The B2B GEO Playbook: What Actually Gets You Cited by AI Buying Agents [2026]
Quick answer: To get cited by AI engines, measure your machine-query footprint in Search Console, lead every page with a direct 40-60 word answer containing a concrete number, publish verifiable pricing and stats (worth up to 40% more AI visibility per Princeton's GEO study), keep ranking top-3 (position 1 gets cited 43% of the time, position 7 gets 5%), and skip llms.txt — 97% of those files never get fetched.
Three weeks ago we published 28 days of data showing AI agents googling our product — a third of our search impressions came from queries no human typed, and they produced exactly zero clicks. The most common question we got back was: fine, so what do we actually do about it?
This is that post. A working GEO (generative engine optimization) playbook for B2B teams — except every step is backed by published evidence or our own Search Console data, because the GEO advice industry is currently about 80% vibes. Some of what vendors are selling demonstrably does nothing. Some boring things work extremely well. Here's how to tell them apart.
Why this is worth your time now
Two numbers from G2's March 2026 survey of 1,076 B2B software buyers ("The Answer Economy"):
- 51% of B2B software buyers now start their research in an AI chatbot, not a search engine.
- 69% chose a different vendor than they originally planned based on what the chatbot told them — and a third bought from a vendor they'd never heard of before the AI suggested it.
Meanwhile, Brandlight's tracking found the overlap between top Google results and AI-cited sources has collapsed from roughly 70% to under 20%. Ranking well on Google no longer guarantees you exist in the answer your buyer actually reads.
Our own data says the machine readers are already here: in our latest 28-day Search Console window (Aug 10 – Sep 6), queries of 8+ words — overwhelmingly assistant-generated — accounted for 32.9% of our 294,750 impressions and produced zero clicks. The evaluation is happening. You're just not seeing the visit.
Step 1: Measure your machine-query footprint (15 minutes)
Before optimizing anything, find out how much of your search footprint is already machine-read. In Search Console, open Performance, add a query filter with Custom (regex), and use:
^(what|which|how|where|why|can|is|are|does|do|should)\b
That catches question-form queries — the signature of AI assistants running query fan-out. Then compare CTR against your overall average.
Ours, for the last 28 days:
| Query type | Share of impressions | CTR |
|---|---|---|
| All queries | 100% | 0.13% |
| Question-form queries | 24.5% | 0.02% |
| Queries of 8+ words | 32.9% | 0.00% |
If your question-form bucket is piling up impressions with near-zero CTR, AI engines are reading you. That's not a problem to fix — it's the channel you're about to optimize. (You'll also find oddities in there: we get queries prefixed with "is it" grafted onto complete other questions, and queries carrying % and + operator artifacts. Those are other people's AI tools malfunctioning in public. Enjoy them.)
Step 2: Put a quotable answer in the first 100 words
The single highest-leverage change, and it costs nothing. When an AI engine fans a prompt out into sub-queries, it reads the top results and extracts whatever answers the question directly. A page that opens with context-setting throat-clearing gives the model nothing to extract.
The evidence: Princeton's GEO study (published at KDD, tested across 10,000 queries) found that the tactics that most improved visibility in generative engine answers were adding statistics, quotations, and explicit citations — each worth roughly 25-40% more visibility. Fluent, keyword-stuffed prose did nothing. Machine-extractable specifics did everything.
In practice, every high-intent page should open with a 40-60 word block that states the answer with at least one concrete number. We rebuilt our Close CRM pricing breakdown this way — it opens with the exact verified per-seat range and the date we verified it. That page now earns over 33,000 impressions a month on pricing queries, and when an AI engine answers "what does Close actually cost per rep," the extractable math is ours.
Step 3: Publish numbers competitors won't
AI engines cross-reference sources before citing them. Generic claims ("flexible pricing," "industry-leading accuracy") are unverifiable, so they're skipped. Specific claims get pulled into answers because they're what makes an answer worth synthesizing.
This is uncomfortable for B2B teams raised on "contact sales." But the SSRN cross-platform citation study found pages with concrete populated attributes — real pricing, real ratings, real specifications — were cited at substantially higher rates than pages with vague equivalents. It's also why our AI BDR tools comparison names actual prices and actual feature gaps for every vendor including ourselves, and why our visitor identification guide publishes tested match-rate ranges instead of "high accuracy."
The rule: if a claim can't be verified by a model reading five other sources, don't lead with it. If your real numbers are good, publishing them is now a distribution strategy, not a leak.
Step 4: Skip llms.txt — the evidence says it's theater
Half the GEO consultants on LinkedIn will sell you an llms.txt file this week. Here's what the data shows:
- Ahrefs analyzed server logs across 137,000 domains: 97% of llms.txt files received zero requests in the measured month. GPTBot, ClaudeBot, PerplexityBot, and Google-Extended crawl your HTML directly and don't even probe for the file.
- No major AI company — OpenAI, Google, Anthropic, Meta — has committed to reading it in production.
- Google's search team has said outright they don't support it and compared it to the keywords meta tag.
- One research team found that removing llms.txt as a variable from their AI-citation prediction model improved the model's accuracy.
It takes ten minutes and does no harm, so ship one if it makes a stakeholder happy. But if a vendor's GEO pitch leads with llms.txt, that tells you what the rest of the engagement will be worth.
Schema markup deserves similar skepticism in moderation: Ahrefs tracked 1,885 pages that added JSON-LD and found AI citations barely moved. Schema with real populated data (pricing, ratings, authorship, dateModified) helps engines trust and attribute your content; schema as an empty ritual does nothing. Fill the fields or skip the exercise.
Step 5: Keep winning at boring old SEO — position still decides citations
The most underreported finding in the AI-citation research: pages at position 1 got cited in 43% of the AI answers where they appeared; by position 7, that dropped to 5%. Each rank position costs you roughly a quarter of your citation odds.
GEO is not a replacement channel. The engines mostly read the same index Google ranks, which means the retrieval layer still runs on classic SEO: crawlability, internal links, topical authority, freshness. All the unglamorous work in our search intent study — matching page format to query intent — matters more now, because you're competing for a handful of citation slots instead of ten blue links.
Rank first. Get extracted second. There is no step where you skip ranking.
Step 6: Consolidate — thin content fails with machine readers too
AI engines synthesize across sources. A page that adds no unique data to the synthesis gets read and discarded. If you spent 2024-2025 shipping templated comparison pages (we did — about 680 of them), the machine reader is even less forgiving than Google's core updates were.
We deleted 161 posts in one day and consolidated the survivors into pillars like our signal-based selling guide and B2B intent data guide. Traffic went up. Fewer, denser pages concentrate your citable claims instead of scattering them across near-duplicates that dilute each other in retrieval.
Consolidation heuristic: if two of your pages could cite each other as sources, they should probably be one page.
Step 7: Change what you measure
If you grade AI-era content on clicks, you will kill exactly the pages doing the invisible work. Our question-form pages look like failures by CTR — 0.02% — while feeding answers to the agents building our buyers' shortlists. G2's data says AI now assembles the majority of B2B shortlists before a human ever visits a vendor site.
What we track instead:
- Citation spot-checks — monthly, we run our 20 highest-value buyer questions through ChatGPT, Perplexity, and Google's AI Mode and log who gets cited. Manual, 45 minutes, brutally clarifying.
- Branded search volume — the buyer who reads an AI answer and later googles your name is the click you earned but never attributed.
- Machine-query share (the Step 1 regex) — trending up means growing machine readership.
- Demo requests with "AI told me about you" — we ask on the booking form. It's no longer a rare answer, which matches G2's finding that a third of buyers purchase from vendors an AI introduced.
And remember the impression isn't the end of the funnel you control. The buyers an AI sends you arrive anonymous — identifying who's on your site and acting on it is how the invisible channel becomes pipeline. That part is literally our product.
The checklist
| Step | Action | Evidence |
|---|---|---|
| 1 | Measure machine-query share with the GSC regex | Our data: 32.9% of impressions, zero clicks |
| 2 | 40-60 word answer with a number in the first 100 words | Princeton GEO: up to +40% visibility from stats/citations |
| 3 | Publish verifiable pricing, rates, and limits | SSRN: concrete attributes cited at substantially higher rates |
| 4 | Skip llms.txt; only ship schema with real data | Ahrefs: 97% of llms.txt never fetched; schema barely moved citations |
| 5 | Keep ranking top 3 | Position 1 cited 43% vs 5% at position 7 |
| 6 | Consolidate thin pages into pillars | Our pruning case study: traffic up after deleting 161 posts |
| 7 | Track citations, branded search, and machine-query share | G2: 51% of buyers start in AI chat |
None of this is exotic. That's the point — the effective version of GEO is mostly disciplined content work aimed at a new reader, and the exotic version being sold as a service mostly doesn't survive contact with server logs.
The buyers your AI-era content wins arrive on your site anonymous. MarketBetter identifies them and tells your SDRs exactly what to do next — book a demo →