is there a concise, structured summary right at the top? A lack of an upfront summary is the most common culprit.
Next, I perform a structural analysis. Is the content a dense wall of text, or is it broken into logical sections with clear headings, lists, tables, or a Q&A format? AI models need these structural elements to segment information effectively. Finally, I scrutinize the in-text E-E-A-T signals. Does the page just make claims, or does it cite and link to authoritative sources? Is expertise attributed to named individuals within the text? Often, a page ranks on Google due to its domain’s overall authority, but the content itself lacks the explicit, text-based trust signals that AIs seem to require for citation.
What is your forecast for the evolution of AI search over the next two years, and how do you believe content strategy will need to adapt to keep pace with these changes?
I believe we’re moving toward a future of “instant synthesis.” Over the next two years, AI search will become less about providing a list of links and more about delivering a direct, comprehensive answer synthesized from multiple top sources. The concept of a single “citation” may even evolve into micro-attributions embedded within a larger AI-generated response. This fundamentally changes the game for content creators.
Content strategy will need to shift from building singular, monolithic “pillar pages” to creating a deeply interconnected “knowledge graph” of content. This means focusing on producing highly-structured, granular pieces that answer very specific questions with extreme clarity. We’ll have to think of our websites not as a collection of articles, but as a well-organized database of answers that an AI can easily query, trust, and synthesize. The brands that succeed will be those that make their expertise the most structured and easily digestible for machine interpretation.
