Artificial intelligence is changing how people discover information, compare products, and evaluate businesses online. For website owners and SEO professionals, one important question is becoming increasingly relevant: what makes an AI system select one webpage as a source instead of another?
A recent analysis by TJ Digital, reported by Search Engine Journal on October 28, 2026, offers an interesting observation. Pages cited in AI-generated answers were more likely to provide a direct answer near the beginning of the page than the comparison pages examined in the study.
The research analysed 193,507 AI answers from ChatGPT, Google AI Mode, and Google AI Overviews. It also used blind ratings to compare cited webpages with other pages that appeared in the research process.
The findings suggest that answer-first content structure is worth investigating as part of an AI search visibility strategy. However, there is an important distinction between identifying a pattern and proving that a particular writing technique causes AI citations.
As someone working across SEO, web development, analytics, and digital marketing, I see this as a useful reminder to evaluate content through both the user’s perspective and the way search systems retrieve information. A page should make its purpose clear, answer the intended question, and provide enough supporting information for readers to understand the subject.
Let’s examine what the research found, what it means for businesses, and how marketers can apply the findings without turning them into another set of unverified SEO rules.
1. What Did the Research Discover?
The analysis was conducted by TJ Robertson of TJ Digital, an agency that provides SEO and Generative Engine Optimization services. It examined AI-generated answers across three major search and conversational AI experiences: ChatGPT, Google AI Mode, and Google AI Overviews.
The research covered 4,167 questions that did not explicitly mention a particular company or product. These questions were associated with 78 client businesses across nine business models.
The data was collected through eight weekly snapshots between August 27 and October 21, 2026, alongside repeat runs using the same questions.
The central observation was straightforward: pages cited by AI systems more often started by directly answering the question being asked.
However, the difference depended on which comparison group researchers used.
In one comparison, cited pages were evaluated against pages that the same AI answers had retrieved but did not cite. In another, cited pages were compared with pages ranking in Google’s top ten for related keywords that the AI systems had not retrieved during the observed searches.
These comparisons help distinguish two different challenges: getting a page into the information pool considered by an AI system and getting that page selected as a source.
That distinction is important for anyone developing an AI search optimization strategy.
2. The Numbers Behind Answer-First Content
The research included two rounds of blind page ratings. In these comparisons, the raters did not know which page had been cited by the AI system.
The first comparison examined cited pages against pages the AI had retrieved but did not cite. In this group, 23% of cited pages opened with a direct answer, compared with 14% of retrieved but uncited pages.
The second comparison produced a wider difference. Among cited pages, 20% opened with a direct answer, compared with just 5% of the Google top-ten comparison pages that the AI had not retrieved.
These findings suggest that answer-first structure was more common among cited pages in both comparisons, but the strength of the association varied.
The smaller difference in the first comparison deserves particular attention. When an AI system had already retrieved a page, opening with a direct answer was not enough to explain why that page was selected for citation rather than another retrieved source.
Other characteristics, including relevance, completeness, source quality, and the information available on competing pages, may also influence the selection process.
The figures therefore support a practical hypothesis: answering the reader’s question early may be a useful content design choice. They do not establish that moving a paragraph to the top of a page will automatically increase AI citations.
3. Why Answering the Question Early Makes Sense
Consider a user asking an AI assistant, “Which website platform is suitable for a small online store?”
A page that begins with several paragraphs about the history of e-commerce may eventually provide a useful comparison, but the reader must work to find the central point.
Another page might begin by explaining that the right platform depends on product complexity, budget, payment requirements, and the level of technical control needed. It could then compare suitable options and explain the trade-offs.
The second approach makes the page’s purpose clear immediately while still allowing room for detailed analysis.
This is not simply about writing shorter introductions. It is about reducing the distance between the user’s question and the information that addresses it.
For SEO professionals, the practical lesson is to review whether the opening paragraphs establish the subject and answer the primary question.
A page about website migration should explain the central migration considerations early. A product comparison should make its evaluation criteria clear. A service page should describe what the service does and whom it helps before moving into secondary details.
The rest of the page should provide the depth needed to support that opening answer.
A concise answer without evidence or useful explanation is not necessarily better than a detailed article. The goal is to combine immediate clarity with meaningful coverage.
4. Getting Retrieved Is Different From Getting Cited
One of the most useful lessons from this study is that retrieval and citation should not be treated as the same outcome.
A page can be discovered by an AI system without appearing as a cited source in its final answer. Conversely, a page that is never retrieved during a particular search cannot be selected from that retrieval set.
The research’s 23% versus 14% comparison highlights this distinction. Even among pages that AI systems retrieved, the difference in answer-first openings between cited and uncited pages was relatively modest.
This means content teams should investigate more than introductions when diagnosing weak AI visibility.
Start by asking whether your page is relevant to the actual question. Does it contain information that directly addresses the user’s need? Does it provide sufficient detail to support the answer? Is the content accessible, accurate, and easy to interpret?
Then consider whether other pages provide more complete or useful information.
For businesses, this requires a broader content review rather than a quick exercise in rewriting introductions. A page may need better evidence, clearer explanations, more specific product information, or a stronger match with the search intent.
Answer-first writing is a reasonable place to start, but it should be evaluated alongside the overall quality and usefulness of the page.
5. What Else Did the Study Find About Content Quality?
The research examined additional differences between cited pages and pages that were passed over.
In the comparison involving Google top-ten pages that the AI had not retrieved, cited pages received an average completeness score of 2.3 out of 5, compared with 1.7 for the comparison pages.
The analysis also found that approximately half of the comparison pages did not answer the relevant question anywhere in the portion of the content reviewed. Among cited pages, the corresponding figure was 23%.
These findings reinforce the importance of addressing the subject adequately.
A webpage may contain relevant keywords and still fail to provide a useful answer. A product page might describe features without explaining which customer needs those features address. A service page might make broad claims without describing the actual process or deliverables.
In such cases, improving the content means adding meaningful information, not simply changing headings or inserting more keywords.
The researchers also found little difference in promotional tone between the two groups. Approximately three-quarters of the pages in both groups were business pages promoting their own products or services.
This is encouraging for businesses. Commercial content does not automatically become unsuitable for AI citations merely because it promotes an offering. The important consideration is whether the page provides relevant information that supports the user’s decision.
6. Other Page Characteristics Associated With AI Citations
The analysis also examined relationships between citation likelihood and several page characteristics, accounting for factors including Ahrefs Domain Rating and page type.
The reported associations included:
- A year in the page title was associated with more than twice the odds of being cited.
- A publication or update date within the previous 12 months was associated with 88% higher odds.
- Tables were associated with 69% higher odds.
- First-hand experience was associated with 45% higher odds.
- Prices were associated with 28% higher odds.
The research also reported lower citation odds for pages with an author byline and pages with a call to action. The reported associations were 28% and 12% lower, respectively.
These findings need careful interpretation.
For example, a year in a title may be more common on content addressing topics where freshness is important. Tables may appear more frequently on pages that compare products or organize complex information. Prices may be particularly useful when users are researching purchasing decisions.
Similarly, first-hand experience can provide practical information that generic explanations may lack.
The data does not establish that adding a year, inserting a table, or removing an author byline will cause more AI citations. The unexpected findings involving author bylines and calls to action should not be treated as instructions to remove these elements.
Instead, consider whether each feature serves the reader. Include dates when they help communicate freshness, tables when they improve comparison, prices when accurate and relevant, and author information when it supports transparency and accountability.
7. How I Recommend Applying These Findings
For businesses looking to improve their visibility in AI-generated answers, I recommend a structured content review.
First, identify the primary question. Before rewriting a page, determine what the intended audience wants to know. A page targeting several unrelated questions may need a clearer focus.
Second, review the opening paragraphs. Check whether they answer the main question directly or make readers navigate through unnecessary background information first.
Third, strengthen the supporting content. Provide explanations, examples, comparisons, limitations, and relevant evidence. The opening answer should introduce the subject, while the rest of the page should demonstrate its value.
Fourth, add first-hand information where appropriate. If your business has genuine experience with a product, service, implementation, or process, explain what you learned. Do not invent case studies, results, or personal experience to make a page appear more authoritative.
Fifth, improve structure where it helps comprehension. Use descriptive headings, lists, and comparison tables when they make information easier to understand. Avoid adding formatting merely to satisfy a presumed AI preference.
Finally, measure the outcome. Track relevant questions and monitor whether your pages are retrieved or cited across the AI search experiences important to your business. Compare results over time and account for changes in prompts, platforms, and competing content.
This approach turns the research into a testable content strategy rather than a universal checklist.
8. What This Means for SEO, GEO, and Content Strategy
Traditional SEO and Generative Engine Optimization share an important objective: helping relevant information reach people who need it. However, visibility in conventional search results and citation in an AI-generated response are different outcomes and should be measured separately.
The study provides useful observational evidence about how cited pages differ from certain comparison pages. It does not establish a definitive formula for AI visibility.
There are also methodological limitations to consider. The research came from an agency’s client questions, rather than a representative sample of every topic on the internet. The comparison pages were selected using Google ranking data for related keywords, not always the exact questions submitted to AI systems.
The study also relied on AI raters for blind page evaluations. The results may therefore reflect the rating model’s own assessment criteria.
These limitations do not make the findings useless. They establish the appropriate level of confidence with which marketers should interpret them.
My recommendation is to combine these observations with your own performance data, customer research, and content experiments. Improve pages because they answer real questions more effectively, then investigate whether those improvements coincide with stronger visibility.
Do not promise clients that a particular heading structure or opening sentence will guarantee AI citations. The evidence does not support that claim.
Conclusion
The recent TJ Digital analysis offers a useful insight for businesses investing in SEO and AI search visibility: pages cited by AI systems were more likely to open with a direct answer than the comparison pages examined in the study.
The difference was 23% versus 14% when cited pages were compared with pages retrieved but not cited. It widened to 20% versus 5% when the comparison involved Google top-ten pages that the AI had not retrieved.
These numbers make answer-first content worth testing, but they do not prove that this writing technique directly causes citations.
My advice is to focus on clarity, completeness, relevance, and evidence. Help readers understand the answer quickly, then give them enough information to evaluate it properly. Add tables, prices, dates, and first-hand experience when they genuinely improve the page.
For Backlinkgen.com readers, the broader message is straightforward: AI visibility should be approached as an ongoing process of useful content creation, technical accessibility, and measurement—not a collection of shortcuts.
A page should not merely look optimized for an AI system. It should genuinely help answer the question that brought the user there.
Disclaimer
This article summarizes observational research and provides general SEO guidance. The reported relationships do not establish causation or guarantee AI citations, rankings, traffic, or conversions. Findings may vary by industry, query, platform, and website. Test content changes using your own data, evaluate the quality of your sources, and avoid treating individual study results as universal search optimization rules.
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About Author:
BacklinkGen.com Editorial Team
The BacklinkGen.com Editorial Team is led by Amit Tyagi, Gaurav Saxena and Rachit Srivastava—professionals with expertise spanning enterprise technology, artificial intelligence, digital marketing, SEO, AI Search Optimization (AIO/GEO), content strategy, and business growth. Together, they bring decades of combined industry experience across global technology initiatives, performance marketing, and digital transformation.
Every article published on BacklinkGen.com is guided by practical experience, in-depth research, and a commitment to editorial accuracy. The team focuses on delivering trustworthy, actionable, and up-to-date insights that help businesses, marketers, and professionals stay ahead in the rapidly evolving world of search, AI, and digital marketing.
