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Your Demand Gen Strategy Is Broken Above the Funnel

darkhorseMK-dev — Mon, 29 Jun 2026 19:31:40 +0000

Every B2B demand gen team is optimizing for a funnel that no longer exists at the top.

The awareness stage — the moment a buyer first realizes there's a category of solution that might address their problem — has moved. It used to happen on Google, in trade publications, at industry events, and through analyst reports. All of those channels still exist. But an increasingly large share of Stage 1 buyer behavior now happens in a single interaction: someone types a question into an AI system and reads the answer.

That answer is the new awareness layer. And most demand gen strategies have no plan for it.

What changed and when

The shift happened faster than most GTM teams noticed because it didn't show up in their attribution models. Buyers started using AI assistants for research in 2023 and 2024. By 2025, it was mainstream behavior among B2B buyers — particularly in technology, financial services, and professional services categories. By 2026, it's simply how buyers start.

The data point that crystallized this for me: in B2B tech, a buyer is now more likely to start a vendor evaluation by asking ChatGPT or Perplexity "what are the best [category] platforms for [use case]?" than by running a Google search. Not universally, not in every vertical, but consistently enough that the LLM shortlist has become the de facto awareness layer for a meaningful share of buyer journeys.

The brands on that shortlist get evaluated. The brands not on it don't exist for that buyer at that moment — not because the buyer chose to exclude them, but because the AI system didn't surface them.

Why this breaks demand gen math

Traditional demand gen is built on a model where you can influence each funnel stage with a specific set of tactics:

Awareness: SEO, paid media, content, PR, events
Consideration: Retargeting, nurture sequences, case studies, analyst coverage
Decision: Sales outreach, demos, proposals, references

The AI filter collapses the awareness stage into a single interaction. If an LLM generates a five-company shortlist in response to a buyer's query, the buyer often goes from "what are the options" to "which of these five should I evaluate" in one step. The consideration stage begins before your demand gen program has had any chance to reach that buyer.

This means:

Brands not on the AI shortlist are excluded from the evaluation before any marketing touchpoint occurs
The "awareness → consideration" gap no longer exists for buyers who start with AI
Attribution models that measure last-touch or even multi-touch don't capture AI-influenced shortlisting at all
Top-of-funnel content investment that isn't structured for LLM citation is not generating awareness for AI-first buyers

The LLM shortlist is not random

Here's what makes this tractable: the brands that appear in AI-generated shortlists are there for observable, structural reasons. LLMs generate recommendations based on citation density — how frequently and authoritatively a brand appears across the specific sources LLMs draw from during training and retrieval.

Those sources are measurable:

High-DA editorial publications (Forbes, trade press, analyst reports)
Reddit and community platforms (46.7% of Perplexity's top citations)
Wikipedia and knowledge graphs (top 3 LLM citation source)
G2 and Clutch (cited in 8–12% of B2B service queries)
LinkedIn (15% of Google AI Mode citations)
Owned content with answer-object structure and FAQ schema
GitHub and developer platforms (for technical queries)

A brand that appears consistently across all of these surfaces appears in LLM shortlists. A brand that appears on one or two doesn't. This is predictable, auditable, and fixable.

What this means for each demand gen channel

Paid media: Unaffected by AI awareness shift — paid channels still reach buyers who don't start with AI. But if your paid media drives traffic to a website that isn't optimized for LLM citation (no FAQ schema, no answer-object pages, weak domain authority), the investment in paid isn't building the citation density that would help you appear in AI-generated shortlists. Paid and organic AI visibility are more interdependent than they look.

Content marketing: The unit of content that drives LLM citation is different from the unit of content that drives traditional SEO. Traditional SEO rewards long-form, comprehensive content. LLM citation rewards declarative, answer-object-structured content — pages that open with a direct answer to a specific buyer query, structured with FAQ schema so AI systems can extract individual question-answer pairs. Most content teams are optimizing for the wrong format.

PR and earned media: Earned media is the highest-weight LLM training signal available. But the format matters. A brand mention in a high-DA publication is a citation signal regardless of the article's structure. A byline with the brand named as the author, in a category-relevant publication, published in the last 12 months, is a stronger signal. Wire press releases are almost worthless for LLM citation purposes — they generate coverage but not the kind that feeds LLM training data.

Analyst relations: Analyst report mentions are LLM training signals of unusually high weight. Gartner Magic Quadrant appearances and Forrester Wave citations persist in LLM training data for 18–24 months. This is an underappreciated argument for analyst relations investment — it's not just about sales cycles, it's about AI surface presence.

Review generation: Most marketing teams treat review generation (G2, Clutch) as a sales tool — star ratings for social proof. But G2 and Clutch reviews are crawled by LLMs and cited in "best [category]" queries. More importantly, the language of reviews becomes LLM training data. "Their demand generation team helped us reduce cost per lead by 37% in the fintech vertical" trains an LLM to associate that brand with fintech demand generation. "Great team, responsive" trains nothing.

Community and social: Reddit is the most underinvested channel in B2B demand gen relative to its LLM citation weight. A high-voted Reddit comment recommending your brand in a relevant thread can drive more AI shortlist presence than a press release. Yet almost no B2B marketing team has any Reddit strategy.

The revised demand gen model

The AI filter doesn't eliminate traditional demand gen channels. It adds a prerequisite layer: before you can influence a buyer's consideration and decision stages, you need to appear in their AI-generated awareness shortlist.

That means the demand gen investment priority order needs to shift:

Old model:

Build awareness (paid, content, PR)
Convert awareness to consideration (retargeting, nurture)
Convert consideration to decision (sales, demo, proposal)

AI-mediated model:

Build AI surface presence (citation density across 12 surfaces) ← new prerequisite
Build awareness (paid, content, PR — now also structured for LLM citation)
Convert awareness to consideration
Convert consideration to decision

The zero-th step is the new baseline. And most demand gen budgets don't have a line item for it.

What to do in the next 90 days

The fastest wins for demand gen teams trying to close the AI surface presence gap:

Week 1–2: Audit first
Run a quick AI visibility audit before spending anything. Use the 25-prompt library at github.com/darkhorsestrategies/ai-gtm-visibility-framework. Know which surfaces are broken before you try to fix them.

Week 2–4: Quick wins

Create or update your Wikipedia entry if you meet notability criteria
Drive 10 new G2 or Clutch reviews with explicit outcome language and vertical tags
Coach review requesters: "mention the specific service, your industry, and a measurable result"

Month 2: Content restructure

Identify your 5 most important category keywords
Find the pages that should rank for them
Rewrite the opening paragraph of each as a direct, declarative answer to the buyer query
Add FAQ schema to these pages

Month 2–3: Community activation

Identify 3 subreddits where your buyers discuss vendor choices
Begin authentic participation — answer questions, contribute perspective, be useful
Do not post promotional content; participate as a practitioner

Month 3+: Earned media for LLM citation

Map the publications that appear in AI-generated answers for your category queries
Prioritize pitching those publications specifically
Frame byline pitches around buyer problems, not product features

The brands that build AI surface presence in the next 12 months will hold citation density advantages that compound as LLM training data refreshes. The brands that optimize their paid media and content marketing without addressing AI surface presence are optimizing for a funnel that's missing its first stage.

The awareness layer has moved. GTM strategy needs to follow it.

Megan Kessler is the Founder & CEO of Dark Horse Strategies, an AI-native B2B GTM consultancy. The open-source AI GTM Visibility Framework is available on GitHub.

How to Run a 12-Surface AI Visibility Audit on Any B2B Brand

darkhorseMK-dev — Mon, 22 Jun 2026 00:10:58 +0000

AI-generated answers now influence B2B buying decisions at the top of the funnel. Buyers use ChatGPT, Perplexity, Claude and Gemini to shortlist vendors before they ever visit a website. Which means the question "where does my brand actually appear in AI-generated answers?" is now a critical GTM intelligence question.

Most brands don't know the answer. This post gives you the methodology to find out.

What an AI visibility audit is — and isn't

An AI visibility audit is not a technical SEO audit. You're not looking for broken links, page speed issues, or crawl errors (though those matter for a related reason). You're auditing citation density — how frequently and authoritatively your brand appears across the specific sources that LLMs draw from when generating recommendations.

Those sources break down into 12 distinct surfaces. A brand can score 9/10 on one surface and 1/10 on eleven others — and the aggregate result is a brand that appears for some queries and not others in ways that seem inexplicable but are structurally predictable.

The goal of this audit is to make that structure visible.

The 12 surfaces — quick reference

Before diving into the methodology, here's the full surface map:

#	Surface	Primary platforms
1	AI Interfaces	ChatGPT, Perplexity, Gemini, Claude, Copilot
2	Search + AI Search	Google AI Overviews, Bing Copilot, Perplexity Search
3	Reviews + Reputation	G2, Clutch, Capterra, industry-specific platforms
4	Earned Media + Publishers	Trade press, business press, analyst reports
5	Owned Content + Website	Brand site, blog, schema markup, answer-object pages
6	Technical + Developer	GitHub, dev.to, Stack Overflow, Hacker News
7	Social + Authority	LinkedIn, executive publishing, award citations
8	Data + Knowledge Graphs	Wikipedia, Wikidata, Crunchbase, ZoomInfo
9	Marketplaces + Ecosystems	Clutch category pages, Agency Spotter, partner directories
10	Case Studies + Proof	Published case studies, award wins, client outcomes
11	Community + Q&A	Reddit, Quora, LinkedIn groups, Slack communities
12	Enterprise + Private AI	Microsoft 365 Copilot, Salesforce Einstein, Perplexity Teams

Full scoring rubrics for each surface are in the Dark Horse AI GTM Visibility Framework on GitHub.

Step 1: Define your query set (20 minutes)

Before running anything, define the queries your buyers actually use. The audit is only as useful as the prompts you test.

Build a query set with three layers:

Layer 1 — Category queries (how buyers discover the space)

"Best [category] solutions for [ICP]"
"What is [category] and who are the leaders?"
"[Category] companies compared"

Layer 2 — Problem-first queries (how buyers frame their need)

"How do I [problem your product solves]?"
"What should I use to [job to be done]?"
"[ICP role] looking for [outcome] — what do you recommend?"

Layer 3 — Brand queries (how buyers verify you specifically)

"What is [your brand] and what do they do?"
"Is [your brand] reputable?"
"Compare [your brand] with [top competitor]"

You should have 8–12 queries total before you start testing. More than that and the audit becomes unwieldy; fewer and you'll miss important visibility gaps.

Step 2: Run Surface 1 (AI Interfaces) — 45 minutes

This is the core of the audit. Run your full query set across at least three LLMs:

Platforms to test: ChatGPT (GPT-4o), Perplexity, Gemini 1.5 Pro, Claude, Microsoft Copilot

For each query on each platform, record:

Did your brand appear? (Y/N)
Position (1st mention, 3rd mention, not mentioned)
Language used to describe your brand (copy the exact words)
Sources cited (when the LLM shows them)
Competitor brands mentioned in the same response

Interpretation:

Not appearing in category queries → Surface 4 (Earned Media) or Surface 8 (Knowledge Graph) gap
Appearing with wrong/stale description → Surface 5 (Owned Content) or Surface 8 (Knowledge Graph) gap
Appearing for branded but not category queries → Surface 4 + Surface 11 (Community) gap
Appearing in some LLMs but not others → Surface-specific signal issues; check which sources each LLM favors

Step 3: Run Surface 2 (Search + AI Search) — 20 minutes

Open Google and Bing. For each of your Layer 1 category queries:

Note whether a Google AI Overview appears (the AI-generated summary box above organic results)
If an AI Overview appears, note whether your brand is mentioned
Note your organic rank for the same query
Switch to Bing and run the same queries; note Copilot's answer in the sidebar

Key diagnostic: If you rank page 1 organically but don't appear in the AI Overview for the same query, your content is not structured as an answer-object. The AI Overview pulls from page content that opens with a declarative answer paragraph. Your page likely opens with brand narrative instead.

Quick fix test: Find a page on your site that ranks page 1 for a category query. Look at the first 100 words. Does it directly answer the query? If it starts with "We are a leading provider of..." rather than "[Category] is a [definition] that helps [buyer type] achieve [outcome]..." — that's your problem.

Step 4: Run Surfaces 3, 9, and 10 (Reviews, Marketplaces, Proof) — 30 minutes

These three surfaces are observable without LLM prompting.

Surface 3 (Reviews):

Search your brand on G2, Clutch, Capterra
Count total reviews
Read the language: do reviews contain service-specific terminology and outcome metrics? Or generic praise?
Note vertical/category tags on your profile
Check whether a competitor has significantly more reviews or better vertical coverage

Surface 9 (Marketplaces):

Search your brand on Clutch, Agency Spotter, The Manifest, UpCity, relevant partner directories
Check whether profiles are claimed and complete
Note which category tags and service tags are applied
These tags are the literal words LLMs use to categorize your brand for buyer queries

Surface 10 (Case Studies):

Count published case studies on your owned site
Assess format: do they include outcome metrics? Named clients? Problem → approach → result structure?
Note whether acquired or predecessor brand proof points are captured under your current brand
Check award wins: are they published on the site in text format (not just badge images)?

Step 5: Run Surface 8 (Knowledge Graph) — 15 minutes

Wikipedia check:

Search "[your brand] site:wikipedia.org"
If no entry exists, assess notability criteria: do you have coverage in multiple independent reliable sources? If yes, you meet the standard
If an entry exists, verify it's current and accurate

Wikidata check:

Search your brand at wikidata.org
If no entry exists, this is a gap — Wikidata feeds Google's Knowledge Graph

Google Knowledge Panel:

Google your brand name
Does a Knowledge Panel appear on the right side? What information does it show?
Stale or missing information indicates a Wikidata/structured data gap

B2B data platforms:

Check Crunchbase, ZoomInfo, Apollo for your brand
Verify: description accuracy, service/category taxonomy, founding date, employee count
These are source layers for AI-generated company research summaries

Step 6: Spot-check Surfaces 7 and 11 (Social and Community) — 20 minutes

Surface 7 (LinkedIn/Social):

Note your LinkedIn company page follower count
Check your CEO/founder's LinkedIn posting frequency (last 30 days)
Count LinkedIn posts that include explicit category and vertical positioning language
Search for your brand in Twitter/X (even minimal brand mention signal matters)

Surface 11 (Reddit):

Search Reddit for your brand name: site:reddit.com "[your brand]"
Search for your category + recommendation: site:reddit.com "[category] recommendations" and note which brands appear
Are you mentioned organically by community members? Or only when someone specifically asks about you?
This is the fastest-changing surface to improve — one high-voted thread can move LLM citation presence within weeks

Step 7: Score all 12 surfaces and identify priorities

Using the scoring worksheet from the framework repo, score each surface 1–10 based on your observations.

Then categorize:

Tier 3 (1–3): Your 30-day priorities. These are structural blockers — brands that are invisible on these surfaces are missing from buyer shortlists entirely.
Tier 2 (4–6): Your 60–90 day investments. Present but not competitive — surface improvements here compound over time.
Tier 1 (7–10): Protect these. Maintain publishing cadence, review velocity, and content freshness.

Common patterns and what they mean

Pattern 1: Strong Surface 5, Weak Surface 4 and 11
The brand has good owned content but weak editorial coverage and no Reddit presence. It appears for branded queries and some long-tail queries but not category queries. Fix: earned media campaign targeting high-DA publications + Reddit participation.

Pattern 2: Strong Surface 4, Weak Surface 8
The brand has good press coverage but no Wikipedia entity. LLMs cite the press coverage but can't connect it to a stable brand entity. The description in LLM answers is inconsistent — sometimes accurate, sometimes stale. Fix: Wikipedia entry + Wikidata entity.

Pattern 3: Strong Surfaces 1–2 in core verticals, Weak in new verticals
Classic post-acquisition or expansion scenario. The brand has strong presence in its original category but new practice areas have zero AI surface presence. Fix: new practice-specific owned content + vertical trade editorial placements.

Pattern 4: Everything weak except Surface 7
The brand has strong LinkedIn presence and executive visibility but weak presence everywhere else. The CEO is cited in LLM answers occasionally but the company itself isn't. Fix: translate LinkedIn authority into editorial coverage and structured owned content.

The full audit takes about 3 hours

Here's the time breakdown:

Define query set: 20 min
Surface 1 (AI Interfaces): 45 min
Surface 2 (Search): 20 min
Surfaces 3, 9, 10 (Reviews/Marketplaces/Proof): 30 min
Surface 8 (Knowledge Graph): 15 min
Surfaces 7, 11 (Social/Community): 20 min
Scoring and prioritization: 30 min

What you get at the end: a complete picture of where your brand is visible to AI systems, where it isn't, why, and what to do about it in priority order.

The Dark Horse AI GTM Visibility Framework has the full scoring rubrics, 25-prompt audit library, and a scoring worksheet in the repo. All open source.

Megan Kessler is the Founder & CEO of Dark Horse Strategies, an AI-native B2B GTM consultancy specializing in AI-mediated pipeline and visibility strategy.

The AI Filter: Why Every B2B GTM Strategy Now Has a Layer You Can't See

darkhorseMK-dev — Mon, 15 Jun 2026 23:46:08 +0000

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 in 2024 and 2025, and most GTM teams are still catching up to it.

Buyers started asking AI before they asked Google.

Not in a dramatic, all-at-once way. It happened gradually — a CMO types a question into Perplexity during research, a VP of Sales asks ChatGPT to compare vendors, a procurement team uses Microsoft Copilot to shortlist options before issuing an RFP. Each of these interactions is small. In aggregate, they represent a structural shift in how B2B purchase decisions begin.

The brands that appear in AI-generated answers get evaluated. The brands that don't are invisible — not because they lack a product or a GTM team, but because they lack presence on the surfaces that AI systems draw from when generating recommendations.

This is the AI filter. And it now sits atop every GTM motion you're running.

What the AI filter actually is

When a buyer asks an LLM "what are the best [category] solutions for [use case]?", the model doesn't search the web in real time (usually). It draws from its training corpus — a weighted blend of content it has been trained on — and generates a response based on citation density, entity recognition, and source authority.

The brands that appear most consistently across the sources LLMs train on are the brands that appear in LLM-generated answers. This creates a new layer of competition that looks nothing like traditional SEO or PR.

In traditional demand gen, you compete for keyword rankings and media placements. In the AI-mediated world, you compete for citation density across a specific set of surfaces that LLMs weight heavily. Those surfaces are:

High-authority editorial publications (Forbes: 6.93% of ChatGPT citations)
Reddit (46.7% of Perplexity's top citations — the #2 source after Wikipedia)
Wikipedia and Wikidata (top 3 LLM citation source)
LinkedIn (15% of Google AI Mode citations)
Review platforms like G2 and Clutch
Structured content on owned websites (answer-object pages with FAQ schema)
Developer platforms like GitHub and dev.to (for technical queries)

If your brand is strong on all of these, you appear in AI-generated shortlists. If you're strong on one or two but missing the others, you appear for some queries and not others — often in ways that seem random but are actually structural.

Why this isn't just an SEO problem

The temptation is to frame the AI filter as "SEO, but for LLMs" — optimize your content, add some schema markup, done. That's partially right but mostly wrong.

Traditional SEO is a single-surface problem. You optimize for Google's ranking algorithm, and you rank or you don't. The AI filter is a 12-surface problem. LLMs pull from editorial content, community platforms, knowledge graphs, review sites, developer documentation, and social authority signals — all simultaneously. A brand that has great owned content but no Reddit presence, no G2 reviews, and no Wikipedia entity will underperform compared to a brand with mediocre owned content that is mentioned consistently across all twelve surfaces.

This is why traditional GTM playbooks don't map cleanly to the AI-mediated world. The unit of competition has changed from the keyword ranking to the citation signal.

What breaks first

From auditing AI surface presence across dozens of B2B brands in the past year, the gaps that show up most consistently are:

The Wikipedia gap. A huge number of B2B companies — including well-funded ones — don't have Wikipedia entries despite clearly meeting notability criteria. Wikipedia is among the top 3 LLM citation sources. Not having an entry is like not having a website in 2010.

The Reddit gap. Almost no B2B brand has any meaningful Reddit presence, despite Reddit comprising nearly half of Perplexity's top citations. The companies that appear in Perplexity answers for category queries are disproportionately the ones whose customers and communities discuss them on Reddit.

The review language gap. Most brands approach review generation as a star-rating exercise. But LLMs don't just cite that reviews exist — they extract the language of reviews. A review that says "their team helped us reduce cost per lead by 40% in the fintech vertical" trains the LLM to associate that brand with fintech demand generation. "Great to work with!" trains nothing.

The knowledge graph gap. Brands that have been acquired, rebranded, or merged often have stale or fragmented entity data across Crunchbase, ZoomInfo, Wikidata, and Google's Knowledge Graph. LLMs drawing from this data describe the brand as it was, not as it is.

The developer surface gap. No B2B services firm — agency, consultancy, or SaaS company — has any meaningful GitHub or dev.to presence. This is the least competitive surface of all 12 for B2B brands. Any brand that publishes substantive content there faces essentially no competition for LLM citations on those platforms.

The GTM reframe

Every channel in a traditional B2B GTM motion has an AI-mediated equivalent:

Traditional GTM	AI-Mediated equivalent
SEO → rank for keywords	AEO/GEO → get cited in AI-generated answers
PR → earn media coverage	PR + Citation Engineering → structure coverage for LLM extraction
Review generation → star ratings	Review generation + taxonomy tagging → LLM-citable category signals
Thought leadership → LinkedIn posts	Thought leadership + Answer-object pages → LLM training signal
Analyst relations → Gartner mentions	Analyst relations + Wikipedia entity → Knowledge graph presence
Demand gen → paid + content	AI surface presence first, then paid amplification

The AI filter doesn't make traditional GTM obsolete. It sits on top of it. But it changes the priority order, the success metrics, and the content formats that drive results.

How to audit your current AI surface presence

The fastest way to understand where you stand is to run five prompts right now:

Open ChatGPT, Perplexity, and Gemini in separate tabs
In each, run: "What are the best [your category] solutions for [your ICP]?"
Note whether your brand appears, at what position, and what language is used
Run: "What is [your brand name] and what do they do?"
Run: "Compare [your brand] with [your top competitor]"

If you don't appear in responses 1–2, you have a critical AI interface gap. If you appear but the description is wrong or stale, you have a knowledge graph gap. If you appear for branded queries but not category queries, you have an earned media gap.

For a structured 12-surface audit framework — including scoring rubrics, root cause diagnostics, and a 25-prompt audit library — see the Dark Horse AI GTM Visibility Framework on GitHub.

What to do about it

The 90-day activation sequence that moves the needle fastest, in priority order:

Days 0–30 (quick wins):

Create or update your Wikipedia entry (if you meet notability criteria — most established B2B companies do)
Drive 10–15 new reviews on Clutch or G2 with explicit outcome language and vertical tags
Publish 2–3 answer-object pages for your primary categories with FAQ schema
Begin authentic Reddit participation in your 3 most relevant subreddits

Days 30–60 (structural):

Restructure your top 10 owned content pages with declarative openings and FAQ schema
Update all B2B data platform profiles (ZoomInfo, Apollo, Crunchbase) with current descriptions and category taxonomy
Activate LinkedIn publishing cadence for CEO/senior leadership with category-positioning language
Migrate any acquired brand's proof layer (case studies, awards) to the primary brand's domain

Days 60–90 (compounding):

Publish a GitHub repository with your framework, methodology, or prompt library
Begin pitching vertical trade publications for byline placements
Draft and schedule Quora answers for your 5 most common buyer questions

The brands that invest in this now, before AI-mediated buying fully matures, will hold citation density advantages that compound for years. The brands that wait will find themselves playing catch-up in an environment where LLM training data is already set.

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.