Recent UK and US roundtables with senior enterprise marketing leaders provide a real-world view of how organisations are approaching generative engine optimisation (GEO) and answer engine optimisation (AEO). These insights come from leaders testing AI search discovery, questioning attribution and seeking an investment case, not guesses or AI-generated predictions about how everyone will search next year. For agencies and technology vendors selling AI search visibility services, the commercial challenge is to help buyers understand what can be improved, what can be measured and what remains outside anyone's control.
A prospective customer can now explore a category, ask for comparisons and gather practical advice through AI interfaces without following the familiar path from search results to a vendor website. That creates both a content problem and a measurement problem. An enterprise CMO may ask whether the brand is represented accurately when AI tools answer questions, whether technical foundations allow information to be found and how any improvement relates to opportunities or revenue.
AEO and GEO need precise definitions
The terms are often used interchangeably, but a proposal should explain its intended scope. AEO generally describes making information understandable and useful when systems provide direct answers to questions. GEO is commonly used for work intended to improve the discoverability and representation of content in generative search environments. These labels are not regulated product specifications and different suppliers may use them differently.
A vendor should translate the acronym into deliverables. Are you reviewing page structure and crawl access? Publishing clearer answers to important buyer questions? Measuring referrals from identifiable AI services? Checking whether approved information is accurately represented in a repeatable set of test prompts? Investigating off-site source material? Each activity has different dependencies and evidence.
The roundtables described experimentation with LLM-oriented content, structured data and analysis of AI referrals. They did not establish a method that guarantees a brand will be quoted or recommended by a model. An honest vendor should begin by stating that distinction.
Enterprise buyers want a business question, not a visibility trophy
Traditional marketing reports already suffer when impressions, rankings and traffic are treated as the end goal. AI search can magnify the problem because generated answers may not always provide a measurable click. A brand mention may be useful, irrelevant or inaccurate depending on the question and the context. A single screenshot of a favourable AI response proves very little about sustained performance.
A more useful buying conversation starts with a commercial decision journey. Which prospective customers are researching problems the business can solve? What questions must they answer before considering a supplier? Which information would reduce confusion about fit, risk, implementation or value? The content programme should be organised around those needs rather than chasing mentions for their own sake.
For a Martech vendor, high-value questions could concern compatibility with existing CRM platforms, treatment of personal data, implementation ownership, contract structure or evidence of ROI. For a professional-service provider, buyers may need to understand the conditions under which an audit, advisory engagement or implementation programme makes sense. AEO and GEO work should improve those explanations for humans first.
Read the roundtable signal without exaggeration
In the UK discussions, a marketing leader described efforts to optimise content for discovery through conversational and LLM-led experiences, using analytics to observe resulting traffic. US discussions covered structured data, technical SEO, AI referrals and the difficulty of connecting emerging search practices with financial return. Some participants were testing AI-related discovery; others were still asking how to justify the investment.
Those are useful buying signals, but they are not a market-wide forecast. The summaries do not disclose a representative sample, a uniform definition of AI-origin traffic or conversion performance for the category. A responsible article and a credible vendor proposal should distinguish leader experience from universal claims.
That caution is commercially useful. Senior buyers are likely to challenge a supplier who guarantees AI answer placement or assigns precise revenue to visits that cannot be observed. A vendor that can describe the uncertainty and set up a reasonable experiment may have a stronger proposition than one selling certainty it cannot deliver.
Begin with content that answers real evaluation questions
A practical audit should examine the information a buyer needs to decide whether a solution belongs on the shortlist. Can visitors understand the intended customer, problem addressed, deployment prerequisites, typical use cases and key limitations? Are claims supported by examples or properly scoped evidence? Is the relationship between the product and the business outcome clear?
Organise priority topics into clusters based on decisions. One cluster might explain an enterprise marketing measurement problem and the available approaches. A second could cover implementation and integration. A third might address governance, procurement and cost. Link detailed pages to a concise category overview so a reader can move from a question to a more complete evaluation.
Answer common questions directly near the point of need. Use descriptive headings, consistent terminology and definitions that a non-specialist can understand. Do not flood a page with artificial FAQ entries or repeat a phrase simply to trigger a search system. Clear information is more durable than a thin page optimised for one imagined prompt.
Technical readiness is a prerequisite, not a shortcut
Enterprise search visibility programmes should still examine basics: accessible pages, appropriate indexing directives, canonical consistency, server responses, rendering and mobile usability. Structured data can help describe certain content types where the markup accurately reflects visible information. It does not guarantee an AI assistant will cite the page, and unsupported schema claims can make technical work actively misleading.
Vendors should document an audit trail. Which important pages can standard crawlers reach? Which information requires interaction, authentication or consent? Are old versions competing with current versions? Does a JavaScript-heavy experience deliver useful content to the tools that can access it? Are title tags, headings and canonical URLs consistent with the intended page?
Not every answer belongs on an open page. Confidential material, client-specific evidence and restricted documents should remain protected. The correct balance between accessible knowledge and controlled information is an editorial and governance decision, not simply a crawlability setting.
How to measure AI-related discovery responsibly
Start with three separate reporting layers. Search visibility covers observed performance in established search systems, such as queries, clicks and indexed landing pages. AI-related engagement covers measurable visits referred by identifiable AI interfaces and approved, documented checks of how selected prompts represent the brand. Commercial outcomes cover actual enquiries, qualified leads and opportunities with appropriately recorded attribution.
Do not collapse these into one number called AI ROI. A user may learn about a vendor through an AI answer and later navigate directly to its website. The reporting system may not identify the original exposure. Conversely, a visit with an identifiable AI referrer does not prove the tool generated a business opportunity. Attribution has gaps, and a mature dashboard should make them visible.
A reasonable pilot could establish a baseline for selected questions, review whether the organisation's pages answer them clearly and record changes in observed referrals and downstream outcomes. The report should distinguish source observations, qualitative checks and editorial interpretation. Repeated, documented tests are better than cherry-picked demonstrations, although even a controlled prompt set cannot represent every user's experience.
The enterprise buyer's AI search vendor checklist
The vendor should be able to answer: Which engines or interfaces are in scope? Which outcomes are directly controlled and which can only be observed? What content is being changed and why? How will technical changes be validated? What is the method for documenting prompts, versions and response variability? Which reporting sources capture referrals, leads and downstream quality?
The buyer should ask how the supplier handles factual accuracy and content provenance. Is a claim backed by public evidence? Can a specialist approve or correct an answer? Does the programme respect robots controls and privacy restrictions? Is there a documented process for refreshing outdated material? A credible agency should be able to describe its limitations and provide a reproducible audit.
Price structure matters too. Is the customer paying for a technical audit, content production, periodic monitoring, referral analysis or all four? How much work depends on the client's subject experts? Will recommendations remain useful after the contract ends? If the buyer owns the content and reporting definitions, the relationship is easier to evaluate.
Avoid turning AI search into another content-volume contest
The UK and US discussions also addressed AI-assisted content production and the tension between speed, originality and review. It may now be easier to publish dozens of similar pages, but that does not make them genuinely useful to a CMO evaluating enterprise solutions. Unverified claims, generic summaries and contradictory advice can undermine a brand's authority.
An effective GEO or AEO content programme should therefore put source quality before output volume. Interview people who understand the subject. Link claims to their context. Distinguish an organisation's position from neutral explanation. Update the information when products, markets or rules change. Show implementation realities rather than recycling a list of trends.
For vendors selling content services, this creates an opportunity to compete on research quality, editorial governance and measurement discipline. Faster publishing may support those capabilities, but it should not replace them.
Fit AI search into the existing customer journey
Some organisations may be tempted to establish AI visibility as a separate team and dashboard disconnected from SEO, content, CRM and sales. That separation creates avoidable blind spots. An enterprise buyer needs to know whether the same prospect questions are being answered consistently across search results, the website, sales conversations and product documentation.
A joined-up approach uses a common set of buyer questions, approved terminology and landing pages with clear next steps. Search and content teams can evaluate visibility, while operations and sales confirm whether enquiries are suitable. Governance teams ensure the information published is safe to share. The aim is to improve discovery and evaluation, not create a new metric without accountability.
Where the organisation sells to several regions, test questions and terminology that reflect those markets. The ability of a page to answer a UK procurement question may differ from its usefulness for a US buyer. Localisation should preserve factual consistency while addressing relevant commercial and regulatory differences.
Construct a fundable pilot
Set a limited subject area and a group of commercially relevant questions. Establish the current state of the website, content and observable traffic. Define the deliverables: perhaps a content audit, a technical accessibility review, revised evaluation pages and a transparent performance report. Include an approval workflow and a date for deciding whether to continue.
A pilot should not promise a universal percentage uplift in citations or sales. It should demonstrate whether the chosen work improved clarity, resolved a technical blocker, increased observable qualified discovery or provided better evidence for a follow-on investment. Record findings even when a platform does not provide the desired attribution.
Finance will reasonably ask why the spend matters alongside established SEO and demand generation. Answer that by showing the specific discovery problem, what the work addresses and how it complements existing channels. Treat any commercial benefit not yet observed as a hypothesis to test.
The implication for GEO and AEO vendors
Senior enterprise marketing leaders are exploring AI-mediated search, but they still need practical answers about evidence, reporting and control. That is a more durable sales opportunity than claiming a secret route into an answer engine. Vendors who can make an organisation's expertise clearer, verify technical foundations and provide responsible measurement are easier to evaluate than those promising unpredictable platform outcomes.
The best proposal makes two promises it can keep: that the work will improve the quality and accessibility of the organisation's information, and that the supplier will report honestly on what changes can actually be observed. Everything beyond that requires evidence from the buyer's own environment.
Speak to The Leadership Board to understand the search, content and AI discovery questions influencing enterprise marketing leaders and how to position your solution credibly.
Frequently asked questions
What is the difference between AEO and GEO?
Both concern how information is presented and discovered in answer-oriented or generative interfaces. Their precise use varies by provider, so enterprise buyers should evaluate the stated deliverables rather than rely solely on the acronym.
Can a GEO agency guarantee citations in AI answers?
No credible supplier can guarantee the behaviour of independent AI systems. It can improve content, technical foundations and measurement, then document observable outcomes and limitations.
How should enterprise teams track AI search ROI?
Keep search metrics, identifiable AI referrals, brand representation checks and verified commercial outcomes separate. Use agreed attribution rules and identify activity that cannot be tracked.
Is traditional SEO still relevant to AEO and GEO?
Yes. Accessible, useful, accurate and well-structured information remains important to human discovery. AI-specific work should complement rather than replace established technical and content fundamentals.