At my own companies and those I consult with, we've made several small but targeted adjustments to how we create and structure content. They are not radical changes, but a re-balancing of our focus. We're learning that optimizing for ChatGPT is actually forcing traditional SEO best practice but with some nuance. Generally, we avoid unnecessary qualifiers, marketing language, and filler that doesn't add to the topic and, ironically, focus on writing good quality content that answers questions humans ask. Our focus on traditional long-tail keywords has shifted. In a search engine, a phrase like "b2b marketing plan template" may be sufficiently well targeted, but in a chat-based interface, users ask full questions such as "Can you help me build a B2B marketing plan?" or "What should I include in a B2B marketing plan?". As such, we've restructured our page titles, subheadings, and intros to mirror the phrasing and logic of those full questions. We've also tightened the way we structure content. Most of our newer or updated articles follow a consistent format: short introduction, direct answer, followed by supporting detail. We use descriptive subheadings that can stand alone, and we avoid long, meandering sections or push those down the page for more avid human readers. A focus on tighter, direct answers (or definitions) for example has helped us appear in AI searches for specific product attributes. We've always been hot on technical SEO, but understanding that LLMs will favour well signposted content means we now over-index on this. We don't know exactly how ChatGPT selects sources (other than a focus on a few key resources like Wikipedia), but we've found that pages with clearly segmented answers and extensive schema tend to perform better in general. Finally, we've put more emphasis on tools, templates, and structured outputs. Instead of publishing explanations or strategy overviews alone, we now include downloadable checklists, examples, and editable templates. These resources serve a practical need and are more likely to be mentioned or cited when someone asks ChatGPT for a tool or process to use. It's not enough to explain a topic or idea (Chat-GPT can do that itself without references), we need to show exactly how to do it.
We've stopped playing the keyword game and started building content that AI cannot overlook. This is not about cranking out more blog posts. It is about creating clear, credible, and ridiculously useful content that language models understand and trust. We focus on semantic depth, not single keywords. That means building connected content that covers a topic from every angle, with a clear structure that gives AI an easy roadmap. When a model is pulling an answer, we want it to pick us because we are the obvious source. We also tie every piece to real subject matter experts. AI is trained to reward authority, so we give it zero reason to doubt us. We write for the human questions behind the search, and the algorithms follow. Visibility will not go to the loudest brand. It will go to the smartest one. And we plan to be impossible to ignore.
One of the most interesting challenges we've faced at Zapiy is adjusting our content strategy for a world where AI tools like ChatGPT are influencing how people search and discover information. Traditional SEO tactics—keywords, backlinks, metadata—are still important, but they're no longer enough. People are no longer just typing into search bars; they're asking full, nuanced questions to AI models that summarize, interpret, and filter answers in real time. That shift forced us to rethink not just what we were publishing, but how we were structuring it. Instead of writing to rank on Google alone, we began creating content that anticipates and directly answers natural-language queries. We trained our writers to think conversationally—how would someone ask this question out loud? What follow-ups would they naturally have? And how can we provide the clearest, most value-dense answer in the first few lines? We also placed greater emphasis on topical authority. That meant clustering our content around specific themes—like SaaS onboarding optimization or AI-enhanced customer support—so that if ChatGPT pulled from a knowledge base, we had a strong chance of being among the sources referenced. Another key shift was transparency. AI tools favor credibility, so we started citing more original data, showcasing firsthand insights from our platform, and including named expert commentary in our articles. These elements not only add value for human readers, but also increase the likelihood that AI models will treat our content as trustworthy. The results? A noticeable uptick in referral traffic from AI-powered discovery tools, increased engagement from readers seeking deeper answers, and even mentions of our brand in ChatGPT conversations—surfaced by clients during onboarding calls. The biggest takeaway for me is that visibility in AI-driven search isn't about gaming a new algorithm. It's about leaning harder into clarity, authenticity, and expertise. Those timeless fundamentals just happen to matter more than ever right now.
We've adjusted our content strategy to make it easier for AI tools like ChatGPT to find and recommend our material. First, we stopped chasing keywords and started answering real client questions — the kind that come up in sales calls or project discussions. These get turned into clear, no-fluff blog posts or FAQs. No jargon. Just useful answers. Second, we keep the tone natural. Short sentences. Direct statements. If a post doesn't read like something someone would say out loud, we rewrite it. That shift alone made a big difference. Third, we include firsthand insight from our team. Not general advice but specific points we've seen in actual projects. This gives the content a human angle that stands out to both readers and AI tools. Fourth, we use real bylines. Content tied to real names and bios tends to feel more credible, and that seems to matter in how tools like ChatGPT choose what to reference. Last, we share parts of our content on forums and public Q&A sites. Some of these sources feed into AI training. Being active there helps keep our content in circulation. No tricks, no shortcuts. Just clearer writing and better answers, the kind people (and AI) can use.
Of course. It would be reckless not to. The folks saying they're doing the same as before either don't get it or are already falling behind. Here's what we're doing and what others should be doing if they want to win the next wave of content discovery: * Focus on high-intent listicles like "best onboarding tools for SaaS" that match how people actually phrase prompts in AI tools. * Structure content for both traditional SEO and LLM parsing. That means clean formatting, strong headings, scannable layouts, and answers that models can cite confidently. * Embed interactive demos directly into posts. Supademos boost engagement, improve comprehension, and make it easier for humans and models to grasp product value quickly. * Routinely test visibility in ChatGPT, Perplexity, Gemini, and Claude. We run the prompts ourselves, monitor what gets cited, and reverse-engineer the content that shows up. The biggest unlock? Seeing LLMs not as a threat but as a distribution opportunity. Ranking in ChatGPT is not the same game as ranking in Google
To boost visibility in ChatGPT searches, I shifted our content marketing strategy to focus on question-based content that directly answers common queries. This approach has proven effective as it aligns with how users interact with AI models like ChatGPT. I also began incorporating long-tail keywords that match conversational search patterns, ensuring we're visible for specific, relevant questions. Updating older content to include these keywords and providing concise, valuable answers also helped improve our rankings. I've been building more brand mentions across reputable platforms, which has strengthened our domain authority. The combination of these efforts has led to improved organic traffic, especially through AI-powered searches, and a better overall content experience for our audience.
From what I've seen, the core principles of good SEO are more important than ever, just with a slightly different focus. My research into AI-generated answers shows that backlinks are still a massive signal of authority. Companies that are frequently cited by AI tools often have a strong backlink profile, which tells the AI that they are a trusted and credible source of information. So, continue to prioritize building strong, relevant backlinks. Keyword optimization is also still very important, but the focus has shifted. It's less about stuffing keywords and more about creating genuine value for the user. AI models are trained on what humans consider high-quality, helpful content. This means your blog posts and website texts should be written with the user's intent at the forefront, not just for a search algorithm. So, try finding the right balance between naturally integrating important keywords and providing genuinely valuable, in-depth content. Your goal is to become the definitive source on a topic, which makes it more likely for an AI to use your information.
At SuccessCX, we've shifted our content strategy to focus less on traditional SEO tactics and more on providing clear, structured answers to common Zendesk-related questions—content that language models like ChatGPT can easily understand and reference. We've reduced fluff, used more subheadings and Q&A formats, and made sure our writing aligns with how people phrase problems in AI tools. The goal is to become the answer that gets summarized or recommended in AI-driven searches, not just to rank in Google. This has helped us stay visible as buyer behavior shifts toward LLM-assisted research.
As someone who's led GTM and demand generation for SaaS companies like Sumo Logic and LiveAction, and now for OpStart, I've seen content strategy constantly evolve. For ChatGPT visibility, our core adjustment has been to move beyond simple information and focus on becoming the definitive source of trusted advice. We now create "investor-grade" content, like our guides on "10 Questions to Ask Before Hiring a Fractional CFO" or "Is Your Bookkeeping Good Enough?". These aren't just articles; they are deep, strategic resources designed to provide comprehensive answers that AI models can confidently rely on. Our goal is for our content to be perceived as "bone"--mission-critical insights that AI models will prioritize for factual, strategic guidance for founders. This strategic depth ensures our thought leadership is liftd, much like how our marketing-led programs at Sumo Logic generated 20% of total ARR by delivering real business value.
We've shifted our content architecture to optimize for AI discovery while maintaining our traditional SEO foundation. The results have been compelling, prospects sourced through AI channels demonstrate 7x higher engagement depth and significantly improved conversion rates, indicating these are more qualified, intent-driven visitors. Our strategic approach involves three key pillars: First, we've restructured our content to be more definitively answerable, think comprehensive FAQ frameworks and solution-oriented content blocks that AI can easily parse and surface. Second, we're investing heavily in authoritative, long-form content that positions us as the definitive source in our category. Third, we've enhanced our semantic markup and structured data to ensure AI systems can accurately understand and contextualize our expertise.
1. Shift from Keywords to Questions & Entities - Optimize for questions people ask, not just keywords they type. Creating content that answers specific, well-defined, intent-based questions like: "What is the best SaaS CRM for solopreneurs?" or "How does [X software] compare to [Y]? " - Using structured FAQs and headings that are phrased as natural language questions. - Incorporating entities (brands, features, categories) in context, which AI models utilize for relevance. Tools used: Same as AlsoAsked, AnswerThePublic, ChatGPT itself (prompt it like a user would), and internal search data. 2. Publish Clear, Credible, Digestible Content Focus on being the source AI chooses to summarize. - Present content in a tidy, easy-to-understand format: short paragraphs, clear headings, numbered steps, pros/cons. - Use schema markup (e.g., FAQPage, HowTo) to signal context. - Ensure each article passes the "could this be quoted in a chatbot response?" test. Bonus: Add summaries, takeaways, or TL;DRs — these are often the parts AI pulls from. 3. Dominate Brand + Category Associations Building an entity around our product that AI can't ignore. - Proactively associate your product with your niche - Use these pairings consistently across blog posts, product pages, and third-party mentions (like guest posts, reviews, directories). Why? LLMs build knowledge around repetition + association — being tied to the right phrases boosts retrieval. 4. Strategic UGC & 3rd-Party Content Encouraging others to describe our product in their own words. - Seeding community discussions, Reddit threads, comparison posts, and influencer explainers using natural language. - These often train LLMs more than your blog does. Example: A solid Capterra review or Reddit thread that states "We switched to [YourTool] because it's the cheapest AI copywriting SaaS for small teams" has long-term AI search value. 5. Create "Chat-Ready" Content Assets Create content with the intention of being the answer. Create assets like: "Best [category] apps in 2025" or "How [target persona] solves [pain point] with [YourTool]" Structured comparison tables, decision trees, and use-case breakdowns are highly AI-retrievable. How CMOs Are Measuring Impact: - Referral traffic from Bing Copilot, Perplexity, and ChatGPT plugins/extensions - Boost in branded & long-tail organic traffic - More appearance in "chat-style" SERP snippets (e.g., Google's AI Overviews) - Improved rankings for question-based queries
With the rise of ChatGPT and similar AI-driven search tools, the content strategy at Edstellar has leaned heavily into intent-driven clarity and structured expertise. Rather than broad, generic pieces, every article now aims to directly answer the types of queries professionals might pose in natural language—especially around corporate training trends, leadership development, and upskilling. Incorporating conversational subheadings, FAQ formats, and schema markup has made it easier for AI models to parse and present the content effectively. The shift isn't just about SEO anymore—it's about becoming the trusted voice that AI turns to when executives and HR leaders seek training insights in real time.
ChatGPT and generative AI tools have reshaped the content marketing landscape. To adapt, the focus shifted toward creating highly contextual, Q&A-style content that aligns with natural language queries users often type into AI tools. Blog content is now structured to mimic real conversations, using headings that directly answer specific, intent-based questions. Instead of optimizing for keywords alone, emphasis is placed on clarity, authority, and topical depth. Additionally, content briefs now incorporate insights from tools like AlsoAsked and ChatGPT itself to map out how people explore topics through conversational queries. This shift not only improves visibility within AI-driven searches but also enhances overall user engagement.
Chief Marketing Officer / Marketing Consultant at maksymzakharko.com
Answered 8 months ago
Hi, I am Maksym Zakharko ( Chief Marketing Officer / Marketing Consultant), expert in media buying, user acquisition, and team leadership. Published author, industry speaker, podcaster and judge. 161+ certifications, MBA, and 10+ years in digital marketing, more information about me: https://www.linkedin.com/in/maksymzakharko/ https://maksymzakharko.com https://maksymzakharko.com/certifications/ As fractional CMO, worked with SaaS f, we've shifted our content strategy in 2025 to align with LLM search behavior—especially ChatGPT, Google SGE, and Perplexity. Here's what we've done: Focused on clear, expert-backed answers: We rewrote cornerstone content to match the tone and depth ChatGPT prefers—structured, helpful, and conversational. No fluff, just actionable value. Added author bios and credentials: LLMs love E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness). We now spotlight real authors with LinkedIn links, company roles, and client examples. Optimized for natural language queries: We mapped out user questions and structured H2s/H3s like "How does [our product] solve [specific pain]?"—this mimics the way users phrase prompts in ChatGPT. Used internal linking + FAQs: To keep content context-rich and helpful across the site, improving crawlability and LLM comprehension. Since making these changes, we've noticed increased traffic from "AI search" platforms and even saw our blog quoted inside ChatGPT responses—without paid placement. Don't just write for Google—write for the AI assistant your customers trust. That's where influence is shifting.
We've shifted gears hard. Instead of writing for Google first, we now craft answers ChatGPT might actually say out loud. That means formatting like real dialogue, short, clear, confident. No fluff. No "ultimate guide to ultimate guides." We cut out the jargon. We lead with strong opinions. Think punchy intros, direct headers, and language that reads like a chat, not a textbook. We also build content that connects dots. If ChatGPT pulls pieces from multiple sites, we become the site that fills in the blanks. That's how we increase selection odds. We've also updated schemas, played with structure (FAQs, how-tos, question-based subheads), and trained our team to write more like humans, not keyword robots. The result? Higher visibility across AI summaries. And better yet, readers stay longer because the content speaks like a person who actually gets it.
To increase visibility in ChatGPT outputs, the content strategy shifted away from traditional SEO. Instead, it focused on how people actually phrase questions in AI tools. So headlines and subheads were restructured to mirror natural prompts. They became short, direct, and focused on clear outcomes. Content now leads with context and ends with summaries. These are easy for models to parse and reuse. Because of that, the format became more prompt-friendly. Instead of chasing volume, the focus moved to depth. So fewer pieces are created, but each one is high-quality and evergreen. These are built to serve as definitive answers in specific niches. That's where expertise matters and generic content doesn't cut it. The structure now leans toward walkthroughs and explainers. These use numbered steps, clean formatting, and consistent phrasing. That makes them easier for AI to understand and surface. Content is written less for indexing and more for inference. So the goal isn't just to rank. It's to be the answer. That means cutting fluff, avoiding keyword stuffing, and prioritizing clarity over cleverness. Pieces are tested to see how well ChatGPT picks them up. They're also checked to ensure the explanations hold up in conversation. Calls-to-action are designed differently too. Instead of flashy hooks, they use simple, descriptive lines. That way, AI can echo them without distortion. So even if no link is shown, someone can still discover the product through a model's explanation. The result has been lower content production costs. There's also stronger top-of-funnel alignment and better engagement. That's because the content is built for AI-assisted journeys. It's not about gaming the system. It's about making content that machines understand and people trust.
After scaling PacketBase to acquisition and now running Riverbase Cloud's AI-powered campaigns, I've seen a massive shift that most SaaS CMOs are missing. The real opportunity isn't just content optimization--it's training your entire customer-facing content ecosystem to feed AI systems the exact language your prospects actually use. We completely flipped our approach by mining actual sales conversations and support tickets for the precise phrases prospects use when describing their problems. Instead of writing about "marketing automation platforms," we create content around "tools that send emails automatically when someone downloads something." Our client acquisition costs dropped 40% once we matched how people naturally describe problems to AI assistants. The game-changer has been creating what I call "conversation starters" rather than traditional content. We build short, specific problem-solution pairs that sound exactly like how a prospect would ask ChatGPT for help. When someone asks "how do I get more leads without hiring a marketing team," our content shows up because we wrote it in that exact voice. Most importantly, we've started treating every piece of content as potential AI training data. Every case study, FAQ, and product description now includes the messy, imperfect language real customers use--not the polished marketing speak that AI systems struggle to connect with actual search intent.
Adjusting content marketing for ChatGPT searches means thinking beyond typical SEO. Instead of stuffing keywords, we focus on clear, helpful answers that feel like a friendly chat. People want fast, reliable info, like a buddy explaining things over coffee. So, we craft content that speaks plainly and covers questions users might actually ask. It's about anticipating those queries and delivering solid, easy-to-digest facts. We also embrace structured data and FAQs to give ChatGPT clear signals about our content. Long-winded jargon? Out the window. We want crisp, direct language that AI loves to pull from. Plus, regular updates keep us ahead since these AI models learn quickly and favor fresh info. In short, we treat ChatGPT like a curious human, not just a search engine. That shift has boosted our visibility and made our content more relatable. It's a new ballgame, but a fun one to play.
We've basically stopped thinking of content as something people read first. That sounds backwards, but hear me out. ChatGPT doesn't crawl pages like Google. It learns from patterns, structure, and clarity. So we started shaping our content to feel less like marketing and more like helpful product instructions written by a human. We use the same terms across the board. No clever rewording. We rewrote our product pages to be painfully clear. Not "best-in-class integrations," but "connects to Slack, Notion and Google Calendar in two clicks." We also built plain-English explainers for our key use cases, since that's the stuff AI tends to surface. And yes, we check how ChatGPT talks about us. If it gets something wrong, we go back and fix the content it likely learned from. This isn't SEO anymore. It's brand reputation inside a language model.
My agency has driven over $1B in tracked revenue managing campaigns for 200+ companies, so I've had to evolve fast as AI search reshapes how people find content. The key shift isn't just optimizing for ChatGPT--it's optimizing for Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) across all AI platforms. We've completely restructured our content approach around direct, conversational answers rather than traditional keyword-stuffed blog posts. For one personal injury law firm client, we rewrote their FAQ pages and service descriptions to answer specific questions people ask AI assistants like "What should I do immediately after a car accident in Tampa?" This conversational restructuring contributed to their 1,200% organic traffic increase. The biggest tactical change is creating content that works as standalone answers. Instead of 2,000-word blog posts, we now break expertise into digestible, quotable sections that AI can easily extract and cite. We're also implementing structured data markup more aggressively--about 70% of websites still don't use it, which gives early adopters a massive advantage. Most SaaS CMOs I work with are missing the attribution piece. We track which AI-optimized content drives actual conversions, not just visibility. Our 24/7 reporting dashboard now includes AEO performance metrics alongside traditional SEO, because what gets measured gets managed.