The rise of AI-driven search has changed the way we think about SEO from optimizing keywords to optimizing knowledge. At Get Digital, we make sure that our content is structured in a way that large language models can understand more than just relevance. They can also understand intent, context, and authority. We believe AI summaries along with zero-click visibility are new ways for buyers to connect with us, where trust comes before traffic. By combining structured data with CRM insights, we can now see not only who visits but also who knows about our expertise, even if they don't click.
We've doubled-down on quality content and how that content is structured, as well as ensuring we have a steady stream of external references via publisher outreach, digital PR and expert commentary (across several team members). From an SEO perspective, in that regard nothing really changes, we're just more conscious of what the LLM's 'think' or 'see' about our brand, but stick to the basics to help inform LLM decision making processes.
Our content strategy has pivoted from long, skyscraper pages to more of a hub-and-spoke strategy with pages optimized for long-tail keywords. AI search seems to use more specific search queries, possibly due to factoring in the profile of the person performing the search. From our testing, optimizing content around internal links with descriptive hyperlinks results in more of our articles being cited in AI search summaries.
At Yext, we have evolved our SEO content strategy significant to account for AI-driven search. The two workflows we've focused on implementing up front are (1) content refreshes and (2) adding structured content sections to blogs for readability and answer extraction. Content refreshes are beneficial for both traditional SEO and AI Search. Recently, we refreshed a blog from several months ago with new information, a few new data citations, and some general messaging updates. Within the first month, the post started ranking for 3 net new target keywords, and for two of them, it was cited in the AIO position. Additionally, its rankings increased for existing keywords - one jumped from page 2 to AIO, and another jumped from page 4 to page 1. We've also begun adding structured content sections to every post to improve readability for AI models. This includes a TLDR at the beginning as well as an FAQ list at the end. We make sure to use the proper markup for this so that it tells LLMs that it's a quick and easy answer. This also does have an added benefit for human readability as well, if someone is just looking to get the key takeaway of the article or scan through the main points, which is ideal, since we are really aiming to write for two audiences at the same time: humans and AI. Additionally, we've added an AEO review step to our editorial process. This also incorporates traditional SEO keywords, ensuring they're in the right places and that all links have the best anchor text. For AI optimization, we also look to implement best practices like writing H2s in question format and answering the question directly first before diving deeper, as well as using tables, comparison lists, and structuring blog sections consistently.
Our entire content strategy pivoted from merely targeting page one rankings to actively seeking citation and entity recognition within the large language models and AI Overviews. This means we are no longer just writing for an algorithm to rank a URL; we are writing for the AI to ingest our specific, authoritative facts and use them as its source material, even if it does not link. The one tactic that has proven invaluable for visibility in the zero-click environment is the strict adherence to a clear, repeatable question and answer structure using structured data, specifically FAQ and HowTo schema markup. This formatting allows AI to easily parse our definitive answers and use them in summaries, and we measure success not just by clicks, but by the growth of our branded search volume, which signals that users are seeing our brand cited in an AI answer and then searching for us directly later on.
We've been including FAQ sections at the bottom of new blog posts to directly answer the web's most searched questions in short snippets (50 words or less) that fit the clear, declarative language that AI models are looking for. We also use FAQPage schema markup to ensure the information is displayed correctly.
We've stopped measuring effectiveness solely through clicks and are now evaluating brand mentions in AI-driven responses and the share of zero-click queries (using Ahrefs in conjunction with other tools). This has a delayed effect, as citations help potential clients learn about your brand, which in some cases leads to orders through branded search queries. We've also focused on content authorship and expertise. Each piece of content is now linked to a specific CTO, engineer, or analyst within the company.
We've stopped chasing keywords and started building context ecosystems. With AI-driven search pulling summaries instead of clicks, our focus is on structure, authority, and visibility. If AI is going to quote us, it should sound like us.
AI search has ended lazy SEO. You can't just throw together a 1,500-word post stuffed with keywords and expect to rank anymore. The brands getting mentioned by LLMs now are creating short, clear, and genuinely useful content. We've started cutting our content down, keeping it fresh, and spreading brand mentions across social and other reputable sites. Right now, it feels like the models crave new information and active brands. If you're not updating and staying visible, you're gone.
Our strategy is to create content that's a moving target because AI is trained on the past. The moment a new regulation is proposed in a state like California or New York, our team writes a new piece of content within 48 hours. AI Overviews can't keep up with that pace, and we get to pull users who are tuned in and engaged with the topic. Since the content is still so new and too nuanced for a generic LLM, it makes more users want to click through to our site to get it.
We want AI search to cite our human experts by name alongside our brand. We're always using Author and ProfilePage schema to tie every in-depth guide to one of our named, public design experts to prove we know what we're talking about. In zero-click search, 'According to marketing manager John Smith...' is far better for SEO and reputation than a brand mention alone. It helps us build E-E-A-T and positions us as a company of authorities with our readers.
We're now using our CRM to figure out where to take our content next. We analyze the reason for loss data in our HubSpot deals to find the most common customer objections and misunderstandings that cause us to lose deals. We then build our entire content strategy around answering those bottom-funnel questions. We work to make sure our content is genuinely relevant to buyers, not so generic that it could easily be answered by AI, and directly supports our sales team in closing more deals. It's harder, but not impossible, to work around AI and still create content that users care about.
We've started treating our individual services as products using Product schema, and we embed our AggregateRating from client testimonials directly into the schema. We've seen AI pull star ratings directly into its answers when users ask to compare local providers, and we want in on that action. Our brand name shows up with a 4.9-star rating, while our competitors are just plain text, which helps us build up that hierarchy of trust before they even click through.
AI is great at summarizing facts but terrible at providing original experience or results. We've shifted our content budget from generic guides to detailed, data-rich case studies that show a real customer's problem and our solution. We're measuring visibility by tracking how many of these case studies are viewed during a new lead's journey in our CRM, as this trust-building content is what ultimately gets the conversion, not the initial AI Overview. Instead of publishing anonymous case studies, we now feature the specific RapidDirect engineer who solved the customer's problem, and we're seeing that leads who mention an engineer have a 25% shorter sales cycle so far!
We've started wrapping our FAQ answers in Google's speakable structured data, while it's still in beta testing. We're anticipating that the next wave of AI search will be voice-first. This schema signals to AI assistants on what part of the text is most suitable to be read aloud. When a user in their kitchen asks their smart speaker, "How do I move a fridge without scratching the floor?" we're positioning ourselves to be the answer read aloud—hopefully with our brand name explicitly shouted out. We want to use AI to build our brand in a completely zero-click, zero-screen environment.
At Clever Real Estate, we shifted our SEO approach to optimize for entities. We are also implementing structured data, so that AI systems understand and can cite our content, even if a user never clicks through. We measure success by traffic and brand mentions and visibility in AI summaries. One strategy that has helped is writing clear, succinct, fact-filled paragraphs that answer a specific search intent and are clear enough for AI to quote. In this new world, content clarity and authority have become as important as keyword rankings.
"Our content strategy has shifted from optimizing for clicks to optimizing for comprehension. We built AEORegistry.com specifically to help businesses structure their data so AI systems can interpret and cite it accurately. In a zero-click world, visibility isn't about ranking, it's about being readable and verifiable by machines. The most effective tactic we've found is creating lean, schema-rich pages that AI engines can trust as definitive sources."
Our SEO strategies for our clients primarily focus on creating a snowball effect by developing a highly detailed outline that will compete and rank against our competitors who are currently recognized. To do this, it's helpful to conduct SERP comparisons and competitor analysis of the ranking pages, and figure out how to create a more robust version than what they have by editing processes and using tools like Clearscope.io. Beyond the competitiveness it takes on creating the most clear and concise answer for Google and LLMs to scan and believe is the best answer to showcase, you still need to create a website experience that is aligned with Google's guidelines for it to start showing in SERPs. Websites that don't take shortcuts with their SEO and align with Google's guidelines are doing just fine with their organic traffic today.
Founder & Community Manager at PRpackage.com - PR Package Gifting Platform
Answered 3 months ago
Instead of chasing blog keywords that can't rank anymore, we shifted to running newsletters. Every post goes straight to active subscribers and gets shared socially through expert roundups. That brings real reach and ad revenue at the same time. Zero-click doesn't hurt when your content itself drives signups - readers join to keep getting the news directly.
We've largely doubled-down on what we'd deem to be traditional SEO tactics, rather than chasing things like Reddit and Quora citations (just because that's what's being quoted within the LLM's for the time being). Ultimately, we feel like brands with true value and expertise to share with real history will prevail, not quick-win tactics that could work today and be gone tomorrow.