×
GlobalIndiaThailandUAEUSA
Back
BackMarTech Consultant
SEO | Artificial Intelligence
Improving website SEO for both organic and AI search requires...
By Vanshaj Sharma
Mar 10, 2026 | 5 Minutes | 
| 
Search has changed more in the last two years than it did in the previous ten. The results page that used to be a predictable list of blue links now includes AI generated summaries, featured snippets, people also ask panels and a growing presence of generative answers that pull from content across the web without always sending the click through. Optimizing for that environment requires a broader view of what SEO actually means in practice today.
The good news is that the fundamentals have not been thrown out. The websites that perform well in organic search and increasingly in AI generated results share the same underlying qualities: clear structure, authoritative content, strong technical health and genuine relevance to the queries they are targeting. What has changed is how those qualities need to be expressed and how search systems, both traditional and AI powered, evaluate them.
Every SEO conversation eventually gets to content, but content performance is constrained by technical health. A website that loads slowly, has crawl issues or serves a poor mobile experience limits how well even exceptional content can perform.
Page speed is one of the clearest technical signals that affects both rankings and user behavior. Google uses Core Web Vitals as part of its ranking criteria. Those metrics measure loading performance, visual stability and interactivity in ways that reflect actual user experience rather than just raw server response time. Largest Contentful Paint, Cumulative Layout Shift and Interaction to Next Paint are the specific signals worth monitoring and improving. Tools like Google Search Console and PageSpeed Insights surface these measurements at the page level and provide specific recommendations for addressing them.
Crawlability is the other side of technical health that gets overlooked. If search engines cannot reliably crawl and index the content on a website, none of the other work matters. A clean XML sitemap submitted to Search Console, a robots.txt file that does not accidentally block important pages and a logical internal link structure that helps crawlers move through the site are all baseline requirements. Crawl errors flagged in Search Console should be treated as genuine priorities rather than low urgency housekeeping tasks.
HTTPS is a baseline expectation at this point. Sites that still serve content over HTTP carry a trust signal disadvantage that has no upside.
Search engines have gotten progressively better at evaluating whether content genuinely answers the question it claims to address or whether it is filling space with words that sound relevant. That evaluation has become more sophisticated with AI assisted ranking systems. Shallow content that covers a topic broadly without going anywhere useful does not perform the way it once did.
The content that earns and holds strong organic rankings tends to share a few characteristics. It addresses the core query directly and early. It anticipates follow up questions and addresses those too. It uses specific examples, concrete details and where relevant, data that supports the claims being made. It is written for the person reading it rather than optimized for a keyword density metric that stopped being a useful frame years ago.
For AI search specifically, the way content is structured matters as much as the substance. Large language models and AI answer engines pull information from web content to generate responses. Content that is clearly structured with descriptive headings, concise paragraph lengths and direct answers to specific questions is easier for those systems to parse and cite accurately. Think of it as writing content that a very capable AI could quote from without needing to interpret or fill in gaps.
A page that technically covers a topic but misses the intent behind the query will not hold a strong ranking position regardless of how well optimized it is on other dimensions. Search intent is the reason someone typed a query and it falls into a few recognizable patterns.
Informational intent means the person wants to learn something. Navigational intent means they are trying to find a specific website or resource. Commercial intent means they are researching options before making a decision. Transactional intent means they are ready to take an action. Different pages on a website serve different intents and the content, structure and call to action on each page should reflect the intent of the queries it is targeting.
Mismatches between content and intent are one of the most common reasons pages underperform in search. A page structured as a product pitch that ranks for an informational query will see high bounce rates and poor engagement signals. Over time those signals feed back into ranking performance. Aligning content to intent is not an SEO trick. It is just giving people what they were actually looking for.
Google articulates its quality evaluation framework around Experience, Expertise, Authoritativeness and Trustworthiness. That framework has become a more prominent part of how content quality is assessed, particularly for topics in health, finance, legal or any category where inaccurate information carries real consequences.
For AI search, the trust dimension is equally relevant. AI answer engines tend to cite sources that carry authority signals. Those signals include inbound links from credible domains, clear authorship attribution, accurate and updated information and a site that behaves in ways consistent with being a genuine, trustworthy source rather than a content farm.
Practically, this means investing in author credibility. Named authors with visible expertise and a consistent publication history carry more trust signal than anonymous content. It means keeping published content accurate and updated rather than letting it go stale. It means earning links from relevant, credible sources through content that is worth citing rather than through tactics that treat backlinks as an end in themselves.
Structured data markup helps search engines understand what a page is about at a more specific level than reading the text alone. Schema markup for articles, products, FAQs, reviews and other content types provides explicit signals about what the content represents and how it should be interpreted.
For AI search, entity clarity is an extension of the same principle. AI systems build understanding of the web in terms of entities, which are the people, organizations, products, concepts and places that content is about. A website that clearly establishes what it is, who runs it, what topics it covers and how it relates to other recognized entities in its space is easier for AI systems to understand and accurately represent in generated answers.
A clear About page, consistent use of the organization name and consistent contact information across all web properties are small signals that contribute to entity clarity. Combined with structured data markup and a coherent topical focus across the content on the site, they build a picture that both traditional search engines and AI systems can interpret with confidence.
The shift from thinking about keyword targets to thinking about topical authority is one of the more significant practical changes in how strong SEO programs are built today. A website that covers a topic area with depth and consistency, across multiple pieces of content that address different angles of the same subject, builds a different kind of authority than one that publishes isolated pages targeting individual keywords without connecting them.
Internal linking between related content reinforces topical clusters and helps search engines understand the relationships between pages. A content hub structure, where a central pillar page on a broad topic links out to more specific supporting pages and those supporting pages link back, is a practical implementation of this principle that also creates a good navigation experience for actual visitors.
For AI search, topical depth is arguably even more important than it is for traditional organic search. AI answer engines are looking for sources that demonstrate genuine expertise on a subject, not sources that happened to mention the right words in the right density.
SEO performance is measurable and measurement should inform where effort goes. Google Search Console provides impression and click data at the query level, which shows not just whether pages are ranking but what queries are driving traffic and how click through rates compare to average position. Pages that rank in positions four through ten with strong impression volume but low click through rates are candidates for title tag and meta description refinement.
For AI search visibility specifically, tracking brand mentions in AI generated answers and monitoring whether the website is being cited as a source in platforms like Perplexity or Google AI Overviews is an emerging measurement practice worth building into reporting. The metrics are less standardized than traditional organic search data but the directional signal about whether content is being recognized as authoritative is valuable.
The websites that improve consistently in both organic and AI search are the ones where someone is paying attention to what the data is actually saying and making deliberate decisions based on it rather than running on assumptions formed when the search landscape looked very different.