How has the rise of AI search engines like ChatGPT and Google Gemini changed your SEO strategy in the last 12 months? What is one SEO tactic that used to work well but is now less effective because of AI-generated answers? If you had to split your budget between traditional SEO and AI search optimization, what percentage would you give each and why?

Before AI search took off, a lot of websites could get away with targeting top-of-funnel keywords, ranking in the top 3, and generate massive amounts of traffic. While the conversion rate wasn't amazing, they managed to still generate topical authority on those subjects. AI search engines are now capturing TOFU questions and providing answers that don't warrant digging into the topic by visiting a website. This shifts the focus on MOFU and BOFU content. Regarding budget split, SEO is still the baseline for successful AI search, and most of regular SEO optimizations of it will result in good AI search appearance. However, AI search also requires to focus on other ventures such as Reddit, LinkedIn, YouTube and listicles.

Hello Opticl team, So here's the thing, SEO has moved beyond just ranking on Google. I now treat AI platforms like another layer that sits right on top of SEO. Because of that, I'm doubling down on authority signals like reviews and brand mentions since that's what AI trusts. And honestly, the old play of mass-producing keyword content just isn't cutting it anymore. It looks good on paper, but it doesn't have depth or credibility. AI systems just don't value that kind of content because it doesn't stand out. When it comes to budget, I'd still lean 65% toward SEO and 35% toward AI optimization. SEO is still doing the heavy lifting, but AI is growing quickly and influencing serious buyers. The real goal now is to be one of the few names AI actually surfaces. Sasha Berson Co-Founder and Chief Growth Executive at Grow Law 501 E Las Olas Blvd, Suite 300, Fort Lauderdale, FL 33301 About expert: https://growlaw.co/sasha-berson Website: https://growlaw.co/ LinkedIn: https://www.linkedin.com/in/aleksanderberson Headshot: https://drive.google.com/file/d/1OqLe3z_NEwnUVViCaSozIOGGHdZUVbnq/view?usp=sharing

We help B2B teams stay visible across long and messy buying journeys, including the shift that is now taking place into AI search. I see too many people treating GEO as though it is replacing SEO when it isn't. What it is doing is changing how we should think of visibility. Ranking still matters but the AI answers are picking the brands that they find easy to understand and which are already being talked about in the right places. At 3WH, we check how often our clients appear in AI answers over time as a simple way to see what is actually getting them mentioned. As for budget, we put roughly 70% into traditional SEO and 30% into AI visibility. The old one is still driving traffic. The new one though, is where future attention is headed so building that muscle early is worth it.

Several months ago, I did a test on my website, Anglero.com. I posted a new post with an FAQ schema with a personality schema specifically so that the LLM web crawlers would pick up on it if they did. The test was to see if traditional SEO, by having the right keywords, the right photo, etc., still had long-tail value versus the up-and-coming LLM juggernaut of replacing the search engines with LLMs and AI. The end result is that in three months I was on the first page of Google, and the LLMs found me, and my website comes up. All my other pages that I had posted previously still were ranking extremely low on Google, but this one page, this one LLM test page, immediately went to not only first place on the LLM ranking but first place on a Google SEO (traditional SEO). This is astounding, and this reiterates that optimizing your web page for LLM search and web crawlers is the new SEO. My advice is that every new post on your website must have a FAQ schema and a personality schema or business schema, depending upon your website. You should seriously reconsider going back to your most popular post or the post that best represents your brand and integrating a FAQ schema and a personality schema or business schema, whichever is appropriate. Search is no longer about search. Search is all about the LLMs like ChatGPT, Google Gemini, Claude, etc. SEO is dying extremely quickly, and LLM web crawlers are the future. Thank you for the opportunity to contribute. If you need any more information from me, please contact me directly at thomas@anglero.com and please link to my website, www.anglero.com. I am a 25-year veteran of multinationals in the IT space. You could learn more about me on my website or just ask. Thank you. I hope you're well. Regards, Thomas Anglero

AI search engines have definitely changed the strategy and reporting side of content marketing. Organic search has been greatly impacted by the introduction of AI and is much harder to effectively measure, because while AI has eaten into organic search performance, it has helped improve other channels such as direct, however the attribution is a lot muddier. Someone may use an LLM for research and then plug the result into Google, which often times creates direct traffic that is effectively misattributed. Ranking in Google Gemini and AI Overviews requires much of the same tactics as in the past, but what this leads to is a zero click result which is much different than in years past where you would expect a click or session. If I had to split my budget between SEO and AI, I would lean 60% AI search optimization and 40% traditional SEO. There is overlap between the two, however understanding which channels feed into AI search and understanding the type of content that AI/LLMs are seeking, can greatly inform how you create content as well as the type.

Over the past 12 months we've augmented our SEO with AEO content by tracking relevant AI queries and adding FAQ-style answers to existing high-ranking pages. That lets us maintain our strong rankings while ensuring those pages are readable and likely to surface in LLM answers. A tactic that used to work well but is now less effective is relying solely on wholesale page rewrites to chase new queries instead of adding targeted, AI-optimized FAQ snippets. Given this approach, I split budget roughly 50/50 between traditional SEO and AI search optimization to preserve organic rankings while building presence in AI answer engines. Separately, conventional SEO takes the stance that all relevant traffic is valuable. But by contrast, LLMs are most valuable for deeper funnel prompting since they have more general stored/training data for upper funnel concepts and therefore, your web content isn't likely to be cited. For example, we are a creative agency and if someone was prompting an LLM for content marketing frameworks, it's not likely the LLM will surface an article of ours as a citation; it's even less likely that the user will click the citation for such general education content. However, when they know they need an agency, they'll search for solution providers and ask LLMs to compare us with competition. It's a better use of our time investment to show up there, since we definitely want to show up, since it's not only much later in the buying process, but they're more likely to click through to our site.

In the last 12 months, my strategy at SEOsiri has shifted from "Content Production" to "Information Gain." With the rise of Google Gemini and ChatGPT, simply answering a question is no longer enough because the AI does that directly on the SERP. My strategy now focuses on providing unique data, personal case studies, and contrarian viewpoints—elements that AI cannot synthesize from existing datasets. We are no longer just optimizing for keywords; we are optimizing for "cite-ability" so that LLMs recognize our brand as a primary source of truth. The tactic that has become significantly less effective is targeting high-volume, "definitional" keywords (e.g., "What is SEO" or "How to...") with standard blog posts. These "zero-click" searches are now almost entirely captured by AI Overviews and chatbots. Previously, these were great for top-of-funnel traffic, but now, the CTR on these terms has plummeted. We've had to pivot those resources toward middle and bottom-of-funnel content where human nuance and transactional trust are required. If I had to split a budget today, I would allocate 60% to Traditional SEO and 40% to AI Search Optimization (GEO). Traditional SEO—specifically technical health and high-authority backlinking—remains the foundation; without it, AI engines won't trust your site enough to source it. However, the 40% for AI Optimization is critical for "Generative Engine Optimization" (GEO). This involves structuring data for LLMs, focusing on brand mentions in AI training sets, and creating "fragmented" content that is easily digestible for AI summaries. You need the traditional authority to be "seen," but you need the AI optimization to be "spoken" by the engines.

AI search has shifted SEO from ranking pages to becoming a reliable source that models can reference with confidence. We have focused more on structured, experience-driven content that clearly answers specific questions rather than relying on broad keyword coverage. Tactics like thin, high-volume content creation have become less effective as AI surfaces concise answers directly. If I had to allocate resources, I would lean more toward AI search optimization while maintaining a strong foundation in traditional SEO. The goal is to build authority that translates across both search behaviors.

1. Our search strategy has changed from targeting keyword volume to targeting 'context-authority' with the growing use of AI search. Our goal is no longer solely to rank as a top blue link but rather to become the authoritative source for LLMs (large language models) to cite back in their findings. If your website provides LLMs with the anchor content of the citation, you'll gain supremacy in the AI discovery layer irrespective of where the traditional link positions itself. 2. The effectiveness of thin and generic 'listicle' content (used to capture long-tail keyword traffic) is at an end. An AI search engine generating a three-sentence answer to a user's need has no incentive to direct them to a low-value, 1,000-word blog post. Any content that doesn't represent unique insights or provide veriable data that cannot be easily reproduced by an AI will be deprioritized moving forward. 3. I would suggest an approximate 70/30 split in your budget between traditional SEO and AI search optimization. Traditional SEO is the foundation for both building the domain authority and content archives that AI models use to develop and validate information. Before you can effectively optimize their discovery through AI, you must first establish their trust with the larger web community. Ultimately, the most effective strategy for the AI period is high-quality, human-centric content. The resources provided are built on human intelligence, and sites that do not supply original and verifiable information will not remain relevant, no matter how effective their search algorithm optimization is.

The rise of AI search has pushed us to focus more on topical authority and structured clarity rather than just ranking individual pages. Over the last 12 months, we've shifted toward building connected content ecosystems that clearly explain a topic end to end, because AI models tend to favor sources that demonstrate depth and consistency, not just isolated relevance. One tactic that has become less effective is publishing thin, keyword targeted pages designed to capture long tail queries. Those used to rank and bring in traffic, but now many of those queries are answered directly by AI, so unless the content is genuinely strong and well supported, it gets bypassed. Name: Dillon Hill Title: Founder and Director of Astonishment Company: Cosmoforge.io

Over the past 12 months the rise of AI search has led me to refocus SEO on clear, authoritative content and structured context that helps AI models and users find accurate answers. One tactic that used to work well but is now less effective is creating many short pages stuffed with exact-match keywords instead of producing comprehensive, answer-focused content. Those thin pages are frequently bypassed by AI-generated answers that favor consolidated, well-sourced material. If I had to split budget, I would allocate 60 percent to traditional SEO and 40 percent to AI search optimization to maintain technical health and backlinks while investing in structured data and content shaped for AI-driven results.

Great topic! The rise of AI search has rekindled the necessity for strong Technical SEO. The emerging Generative Engine Optimization (GEO) trend can't be ignored, and while it has unique tactics that differ from traditional SEO, much of it is the same, assuming you've been doing high-quality SEO consistently. The main GEO tactics that we see working include: 1. Structured Data: LLM web crawlers reward semantically ordered HTML like sequential Heading tags, and Structured Data markup to help them understand the content they are extracting. 2. Brand Mentions Over Backlinks (to an extent): Unlike Google (historically), LLMs value high-quality brand mentions from reputable sources even if they're unlinked. As a 20 year SEO however, I'll still take the link with anchor text, and a brand mention along with it, as that's just a stronger signal in my opinion. Really we're talking about growing your quality citations. 3. Extractable Content: This goes along with the structured data, but also, simple design and user experience elements like bulleted lists, FAQs and pull quotes seem to be favored by the AI web crawlers when looking for content to extract and display in an AI search result. 4. Classic SEO: The three bullet points above are really still quality SEO. Locking down your Technical SEO is critical. Creating content that is searched for is still the primary way to get into AI search results. Create web content that can be easily accessed, extracted, and displayed in search results, no matter the application. With this shift to GEO, we are also seeing some big mistakes: These AI web crawlers are not as sophisticated as Google. They behave much more like the early days of GoogleBot since they don't execute JavaScript. What I see time and again is brands are overusing JavaScript to display their content and links in navigation. If the bots can't crawl your website because they can't "see" your content and links, then your brand will be invisible in AI applications. They rely heavily on real-time (grounded) searches of the web, and of Google and Bing search results. Looking at 2026 and beyond: Monitoring and optimizing for AI visibility will be critical. For SEO my agency has always beaten the drum of; you can't improve what you can't measure. Utilizing an AI visibility monitoring tool, and preferably one with GEO insights built-in is the way forward as more and more people turn to AI search to conduct research and make purchasing decisions.

I moved our SEO strategy toward writing as many authentic, and ultimately, useful travel guide articles over the past year, specifically to assist both the reader and the summary/featured snippet generated by an artificial intelligence (AI) tool. One technique used in the past, but now much less effective than previously, was optimizing a site's pages to maximize their potential for rankings without generating truly valuable content. If I were forced to apportion my budget, I would divide approximately 70% to creating new content, based upon my desire to generate long-term human credibility, and 30% for making changes necessary to optimize my website for AI searches.

We killed our long-tail keyword strategy six months ago and traffic actually went up 31%. That sounds backwards until you realize AI search engines don't care about your perfectly optimized "best 3PL for Shopify stores in Texas" page anymore. Here's what changed. When I built Fulfill.com, we initially followed the traditional playbook: target long-tail keywords, create hundreds of specific landing pages, optimize for featured snippets. Worked great in 2022. By mid-2023, those pages were ghost towns. ChatGPT was answering fulfillment questions directly without sending anyone to our site. We were spending thousands on content that Google showed but users never clicked. The tactic that died hardest? FAQ pages optimized for voice search. We had this beautiful resource with 200+ fulfillment questions and answers, perfectly structured for Google's answer boxes. Traffic from that page dropped 64% year over year because AI just scrapes the answer and serves it up. Nobody clicks through anymore. So we pivoted hard. Instead of chasing keywords, we started creating content AI can't replicate: our actual marketplace data. We published a report showing the average cost difference between coastal and midwest 3PLs using real numbers from our platform. We shared case studies with specific dollar amounts, like how Nature Hills saved $334,000 annually. AI can summarize general advice, but it can't generate proprietary insights from a database of 800 verified providers. Budget split? I'm doing 70% on what I call "AI training content" and 30% traditional SEO. The 70% is high-value content designed to be cited by AI engines, referenced in their training data, and quoted when someone asks about fulfillment. Think original research, contrarian opinions, specific case studies with real numbers. The 30% is basic technical SEO and maintaining authority on core terms we actually care about. The future isn't optimizing for Google or ChatGPT separately. It's becoming the source both of them quote when answering questions in your space.

We measure SEO ROI by tracking actual business results such as form submissions, demo requests, and signed deals. Using CRM and attribution tools, we follow the full path from search to sale, ensuring traffic converts into action. AI supports our content production by researching topics, structuring ideas, and maintaining output but every piece lives inside a strategy focused on qualified leads and consistent traffic. Old methods like keyword-stuffed or generic blog posts perform worse now because AI search engines favor authoritative, structured, helpful content. That's why our approach pairs traditional SEO foundations (technical optimization, link authority, on-site improvements) with AI-specific optimization (structured data, rich answers, conversational content). A practical budget split is roughly 60/40, reflecting this balance. Sharing our methodology in content builds credibility with readers and Google, earns backlinks naturally, and shows we solve real problems with evidence. SEO works best when it connects business outcomes to content performance.

Over the last 12 months, AI-driven search has shifted my SEO strategy from keyword targeting to intent coverage. Instead of optimizing for individual queries, we now focus on building content that answers a complete user journey, including context, follow-up questions, and clear explanations. The goal is to make content useful not just for rankings, but also for AI-generated summaries. One tactic that has become less effective is creating content purely around exact-match keywords. Earlier, this approach could help pages rank quickly, but now it often fails to perform because AI-driven results prioritize depth, clarity, and relevance over keyword repetition. If I had to split the budget today, I would lean toward a 70-30 balance. Around 70% would still go to traditional SEO, because it remains the foundation for visibility and traffic. The remaining 30% would focus on AI search optimization, such as structuring content for answer engines, improving topical authority, and ensuring content is easy to interpret and surface in AI-driven results. This balance reflects where things are today, while preparing for where search is heading.

Over the past 12 months we shifted our SEO strategy to add FAQ-formatted sections on top-performing and near-top pages so AI summaries and search snippets can more easily pick up clear questions and answers. This change helps us surface the exact phrases prospects use and improves both human readability and AI citations. A tactic that is less effective now is long, keyword-stuffed paragraphs that do not present clear question-and-answer structure. I would split the budget roughly 70% to traditional SEO and 30% to AI search optimization to preserve technical and link-building work while investing meaningfully in structured content formats that AI engines use for snippets and citations.

The biggest addition to our previous SEO strategy is researching the search queries AI tools like ChatGPT are using, and making sure we have topical content, sections, and FAQs that address those specific phrases. We've pivoted toward Intent-Based Topical Authority. This involves auditing our content to ensure we have dedicated sections and FAQs that address the specific, long-tail conversational phrases used by AI search engines. Our goal is no longer just to rank for a keyword, but to be the definitive source that AI tools cite when answering user queries. __ The generic informational listicle has lost its competitive edge. Since AI-generated summaries now instantly answer 'top 10' or 'how-to' queries directly on the search results page, websites built solely on aggregated list content have seen significant traffic erosion. We've shifted our focus away from top-of-funnel informational listicles and toward high-intent, conversion-oriented content that requires the 'human-in-the-loop' expertise AI cannot yet replicate. __ Currently, we allocate about 28% of our budget to AI search optimization, but I don't view these as competing line items. Instead, I see them as a unified effort to meet the user wherever they happen to be in their journey. I look at the digital landscape as one interconnected ecosystem. Here is why the 70/30 split works for us: The Intent Funnel: AI search excels at "top of the funnel" informational research, broad comparisons, and early discovery. Traditional SEO is where we capture the "bottom of the funnel," showcasing specific offers that meet a user's exact needs once they've moved past the initial AI-assisted research phase. The Authority Loop: You can't have one without the other. Off-page SEO (e.g. quality backlinks) builds the domain and topical authority that Google craves. When your authority rises, you rank higher in organic search. Because AI models often crawl the top-ranking results to synthesize answers, your traditional SEO success directly fuels your AI visibility. Validation Through Engagement: High click-through rates and on-site conversions signal to search engines that your content is the definitive answer. This "proof of relevance" is exactly what AI tools look for when choosing which brands to cite in their responses. Ultimately, traditional SEO builds the foundation of authority and AI optimization ensures that authority is "readable" by the next generation of search. They aren't rivals; they are teammates.

AI search has shifted SEO from ranking pages to being cited as a trusted source. We have moved toward creating clear, experience-backed content that directly answers specific questions rather than relying on broad keyword targeting. Tactics like high-volume, low-depth articles or content written mainly for search engines have become less effective as AI surfaces concise answers. Budget decisions now lean toward strengthening authoritative content while still maintaining core SEO fundamentals. The focus is no longer just visibility in search results, but relevance in how answers are generated and presented.

Over the last 12 months AI search engines have shifted our SEO strategy toward using AI as a drafting and structuring tool while keeping human editors at the center of the process. We now routinely use AI to build outlines and draft angles so we can scale content faster. Every AI-generated draft is reviewed and revised for tone, context, and brand voice before publication. As a result, one tactic that used to work well but is now less effective is publishing unedited, AI-first content that prioritizes keywords over narrative. That kind of content often misses audience nuance and brand authority that humans add. For budget, I would allocate about 60 percent to traditional SEO and 40 percent to AI search optimization. This split reflects using AI for speed and structure while investing more in human-led editing, storytelling, and audience insight to maintain authenticity and long-term visibility.