AI search visibility kit: how AI finds and cites you
Practicum for Volume 2, Chapter 6, "When the Customer Is Not Human." Version: 2026-07. Time: 30 to 40 minutes. This is a manual worksheet for a first measurement. The details change quickly, so check the date.
Search is turning into an answer. A person may ask ChatGPT or another assistant what to buy or whom to hire, then receive a shortlist without opening ten links. A high search rank or an SEO trick is not enough. You need to appear in sources the AI trusts and can cite. "We have a website" becomes "the same checkable facts about us appear across sources a machine can read."
Gartner forecasts declining traffic from traditional search, with estimates of 25% less search volume by 2026 and 50% less organic search traffic by 2028. The KDD 2024 paper "GEO" by Aggarwal et al. found that source citations, quotations, and statistics produced the largest gains in visibility inside generative answers, about 30% to 40% for the best-performing methods. Treat these figures as reference points. Primary sources appear below.
Step 1. Map AI answers
Write five to seven real questions your customers ask. Run each question through three or four AI systems, such as ChatGPT, Gemini, Perplexity, or a locally popular assistant. Record what appears in the answer:
| Customer question | Brands in the answer | Sources cited | Tone about you |
|---|---|---|---|
________ |
positive / neutral / negative / absent | ||
________ |
|||
________ |
If you do not appear, the answer has left you out. If you appear in a weak or negative context, trust can fall before the customer reaches your site.
Step 2. Check the three conditions for being chosen
The chapter gives you three levers. Mark the weak ones:
- Findability: Can the agent find you in the places it uses to build an answer?
- Evaluability: Can the agent quickly understand your value from a clear description, price, and examples? The technical term is token-to-value: how much useful information the agent gets from a small amount of text.
- Actionability: Can the agent take the next step without a negotiation, using a clear contact, checkout, or payment path?
Weakest lever: ________________. Start there.
Step 3. Measure AI search visibility
Do not track only a Google position. Track your presence inside answers:
| Metric | What it means |
|---|---|
| Share of AI mentions | how often you appear in answers to your test questions |
| Position / order | whether the answer names you first or near the end |
| Sentiment | whether the description is positive, neutral, or negative |
| Source share | which sources the system cites about you and whether it trusts them |
| Co-mentions | which competitors or categories appear next to you |
| NAP consistency | whether your name, address, and contact details match across sources |
Remember: API does not equal UI. A model response from an API is not the same as the answer a person sees in a product interface. Personalization, locale, and device can change the result. AI search measurement is probabilistic, not exact. Run each test more than once, then record the date and model.
Step 4. Find the sources that matter in your region
You earn citations in places the AI trusts. Your own site may not be the strongest source. Industry publications, professional communities, marketplace listings, local directories, and review platforms may matter more. Build a current list for your market. Your goal is to place a checkable fact or quotation there, not an advertisement.
Step 5. Make a 14-day plan
- Days 1 to 3. Map AI answers from Step 1. Find the questions where you are absent or described poorly.
- Days 4 to 9. Fix one weak lever from Step 2. Add a machine-readable description, price, or example. Prepare two or three specific facts, case results, or quotations for the sources from Step 4.
- Days 10 to 14. Publish that material where AI systems get answers. Repeat the map and compare your share of mentions and sentiment. Record the date and model.
Output: one repaired lever and a baseline for another measurement next quarter.
Next
- For a quick self-check, use Can an agent see me?.
- For agent payments and protocols, see agent commerce protocols. For the software a customer agent uses, see agentic browsers.
- For the full route, open the Volume 2 workbook.
Sources (verified 2026-07)
- Aggarwal et al., "GEO: Generative Engine Optimization" (KDD 2024): methods that increase citations in generative answers. https://arxiv.org/abs/2311.09735
- Gartner: forecasts for declining traditional search traffic. https://www.gartner.com/en/newsroom
Version: 2026-07. What changed: Sources were added for GEO/KDD 2024 and Gartner. Forecast figures are marked as reference points. Open verification notes were resolved. AI search measurement remains probabilistic because API results do not equal UI results.