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Most teams I talk to jump straight to the question "how do we get ChatGPT to mention us?" and skip everything before it. Getting mentioned is just one piece of the puzzle. If ChatGPT is becoming a new discovery and marketing channel, you need to think about it more like a funnel. I put together a ChatGPT Marketing Roadmap with 3 levels: Level 1: Foundations Before worrying about visibility, understand how ChatGPT works. • How ChatGPT uses training data vs. the live web • How ChatGPT Search differs from traditional ChatGPT • How sources are selected • The role of freshness and trusted domains • How AI search crawlers interact with your site Level 2: AI Search Visibility This is where marketing starts to get interesting. • Allow the right AI crawlers • Structure content so AI systems can understand and extract it • Build third-party authority through places like Reddit, Wikipedia, reviews and PR • Track your share of voice and citation share • Measure whether those citations actually drive referral traffic Level 3: ChatGPT Ads The next layer is paid acquisition. • Where ads may appear • How targeting and intent could work • What makes creative work inside an answer-driven environment • How to think about attribution, assisted conversions and incrementality The important part is the order. You shouldn't jump straight to "ChatGPT ads" or start pumping out content because you want to be mentioned by AI. Fix the foundation first Then earn citations Then pay for reach Then measure whether you're actually gaining visibility. And eventually, as advertising develops, figure out how paid acquisition fits into the picture. That's the shift I'm seeing in AI marketing. We're moving from "optimize for Google" to managing visibility across a collection of AI discovery surfaces. I made this roadmap as a practical starting point for anyone trying to figure out what to do next. Save it. Share it with your marketing team. And use it to identify where your current AI Search strategy has a gap. If you want to see how your brand is currently showing up in AI search, you can track your AI citations for free - https://pierview.ai/

77% of the pages AI cites were edited in the past year. We looked at every page AI assistants cited between Sep 6 and Oct 6, 2026, and checked when each one was first published and when it was last edited. The takeaway: More than three quarters of the pages were edited within the past year. And it gets sharper the closer you look: - 39% were edited in just the last 3 months. - 41% were edited since July, the largest group by far. - Only 7% were first published before 2020, so old pages can still get cited, but they are the exception. Publish date matters much less than most people think. The date you last touched the page matters a lot more. It also varies by assistant: - Google AI Mode leans freshest, with 53% of its cited pages edited in the last 3 months. - Claude is at 45% - Gemini 44% - Perplexity 40% - ChatGPT and Google AI Overviews sit at 37% - ChatGPT is the most forgiving of older content, with 28% of its cited pages first published before 2023. So what should you do with this? - Pull up the pages you want AI to cite. Check when each was last updated. - Refresh the ones that matter most with new data, current examples, and answers to questions people are asking now. - Make sure the update is real. A changed date with no new substance is not a refresh. - Put this on a calendar. Quarterly is a good starting rhythm. A blog post you wrote in 2023 and never touched is quietly losing ground to a competitor's page that was updated last month. AI isn't necessarily looking for brand-new content. It's finding older pages that have been kept relevant. If you have 500 pages on your website, you probably don't need another 50. The brands that win won't necessarily be the ones producing the most content. They'll be the ones continuously improving the content AI systems already consider useful. If you want to see which of your pages AI is citing today, and which ones are going stale, check out - https://pierview.ai/

Reddit used to be one of the safest bets for AI Search visibility. Then ChatGPT changed. So should brands stop investing in Reddit? Cue the hot takes that Reddit is dead for AI visibility. I think that's the wrong read. The cause isn't confirmed, the data is still being validated, and this has happened before. Reddit's ChatGPT citations collapsed in September 2025 and then came back. Google's AI products also barely flinched in comparison. Also, remember the $60M deal with Google and $70M deal with OpenAI for data access? That's still active. I feel the way we think about a brand's Reddit strategy needs to change. Reddit shouldn't be a “GEO hack.” It should be a source of customer conversations, brand perception, competitive intelligence and, potentially, AI visibility. The key word is "potentially". Because AI search engines change how they retrieve and cite information all the time. What gets cited heavily today might barely get cited tomorrow. That's why I'd approach Reddit in 3 phases: Days 1–30: Listen • Track your brand, products and key topics • Find the subreddits where your audience actually talks • Establish your sentiment baseline • Understand what people are saying about you and your competitors Days 31–60: Engage • Reply where your expertise is genuinely useful • Identify recurring questions and objections • Benchmark your presence against competitors • Build a response playbook from real conversations Days 61–90: Scale and measure • Track sentiment over time • Measure changes in AI visibility across multiple platforms • Expand coverage based on what the data shows • Turn Reddit insights into something your broader marketing team can use The biggest mistake is treating Reddit as a publishing channel. It's a conversation layer. And increasingly, it's also a data layer for understanding how people talk about your category when marketers aren't in the room. But don't assume that because ChatGPT cited a Reddit thread 10 times last month, it will cite it 10 times again next month. Measure it. Across platforms. Over time. That's the difference between having a Reddit presence and actually having a Reddit strategy. Full 90-day framework in the infographic below.

Muse is shopping for you. Instinct is shopping for you. And that might be the beginning of one of the biggest shifts in commerce we’ve seen in decades. We are entering the age of agentic commerce, where AI agents don't just help consumers find products. They make decisions for them. Think about how shopping works today: - You search for a product. - You open 10 tabs. - You compare prices. - You read reviews. - You watch videos. - You ask friends. - Then you make a decision. Now imagine saying: "Find me the best stroller under $500 for Canadian winters." And an AI agent does everything: - It understands your requirements. - Searches the market. - Evaluates products. - Reads reviews. - Compares alternatives. - Makes a recommendation. - And eventually, completes the purchase. The search box starts disappearing. The website becomes less important. The agent becomes the interface. And this creates a completely new problem for brands. For the last 20 years, brands have optimized for humans. Then we built an entire industry around helping search engines discover and rank us. Now we have another audience to optimize for - AI agents making decisions on behalf of consumers. The question is no longer just: "Can people find my brand?" It's: - "Will an AI agent discover me?" - "Will it understand what I offer?" - "Will it trust the information it finds?" - "Will it consider me against my competitors?" - "And when it has to make a recommendation, will I make the shortlist?" That is a very different kind of visibility. And I think this is where things get really interesting. The winners in agentic commerce may not simply be the brands with the biggest marketing budgets. They may be the brands that are easiest for machines to understand, trust and recommend. We've spent decades optimizing the internet for human attention. We're now entering an era of optimizing the internet for machine decisions. And I think we're still at the very beginning. The biggest commerce platform of the next decade might not have a homepage. It might be an agent.

44% of AI citations come from the first 30% of a page. But most writers bury the actual answer three paragraphs deep, right where AI stops looking. That single stat changed how we think about writing for AI search entirely. Here are the 10 habits we've seen actually move citation rates. 1. Lead with the answer: Don't make AI dig through five paragraphs to find the point. Answer the question early, then provide the supporting detail. 2. Make every section useful on its own: AI often retrieves specific content chunks, not an entire page. Each section should be understandable without relying on everything around it. 3. Mention your sources: Support important claims with data, sources, and dates. Clear attribution gives AI more context about where your information comes from. 4. Write with confidence: Remove unnecessary hedging. If you have the data and expertise to support a claim, say it clearly. 5. Get cited beyond your own website: Your website isn't the only source AI uses to understand your brand. Build a presence on the platforms, publications, communities, and sites that AI already trusts. 6. Add structured data: Use relevant schema to help machines understand the content and entities on your pages. 7. Own the entire topic: Don't create one article and call it a content strategy. Cover the important sub-questions around your topic and build genuine topical depth. 8. Publish original data: Generic content is everywhere. Original research, benchmarks, statistics, and case studies give AI something unique to reference. 9. Make sure AI can access your content: Technical accessibility still matters. If important content can't be crawled or understood by AI systems, everything else becomes harder. 10. Refresh what already works: AI Search visibility changes. Track your citations and visibility over time, identify content that is losing traction, and refresh it before it becomes stale. The common thread? AI Search isn't something you "optimize" once. It's an operating habit. Measure what AI says about your brand. Understand what it cites. Find where competitors are winning. Improve the content and sources influencing those answers. Then measure again. That's the loop. At Pierview, we're building the platform to help brands run that loop without manually checking hundreds of AI responses. Track your AI Search visibility, citations, competitors, and prompts for free - https://pierview.ai/

Most AEO teams are still doing everything manually. - Run prompts. - Copy AI responses. - Check citations. - Compare competitors. - Find content gaps. - Write recommendations. It works. Until you need to do it across hundreds of prompts, multiple AI platforms, and dozens of competitors. That's where Claude can become incredibly useful. I put together 6 AEO skills that turn Claude into an AI Search analysis workflow: 1. /ai-visibility-health-check Quickly understand where your brand stands across AI Search platforms. 2. /missed-prompt-gap Find the prompts where competitors are being mentioned and you're not. 3. /competitive-intelligence Understand what AI models are actually saying about your competitors. 4. /brand-perception See how AI models describe your brand, including sentiment and positioning. 5. /on-brand-content-brief Turn visibility gaps into ready-to-write content briefs. 6. /citation-strategy Identify the domains and pages AI models cite most often so you can understand where your citation opportunities are. The interesting part isn't just automating the analysis. It's connecting the entire workflow: Measure → Find gaps → Understand competitors → Build content → Improve citations That's where I think AEO is heading. Less manual prompt checking. More repeatable AI Search workflows. I've packaged all 6 skills, plus 2 bonus skills, into a free document you can use with Claude. Want access? Comment "Skills" below and I'll send you the full skill pack.

SEO is changing. But the biggest change isn't just where people search. It's what search engines need to understand about your brand. Old SEO was largely about getting a page to rank. New AEO is about making your brand understandable, trustworthy, and citable across AI systems. Here are 7 shifts I think every SEO team should pay attention to: 1. From "fixing AI visibility" to building AI trust Adding schema or technical fixes can help, but AEO is bigger than that. The goal is to give AI systems enough consistent information to understand and trust your brand. 2. From dumping pages into llms.txt to curating what matters More pages isn't necessarily better. Think about which pages actually help an AI understand your products, expertise, and brand. 3. From keyword stuffing to entity-based content AI doesn't just look for exact keyword matches. Build content around topics, entities, relationships, and the questions your audience actually asks. 4. From guessing to measurement If you don't measure AI visibility, you're optimizing in the dark. Track citations, brand mentions, competitors, sources, and share of voice. 5. From optimizing for one platform to multi-platform visibility Your brand might be visible in Google AI Overviews but missing from ChatGPT. Or strong in ChatGPT but barely mentioned in Perplexity. AEO needs to account for the platforms your audience actually uses. 6. From generic E-E-A-T content to proprietary knowledge AI systems have access to an enormous amount of similar content. Original research, proprietary data, expert insights, and real case studies give your brand something distinctive to cite. 7. From optimizing only your website to optimizing the broader information ecosystem Your website isn't the only thing AI uses to understand your brand. Reddit, reviews, publications, directories, communities, and other third-party sources can all influence what AI says about you. That's the fundamental shift: Old SEO asked: "How do I rank this page?" New AEO asks: "How do I make my brand the source AI wants to cite?" And that requires a very different measurement and optimization strategy. At Pierview we're building tools to help brands understand exactly how AI sees them, which sources it cites, where competitors appear, and where their biggest visibility gaps are. If you're trying to figure out your AEO strategy, start by measuring your current AI visibility. Track it for free: https://pierview.ai/

Before you launch your next ChatGPT Ad, there are 10 things you should understand. ChatGPT Ads are not just Google Ads in a different interface. The way users discover, interact with, and respond to ads inside an AI conversation is fundamentally different. Here are the 10 things to keep in mind: 1. Ad format Sponsored responses and carousels are designed to appear within the conversational experience. 2. Targeting Think context and intent, not traditional keyword targeting. The conversation itself is part of the targeting signal. 3. Matching Describe the type of conversation you want to reach instead of simply building a list of keywords. 4. Creative The creative constraints are different too. Your image, headline, description, and logo need to work within a much tighter format. 5. Compliance Some categories are restricted, and certain financial ads can require additional review. 6. Campaign objectives Depending on the goal, you can optimize around reach, clicks, or conversions. 7. Measurement First-party measurement and conversion signals become important if you want to understand what happens after the click. 8. Lead quality Don't optimize blindly for cheap clicks. A click from someone with strong purchase intent can be worth far more than a large volume of low-intent traffic. 9. Budgeting Treat ChatGPT Ads as a learning channel initially. Give yourself enough time and budget to understand what works before judging the channel. 10. The biggest one: understand the conversation This is the part I think marketers will underestimate. On traditional search, you know the keyword someone typed. On ChatGPT, the surrounding conversation can provide much richer context about what the person wants, what they are considering, and where they are in their decision process. That changes how we should think about advertising. The winning question won't simply be: "What search term should I bid on?" It will be: "What conversation should my brand be part of?" And this is where paid and organic AI visibility start to converge. If ChatGPT is recommending your competitor organically before your ad appears, that's important context for your paid strategy too. At Pierview, we're building the visibility layer for AI Search so brands can understand where they appear, how competitors are being recommended, which sources influence AI answers, and where the gaps are. The future of AI advertising won't be just about buying impressions. It will be about understanding the AI conversation. Track your AI Search visibility for free: https://pierview.ai/

Most companies think measuring AI Search looks like this: Ask ChatGPT a few questions → check if you're mentioned → screenshot the answer → repeat next month. That's not AI Search measurement. That's manual spot checking. If you actually want to understand how your brand performs across AI Search, you need a system. Here's what that system looks like: 1. Build a realistic prompt library Don't just search your brand name. Build prompts across informational, commercial, and comparative intent based on the questions your buyers actually ask. 2. Query multiple AI platforms Run the same prompt set across ChatGPT, Perplexity, Gemini, Claude and other relevant models. A brand can be highly visible on one platform and almost invisible on another. 3. Capture the raw responses Don't only record whether you appeared. Save the actual answers. That's where the useful intelligence lives. 4. Extract citation data Track: • Brand mentions • Competitor mentions • Cited sources • Answer position • Sentiment Now you can start seeing patterns instead of isolated answers. 5. Analyze the competitive landscape Turn that raw data into: • Share of voice • Competitive benchmarks • Co-citation networks • Citation gaps • Visibility trends This is where AI Search measurement becomes actionable. You can start asking questions like: - "Why is my competitor being cited more than me?" - "Which sources are influencing AI answers about my category?" - "Which prompts are we winning or losing?" - "Where are our biggest citation gaps?" That's the difference between checking AI Search and actually managing it. At Pierview, we help brands monitor AI visibility, citations, competitors, prompts and the sources influencing AI answers, without having to manually run hundreds of prompts every month. Track your AI Search visibility for free: https://pierview.ai/