Two agencies offer the same service.
The first says it provides innovative digital solutions tailored to every client’s needs. Its service page lists SEO, content, paid media, and automation, but leaves the proof elsewhere, the location unclear, and the next step hidden behind a “Learn More” button.
The second says exactly whom it helps, which problems it handles, how its process works, where it operates, and what a prospective client should do next. Its case examples show the starting problem, the work completed, and the commercial result. Its service pages, business profiles, and third-party mentions describe the company consistently.
A buyer can compare the second agency faster, and a search system can interpret it with less guesswork. That difference captures the pressure AI search creates: buyers can encounter, assess, and narrow their options across search results, generated answers, reviews, social proof, and company websites before any provider controls the experience.
Success in that journey depends on whether the business can be understood, trusted, and selected. Chasing the latest GEO tactic is a distraction when the offer, evidence, or decision path remains difficult to interpret.
SEO remains the foundation in AI search, with a higher standard for being understood, trusted, and selected.
The Buyer May Compare You Before Visiting You
Discovery is no longer one neat journey from a keyword to a blue link to a homepage.
A buyer might start with Google, encounter an AI Overview, refine the question in AI Mode, ask ChatGPT for options, check reviews, visit LinkedIn, search a company name, and only then open two service pages. Another buyer may click immediately. A third may get enough information from the answer and never visit any cited site.
The route varies, but more of the comparison can now happen before you control the experience.
Research from Pew makes that pressure visible. In an analysis of 68,879 Google searches associated with 900 U.S. adults, users clicked a traditional search result on 8% of visits where an AI summary appeared, compared with 15% of visits without one. Links cited inside the AI summary received clicks on 1% of visits with a summary.
That study reflects one U.S. dataset and one period in 2025, so it should not be treated as a universal forecast for every market or query. Its commercial value is narrower: traffic alone is becoming a weaker proxy for visibility. A business may influence a decision without receiving the first click, or disappear from consideration before the buyer reaches its website.
A platform-by-platform content strategy would add complexity without resolving the underlying visibility problem. The practical priority is to make the business easier to interpret wherever the buyer encounters it.
GEO Is Not A Replacement For SEO
GEO, or generative engine optimization, is commonly used to describe efforts to improve visibility in AI-generated answers. AEO, or answer engine optimization, is used in a similar way.
The labels help describe a changing search environment, although they often attract more hype than practical guidance.
Google’s position is direct: its generative AI features are rooted in its core Search ranking and quality systems. From Google’s perspective, optimizing for generative AI search is still SEO.
Google also says you do not need an llms.txt file, special AI markup, forced “chunking,” or pages rewritten specifically for AI to appear in its generative search features. It warns against creating large numbers of pages for every possible query variation.
Search interfaces, source selection, query expansion, click behavior, and measurement are changing. The fundamentals still carry the load: crawlable pages, clear site structure, useful content, accurate business information, and evidence that helps people make decisions. The labels are new and the pressure is higher, but substance still determines whether the business gives a buyer anything worth considering.
First, Can Search Systems Understand The Business?
Return to the two agencies.
The vague agency may be technically crawlable and its pages may be indexed, yet “full-service solutions for businesses of all sizes” tells a buyer very little. Indexing creates access; understanding still depends on whether the page establishes the strongest service, intended client, relevant market, and problem the agency is equipped to solve.
Clear understanding starts with basic questions:
- What does the company actually do?
- Who is the service for?
- Which problem does it solve?
- Where does it operate, when location matters?
- How do its services relate to one another?
- Which page is the best source for each service?
The answers should not be buried in one blog post while the service page stays vague. They should be visible in page titles, headings, body copy, navigation, internal links, business profiles, and structured business information where appropriate.
Technical access matters too. Google says pages must be indexed and eligible to appear with a snippet to be eligible for its generative AI features. OpenAI says public websites can appear in ChatGPT Search, while content intended for summaries and snippets should not block OAI-SearchBot.
These requirements create eligibility without promising inclusion. They also cannot rescue an offer that remains vague after the page is crawled.
A Quick Understanding Check
Open the main service page without the navigation or homepage for context. Ask someone unfamiliar with the company to read it for 30 seconds.
Can they state the service, intended buyer, business problem, and next step accurately?
If a person has to infer the offer, a search system is also being asked to resolve unnecessary ambiguity.
Then, Can The Buyer Trust The Claims?
Clarity earns consideration. Trust requires evidence.
Both agencies can claim they are strategic, data-driven, experienced, and results-focused. Those words are nearly free. The buyer needs a reason to believe them.
The stronger agency gives the claim somewhere to stand:
- A case example explains the original leak, the intervention, and the measured outcome.
- A service page names the process and the decisions involved.
- An article interprets a real change in the market instead of summarizing common advice.
- Reviews describe specific work rather than generic satisfaction.
- Team or author information makes relevant experience visible.
- Third-party sources describe the company consistently and authentically.
Google’s guidance emphasizes unique viewpoints, first-hand experience, and non-commodity content. These are sensible trust inputs, yet neither Google nor other AI-search platforms publish a universal formula that guarantees a business will be cited or recommended.
Treat evidence as a buyer requirement, not an algorithm trick.
This distinction matters because the wrong question produces the wrong work.
“How do we get mentioned by AI?” can lead to manufactured mentions, shallow list placements, and content designed around guessed machine preferences.
“What would a careful buyer need to verify this claim?” leads to stronger case evidence, clearer expertise, useful comparisons, and better source material. Those improvements help whether the buyer arrives through Google, an AI answer, a referral, or a sales conversation.
Finally, Can The Buyer Select You?
A business may be understandable and credible while still making the buying decision unnecessarily difficult.
Suppose the second agency has clear services and strong evidence, but its pages never explain fit. There is no indication of project scope, working model, common constraints, or what happens after the form is submitted. Every CTA says “Get Started,” but none explains what starting means.
The buyer is left with another research task.
Selection content should help a serious prospect answer practical questions:
- Is this built for a company like ours?
- Does the provider understand the problem we actually have?
- How does the engagement work?
- What evidence is relevant to our situation?
- What should we expect next?
- Is there a clear way to ask a specific question?
Many content strategies leak at this point. They optimize for discovery, publish educational material, and then send every reader to a generic homepage or contact form.
When trust has no decision path, interest has nowhere useful to go. An effective path might move from an article about weak search visibility to a focused SEO service page, then to a clearly described review. The CTA should feel like the next diagnostic step rather than a sudden sales demand.
The Four-Question AI Search Visibility Check
A useful first pass can stay focused on four questions.
1. Can Search Systems Clearly Understand What You Do And Whom You Serve?
Check whether each core service has one clear page, whether the intended buyer and problem are explicit, and whether important pages can be crawled and indexed. Review titles, headings, navigation, internal links, and business details for ambiguity.
Fix the offer language before producing more supporting content. More pages will not clarify a service the company still cannot describe.
2. Can Buyers Find Credible Evidence Supporting Your Claims?
List the main claims on each service page. Then locate the proof.
If “experienced,” “strategic,” or “results-driven” has no case detail, named process, relevant example, or expert explanation nearby, it is positioning copy without support.
Useful evidence can protect confidential client information while still showing, with enough specificity, how the company thinks and works.
3. Are Your Positioning And Business Details Consistent?
Compare the website, Google Business Profile where relevant, major directories, social profiles, review platforms, and meaningful third-party coverage.
Look for conflicting service descriptions, old locations, inconsistent company names, outdated team information, and profiles that position the business differently from the website.
Consistency means removing contradictions that make verification harder, while allowing each channel to describe the business in language appropriate to its audience.
4. Is There A Clear Path From Discovery To Comparison To Contact?
Trace the journey from an informational article or external mention to the relevant service page and next action.
Does each step answer the next likely question? Or does the buyer land on a broad page and have to restart the search?
A useful conversion path reduces uncertainty by answering the next buyer question; adding more buttons achieves little when those questions remain unresolved.
Measure What You Can. Label What You Cannot.
AI-search measurement is improving, although important gaps remain.
Google introduced dedicated generative-AI performance reports in Search Console in June 2026. The initial rollout covered a subset of sites. Where available, the reports show impressions, pages, countries, devices, and dates for visibility in generative AI features such as AI Overviews and AI Mode.
Check whether the report is available in your property. Do not assume every site has it yet, and do not confuse an impression with a qualified visit or lead.
For ChatGPT Search, OpenAI says referral links include utm_source=chatgpt.com, which makes inbound sessions identifiable in analytics. Those referrals show visits, while leaving every unclicked consideration, summary, or exclusion outside the measurement view.
Comparable visibility and citation data across ChatGPT, Gemini, Perplexity, and other systems remains platform-dependent and incomplete. Third-party monitoring tools can help observe a selected prompt set, but they do not have access to the platforms’ internal ranking systems. Prompt outputs can also change by wording, location, timing, personalization, and product behavior.
Use a layered measurement view:
- Search visibility: traditional Search Console data and the generative-AI report where available.
- Referral behavior: visits from ChatGPT and other identifiable sources.
- Branded demand: changes in branded searches and direct interest, interpreted carefully.
- Buyer behavior: qualified visits to service and comparison pages.
- Commercial outcomes: inquiries, assisted conversions, lead quality, and sales feedback.
False precision is the main measurement risk. A clean dashboard cannot fill the gaps that the platforms do not expose.
Avoid Shortcuts That Create More Noise
Building a separate page for every imagined fan-out query creates volume without making the business more useful. Buying inauthentic mentions creates the appearance of authority without credible evidence behind it.
Special files and markup deserve the same skepticism when someone presents them as shortcuts into Google AI answers. Google explicitly says its generative search features do not require llms.txt, special AI markup, forced chunking, or content rewritten for language models.
The same principle applies to content production. Rewriting useful human content into stiff fragments or duplicating a generic answer across dozens of pages weakens the material buyers are meant to evaluate. Generic content is much easier to ignore. The winning move is sharper usefulness.
The practical work is less theatrical: make the business legible, support its claims, remove contradictions, build useful comparison material, and connect discovery to a sensible next step.
See How Clearly Your Business Shows Up
Being present in every generated answer is neither realistic nor necessary. The commercial objective is clearer: help the right buyer understand the business, verify its claims, and choose it when the fit is real.
Digitful can review how your services, expertise, evidence, and search presence work together across traditional and AI-assisted discovery.
Start with an AI search visibility review.
Sources And Evidence Notes
- Google Search Central, “Optimizing your website for generative AI features on Google Search”
- Google Search Central, “Introducing Search Generative AI performance reports in Search Console”
- Pew Research Center, “Google users are less likely to click on links when an AI summary appears in the results”
- OpenAI Help Center, “Publishers and Developers - FAQ”
- OpenAI Help Center, “ChatGPT Search”