How to show up where AI is listening: Building AI visibility from buyer conversations

Your buyers are telling you exactly what they want to know, in their own words, every day.

Where? In your sales calls. Your support tickets. Your win-loss interviews. Your demo Q&As. Your cancellation surveys. Your Amazon Q&A.

They’re the exact questions your buyers type into ChatGPT and Perplexity. And they’re sitting in your systems right now, unused.

No competitor has access to your buyer language.

Buyer data is just the starting point. The full strategy goes beyond content creation. It means deciding which questions matter most, building PR and communications around those questions, showing up in the social and review platforms AI reads, and making sure your story is consistent across every surface where AI forms an opinion about your brand.

Being found in AI search is the entry condition. It’s how you know you’re in the game. But the workflow that turns your buyer data into always-on visibility is what makes you unreplicable. Share of model gets you in. Share of workflow keeps you ahead.

I want to show you the full strategy we built at WRITER, not just the content engine. Why it works, how it connects to business outcomes, and how you can start building something similar this week.

Why do most teams start GEO wrong?

When teams start optimizing for AI search (what most people call GEO and AEO), they start where they should: with their existing keyword data. They pull search volume from Semrush or Ahrefs, convert those keywords into natural language queries, and build content around them. We’ve built agentic playbooks that do exactly this. They take your keywords, map in some persona data, and turn them into the questions buyers ask AI engines.

That’s a strong entry point. Your keyword data tells you what the market is searching for. But it has a limit: it tells you what people searched for in the past, not what your specific buyers are asking right now. And it can’t tell you which questions map to revenue.

Most teams also treat GEO as a technical exercise: structured data (schema markup), crawlability, and information architecture. Those things matter. But they’re the 20%.

The other 80% is strategic. It’s your brand, your positioning, your proof points, and how consistently your story shows up across the web. That’s why this is a CMO problem, not just an SEO problem. It touches brand, content, analyst relations, and PR at the same time.

We’re not the only ones saying this. Marketbridge analyzed more than 17,000 AI citations across multiple industries and reached the same conclusion: “Organizations that treat GEO as a technical add-on risk missing the bigger picture.” Forrester makes the same case: “Visibility becomes a shared KPI — spanning content, messaging, operations, and strategy — rather than a byproduct of search optimization.”

The strategic advantage: buyer conversations on top of keyword data

The strategic advantage comes when you add buyer conversations on top of your keyword data. Pull the actual language your buyers use, not just what the market searched for. A B2B software company’s sales calls reveal questions like “how does this platform handle data residency for EU customers?” A consumer brand’s Amazon Q&A surfaces “does this product work for sensitive skin?” A financial services firm’s support tickets show “what happens to my account if I switch banks?”

Those are the exact phrases buyers type into AI engines. The real framing. The real concerns. And unlike keyword data, you can tie them directly to revenue because you know which deals they came from.

Which questions should you be the answer to?

Not every buyer question deserves your time. A B2B SaaS company might collect 800 questions from sales calls in a quarter. You can’t build content, PR, and social strategy around 800 questions. You need to prioritize.

Here’s how we think about it at WRITER:

Map questions to purchase decisions. Which questions come up in deals that actually close? Which ones appear in your win-loss interviews as the make-or-break moment? A question like “how does this platform handle data residency for EU customers?” might come from one prospect who never buys. But if it shows up in five deals that closed last quarter, that’s a question you need to own.

Tie questions to revenue. When a buyer asks “what’s the difference between WRITER and ChatGPT for enterprise use?,” that question sits at the top of the funnel. It determines whether you make the shortlist. If you’re not the answer, you’re not in the conversation. That question is worth more to your business than a question about a niche feature that only existing customers ask.

Group by theme. Cluster your prioritized questions. You’ll find that 20 questions collapse into 5 themes. Those themes become your content priorities, your PR priorities, and your social priorities. One strategy, expressed across every channel where AI listens.

The output of this step is a shortlist: 10 to 15 questions that matter to your business, grouped into 3 to 5 themes. That list drives everything that follows. Content creation, PR outreach, social media, analyst briefings. All focused on the same questions.

Most teams jump straight to content production without deciding which questions are worth answering. Then they have a library of content that doesn’t map to the moments that move revenue.

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How do you turn buyer conversations into content?

At WRITER, we built an autonomous knowledge hub that turns buyer conversations into content every day, using WRITER Agent and pre-built agentic playbooks that connect directly with call recording software like Gong.

Here’s what happens every morning, step by step:

Turn buyer conversations into content with autonomous agent playbooks

1. Listen. Every morning, a WRITER Agent pulls sales call transcripts from Gong from the previous 24 hours. The playbook connects to Gong, retrieves the transcripts, and passes them to the next stage.

2. Extract. Worker agents analyze each transcript, extract the real buyer questions, deduplicate them, and tag them by theme. The actual questions, in the buyer’s words. They also create an unbranded version of each question.

3. Store. The questions are written to a system of record (in our case, a Google Sheet), tagged and organized by theme so they’re searchable and actionable.

4. Cluster and plan. A content supply chain workflow reads the questions, clusters them by topic, and, if the question maps to our strategic goal, dispatches a virtual content team to determine what type of content each cluster needs. Is this a comparison piece? A how-to guide? An FAQ? The system decides based on the question pattern.

5. Create. Researcher agents gather what’s needed: competitive context, existing content gaps, supporting data. Content manager agents produce the drafts.

6. Publish. The content is pushed to our CMS in a private state, on-brand, compliant, and ready for human review. Our content team approves, refines, and adds the perspective only they can provide.

The result: a daily content pipeline built from real buyer questions that feeds AI visibility. Every day, fresh content that answers the exact questions your buyers are asking, not the questions you guessed they might ask.

This is what the first-party data advantage looks like in practice. You’re not guessing what your buyers want to know. You’re building content from the exact language they use, at the speed your buyers expect, in the voice your market recognizes. Your organizational context (your buyer language, your brand standards, your domain expertise) is what makes this work, and no competitor has it.

The pattern is the same, whatever tools your team uses. B2C? Replace Gong with Amazon Q&A. The questions exist in your systems right now. You’re just not using them for GEO yet.

The human stays in the loop. What changes is what the human’s time is worth.

In most content operations, review is the bottleneck. Drafts pile up. Editors become the constraint. Work slows because someone has to fix the voice, check the terminology, rewrite the off-brand sentences before anything ships.

That bottleneck disappears when your brand’s context (your first-party buyer data, your messaging, your positioning, your voice) is encoded into the system rather than applied by hand at the end. Every draft starts on-brand. The agents don’t guess at your voice. They work from it. The approval and editing phases stop being a repair shop and start being a judgment call. Your editors stop fixing and start shaping.

This is what Diego means when he says the moat is the workflow. The system produces content from your unique buyer data every day at a pace no human team could match. Your people’s judgment guides what the system produces and reviews the output. The agents handle the execution. Your people handle the creation. That combination is what no competitor can replicate.

But content is only one part of the strategy. The questions you identified in the last section don’t just need content answers. They need presence across every surface where AI forms opinions about your brand.

What content gets cited in AI search?

Not all content performs equally in AI search. Some content types earn citations. Others are becoming invisible.

What wins:

• Original data and research. First-party data, proprietary surveys, and original analysis are the most citable content you can produce. AI engines favor sources that offer something no one else has. Princeton’s GEO research found that content with statistics and citations earns up to 40% more visibility in AI responses. The original data from your buyer conversations is the statistics no competitor can produce.
• Middle-funnel content. “How do companies like ours solve X in context Y?” taken from actual buyer questions. This is where buyers are making decisions, and where AI synthesizes answers from multiple sources.
• Comparison and alternatives. “How does X compare to Y for Z?” These are the shortlist-defining queries where buyers narrow their options. If you’re not present in the comparison, you’re not in the conversation.

What to deprioritize:

• Top-of-funnel definitional content. “What is X?” AI answers these directly now. If your content just defines a term that ChatGPT can define in three sentences, it’s not earning citations. Reallocate that budget down-funnel where it compounds.

Velocity matters. AI weights recency heavily. A content operation that takes four months to produce a piece is working at a structural disadvantage. When you speed up that cycle with AI-powered workflows, speed becomes the advantage.

How do you show up in the sources AI trusts?

This is where most GEO guides stop. They tell you to create content and structure it well. But Bain and ScrunchAI’s research, analyzing roughly 500 million citations, found that 89% of unbranded AI prompts are fulfilled by third-party sources rather than brand-owned media. The sources AI trusts most are the ones you don’t directly control.

That means content alone won’t get you there. You need presence across three tiers of sources, all aligned to the same questions, all telling a consistent story.

The three tiers of AI visibility

Owned. Your product pages, your comparison content, your original research. The question isn’t “are we producing enough?” but “is this the right type of content, fast enough, structured for AI extraction?”

Reviews. Keep your review profiles current with your latest positioning. Use AI workflows to encourage reviews and respond quickly. This tier is often overlooked, but it’s crucial, especially for B2B, where G2 and Capterra profiles carry enormous weight in AI synthesis.

Earned. Industry analyst reports, Wikipedia, sustained PR investment. This is the hardest category, but the highest leverage. The sources AI trusts most are the ones you don’t directly control.

Trust neighborhoods: where AI goes to form opinions

• Regulated industries (healthcare or financial services): Healthgrades, U.S. News Hospital Rankings, CMS quality scores, A.M. Best ratings, NAIC complaint indexes, Morningstar.
• Professional services: The analyst trio — Gartner Magic Quadrants, Forrester Waves, ALM Intelligence.
• CPG or household goods: Amazon, Wirecutter, Good Housekeeping, retailer PDPs, and Reddit.
• Seasonal consumer services: Category-specific Reddit communities, App Store reviews, and comparison aggregators.

The key insight: each of these neighborhoods has a different set of trusted URLs. You need to know which sources AI reads for your category, and you need presence in those sources tied to your priority questions.

Building PR and communications around your questions

Your PR strategy should answer the same questions your content answers. If one of your priority questions is “what’s the best AI platform for enterprise marketing teams?,” your PR team should be pitching stories, securing analyst briefings, and building relationships with the publications that AI reads when it answers that question.

This means:

• Analyst relations. Brief analysts on your perspective on the questions that matter. When a buyer asks an AI engine for a recommendation, analyst reports are among the most cited sources. If the analyst hasn’t heard your perspective on the question, you won’t be in their report, and you won’t be in the answer.
• Earned media. Pitch stories tied to your priority questions, not just your product launches. When Forbes writes about how companies track AI ROI, and your survey data is the evidence, you earn a citation that AI reads for months.
• Wikipedia. If your brand or category has a Wikipedia page, keep it accurate and current. Wikipedia is one of the most cited sources across all AI engines. It’s not a place for marketing language. It’s a place for factual, sourced information about your company and category.

Social platforms are part of the trust neighborhood too. LinkedIn discussions, Reddit threads, YouTube reviews, and podcast appearances all feed the sources AI reads. The same questions you identified should guide your social strategy:

• LinkedIn. Your team should be contributing to discussions around your priority questions. Not promotional posts. Genuine perspectives that add to the conversation AI is reading. When someone asks “how do enterprise AI platforms handle data governance?” on LinkedIn, your team’s answer should be part of what AI finds.
• Reddit and forums. These are harder to influence, and that’s the point. AI trusts them because they’re authentic. If your product genuinely earns praise in Reddit threads, that shows up in AI answers. You can’t fake this. You can make sure your customers know where to share their experiences.
• Podcasts. Podcast appearances let you answer your priority questions in depth, in your own voice. The transcripts feed AI engines. When I appeared on the Humans of AI podcast to talk about rebuilding inbound marketing for AI search, that conversation became a source AI reads when buyers research AI visibility.

Consistency across surfaces

Getting visibility across all three tiers, with consistent messaging, is what separates brands that appear in AI answers from brands that don’t. If your owned content says one thing and your reviews say another, AI synthesizes the inconsistency and your positioning gets blurred. If your PR team pitches one story and your social team tells another, AI reads the gap.

This is why query prioritization matters so much. When everyone in your organization — content, PR, social, analyst relations — is focused on the same 10 to 15 questions, your messaging becomes consistent across every surface. AI reads that consistency and ranks you higher in the answer.

How do you get started in 30 days?

Before you build an always-on engine, get a 30-day baseline. Here’s the four-step framework I recommend:

1. Find your data sources. Pull 20–30 questions in your buyers’ own language from calls, tickets, and reviews. Don’t synthesize. Don’t paraphrase. Get the real words.

2. Prioritize and map to revenue. Not all questions matter equally. Which ones map to closed deals? Which ones show up in your win-loss data? Pick the 10 to 15 questions that tie to real purchase decisions. Group them into 3 to 5 themes. This list drives everything.

3. Identify high-intent prompts. Map those questions to real purchase decisions. Check where you appear across ChatGPT, Perplexity, and Google AI Mode. Set your baseline. Here’s why you need to check all three: only 11% of domains are cited by both ChatGPT and Perplexity, according to CiteMetrix’s analysis of 680 million tracked AI citations. The sources AI engines cite are almost entirely different from one platform to the next. If you only check one, you’re blind to the other 89%.

4. Pick three high-value surfaces. Your main site page, your primary review profile, maybe Wikipedia. Find the gaps in messaging alignment and fix them for quick wins.

5. Cross-reference questions against current content. Where are the content gaps? Are you addressing the real buyer questions? If not, you have a clear roadmap.

This gives you the signals to justify building the always-on engine. Once you can see the gaps, the questions your buyers ask that your content doesn’t answer, you have your business case.

What metrics should you track for AI visibility?

This is the question I get most often from CMOs. The honest answer: AI visibility is harder to measure than SEO, but not impossible. You just need different metrics and a different cadence.

Here’s what I track at WRITER, and what I’d recommend you track from day one:

• Brand visibility. This is the core metric. Pick 20 to 30 category prompts (the questions your buyers actually ask) and run them weekly across ChatGPT, Perplexity, and Google AI Mode. Log whether your brand is mentioned in each answer. If your brand shows up in 7 out of 10 answers, your brand visibility is 70%. This tells you whether you’re in the conversation at all.
  

• Share of model. Once you know you’re showing up, measure your share of the conversation. Share of model is your brand’s mentions divided by all brand mentions combined across your tracked prompts. If you appear 7 times and competitors appear 13 times total, your share of model is 35%. It’s a relative metric — if competitors gain mentions, your share may drop even if your total mentions stay the same. Track it as a rolling six-week average. One week tells you nothing. Six weeks tells you something real.
  

• Branded search. When branded search rises and you can’t explain it with a campaign, that’s often buyers who found you in an AI answer and came to learn more. It’s the best leading signal we have.

• Self-reported attribution. We added “How did you hear about us?” back to our contact forms. When someone comes through a paid ad but tells us they first heard about us through an AI assistant, we update the attribution. Most dashboards massively underreport AI’s influence because AI research doesn’t leave a standard referrer string. People rarely click through to websites from AI search (known as the “dark funnel”). Ask your buyers directly. They’ll tell you.

• Sentiment. Track not just whether you appear, but what’s said about you. AI engines can surface outdated information, negative reviews, and factual errors with the same confidence as a glowing recommendation. When you find something wrong, the fix is the same as everything else in this guide — publish authoritative content that corrects the record.

What does this look like with real numbers?

Our Enterprise AI Adoption 2026 survey is original research, first-party data no competitor has. We didn’t have to ask our buyers what they wanted to know. We already knew. The autonomous knowledge hub pulls their questions from our Gong recordings every day. The survey asked the market the same questions our buyers were already asking: Is the strategy real? Are employees actually using these tools? Is the ROI there?

We released the survey in early April 2026 and used WRITER to synthesize the visibility report itself. Then we tracked the survey’s performance across AI engines over 100 days.

The results show what happens when original data meets the workflow I’ve been describing:

Hundreds of sessions referred from AI engines, even with the low click-through rates from AI search. Not a launch spike that faded, but steady, daily sessions for 15 weeks. The traffic held flat month over month.

Fifth most-cited owned source in our LLM tracking, behind only structural pages like our homepage and support docs. Among individual content assets, it sits near the top.

Position 1 in Google AI Overviews for “enterprise ai adoption,” a term with 590 monthly searches. Eight AI Overview placements, all at position 1.

LLM-referred traffic outpaced organic search by nearly 20 to 1: For this piece, AI is the primary traffic engine, not Google.

The survey didn’t just earn citations from AI engines. It continues to be cited by major media outlets like Forbes, months after release. Every earned media mention sends a signal to LLMs: this source is authoritative, current, and trusted by other publishers. The 89% third-party source stat I mentioned earlier works both ways. You want your content showing up in the sources AI reads, and media citations are one of the strongest ways to get there.

That’s what the numbers look like when the system works. Original data earns citations. Citations drive brand visibility and traffic (direct or indirect). Earned media amplifies both. The measurement tells you whether it’s happening.

Here’s the connection that makes this work: measurement needs to tie back to the workflow. When I see a gap in share of model, a prompt where we don’t appear or appear with the wrong positioning, that gap goes straight into the content pipeline. The buyer questions from our sales calls tell us what to create. The measurement tells us whether it’s working. The workflow closes the loop.

That’s the system: listen to your buyers, decide which questions matter, build content and PR and social presence around those questions, measure whether you show up in AI answers, and feed the gaps back in. Every day.

The first move takes 30 days

AI didn’t change what good marketing is. It changed where the audience is listening.

The work of clarity of positioning, understanding your buyer, consistency across channels: that’s the work of a great marketing team, and it hasn’t changed. What has changed is where the conversation is happening: off your website, on platforms you don’t fully control.

The first move takes 30 days. Start with your buyer’s own words. Decide which questions matter.  Build from there, across every surface where AI listens.

And if you want the 30-day baseline template to start this week, download it here.

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Christian Westcott is Director of AI Visibility at WRITER. He leads WRITER’s GEO strategy and built the autonomous knowledge hub that turns buyer conversations into citable content.