Dark Horse
Aug. 25, 2026
AI systems now sit between your brand and your buyers. Here's what that means for every GTM motion you're running — and what to do about it.
Something changed in B2B buying over the past two years, and most GTM teams are still catching up to it.
Buyers started asking AI before — or alongside — Google.
This happened gradually: a CMO uses Perplexity during market research, a VP of Sales asks ChatGPT to compare vendors, a procurement team uses Microsoft Copilot to build an initial shortlist, or a buyer asks Gemini to explain the differences between competing approaches.
Each interaction is small but, in aggregate, they represent a structural change in how B2B purchase decisions begin.
The brands that appear, are described accurately and are supported by credible sources have an advantage. The brands that don't may never make the initial consideration set.
This is the AI filter that now sits above every GTM motion you're already running.
The first thing GTM leaders need to understand is that modern AI systems do not rely on one universal database of information.
An AI answer can be influenced by several layers:
Model knowledge — information encoded during pretraining and post-training from public web content, licensed datasets, code, documents, synthetic data and other sources.
Search and retrieval indexes — information located when an AI system searches the web or another index in response to a user's question.
Live web retrieval — individual pages fetched at answer time.
Licensed and partner data — content made available through commercial agreements, APIs or platform partnerships.
Structured entity data — information that helps systems resolve what a company, product, person or category actually is.
That distinction matters a lot.
Marketers are now catering to two unique buyers - the human prospect and the AI systems working to discover, understand, verify and retrieve a brand.
And because different AI engines retrieve differently, there is no single universal set of sources to optimize for.
Large-scale 2026 citation analyses consistently show substantial differences among ChatGPT, Gemini, Perplexity, Claude, Copilot and Google AI products. One recent cross-engine study found that roughly three-quarters of cited sources appeared on only one engine, while another found only 3.8% of sources were shared by ChatGPT, Gemini, Perplexity and Google AI Overviews for the same prompts.
This means AI visibility is not about dominating one website.
It is about establishing enough corroborated digital evidence that multiple systems can independently arrive at the same conclusion about your brand.
Your own domain has become more important, not less.
Current AI systems can retrieve company websites directly, and some engines disproportionately rely on first-party sources for product and company questions. The most useful pages are not generic marketing copy, but are pages containing explicit, extractable facts:
Structure matters because retrieval systems often extract small passages rather than interpreting an entire website.
The objective is to create answerable content, not simply searchable content.
Third-party validation remains one of the strongest ways to establish what a company is known for.
Trade publications, national media, respected industry sites, associations, research organizations and credible specialist publishers create independent corroboration.
This matters when AI systems are answering questions such as:
“Who are the leaders in this category?”
“What companies specialize in X?”
“What is this company known for?”
“What alternatives should I evaluate?”
Earned authority cannot be manufactured on a brand's own domain — which is why it is so valuable.
Wikipedia and Wikidata remain important because they help machines resolve entities, relationships, names, founders, acquisitions, locations and categories.
But Wikipedia should not be treated as a marketing channel.
Companies should only have articles when they independently satisfy Wikipedia's notability requirements, and company representatives should follow Wikipedia's conflict-of-interest policies rather than creating promotional entries themselves.
Beyond Wikipedia, consistent entity information across authoritative company profiles, business directories, industry databases and the brand's own structured markup helps reduce ambiguity.
The objective is to make sure the web agrees about who you are. Acquisitions, rebrands and other company milestones can confuse AI systems when it comes to entity identity, so make sure that updating these properties becomes a routine part of those efforts.
Review sites such as G2, Gartner Peer Insights, Capterra, Clutch and relevant industry-specific platforms can provide highly valuable buyer-language signals.
What matters most now is accumulating authentic customer evidence describing:
AI systems increasingly retrieve specific claims and passages. A detailed review containing real implementation context conveys substantially more useful information than a generic five-star endorsement.
YouTube deserves significantly more attention than it received in the original AI visibility playbook.
Video itself is only part of the opportunity. Titles, descriptions, chapters, captions and transcripts create a machine-readable information layer around the video.
Emerging 2026 citation research shows YouTube appearing frequently across AI-search results, particularly throughout Google's ecosystem and for explanatory, instructional and experiential queries.
For B2B brands, expert interviews, demonstrations, explainers, executive conversations and research commentary can therefore create both a human engagement asset and an AI-retrievable knowledge asset.
LinkedIn remains strategically valuable for B2B authority, but it should not be described as a universal LLM training surface.
Its importance comes from a combination of professional identity, executive authority, public content, search discoverability and the broader Microsoft ecosystem.
LinkedIn itself also uses member content in certain circumstances to train its own and affiliated generative-AI systems, subject to geography, settings and opt-outs.
The implication for brands is simple: executive expertise should be consistently associated with the categories and problems the company wants to own.
But LinkedIn should complement — not replace — durable, indexable content on owned and earned domains.
For technical categories, GitHub remains unusually valuable.
Repositories, documentation, examples, benchmarks, integrations and open-source frameworks produce highly structured, factual material that is easy for machines to interpret.
That does not mean every B2B company needs a GitHub strategy.
A consulting firm publishing a substantive methodology, benchmark dataset, evaluation framework or useful tool may have a reason to be there.
A brand publishing an empty repository purely for “GEO” does not.
Reddit needs to be treated very differently than it was even a year ago.
Reddit has continued to restrict unauthorized scraping and general API access as it protects its data and moves more access through controlled, licensed and platform-specific channels.
At the same time, Reddit has not disappeared from AI answers altogether. Its content can still enter some AI experiences through search indexing, licensed arrangements and other approved pathways. Research in 2026 has documented Reddit content appearing in Google's AI products.
But its value is volatile and model-specific.
In fact, Reddit's share of ChatGPT Search citations fell sharply during July and August 2026, illustrating exactly why brands should not build an AI-visibility strategy around any single community platform.
Rather, treat Reddit as earned community evidence.
If real customers are discussing your category there, understand those conversations and engage with valuable insight. This requires commitment and skilled practitioner involvement.
The temptation is to frame all of this as “SEO for LLMs.”
That's too narrow.
Traditional SEO asks:
How do I make this page rank?
AI visibility asks:
If a machine tries to answer a buyer's question about my market, what evidence will it find — and will independent sources agree about the answer?
That creates a fundamentally different competitive environment.
A brand may have:
Excellent SEO, but weak third-party validation.
Strong PR, but unclear product pages.
Hundreds of reviews, but inconsistent category positioning.
Strong executive thought leadership, but no durable owned evidence.
Great content, but contradictory company descriptions across the web.
The AI system has to reconcile all of those signals.
The strategic unit is therefore not the keyword.
It is the entity + claim + evidence relationship.
You want machines to encounter the same association repeatedly:
Brand → category → use case → audience → evidence → outcome
across credible, independently useful sources.
Across B2B brands, several AI visibility fixes are particularly important.
Companies that have been acquired, renamed, repositioned or merged often leave conflicting information across the web. Before creating more content, ensure your company narrative is correctly reflected (and updated) across all knowledge graph surfaces.
Many companies make strong claims on their own websites but have almost no independent evidence supporting them. PR, customer reviews, analyst coverage, awards, original research and industry participation now form a critical machine-readable proof layer.
A beautifully written 1,500-word thought-leadership article may contain the answer an AI system needs without ever stating it cleanly. AI-retrievable content benefits from clear definitions, declarative statements, comparison tables, FAQs, explicit statistics and well-structured sections.
The AI filter does not replace traditional GTM, but it does change what your existing programs are building: a credibility ecosystem from which humans and machines form an opinion of your brand.
Do not test only one engine.
Build a consistent set of buyer-intent prompts and run them across:
Test three different classes of questions:
Category discovery
“What are the best [category] companies for [specific buyer/use case]?”
Entity understanding
“What is [brand] and what does it specialize in?”
Competitive evaluation
“Compare [brand] with [competitor] for [specific use case].”
Then capture more than ranking.
Track:
Mention — Did the brand appear?
Description — Was it described correctly?
Positioning — What categories and attributes were associated with it?
Evidence — What sources supported the answer?
Competitive set — Which brands appeared around it?
Consistency — Did the answer change materially across engines?
That produces a much more useful diagnostic than asking whether you “rank in ChatGPT.”
The fastest 90-day program starts by fixing the evidence layer.
The competitive advantage comes from creating a brand environment in which multiple independent systems repeatedly reach the conclusion you want buyers to reach.
And it is why AI visibility belongs inside GTM strategy — not as a side project owned exclusively by SEO.
Megan Kessler is the Founder & CEO of Dark Horse Strategies, an AI-native B2B GTM consultancy. The AI GTM Visibility Framework referenced in this post is open-source and free to use.