Every marketing function that creates market-facing content is already shaping how AI systems describe, recommend, and compare your company to buyers. Most teams are doing it accidentally. The ones that start doing it intentionally will be harder to displace every month they stay ahead.
This is the sixth and final post in our AI Visibility Gap series. As a quick recap, we’ve covered:
- Why AI is now a filter in buyer research before any human interaction occurs
- How your market signals reach AI systems and where they fragment
- How to measure what AI actually does with those signals
- Why optimizing visibility without fixing the foundation underneath it is going to be a costly mistake
- What has to change at the organizational level to manage this as a strategic capability.
Post #6 delivers the functional detail, the strategic extension each marketing function needs to add to what it already does.
This is not a technical implementation guide. We’d be here all day. And we’d need snacks.
I’m just trying to give you a clear sense of what changes within each function, why it matters, and what it looks like in practice.
What These Changes Have in Common
Before going function by function, let me throw out a principle that holds across all of them.
AI visibility isn’t a separate workstream that sits alongside your existing marketing operations. It’s a new dimension of work that extends into every function already producing market-facing content.
The disciplines you already have — content strategy, SEO, product marketing, brand, PR — are the foundation. What changes is the scope of what each function is responsible for, the channels and sources it manages, and the signals it’s deliberately creating.
This is additive, not replacement. A team that’s already doing these functions well has a shorter path to LLM-era competence than one that’s still trying to get the basics right.
You may be thinking… “We already have a content strategy”. Great! But that’s not the same thing as “our content strategy accounts for how AI systems extract and reuse our work.”
That gap is where most mid-market teams currently live.
Content Marketing: Own the Questions Before AI Does
Content marketing’s job has always been to answer buyer questions. In an LLM-mediated research environment, that job gets more consequential.
When a buyer asks an AI tool a question about your category, AI pulls from whatever go-to explanation already exists at scale. If your content owns that question clearly, AI references your version of the answer. If it doesn’t, AI synthesizes from whoever does.
This isn’t a new idea. Marcus Sheridan built They Ask, You Answer on exactly this principle — that the companies willing to answer their buyers’ real questions honestly and completely win the trust that drives revenue.
The companies that implemented it built durable content advantages. The ones that didn’t now face a compounded version of the same problem, because AI doesn’t just reward answer ownership. It systematically surfaces whoever owns the answer and sidelines whoever doesn’t.
What’s changed is the operational discipline required to execute it in an AI-first environment. Josh Grant at Stacked GTM calls this question mining: systematically identifying and owning the highest-stakes questions your buyers actually ask at each decision stage.
Where Sheridan’s framework focuses on building trust with human readers moving through self-directed research, question mining is designed for an environment where AI is constructing the answer on the buyer’s behalf before they ever visit your site.
The discipline works like this:
- Map the real questions buyers ask during discovery, evaluation, comparison, and post-purchase decisions.
- Source them from actual buyer behavior, not internal brainstorming – Sales call transcripts, support tickets, review sites, Reddit threads, and community discussions are the right inputs.
- Prioritize by revenue proximity and decision-shaping weight, not search volume.
- Build clear, structured answers that become the shared source of truth for marketing, sales, and the website.
Make sure those explanations are structured for AI extraction: direct answers up front, clear formatting, honest tradeoffs included. AI systems favor content that gives a complete, confident answer to a specific question. Hedged, meandering content doesn’t get cited.
The reframe that matters most for mid-market B2B teams: this discipline only works if your ICP clarity is strong enough to know whose questions matter most.
You can mine every question a buyer might ask. You can’t mine the right ones if you haven’t defined the right buyer. Scattered ICP assumptions produce scattered question coverage, which produces weak AI signals on the exact queries where strong signals would generate pipeline.
Question mining is a content strategy for companies that know who they’re for. If that clarity doesn’t exist yet, this work is a downstream fix to an upstream problem.
SEO and Web: Machine Legibility Beyond Google
SEO teams already think about how crawlers parse and index content. The extension into LLM-era thinking isn’t a departure from that work — it’s an expansion of the surfaces that matter and the systems being optimized for.
AI systems that power conversational research have their own crawling and indexing infrastructure. Some overlap with Google’s. Much of it doesn’t. A site that’s well-optimized for Google can still be difficult for AI systems to parse if the underlying structure makes it hard to extract clear, attributable claims.
The practical changes for SEO and web teams:
Verify AI crawler access. Check your robots.txt to confirm you’re not inadvertently blocking the crawlers that feed major AI platforms. This is a five-minute audit that many teams have never done.
Implement schema markup for the most AI-extractable content types:
- FAQs
- How-to content (articles)
- Case studies (articles)
- Product comparisons
- Include author information and publish date whenever possible.
Schema markup gives AI systems explicit signals about what a piece of content is and what claims it’s making. It’s structured data that functions as a translation layer between your content and AI retrieval systems.
Structure pages for semantic clarity. AI systems extract meaning from heading hierarchy, paragraph structure, and the relationship between claims and their supporting evidence. A page that’s visually clean but semantically flat — long paragraphs, no clear hierarchy, claims buried in the middle of blocks of text — is harder for AI to parse accurately than a page where the structure itself signals what matters.
The underlying principle: legibility to AI systems is the same thing as legibility to a thoughtful human reader who’s skimming for the main point. Content that requires deep reading to understand its core claim will lose to content that makes its claim immediately and then supports it.
Brand and Thought Leadership: Build the Signals AI Trusts
Brand and thought leadership content does two jobs in an LLM-mediated environment: it establishes topical authority that AI associates with your company, and it creates citable material that AI references when explaining your category or recommending your firm.
The “named framework” principle is central here. AI systems build a picture of a company’s expertise from the concepts it consistently introduces, names, and explains. For example, at Forge & Fathom we’re focused on owning the category of ‘B2B growth diagnostics‘ and have built a unique methodology called Fathom360™ to uncover issues in the growth engines of B2B companies.
Generic commentary on industry trends creates weak authority signals. Introducing a named concept — one you own, explain, and talk about consistently — creates strong signals. Every time the concept is referenced across your content library, the association between your company and that concept strengthens in AI’s training data.
For mid-market teams, this means being deliberate about the frameworks and language you’re introducing.
- What do you call the problem you solve?
- What do you call the pattern you’ve named that others haven’t?
- What’s the specific, ownable vocabulary your team uses consistently in content, sales conversations, and external appearances?
If the answer is “we don’t have one,” the thought leadership layer has no foundation to build from.
We Need to Talk About LinkedIn
The LinkedIn landscape shifted in 2026. Multiple studies from Semrush and Profound confirm that LinkedIn has become one of the most-cited domains in AI-generated responses to professional queries. It ranks as the top source for professional content across ChatGPT, Gemini, Copilot, and Perplexity.
More importantly, content citations (posts, articles, newsletters) now significantly outpace profile citations. AI systems are referencing what your leaders publish, not just who they are.
Frequent publishers, defined as those who published five or more posts in the previous four-week period, account for roughly three-quarters of all cited LinkedIn post authors. Consistency isn’t just an audience-building discipline anymore. It’s a citation-eligibility threshold. Fall below that publishing cadence and your content doesn’t enter the pool AI draws from.
For format, LinkedIn articles in the 500 to 2,000-word range are the most frequently cited.
The Strategic Implication For Mid-Market Companies
Your founders’ and leaders’ LinkedIn presence is now an AI-readable positioning layer that shapes how AI systems describe, recommend, and compare your company in response to buyer queries.
A leader publishing consistently with a clear, focused point of view builds compounding advantage across two surfaces simultaneously: human trust and AI citation.
A leader publishing consistently without that clarity makes the muddled signal louder. Volume without foundation doesn’t help. It entrenches the wrong picture.
Product Marketing: Structure the Evaluation Conversation
AI systems frequently serve as the first layer of vendor evaluation. When a buyer asks an AI tool to compare solutions in a category, the quality of that comparison depends almost entirely on the structured evaluation content that exists publicly.
Most product marketing teams create content for human readers moving through a self-directed research process. The LLM-era extension is to create content that helps AI construct accurate comparisons on your behalf.
This means building three specific content types:
Clear fit criteria. Explicit “we’re right for you if” and “we’re not right for you if” content. AI systems extract and use this kind of explicit fit framing when answering buyer evaluation questions. Companies that force buyers to infer fit criteria leave AI with less to work with, which means less precise representation in AI-mediated comparisons.
Structured comparison pages. If you don’t build the comparison against your top competitors, AI will construct one from whatever signal sources it has. Taking ownership of at least the framing of your most common competitive comparisons — your differentiation, the tradeoffs involved, the use cases where you win — gives AI structured, attributable content to reference.
Use-case-specific content. AI matches buyers to vendors partly by associating companies with specific use cases and buyer profiles. The more clearly you’ve documented which specific problems you solve for which specific buyers in which specific contexts, the more accurately AI performs that matching.
Product marketing is also where the ICP clarity dependency shows up most practically.
Fit criteria only work if you’ve defined fit.
Use-case content only surfaces the right buyers if you’ve mapped the right use cases to the right profiles.
The upstream clarity problem creates downstream AI accuracy problems, which is why we STRONGLY RECOMMEND the diagnostic comes before the optimization.
PR and External Signals: Third-Party Corroboration
AI systems build authority through corroboration. A company that says good things about itself gets one type of signal. A company that gets mentioned accurately and positively by third parties across multiple independent sources gets a different, stronger type of signal. AI weighs the latter more heavily.
For mid-market companies that don’t have massive analyst coverage or widespread press, this means being strategic about a smaller number of corroboration sources rather than trying to replicate an enterprise-scale PR program.
The highest-impact surfaces:
- Review platforms (G2, Capterra, TrustRadius) where actual customers describe your product in natural language that matches how buyers describe their problems
- Directory listings that reflect your current positioning and capabilities (not the version from two years ago)
- Partner pages and integration listings that confirm third-party relationships
- Contributed articles or podcast appearances that establish your team as subject-matter authorities in named publications.
The audit most mid-market companies have never done: systematically checking all of the places they exist as a named entity in public sources and comparing what those sources say to what the company says about itself.
The divergences are where AI’s muddled or outdated picture comes from. Closing that gap is less glamorous than producing new content, but it often has more immediate impact on AI accuracy scores.
Monitoring and Governance: Manage AI Perception Like a Strategic Asset
The functions above create and distribute signals. This function catches the drift.
AI perception of a company changes over time, both because training data is refreshed and because the competitive landscape shifts. A company that builds strong AI visibility in Q1 and doesn’t revisit it until Q4 will find the landscape has moved. Citations decay (quickly). Competitors publish new material. AI models update. The company that monitors continuously compounds its advantage. The company that audits once a year plays catch-up.
The Practical Monitoring Framework For a Mid-Market Team
Run structured AI queries monthly for the questions your buyers are most likely to ask at the discovery, evaluation, and comparison stages. Document what AI says about you. Track changes. This is a two-hour exercise that creates a monthly signal — not a comprehensive assessment, but a consistent pulse check that catches major drift before it compounds.
For teams that want a structured, scored baseline rather than a manual spot check, our AI360 Analyzer runs this across multiple AI platforms simultaneously and produces a report you can benchmark against month over month. Let us know if you’d like to take a look here.
When you run these tests, it’s important to evaluate accuracy specifically, not just inclusion. Being mentioned by AI is useful. Being mentioned accurately and with the current differentiation claims intact is what actually matters. If AI is describing your company using positioning language from 18 months ago, or attributing capabilities to you that you’ve since evolved, accuracy drift has started.
Track competitive positioning in AI-generated comparisons. What does AI say when asked to compare you to your top two or three competitors? Is the framing fair? Are the differentiators correctly identified? Are the tradeoffs accurately represented? This is the comparison that buyers are increasingly asking AI to construct for them before they ever visit your website.
For most mid-market marketing teams, this monitoring function doesn’t yet have an owner. It falls between the responsibilities of content, SEO, and brand — nobody’s job in the current org structure. That’s the organizational gap Post #5 named. The monitoring and governance function is what turns AI visibility from a project into a discipline.
The Compounding Logic
Each of these functions creates a different layer of AI signal. Content marketing establishes question ownership. SEO and web ensure machine legibility. Brand and thought leadership build topical authority. Product marketing structures the evaluation conversation. PR creates third-party corroboration. Monitoring catches drift before it becomes a deficit.
None of these functions are new. The work you’re already doing in each of these areas is creating AI signals whether you’re managing them or not. The shift is from accidental to intentional. From “our signals are going in” to “we know what signals are going in, and we’re checking what they’re producing.”
The companies that build this capability now — before their category gets competitive on AI visibility — will find the investment compounds. AI systems build authority through consistency and corroboration over time. The longer a company is consistently represented accurately across the surfaces AI draws from, the more embedded that representation becomes.
That’s the compounding logic the entire series has been building toward. AI is already a buyer research channel. Your next generation of buyers are already using it. The question is whether your company shows up in that research clearly, accurately, and competitively enough to make their shortlists.
The window is open. It’s narrowing. Build the capability while the building still takes less effort than catching up.
Want help building AI visibility into your marketing operating model?
Let’s talk about what that looks like for your team. If you want to start with measurement first, we’d be happy to run a FREE AI360 assessment to see where your company currently stands across visibility, accuracy, and competitive positioning.
