April 2026 · Version 2.0

Acknowledgements

This guide is a product of practice, not just theory. The ideas in it were shaped by conversations, challenge, peer review, and the generosity of colleagues who gave their time and thinking freely. I am grateful to the following individuals for their contributions.

Peer Reviewers

The following individuals reviewed drafts of this guide and provided feedback that materially improved its accuracy, clarity, and practical value:

 Contributors & Advisors

The following individuals contributed ideas, data, challenges, or encouragement that shaped the thinking in this guide:

• Ariane Redder, VP Marketing, Graphisoft  Community

This guide was presented to and improved by members of the following marketing communities. Their questions, pushback, and real-world experience made it more useful.

 A note on attribution: Inclusion in this section reflects a contribution to the development of this document. It does not imply endorsement of its conclusions, which remain the author’s own.

Ron Close

Budapest · April 2026

Table of Contents

1. Introduction
2. Preface: Why This Matters Now
3. The Reality of Zero-Click
4. Section 1 — Discovery: The Digital Trust Stack
   1. AI Answers via Direct SourcingMedia Publications
   2. Indirect Authority Signal via Social Media
   3. Structured Sourcing via Review Platforms
   4. Discovery Checklist
5. Section 2 — Measurement: Building the AEO Dashboard
   1. The Core AEO Metrics
   2. Building the Prompt Testing System
   3. Testing Protocol
   4. Dashboard Models
   5. Data Driving Improvement
   6. Measurement Maturity Model
   7. Measurement Checklist
   8. Understanding the Platforms: ChatGPT vs. Gemini
   9. ChatGPT: Training-Data-First
   10. Gemini: Retrieval-First
   11. The Critical Difference: Determinism vs. Dynamism
   12. What This Means for Your Measurement Dashboard
   13. Case Study 1 — AEO in Practice: MES Market
   14. How We Ran It
       1. Finding 1: The Two Platforms Behave Completely Differently — and Require Different Strategies
       2. Finding 2: ChatGPT Cited Zero Sources. Gemini Cited Sources in 94% of Responses
       3. Finding 3: ChatGPT Is Dominated by Incumbents — and the Concentration Is Extreme
       4. Finding 4: Gemini Is a Genuinely Open Playing Field for Smaller Vendors
       5. Finding 5: ChatGPT Appears to Rotate Vendor Order — and This Matters for How You Measure Success
       6. Finding 6: Geographic Query Origin Influences Gemini Results
   15. What This Means for Your AEO Strategy
   16. Case Study 2 — AEO in the Contested Middle: CAD Market
   17. How We Ran It
       1. Finding 1: SOLIDWORKS Is the Unchallenged Default — But Its Nature Differs by Platform
       2. Finding 2: The CAD Citation Ecosystem Is Dominated by Resellers and Aggregators — Not Trade Press
       3. Finding 3: The Discovery Gap — Onshape and Shapr3D Tell Different Stories
       4. Finding 4: The Adjacent Product Universe Is Larger Than Expected
   18. Finding 5: The Geography Effect Is Market-Dependent
6. What This Means for CAD Vendor AEO Strategy
7. Cross-Study Observations: What Two Studies Tell Us
8. Section 3 — The Authority Model: Keywords & Beyond
   1. Jump Start — Let AI Help You Build the Prompt List
   2. The Foundational Reality — Keywords Still Matter.
   3. Why Keyword Seeding Alone No Longer Works
   4. The Four Shifts in Keyword Strategy for AEO
   5. Operationalizing Semantic Breadth: Use “Analyst Vernacular”
   6. From Keyword Lists to Prompt Maps
   7. Rebalancing Your Keyword Strategy
   8. Keyword Checklist
9. Section 4 — Getting Started: Quick Wins Top Ten
   1. A Forward Look: MCP and the Next Layer of AI Visibility
   2. What This Means in Practice
10. Glossary
11. Appendix 1 — Master Prompt Template
12. Sources
13. BibliographyFootnotes Used In-Text

Introduction

As a B2B marketing leader, you’ve likely heard this from your stakeholders: “What are you doing to optimize our website for AEO (Answer Engine Optimization)?” Over the past year, I’ve heard this question from CEOs, Sales Leaders, Investors, Advisors, and my wife. The question is not just about the website. It invites a review of your entire marketing strategy.

I reached for answers on ChatGPT and other sites. There’s a plethora of interesting information out there, but I was missing an organized approach to operationalizing and measuring AEO.

Hence, this guide.

I created the first version of this guide based on theory and knowledge I had acquired. But I wanted to put it to the test, and I used the ideas in this guide on a real company to develop an AEO strategy. The results were quite useful, so I ran structured analyses across two markets — Manufacturing Execution Systems (MES) and CAD software — to produce findings that are shareable and comparative. In doing so, I uncovered significant differences between the two most-used tools — ChatGPT and Gemini — as well as meaningful differences between markets.

Why focus on ChatGPT and Gemini — and not the others?

At the time of writing, ChatGPT held a 60% share of volume, with Gemini creeping up at 13%¹. It’s important to note that Gemini’s growth was at the expense of ChatGPT. AI tools are evolving, but I hope that the basics of this methodology will be useful for any general-use AI tool you’d like to focus on for your marketing efforts.

I am not an expert. This guide is what I have gathered and put into practice.

Feel free to skip around. I’ve organized this guide as follows:

1. Why it’s important
2. Digital Trust Stack
3. Measurement
4. Tactics
5. Quick Start Guide
6. Examples

And yes, I used AI to help. Wouldn’t you?

Preface: Why This Matters Now

Search behavior is undergoing its most significant structural shift since 1998. If 2023–2024 was the ‘Wild West’ of AI experimentation, we have now entered the ‘Gold Rush’—the window where companies either claim their territory in AI answers or get left behind.

As a marketer, this isn’t a future-state problem; it’s table stakes. Buyers have moved away from a linear path of ‘Query → Ranking → Website’. Today, they seek a synthetic consensus: an AI summary validated by social proof and peer reviews before they ever click a vendor link.

In practice, the buyer journey is never as linear as the charts depict. It’s fragmented, highly situational, and occasionally chaotic. Google refers to the journey from discovery to vendor selection as the ‘messy middle’—an apt term for our purposes.

Buyers may:

• Discover vendors at trade shows or industry events
• Encounter companies through LinkedIn advertising or social content
• Hear about solutions via peer referrals
• Begin with an internally recognized problem rather than external research
• Skip validation steps or reverse the sequence entirely

But there is strategic value in using the idealized version of the buyer journey because it serves as a useful framework for providing directional guidance. Think of the journey model in this guide as an idealized influence flow, not a rigid funnel. In these journeys, buyers are looking for a consensus — not a link. In fact, the user may get their answer directly on the search or AI surface — and never click through to your website.

 

THE ZERO-CLICK REALITY

Buyers are looking for a consensus — not a link. They may never click through to your website at all.

The Reality of Zero-Click

• When Google shows an AI summary, users are less likely to click traditional results (8% of visits with an AI summary vs. 15% without) ².
• Users click links inside AI summaries rarely — about 1% of ³
• Search sessions end on the results page more often when an AI summary appears

(26% vs. 16%) ⁴.

• Even before AI summaries scaled, most Google searches ended with no click to the open web (US estimate: ~58.5% zero-click). ⁵
• Survey-based research suggests large portions of consumers rely on AI/zero-click

summaries for a meaningful share of searches. ⁶

• This reliance creates significant modeled downstream pressure on organic web traffic. ⁷

• Generative summaries are increasingly common across many query sets, with studies showing AI Overviews appearing at substantial rates. ⁸

• These AI Overviews draw sources overwhelmingly from top-ranking ⁹
• Current data indicates that these AI Overviews continue to fundamentally alter click-through rate (CTR) dynamics for all ranking positions. ¹⁰

Section 1 — Discovery: The Digital Trust Stack

In the traditional SEO era, we built “backlinks.” In the AI era, we build the Trust Stack.

Trust is no longer a binary signal; it is a layered ecosystem of data sources that AI models use to determine if your brand is a credible answer to a user’s prompt. While your website remains the “Source of Truth,” the rest of the web provides the “Proof of Truth.”

The Stack consists of three primary layers:

1. Direct Sourcing (Your Website): Technical clarity and modular
2. Indirect Authority (Social/Community, Media): Brand salience and topic
3. Structured Sourcing (Review Platforms/Analyst Firms): Comparative data and sentiment

Layer	Buyer Behavior	AI Processing Signal	Your Actions
AI Answers	Initial synthesis, discovery	Direct sourcing	Win direct sourcing — see detailed section below.
Trade Publications & Earned Media	Initial synthesis, discovery	Indirect authority signal	Fine-tune your media game to the outlets that AI platforms leverage.
Social Media	Peer validation — a ‘Salience Signal’ telling the AI that people are actively talking about you.	Indirect authority signal	Maintain and elevate your social media presence.
Community	Practitioner advice	Emerging training signal	Maintain and elevate your community game.
Review Platforms	Product validation	Structured sourcing — AI uses these for sentiment analysis and feature benchmarking.	Elevate your review platform presence; be especially sensitive to negative reviews.
Analyst Firms	Category framing	High authority weighting	Engage analysts and make sure you are on their radar. ChatGPT appears to reward those featured by analysts.

 

The trust you have been building in your integrated marketing engine (social media, community, review platforms, and analyst firms) becomes even more important for AEO.

 

KEY INSIGHT

Brand plays a vital role in optimizing AEO. The signals you’ve built over years — in media, social, reviews, and analyst coverage — are now your most valuable AEO assets.

 

AI Answers via Direct Sourcing

This is where you learn how to “feed the beast” directly from your own site. AI systems pull, summarize, and synthesize information directly from indexed or retrieved content sources. These AI Answers are central to our strategy.

In the MES case study, Gemini demonstrated more sensitivity to direct sourcing and cited sources for 94% of the prompts used in our benchmark.

 

AI Answers are constructed from:	When your content appears in:	Your Actions (The AEO Content Playbook):
•  Pretraining web corpora •  Licensed datasets •  Retrieval layers (RAG) •  Structured knowledge graphs	•  Definitions •  Comparison pages •  Buyer guides •  Research reports •  Documentation hubs	•  Publish definitional content (“What is X?”) •  Create comparison frameworks •  Structure pages for summarization •  Produce original research •  Build glossary hubs •  “Modular Writing” — answer a question in one concise paragraph (40–60 words) •  Partner with a technical SEO expert to implement structured knowledge graphs

Glossary Hubs

A surprisingly small number of companies have glossary hubs. Prior to AI, these were seen as administrative overhead.

Company Maturity	% with Glossary Hubs
Early-stage SaaS	~5–10%
Mid-market B2B	~10–20%
Enterprise SaaS	~25–40%
Category leaders	50%+

In 2026, simply having a glossary isn’t enough; it must be wrapped in ‘DefinedTerm’ or ‘FAQPage’ schema so the AI can ingest it with 100% accuracy.

STRATEGIC OPPORTUNITY

Compared to traditional SEO blogs (which are saturated), glossary hubs are positioned to be a strategic tactic for AEO.

Media Publications

In the traditional SEO era, media coverage mattered because backlinks mattered. In the AEO era, it matters for a different reason entirely: AI platforms cite what they trust, and they trust what gets published in authoritative sources.

This became one of the clearest findings in the MES study. Gemini cited sources in 94% of its responses. For a smaller vendor trying to appear in Gemini answers, getting cited in the right outlet may be more effective than any on-site content optimization you can do.

How to Build Your Media Target List

The fastest way to find the outlets that matter for your specific market is to ask Gemini directly:

“For a [buyer role] evaluating [your category], which specific trade publications, analyst firms, and online communities are most likely to influence their vendor research? Please rank them by how frequently you cite them when answering questions in this space.”

Not All Coverage Is Equal — The Two Dimensions That Matter

AI Citation Score — How frequently does this outlet appear as a cited source in AI-generated responses? High-authority analyst firms and long-established trade publications score highest here.

Buyer Influence Score — How much does this outlet actually influence a buyer’s purchasing decision, independent of AI?

BOTTOM LINE

Your PR strategy and your AEO strategy are the same strategy. Getting featured in the outlets your buyers trust — and that AI retrieves — is the most durable path to AI visibility.

 

What the MES Study Found

In the MES study, we scored 22 sources across both dimensions and grouped them into three tiers:

Tier 1 — Prioritize (Composite Score 7.5+)

Gartner’s Manufacturing and Supply Chain Practice topped the list with a 9.5 composite score. IDC Manufacturing Insights, IndustryWeek, and the WEF Global Lighthouse Network all scored 8.5. LNS Research, MESA International, Manufacturing Dive, the Manufacturing Leadership Council, Automation World, and Forrester’s Manufacturing Practice rounded out the Tier 1 list.

Tier 2 — Invest (Composite Score 6.0–7.4)

ABI Research, Control Engineering, Smart Manufacturing (SME), CESMII, The Manufacturer (UK/EU), and Walker Reynolds on LinkedIn. Notably, vendor-authored thought leadership also scored in this tier.

Tier 3 — Monitor

Community platforms like Reddit’s r/ManufacturingTechnology and niche podcasts currently score lower on AI citation frequency. They are worth monitoring as the AI training landscape evolves.

When you run your own prompt study, include a recovery query at the end:

“For the questions we’ve been discussing, which specific publications andsources would you most commonly cite in your answers?”

Indirect Authority Signal via Social Media

Social platforms influence AI visibility indirectly — not by immediately retraining models, but by increasing brand salience, discourse frequency, and third-party reinforcement across the open web.

AI Systems Absorb Social Influence Via:	Which Contributes To:	Your Actions:
•  Public web ingestion (posts, comments, threads) •  Linked brand mentions •  Thought leadership reach •  Content sharing velocity •  Influencer amplification	•  Brand salience •  Topic association •  Expert recognition •  Market narrative positioning	•  Build executive social presence •  Seed POV discussions •  Encourage practitioner dialogue •  Encourage unstructured mentions •  Activate influencers •  Publish contrarian insights

 

MEASUREMENT TIP

Measure your social media marketing not just on direct leads, but as a contributing factor to your AEO efforts.

 

Structured Sourcing via Review Platforms

Review platforms provide highly structured, comparative, and sentiment-rich datasets that AI systems use for vendor evaluation and recommendation synthesis.

Why They Matter Disproportionately	How Influence Appears in Answers	Your Actions:
•  Feature comparisons •  Ratings •  Category placement •  Use-case tagging •  Customer quotes •  Pros/cons summaries — this structure makes them easy for AI to parse, summarize, and cite.	•  Reference review leaders •  Summarize user sentiment •  Compare ratings •  Pull use-case strengths •  List category leaders •  Even when not cited directly, review rankings influence recommendation ordering.	•  Drive review volume •  Improve rating averages •  Expand use-case tagging •  Encourage detailed customer narratives •  Participate in category definitions

 

Pro/Cons summaries: AI assistants often summarize a vendor by looking at the “Cons” section of review sites to provide a “balanced” view. Be sure to audit your “Cons” on G2/Capterra. If the AI is repeating a specific weakness, the fix isn’t just SEO — it’s updating your product documentation to address that specific AI-extracted pain point.

Discovery Checklist

• Audit Your “Cons”: Query ChatGPT and Perplexity about your product’s weaknesses. Cross-reference these with your G2/Capterra reviews and update your documentation to address these specific AI-extracted pain points.
• Deploy Schema Markup: Ensure your Glossary Hub uses ‘DefinedTerm’ schema and your FAQ sections use ‘FAQPage’ schema to guarantee 100% ingestion accuracy by AI agents.
• Implement “Modular Answer” Blocks: Audit your top 10 most important pages. Ensure every H2 question is immediately followed by a concise, 40–60 word descriptive paragraph.
• Launch a “What is [Category]?” Pillar: If you don’t have a definitive guide to your category, build one now.
• Seed the Knowledge Graph: Identify 5 key industry influencers or practitioners and encourage them to mention your brand alongside your primary “Citation Keywords” on LinkedIn or Reddit.

Section 2 — Measurement: Building the AEO Dashboard

If I were reading this, I would have skipped the first section and come here first. It’s intuitive that we will need to build authority — but how will we know if we succeed? I’m feeling a nostalgic sort of PTSD from measuring branding efforts…

Measurement is, however, the operational backbone of Answer Engine Optimization. We need to get this right to ensure the right level of investment is available and we are accountable for a tangible return.

Traditional SEO dashboards track rankings, traffic, and click-through rates. AEO requires a different system: measuring visibility inside answers.

The Core AEO Metrics

To prove impact, we focus on four primary signals:

• Inclusion Rate: The percentage of tested prompts where your brand is Calculation: (Brand Mentions / Total Prompts) × 100. Benchmark: 40–60% is considered “Strong.”
• Citation Rate: The percentage of prompts where your brand is used as evidence or linked as a source. This is a higher authority signal than simple inclusion.
• Position: Whether you are the first-cited source or a secondary
• Share of Answer (SoA): Your brand’s mentions relative to your top 3
• AI-Influenced Pipeline: Revenue opportunities where discovery was driven by AI tools. Track this via CRM fields (“How did you research us?”) and GA4 referral traffic from chatgpt.com or perplexity.ai.

MEASUREMENT NOTE

Some “AI discovery” will never show up in traffic data. Inclusion Rate is a leading indicator even when clicks are zero.

Building the Prompt Testing System

You cannot rely on a single person’s search history. To get factually correct data, you must use a Testing Protocol:

• The Library: Start with a library of 50 prompts — 10 Definitions, 10 Comparisons, 10 Use Cases, 10 Integrations, and 10 “Best of” lists.
• The Environment: Use a dedicated, neutral testing

• The Controls: Always test in Incognito/Private mode with a cleared-cookie state to avoid personalization bias.
• The Cadence: Test monthly to track AI models update their weights and indices frequently; a one-time check is useless.

Prompt Sourcing

Pull prompts from sales discovery calls, Gong transcripts, RFP documents, analyst reports, keyword research, and competitor content.

Tip: Check Appendix 1 for a ready-built prompt to let AI help you find the first set of prompts.

Testing Protocol

AI visibility testing must control for personalization and environmental bias. The objective is to simulate neutral buyer conditions while maintaining repeatable benchmarking.

AI Assistants (ChatGPT, Claude, Perplexity)

Incognito mode provides partial control only. Additional controls required:

• Start fresh chat threads
• Disable memory features where possible
• Avoid long brand-specific conversations before testing
• Standardize prompt order

Dashboard Models

The Executive View (Quarterly)

Focus on the macro trend of Share of Answer and AI-Influenced Pipeline. This justifies the investment to the CEO and Sales leaders.

Metric	Q1	Q2	Q3	Trend
Inclusion Rate	26%	34%	48%	↑
Citation Rate	14%	21%	30%	↑
Position	6	5	5	↑
Share of Answer	19%	27%	36%	↑
AI-Influenced Pipeline	$1.4M	$2.3M	$3.8M	↑

 

Executive framing: AI visibility is becoming a leading indicator of pipeline creation.

The Marketers View (Monthly)

The monthly operational view should segment visibility gaps by category.

Category	Inclusion Rate	Citation Rate	Priority
Definitions	62%	41%	Maintain
Comparisons	38%	22%	Improve
Integrations	21%	12%	Priority

 

DASHBOARD TIP

This dashboard drives content roadmap decisions — use it to prioritize where to invest next month’s content budget.

Data Driving Improvement

• Low Inclusion? Expand your Glossary Hub and “What is”
• Low Citation? Add structured “Modular Answer” blocks and original research reports.
• Losing Share of Answer? Build out your “Vs.” library to close comparison

Measurement Maturity Model

Level	Stage	Description
Level 0	No tracking	AEO not yet part of active strategy
Level 1	Baseline Established	Structured prompt library tested with actionable outcomes
Level 2	Plans in Motion	Content and marketing approach adjusted to improve prompt library results. Monthly cadence established.
Level 3	Competitive Benchmarking	Insights into competitive moves gaining attention from AI channels
Level 4	Revenue Attribution	Revenue measurably linked to AEO activities

 Target: Level 3 within the first 6–9 months.

Measurement Checklist

• Finalize Prompt Library: Source 50 questions from sales calls and Gong
• Establish Baseline: Run your first “Incognito” audit across ChatGPT, Perplexity, and Google AI Overviews.
• CRM Update: Add a “Discovery Method” field to your lead forms to capture AI-specific research behavior.
• Referral Segment: Create a custom segment in GA4 to track traffic specifically from AI domains.

Understanding the Platforms: ChatGPT vs. Gemini

One of the most practically important things to understand about AEO is that ChatGPT and Gemini are not just different interfaces — they are architecturally different systems with fundamentally different approaches to constructing answers. This distinction directly shapes which AEO tactics work on each platform, why your measurement data looks different across them, and where to invest your content effort first.

ChatGPT: Training-Data-First

ChatGPT is built on a large language model trained on a corpus of text with a knowledge cutoff (approximately August 2025 for GPT-5.3 at the time of writing). When you ask it a question, its default behavior is to answer from what it learned during training — from memory, not from retrieval. It is, in this sense, more like a very well-read expert than a search engine.

ChatGPT does have a browsing tool that allows it to search the web, and it uses this selectively — typically when it judges the question to require current information, when it lacks confidence in its training-data answer, or when comparison and evaluation framing triggers its retrieval behavior. This explains the divergence between the two case studies: in the MES study, ChatGPT cited zero sources because it had confident, well-formed training-data answers for an established market. In the CAD study, it cited sources 58% of the time because CAD evaluation queries — ‘SOLIDWORKS vs. Onshape’ — are exactly the kind of nuanced comparison that triggers its browsing tool.

The strategic implication: ChatGPT’s answer about your market is largely a function of what was written about you before its training cutoff. You cannot change a ChatGPT answer overnight by publishing new content. Your AEO target on ChatGPT is the training corpus — historical brand weight, analyst coverage, and the accumulated volume of authoritative mentions that existed before the model was trained. This is a slow-moving, high-inertia system.

CHATGPT STRATEGY

Target the training corpus. Build brand weight through analyst coverage, industry awards, academic citations, and sustained third-party press. Counter-consensus data — original research that contradicts established narratives — is the fastest lever, because it triggers the browsing tool even in an otherwise training-data-driven response.

Gemini: Retrieval-First

Gemini is deeply integrated with Google’s search infrastructure. Rather than answering primarily from a fixed training corpus, Gemini constructs answers by drawing on Google’s live search index as a primary input. Citations are not a supplement to its answer — they are a natural byproduct of how it builds the answer in the first place. This is why Gemini cited sources in 82–94% of responses across both studies, while ChatGPT cited sources only selectively.

Think of Gemini as an AI that behaves more like an intelligent search engine than a trained expert. It is asking “what does the web currently say about this?” rather than “what do I know about this?” This means Gemini’s answers are more responsive to fresh content, recent citations, and updated structured data — but it also means that what gets cited depends heavily on what is currently indexed, authoritative, and well-structured in Google’s eyes.

The strategic implication: your AEO target on Gemini is the live retrieval layer. New content can affect Gemini visibility faster than ChatGPT visibility. Structured pages, schema markup, fresh comparison content, and coverage in Gemini’s preferred source ecosystem all have near-term impact. The CAD study showed this in practice: Onshape’s strong Gemini presence (40% mention rate) relative to its ChatGPT presence (22%) reflects its investment in structured, cloud-native educational content that Gemini’s retrieval layer rewards.

GEMINI STRATEGY

Target the live retrieval layer. Invest in structured content, schema markup, fresh comparison pages, and coverage in the sources Gemini cites for your category. Ask Gemini directly which publications it cites for your market — those are your PR targets. Results are faster than ChatGPT but more volatile; Gemini answers can shift as the web changes.

The Critical Difference: Determinism vs. Dynamism

This architectural distinction produces a second-order effect that is just as important for strategy: answer determinism.

Dimension	ChatGPT	Gemini
Primary source	Training corpus (knowledge cutoff ~August 2025)	Live Google search index
When it reaches for sources	Selectively — when it lacks confidence or query triggers browsing	Almost always — retrieval is core to answer construction
Answer stability	High — answers about established markets change slowly, tied to model updates	Moderate — answers can shift as indexed content changes
Speed of AEO impact	Slow — new content affects answers only after model retraining	Faster — new indexed content can affect answers within weeks
What drives incumbency	Training data weight — brand authority accumulated before cutoff	Content signal strength — current indexed quality and citation patterns
Best content lever	Counter-consensus data; analyst coverage; brand-building at scale	Structured comparison pages; schema markup; reseller/aggregator coverage
Geographic sensitivity	Low — training data is largely global and English-centric	Higher — integrates with regional Google search infrastructure

ChatGPT’s high answer determinism is why the MES study found Siemens Opcenter in 76% of responses regardless of the question. That’s not retrieval behavior — it’s the model confidently reciting what its training data established as the category leader. Displacing it requires either a model update (outside your control) or counter-consensus data compelling enough to trigger the browsing tool.

Gemini’s answer dynamism is why small vendors like Tulip in MES and Onshape in CAD can earn meaningful visibility through content investment. When their content is well-structured, freshly indexed, and cited in the right sources, Gemini surfaces them — even against incumbents with far larger brand footprints.

KEY INSIGHT

Don’t treat ChatGPT and Gemini as two versions of the same tool. They are different epistemic systems. ChatGPT answers from memory; Gemini answers from retrieval. Your content strategy, measurement approach, and competitive expectations should be calibrated separately for each.

What This Means for Your Measurement Dashboard

Understanding this distinction changes how you interpret your AEO metrics:

Metric	If ChatGPT Score Is Low…	If Gemini Score Is Low…
Inclusion Rate	Brand weight problem. You are not prominent enough in the training corpus. Long-term play: analyst coverage, industry press, sustained brand building.	Content signal problem. Your content is not being retrieved and cited. Short-term play: structured pages, schema, comparison content.
Citation Rate	ChatGPT rarely cites in established markets. Low citation rate may simply reflect market maturity. Focus on inclusion rate instead.	Source ecosystem problem. You are not appearing in the sources Gemini retrieves. Fix: get cited in the right publications and aggregators.
Discovery Gap (Discovery vs. Eval)	Likely structural — ChatGPT defaults to incumbents on open queries. Hard to move without training data weight.	Content depth problem. Gemini should surface you on open queries if your educational content is strong enough. Invest in ‘What is’ and workflow guides.

Case Study 1 — AEO in Practice: MES Market

A 50-Prompt Analysis of MES Vendor Visibility Across ChatGPT & Google Gemini · March 2026

I wanted to put this guide’s ideas to the test, so I ran a structured AEO study on the Manufacturing Execution Systems (MES) market — an industry with a clear set of established vendors, well-defined buyer questions, and enough competitive diversity to produce meaningful results. The full report is available separately, but this section gives you the findings that matter most for your own AEO strategy.

How We Ran It

We submitted 50 buyer-intent prompts to both ChatGPT (GPT-53) and Google Gemini — 100 responses total — spanning 14 thematic categories including production scaling, traceability, digital transformation, compliance, and workflow digitization. Each prompt was scored on commercial relevance (1–5 composite), and each response was tagged for answer type, vendor mentions (up to four per response), and citation behavior.

Protocol controls: Fresh user accounts, incognito browsing, and no prior brand-specific conversations. Queries were run from Budapest, Hungary, which introduced a minor geographic variable discussed below.

Finding 1: The Two Platforms Behave Completely Differently — and Require Different Strategies

ChatGPT delivered Hybrid responses across all 50 prompts — blending educational context with direct vendor recommendations. Gemini, by contrast, started as almost entirely Educational and only began naming specific companies as the query sequence progressed.

FINDING 1

Content that earns Gemini visibility is not the same content that earns ChatGPT visibility. A single content strategy optimized for one platform will underperform on the other.

Finding 2: ChatGPT Cited Zero Sources. Gemini Cited Sources in 94% of Responses.

This is not a minor UX difference. It’s a structural gap that changes the entire AEO opportunity on each platform. Gemini’s citation behavior creates a dual-channel opportunity: appear in the AI-generated answer itself and appear in the cited sources. ChatGPT’s zero-citation behavior means the only path to visibility is being named in the response body.

FINDING 2

Gemini rewards content marketing investment with a measurable return (you can track citation appearances) in a way that ChatGPT currently does not.

 

Finding 3: ChatGPT Is Dominated by Incumbents — and the Concentration Is Extreme

Siemens Opcenter appeared in 38 of 50 ChatGPT responses — a 76% mention rate. Rockwell FactoryTalk and Dassault DELMIA Apriso formed a distant second tier. Across 50 prompts,

ChatGPT recommended essentially the same five vendors regardless of the specific question asked.

This is incumbency lock-in in its most visible form. To disrupt this, you must publish “Counter-Consensus Data” — original research that contradicts the established enterprise narrative. When ChatGPT uses its browsing feature to answer a nuanced question, it finds your unique data point and has to cite you to provide a balanced answer.

FINDING 3

If you’re a smaller vendor, don’t make ChatGPT your primary AEO target. The return on effort is low relative to what’s available on Gemini.

Finding 4: Gemini Is a Genuinely Open Playing Field for Smaller Vendors

Gemini’s top vendor was Tulip, appearing in 11 of 50 responses (22%). Fabricio came in second at 9 mentions (18%). The long tail included vendors most marketers have never heard of — Evocon, MachineMetrics, Jidoka Tech, HiveMQ, HighByte, Arkite, Shoplogix — surfaced because their content matched the query, not because of their brand size.

FINDING 4

If you’re a small or mid-size vendor, Gemini-first is your clearest near-term AEO opportunity. Build educational depth, get cited in the right publications, and earn your Gemini visibility before worrying about ChatGPT.

Finding 5: ChatGPT Appears to Rotate Vendor Order — and This Matters for How You Measure Success

ChatGPT didn’t consistently lead with the same vendor for similar prompts. This suggests ChatGPT may use a response-diversity mechanism designed to avoid appearing to endorse a single vendor.

FINDING 5

Share of Answer matters more than position. The goal isn’t to always appear first — it’s to remain consistently within the recommended set across the full prompt library.

Finding 6: Geographic Query Origin Influences Gemini Results

Because this study was run from Budapest, EU-based vendors appeared in several Gemini responses that likely wouldn’t surface the same way in North American queries — ANASOFT, Proxus, and Transition Technologies being clear examples. Gemini integrates more actively with Google’s regional search infrastructure.

FINDING 6

If your buyers are global, your AEO program should test from multiple locations. The local competitive landscape may look very different from what your home-country team sees.

What This Means for Your AEO Strategy

1. Run your own version of this study Use the 50-prompt methodology from Appendix 1 and the testing protocol from Section 2. Until you know your owninclusion rate, citation rate, and competitive position on each platform, you’re investing blindly.

2. Treat ChatGPT and Gemini as separate channels with separate Different content levers, different citation mechanics, different competitive dynamics.
3. If you’re a smaller company, start with Gemini. The data shows it’s genuinely accessible to vendors with strong content and earned media — not just incumbents with brand mass.

The full MES AEO report — including the Media Signal Priority Matrix, vendor visibility rankings, and platform comparison tables — is available by request from Ron Close.

Case Study 2 — AEO in the Contested Middle: CAD Market

A 50-Prompt Analysis of CAD Vendor Visibility Across ChatGPT & Google Gemini · March 2026

Following the MES study, I ran a second structured AEO analysis — this time on the CAD software market. I chose CAD because it offered a deliberately different market structure: rather than a single vertical with a clear incumbent, CAD serves multiple industries and user types, with genuine competitive dynamics in what I call the ‘Contested Middle’ — the segments where engineering teams make real purchasing decisions rather than following OEM mandates.

The study focuses on five verticals: Industrial Machinery, Consumer Electronics, Medical Devices, Automotive Tooling & Fixtures, and HardTech Startups. Aerospace and Automotive OEM were deliberately excluded — in those markets, OEM mandates often pre-determine tool selection, making AEO a lower-leverage investment.

How We Ran It

We submitted 50 buyer-intent prompts to both ChatGPT (GPT-53) and Google Gemini — 100 responses total — spanning 7 verticals. Queries were run from Budapest, Hungary in late March 2026 using the same protocol as the MES study: incognito Chrome, no account login, fresh threads for every prompt.

This study introduced one methodological innovation: a deliberate Discovery vs. Evaluation stage split. 30 prompts were written as pure discovery queries — open-ended buyer questions with no vendor names in the prompt. 20 prompts were evaluation-stage queries explicitly naming two or more vendors for comparison. This split allows us to measure not just who appears, but whether vendors earn visibility organically or only when named.

Vertical	Prompts	Focus Area	Key Watch Vendor
Cross-Vertical	8	Platform-agnostic comparisons & definitions	Onshape
Industrial Machinery	8	SOLIDWORKS stronghold; Siemens pushing down, Onshape up	Onshape
Consumer Electronics	8	Fast cycles favor cloud tools; Fusion & Onshape winning	Shapr3D
Medical Devices	8	SOLIDWORKS dominant; Onshape gaining on regulatory workflow	Onshape
Auto Tooling & Fixtures	8	Toolmakers ≠ vehicle designers; Shapr3D penetrating segment	Shapr3D
HardTech Startups	6	No legacy lock-in; highest AI discovery likelihood	Onshape
Adjacent Tools	4	PLM/PDM/simulation intersection	PTC Creo

 

 

Finding 1: SOLIDWORKS Is the Unchallenged Default — But Its Nature Differs by Platform

SOLIDWORKS appeared in 38 of 50 ChatGPT responses (76%) and 36 of 50 Gemini responses (72%). On ChatGPT, SOLIDWORKS surfaces as the default recommendation regardless of vertical, use case, or buyer context. On Gemini, its first-position rate declines noticeably in startup-oriented and cloud-native queries, where Onshape or Fusion 360 lead the response.

FINDING 1

ChatGPT returns SOLIDWORKS mechanically regardless of context. Gemini applies contextual weighting — Onshape rises significantly in cloud-native and startup queries. The incumbency pattern holds, but Gemini’s version is more nuanced.

 

Vendor	ChatGPT Mentions	ChatGPT Rate	Gemini Mentions	Gemini Rate
SOLIDWORKS	38	76%	36	72%
Autodesk Fusion 360	20	40%	19	38%
Onshape	11	22%	20	40%
Siemens NX	13	26%	16	32%
PTC Creo	10	20%	8	16%
CATIA	17	34%	9	18%
Shapr3D	4	8%	4	8%
Autodesk Inventor	8	16%	8	16%
Solid Edge	1	2%	0	0%

Finding 2: The CAD Citation Ecosystem Is Dominated by Resellers and Aggregators — Not Trade Press

Gemini cited sources in 82% of CAD responses. ChatGPT cited sources in 58% — significantly higher than its zero-citation behavior in the MES study. This means the source ecosystem matters for CAD AEO on both platforms, not just Gemini.

The top ChatGPT sources were Xometry (11 citations), cadsoftusa.com (10 citations), and GoEngineer. Gemini drew from a broader pool including Reddit, niche aggregators, and reseller blogs. Trade publications were cited rarely or not at all across 100 responses.

More surprisingly, vendor-authored comparisons were among the most trusted sources. Onshape and Shapr3D in particular published direct competitive comparisons that were cited as credible reference material. The AI platforms do not appear to discount vendor-sourced comparisons the way a discerning human reader might — a significant content opportunity that trade press absence has left largely uncontested.

FINDING 2

In CAD, the source citation ecosystem is dominated by resellers, aggregators, and vendor-authored comparisons — not trade press. This differs structurally from MES and creates a clear, addressable content opportunity for vendors willing to publish rigorous comparison content.

 

ChatGPT Top Sources	Gemini Top Sources
Xometry (11 citations)	Reddit (4 citations)
cadsoftusa.com (10 citations)	udit.es / clevr.com (4 citations each)
GoEngineer / goengineer.com (3+)	fitgap.com / monograph.com (3 citations each)
Wikipedia (4 citations)	researchgate.net / cadd.net.in (3 citations each)
trimech.com (3 citations)	javelin-tech.com / TriMech (2 citations each)

Finding 3: The Discovery Gap — Onshape and Shapr3D Tell Different Stories

The Discovery vs. Evaluation stage split revealed the most strategically important finding of the study. Onshape appeared in only 3 Discovery responses on ChatGPT, but surged to 8 in Evaluation queries. On Gemini, the gap is less pronounced (12 Discovery vs. 8 Evaluation), suggesting Gemini surfaces Onshape more organically through its educational content footprint.

Shapr3D’s position is more acute: all 4 ChatGPT appearances came in Evaluation-stage prompts where Shapr3D was explicitly named. Zero unprompted Discovery mentions on either platform.

FINDING 3

Visibility that depends entirely on the buyer knowing to ask about you is not true top-of-funnel awareness. Onshape has cracked the comparison prompt on ChatGPT — but not the discovery prompt.

 

Vendor	GPT Discovery	GPT Evaluation	GEM Discovery	GEM Evaluation	GPT Gap	GEM Gap
SOLIDWORKS	21	17	19	17	+4	+2
Autodesk Fusion 360	13	7	12	7	+6	+5
Onshape	3	8	12	8	−5	+4
Siemens NX	8	5	10	5	+3	+5
PTC Creo	7	3	5	2	+4	+3
CATIA	13	4	6	3	+9	+3
Shapr3D	1	3	1	3	−2	−2

Note: Discovery Gap = Discovery mentions minus Evaluation mentions. Negative = vendor only surfaces when named.

Finding 4: The Adjacent Product Universe Is Larger Than Expected

Across 100 responses, over 216 unique products were mentioned. CAD queries routinely surface simulation tools (ANSYS, COMSOL, Abaqus), PDM/PLM platforms (SOLIDWORKS PDM, Autodesk Vault, PTC Windchill, Siemens Teamcenter), CAM tools, and visualization software.

FINDING 4

Adjacent vendors have a genuine — and often overlooked — AEO stake in CAD-oriented queries. Building content that connects your product to CAD workflows is a direct AEO lever for simulation, PLM, PDM, CAM, and visualization vendors.

Finding 5: The Geography Effect Is Market-Dependent

Unlike the MES study — where Budapest queries surfaced EU vendors not seen in US queries — the CAD study showed minimal geographic bias. Global brand dominance and English-language content neutralize regional weighting in CAD.

FINDING 5

The geography effect identified in MES is real — but market-dependent. In concentrated markets dominated by global brands, geographic query origin has minimal impact. In fragmented markets with regional vendors, it matters significantly.

What This Means for CAD Vendor AEO Strategy

1. Own the comparison content Create dedicated comparison pages for every major competitor. Vendor-authored comparisons earn AI citations — the platforms treat them as credible sources.
2. Activate your reseller content Reseller blogs and comparison pages are a high-leverage AEO asset. Develop co-branded content programs that give resellers the data they need to publish authoritative articles.
3. Close the Discovery Gap before the Evaluation Gap. Run 30 discovery prompts with no vendor If you don’t appear, invest in educational depth, modular answer blocks, and vertical-specific workflow guides.
4. For challengers, Gemini-first is still the right starting Onshape’s 40% Gemini mention rate vs. 22% on ChatGPT illustrates the platform gap. Gemini’s contextual weighting rewards content investment in a way ChatGPT’s training-data incumbency does not.

The full CAD AEO report — including complete vendor visibility rankings, source ecosystem analysis, and the stage-split data table — is available by request from Ron Close.

Cross-Study Observations: What Two Studies Tell Us

Running the same methodology across two different markets — MES and CAD — produced findings that are more useful in combination than either study yields alone. Some patterns held across both markets and appear structural. Others diverged in ways that reveal how market characteristics shape AI behavior.

Dimension	MES Market	CAD Market	Cross-Study Insight
ChatGPT Incumbency	Extreme — Siemens Opcenter in 76% of responses. Same 5 vendors regardless of question.	Confirmed extreme — SOLIDWORKS in 76% of responses. Same pattern, different incumbent.	ChatGPT encodes the market leader regardless of category. Pattern appears structural.
Gemini Behavior	Content-driven — long-tail vendors surfaced. Tulip #1 at only 22% mentions.	Concentrated but contextual — SOLIDWORKS 72%, Onshape rises to 40% (2nd place). Less long-tail diversity than MES.	Gemini rewards content investment, but market concentration limits long-tail emergence.
Citation Behavior	ChatGPT: 0 citations. Gemini: 94% of responses cited sources.	ChatGPT: 58% cited sources. Gemini: 82%.	CAD diverges from MES: ChatGPT actively cites in CAD. The source ecosystem matters on both platforms.
Source Types	Source types not systematically tracked in this study.	Resellers, aggregators, and vendor-authored comparisons dominate. Trade press nearly absent.	The absence of trade press from CAD citations is structural. A publisher opportunity and a vendor content opportunity.
Discovery Gap	Not explicitly measured — single prompt type used.	Confirmed and striking — Onshape: 3 Discovery vs. 8 Evaluation on ChatGPT. Shapr3D: 0 Discovery mentions on either platform.	The Discovery Gap is a new metric introduced in the CAD study. Recommended for all future AEO studies.
Geography Effect	Budapest queries surfaced EU vendors (ANASOFT, Proxus) not seen in US queries.	Minimal effect — global brand dominance neutralizes regional weighting in CAD.	Geography matters more in fragmented markets than concentrated ones. Market structure is the variable.

 

KEY INSIGHT

The most durable cross-study finding: ChatGPT encodes the incumbent in every market. CAD diverges from MES in one critical way: resellers and aggregators — not trade press — dominate CAD citations on both platforms. This changes the AEO playbook for CAD vendors significantly.

 

Section 3 — The Authority Model: Keywords & Beyond

Keywords get you retrieved. Authority gets you cited.

In the traditional SEO era, we optimized for the engine. In the AEO era, we optimize for the LLM’s Knowledge Graph. AI systems still use keywords to identify relevance, but they use authority to decide who gets the mention.

Jump Start — Let AI Help You Build the Prompt List

Remember the good old days, when you could run keyword analysis for Google AdWords? There is a similar methodology for prompts in Appendix 1.

The Foundational Reality — Keywords Still Matter

AI answer systems still rely on search infrastructure to identify relevant sources. This includes crawling, indexing, semantic clustering, and topic relevance scoring. Without keyword relevance, your content may never be retrieved — and therefore never cited.

So yeah, you’re not off the hook.

Why Keyword Seeding Alone No Longer Works

Traditional SEO playbook: own the keyword → win the ranking. AI-era reality: own the topic → win the citation.

AI systems do not reward keyword density, exact-match repetition, or keyword stuffing. They reward comprehensiveness, contextual authority, multi-source reinforcement, conceptual clarity, and information gain/originality. Over-seeding can actually reduce summarization clarity.

The Four Shifts in Keyword Strategy for AEO

Shift	From (Traditional SEO)	To (AEO Strategy)	AI Processing Intent
Mapping	Keyword Lists	Prompt Maps	Contextual Retrieval: Understanding the user’s problem, not just their search term.
Target	Volume Keywords	Citation Keywords	Source Attribution: Identifying which domain provides the best “definitional” fact.
Structure	Single Keywords	Topic Clusters	Expertise Scoring: Assessing the depth of your site’s knowledge on a whole category.
Language	Exact Match	Semantic Breadth	Entity Association: Linking your brand to “Analyst Vernacular” and industry synonyms.

Operationalizing Semantic Breadth: Use “Analyst Vernacular”

AI models are trained heavily on high-authority datasets like Gartner, Forrester, and McKinsey reports. To increase your citation probability, use the specific terminology these analysts use to describe your category.

Instead of just:	Use analyst-preferred terms:
Automated AI	Agentic AI, Autonomous Decision Agents
Data tracking	Digital Thread, Lifecycle Data Fabric

From Keyword Lists to Prompt Maps

Traditional Keyword	AI Prompt Variant
Revenue intelligence software	What is revenue intelligence?
PLM Platform	Best PLM platforms for aerospace
Factory OS	How does factory OS work?

 Note: Buyers rarely ask one question. They ask follow-ups (e.g., “What is revenue intelligence?” followed by “How does it integrate with Salesforce?”).

From Transactional Focus to Educational Coverage

Content Type	AEO Citation Value
Definitions	Very High
Comparisons	Very High
Frameworks	High
Benchmarks	High
Pricing	Medium
Product Pages	Foundational

 From Single Keywords to Topic Clusters

AI models reward topical completeness. Instead of optimizing for a single term like “Digital thread software,” optimize the full cluster:

• What is a digital thread
• Digital thread architecture
• Digital thread vs PLM
• Digital thread ROI
• Digital thread implementation

From Exact Match to Semantic Coverage

Core Term	Semantic Expressions
Agentic AI	Autonomous AI, decision agents

 

Factory OS	Manufacturing OS, production orchestration
Digital Thread	Lifecycle data fabric, traceability systems

 

KEY PRINCIPLE

Semantic breadth increases citation probability. Covering synonyms, adjacent terminology, analyst language, and ecosystem vocabulary is just as important as the core keyword.

Rebalancing Your Keyword Strategy

Keyword Category	% of Strategy Focus
Definitional	25%
Comparative (especially 3rd party validation)	20%
Use cases (especially 3rd party validation)	15%
Implementation	15%
Integrations	10%
Transactional	10%
Pricing	5%

 

Keyword Checklist

• Create a Prompt Map: Take your top 20 keywords and rewrite them as the top 20 questions a buyer would ask ChatGPT.
• Audit for “Keyword Stuffing”: Remove repetitive exact-match phrases that hinder summarization clarity.
• Audit for Information Gain: Does this page provide a fact, chart, or perspective that only your brand provides?
• Bridge the Semantic Map: Identify the terms Gartner/Forrester use for your product and ensure they appear in your H2 headers.
• Table-ize Comparisons: Convert competitive text into structured tables to make it

“grab-and-go” for AI summaries.

• Build the “Vs.” Library: Create comparison pages for every major
• Map Conversational Threads: Ensure your topic clusters answer the first question and provide the logical “next step” answer.

Section 4 — Getting Started: Quick Wins Top Ten

For a marketing practitioner, the transition to AEO can feel overwhelming. To gain momentum, focus on these ten actions that materially improve AI visibility within 30–90 days.

1. Establish a Baseline — Develop your 50 prompts and evaluate them against AI tools such as ChatGPT and Use the results to inform which prompts to lean in on, content topics, and competitive positioning.
2. Secure Analyst Briefings — AI models are heavily trained on analyst Ensuring you are mentioned in Gartner or Forrester reports feeds the AI’s understanding of your category.
3. Build a Category Glossary Hub — Own the definitions AI uses to explain your market. Simply being the “source of record” for an industry term can trigger consistent citations.
4. Publish a “What Is [Category]?” Pillar — This is your highest-value definitional asset for winning “Direct Sourcing” citations.

7. Create Vendor Comparison Pages — AI frequently sources these for “shortlist” prompts. If you don’t provide the data, the AI will pull it from a competitor or a third-party site you don’t control.
8. Launch an Industry Benchmark Report — Original, proprietary data is the ultimate “Information Gain” signal that AI models prioritize for citations.
9. Expand Review Platform Coverage — Since AI uses structured sentiment data to influence recommendations, driving review volume on G2 or Capterra is a technical AEO tactic.
10. Produce Integration Documentation — Deep technical content signals enterprise credibility and helps you appear in “How does [Brand] work with [Tool]?”
11. Activate Executive Thought Leadership — Use social platforms to build brand salience. When your leaders are part of the industry discourse, it strengthens the association between your brand and the topic.
12. Structure Content for Summarization — Audit your existing high-traffic pages and add FAQs, tables, and “Modular Answer” blocks (40–60 words) to make them “grab-and-go” for AI.

WHERE TO START

Start with items 1 and 2 above — they will inform everything else. Without a baseline and analyst engagement, you are optimizing blindly.

 

A Forward Look: MCP and the Next Layer of AI Visibility

Model Context Protocol (MCP) is an emerging open standard, developed by Anthropic and adopted by a growing number of AI platforms, that allows AI models to connect to external tools, databases, and data sources in real time during a conversation. Rather than relying solely on training data or RAG retrieval from indexed web content, an MCP-enabled AI can reach into a live CRM, a product catalog, a knowledge base, or a structured API to construct its answer.

For AEO practitioners, MCP matters for one specific reason: it changes the sourcing layer. Today, AI visibility depends on whether your content is indexed, cited, and retrieved from the open web. In an MCP-enabled environment, AI visibility may increasingly depend on whether your product data, specifications, and content are structured and accessible via machine-readable connections — feeds that AI can query directly, bypassing the open web entirely.

What This Means in Practice

MCP is still early-stage and primarily a developer and enterprise concept. But three signals are worth watching:

• Structured product data becomes more valuable. If AI can query your product specifications, feature lists, and comparison data via a structured connection, vendors with clean, machine-readable product data have an advantage over those whose information exists only as narrative web content.
• The Digital Trust Stack may gain a new layer. The Trust Stack described in Section 1 may eventually need to include structured data connections alongside web content, citations, and review platforms. Vendors who build MCP-compatible data layers early will be positioned ahead of this shift.
• It does not replace current AEO The web content, comparison pages, glossary hubs, and citation signals described in this guide remain the foundation of AI visibility for the foreseeable future. MCP is additive — a future layer, not a replacement for today’s practices.

 

WATCH LIST

Monitor MCP adoption by ChatGPT, Gemini, and Perplexity over the next 12 months. The vendors best positioned will be those with clean, structured product data and developer-friendly APIs — not just strong web content.

 

Glossary

 

Term	Definition
AEO	Answer Engine Optimization: The practice of optimizing content to be retrieved and cited by AI models.
SEO	Search Engine Optimization: Traditional optimization for link-based search results.
Inclusion Rate	The % of prompts where your brand is mentioned.
Citation Rate	The % of prompts where you are explicitly cited as a source.
Discovery Gap	The difference between a vendor’s visibility in open discovery queries (no vendor names) vs. named evaluation queries. A large negative gap indicates visibility depends on being named — not earned organically.
Prompt Map	A simple way to organize the questions people ask AI tools about a topic or problem. By grouping these questions into themes, it helps marketers see where their company should provide helpful content so they can appear in AI-generated answers.
Pretraining web corpora	The large-scale collections of publicly available internet text used to train foundational AI models on language patterns, knowledge, and context before any task-specific tuning occurs.
Retrieval layers (RAG)	Retrieval-Augmented Generation. These enhance AI answers by retrieving relevant external information in real time, grounding responses in current, authoritative sources.
Structured knowledge graphs	These organize entities and their relationships in machine-readable formats — such as schema markup, graph databases, and linked data — enabling AI systems to understand how concepts connect beyond narrative text.
DefinedTerm Schema	A small piece of structured code added to a webpage that tells search engines and AI systems, “This page defines a specific term.”
FAQPage Schema	Structured markup that identifies a page as containing official question-and-answer pairs, making it easier for search engines and AI systems to extract and reference the answers.
Share of Answer	How often a company is mentioned in AI-generated responses to important questions in its market.
Zero-Click	Describes a situation where someone gets the information they need directly from an AI answer or search result without clicking through to a website.
AI Overview	A feature in Google Search that generates an AI-produced summary at the top of search results. Because AI Overviews are powered by the same signals as Google Gemini, optimizing for Gemini AEO and optimizing for AI Overviews are effectively the same effort.
MCP	Model Context Protocol: An emerging open standard that allows AI models to connect to external tools and data sources in real time, potentially enabling AI answers to be constructed from live structured data rather than only from indexed web content.

Appendix 1 — Master Prompt Template

Use this prompt whenever you want to generate ranked, commercially valuable prompts for any company or industry.

 I want to reverse engineer the highest-value prompts for:

• Industry: [INSERT INDUSTRY]
• ICP: [INSERT BUYER TYPE / ROLE]
• Company positioning: [INSERT COMPANY SUMMARY IN 2–3 SENTENCES]

Generate the top 50 prompts that:

1. Reflect real operational friction or regulatory pressure
2. Indicate commercial buying intent or budget proximity
3. Are likely to be asked in AI tools like ChatGPT, Claude, Perplexity
4. Map to measurable business outcomes (revenue, risk reduction, cost savings)

Then:

• Score each prompt (1–5) across:
  • Revenue Impact
  • Operational Burden
  • Buying Proximity
  • AI Surface Likelihood
  • Differentiation Fit
• Apply weighted scoring:
  • Revenue Impact (30%)
  • Operational Burden (25%)
  • Buying Proximity (20%)
  • AI Surface Likelihood (15%)
  • Differentiation Fit (10%)
• Rank them from highest to lowest composite

Output:

• Top 15 highest-value prompts
• Strategic clustering themes

Sources

Bibliography

1. Similarweb / Similarweb / Datos. AI tool market share analysis, March 2026.
2. Pew Research Pew Research Center. “Google users are less likely to click on links when an AI summary appears in the results,” July 22, 2025.
3. Fishkin, Rand (SparkToro). Fishkin, Rand (SparkToro). “2024 Zero-Click Search

Study,” July 1, 2024.

4. Bain & Bain & Company. “Consumer reliance on AI search results signals new era of marketing,” February 19, 2025.
5. Bain & Bain & Company. “Goodbye Clicks, Hello AI: Zero-click search redefines marketing,” 2025.

6. seoClarity. “Impact of Google’s AI Overviews: SEO Research Study,” 2025.

7. Conductor. “AI Overview Analysis & Study of 118M+ Searches (September 2025 update),” 2025.
8. Seer Seer Interactive. “AIO Impact on Google CTR: September 2025 Update,” November 4, 2025.

Footnotes Used In-Text

1. Similarweb / Datos AI tool market share analysis (2026).
2. Pew Research Center (2025). Analysis of click-through
3. Pew Research Center (2025). Analysis of internal AI link
4. Pew Research Center (2025). Analysis of search session
5. Fishkin, Rand (SparkToro). “2024 Zero-Click Search”
6. Bain & Company (2025). “Consumer reliance on AI search”
7. Bain & Company (2025). “Goodbye Clicks, Hello AI”
8. seoClarity (2025). “Impact of Google’s AI ”
9. Conductor (2025). “AI Overview Analysis & ”
10. Seer Interactive (2025). “AIO Impact on Google ”