AI search optimisation is the process of making your business information easier for AI-powered search experiences to discover, understand, verify and reference. It is not a way to dictate an answer or guarantee a citation. The practical goal is simpler: give search systems and customers clear, useful, well-supported information about what you do, who you serve and when you are relevant.
For Google’s generative search features, the foundation remains solid SEO: pages must be accessible, indexed and eligible to appear in Search. Google also stresses unique, useful content and warns against mass-producing pages for every possible query variation. Google’s generative AI search guidance is a useful reference point, even though other AI search experiences work differently.
We use AI search optimisation as an operating process. Audit the current representation of your business, map the questions and entities that matter, improve the relevant pages, then test the same prompts repeatedly. The seven steps below give you a practical way to do that.
Table of contents
- What AI search optimisation includes, and what it does not
- What you need before you start
- 1. Audit what AI search can currently find and say about your business
- 2. Map customer questions to the entities your business needs AI systems to understand
- 3. Prioritise pages by business value, evidence gap and effort
- 4. Make priority pages easy to extract, verify and use
- 5. Strengthen credibility and keep entity details consistent
- 6. Fix technical quality before adding more optimisation layers
- 7. Measure repeatably, learn and update the pages that matter
- Common AI search optimisation questions
- Build a system, not a collection of AI search tricks
What AI search optimisation includes, and what it does not
You may see overlapping labels including AI SEO, answer engine optimisation (AEO) and generative engine optimisation (GEO). The labels are not used consistently. In this guide, AI search optimisation means improving your website and wider business information so it is more understandable and useful in answer-led search experiences, including Google’s generative features, ChatGPT search, Gemini, Perplexity and similar products.
Traditional SEO still matters because it supports discovery, crawling, indexing and useful landing pages. AI search optimisation adds a sharper focus on accurate entity information, direct answers, evidence, source-worthy detail and repeatable prompt-based testing. It does not require secret markup, a special llms.txt file, or pages built around every wording variation. It also does not make a brand recommendation inevitable.
What you need before you start
Prepare these inputs before changing pages:
- Access to your CMS, analytics, Search Console and site crawl or auditing tool.
- A shortlist of your highest-value products, services, locations, audiences, use cases and comparisons.
- Ten to twenty realistic customer questions, including non-branded discovery, comparison and problem-solving prompts.
- A simple spreadsheet or workspace to record the exact prompt, platform, date, location, answer, cited sources, mention status and next action.
- A person who can verify claims about your business, products and subject-matter expertise.
Choose one business area first. A narrow pilot, such as a service page plus two supporting articles, produces better learning than attempting to optimise every page at once.
1. Audit what AI search can currently find and say about your business
Start with the pages that should represent your business: key service or product pages, location pages, comparison pages, guides and proof pages. Check whether each page returns a 200 status, is indexable, has a self-referencing canonical where appropriate, is internally linked, and exposes its main content without an interaction or login.
Then run your chosen questions in the relevant AI search experiences. Record both positive and negative outcomes: mentions, omissions, incorrect facts, competitor mentions, citations, and whether the answer sends a user to a page that can actually help them.
Use a fixed test set. For example, a B2B payroll provider might test “best payroll software for a 50-person UK company”, “how to reduce payroll errors” and “payroll software alternatives for construction firms”. Do not judge performance from one appealing answer. Answers can change with wording, location, user context and time.
Expected result: a baseline inventory that connects priority prompts to the pages, facts and gaps behind the current answer.
Troubleshooting: if a core page is not indexed or renders incomplete content, fix that before pursuing content expansion. If answers vary, keep the prompt, location and test date consistent, then look for patterns across several checks. Our AI visibility checker follows this sample-based approach for category questions and records mentions and source links rather than treating one answer as an overall ranking.
2. Map customer questions to the entities your business needs AI systems to understand
AI search does not operate on keywords alone. A useful page needs to make the relationships clear between your business and the entities a customer cares about: products, services, people, locations, industries, capabilities, problems, outcomes and alternatives.
Build a question-to-page map for your pilot area. For each question, note:
- The searcher’s job to be done.
- The entities that must be explained correctly.
- The page that should answer the question.
- The evidence or first-hand detail needed to support the answer.
- Whether the page needs a new section, a refresh, or an entirely new resource.
For example, a cybersecurity consultancy may want to be understood in relation to “penetration testing”, “ISO 27001”, “SaaS companies”, “London”, “pricing model”, “remediation support” and named comparison questions. A generic services page rarely explains all of those relationships well. A focused service page, industry page and evidence-backed guide can.
Start from customer need, then use keyword research to understand topic demand and prompts to understand the fuller question. Our guide to how keywords and prompts work together explains why a single keyword can relate to several prompts, and why related keyword volumes should not be added together as if they measured one audience.
Expected result: every priority question has an owner page and a clear list of facts, entities and evidence it must contain.
Troubleshooting: if one page appears to answer every question, split by intent rather than creating an overloaded guide. A buyer comparing options needs different information from a prospect learning the basics.
3. Prioritise pages by business value, evidence gap and effort
Do not publish indiscriminately. Score each proposed improvement using five practical criteria:
- Business value: Does the question relate to a service, product, qualified audience or important customer outcome?
- Customer demand: Is there observable search demand, recurring sales conversations or prompt evidence for the question?
- Current visibility: Does your business already appear, appear inaccurately, or have no useful page to cite?
- Evidence gap: Can you add original experience, data, examples, process detail or an expert perspective that competitors lack?
- Effort and confidence: Can the page be improved accurately, reviewed by an expert and maintained?
Choose the high-value items with a credible information advantage. This avoids a common failure mode: creating thin pages for many prompt variations without adding anything a reader could not find elsewhere.
Expected result: a short backlog with clear next actions, not a long list of unqualified content ideas.
Troubleshooting: if two ideas have similar demand, choose the one closest to a proven customer need or where your team can supply better proof. Search volume is an input, not a publishing command.
4. Make priority pages easy to extract, verify and use
Rewrite priority pages so the answer appears early and the supporting detail follows logically. We recommend this page pattern:
- A direct opening answer that defines the problem, audience and outcome.
- Descriptive headings that reflect the questions a reader needs answered.
- Short, self-contained explanations before deeper detail.
- Specific examples, constraints, comparisons and decision criteria.
- Evidence for material claims, including source links, methodology or first-hand experience.
- Clear information about the business, author or reviewer, and when the page was updated.
For a service page, explain what is included, who it suits, what inputs are required, what the process looks like and what limitations apply. For a guide, include a practical method, examples and a decision framework. For a comparison, explain the criteria and disclose your relationship to the options discussed.
Lists and tables can help users scan genuine comparisons or multi-step processes, but they are not automatic citation devices. The core test is whether a person could understand and validate the answer quickly. Google’s guidance similarly emphasises valuable non-commodity content, clear organisation and information grounded in original expertise or experience.
Expected result: a reader can identify the main answer, supporting proof and next step without interpreting vague marketing language.
Troubleshooting: remove unsupported superlatives and generic claims. If your page could describe any competitor, it needs more specific experience, evidence or scope.
5. Strengthen credibility and keep entity details consistent
AI answers may draw from multiple sources, so conflicting information creates avoidable ambiguity. Review the facts that should be consistent across your site and reputable third-party profiles:
- Official business name, website, locations and contact information.
- Product and service names, categories, descriptions and availability.
- Founders, authors, reviewers and demonstrable subject expertise.
- Case studies, certifications, policies, pricing qualifiers and claims.
- Brand descriptions used in directories, partner profiles, media coverage and social profiles.
Make authorship meaningful. An author byline should connect to relevant experience, and high-stakes or technical claims should have an identifiable reviewer where appropriate. Link claims to primary research, standards, official documentation or a transparent method. Legitimate digital PR, partnerships and independent coverage can help customers corroborate who you are, but do not pursue mentions that misrepresent your business.
Expected result: core facts about your organisation are accurate, consistent and backed by evidence that a customer can inspect.
Troubleshooting: if different pages use different service names or outdated claims, decide on the canonical wording and update the highest-visibility pages first. Keep an internal source of truth for details that frequently change.
6. Fix technical quality before adding more optimisation layers
Technical SEO remains a prerequisite. Check that important content is crawlable, indexable, mobile-friendly, fast enough to use and available in the rendered HTML. Review robots directives, canonical tags, XML sitemaps, redirect chains, broken internal links and accidental duplicate pages.
Structured data can clarify page details, but it must not contradict the visible page. Google’s structured data guidelines require markup to represent the page content accurately and advise against marking up content users cannot see. Treat schema as a consistency check, not a shortcut to an AI answer.
Also review access rules for the specific experiences that matter to your strategy. For example, OpenAI documents that its OAI-SearchBot is used for ChatGPT search, separately from GPTBot, which relates to model training. OpenAI’s crawler documentation explains the distinction. Review any change with your legal, security and engineering teams, especially on sensitive or gated sites.
Expected result: important pages are available to the relevant systems and present the same essential facts in visible content, metadata and structured data.
Troubleshooting: use URL inspection and rendered-page testing to diagnose JavaScript, indexing and canonical issues. Do not make crawler-access changes simply because a blog post recommends them. Confirm the business, privacy and security implications first.
7. Measure repeatably, learn and update the pages that matter
Create a recurring AI search optimisation review, ideally monthly for a new programme and quarterly for established pages. Re-run the same prompt set across your chosen platforms, then add new questions from sales calls, support tickets, Search Console and on-site search.
Track more than a mention count:
- Mention rate and accuracy for priority prompts.
- Citations or source links to your domain and the destination page quality.
- Competitors and third-party sources repeatedly used in answers.
- Prompt coverage by audience, use case and stage of the buying journey.
- Branded search interest, organic clicks, referral traffic and assisted conversions.
- Page-level improvements made, test dates and the evidence behind each decision.
Use the record to decide what to refresh. If a competitor is repeatedly cited for a comparison, find the missing decision criteria or evidence. If your brand is mentioned inaccurately, correct the underlying pages and external profiles. If a well-built page is never discovered, return to the technical audit and internal linking.
A practical 90-day rollout looks like this:
- Days 1 to 30: audit priority pages, establish prompt tests, fix urgent crawlability and accuracy problems, and complete the first entity map.
- Days 31 to 60: improve the three to five highest-priority pages with direct answers, original evidence, clearer structure and consistent entity details.
- Days 61 to 90: repeat testing, compare results against the baseline, refine weak pages and turn validated gaps into the next content backlog.
Expected result: a repeatable programme that improves the accuracy and usefulness of your presence over time, rather than a one-off campaign chasing screenshots.
Troubleshooting: do not infer a trend from one platform or one prompt. Keep tests repeatable, record context and combine AI-search observations with organic and commercial outcomes.
Common AI search optimisation questions
Is AI search optimisation different from SEO?
It builds on SEO rather than replacing it. Good technical foundations, useful content and a clear site structure remain essential. AI search optimisation adds deliberate work on entity clarity, answer quality, evidence, prompt coverage and accuracy across answer-led search experiences.
Should we create a page for every prompt?
No. Create or improve pages when the underlying customer need, intent and information requirement are distinct. Publishing many near-duplicate pages for wording variations is unlikely to help users and can create maintenance and quality problems.
Does structured data guarantee an AI citation?
No. Structured data can help keep page information explicit and machine-readable, but it does not guarantee display, a mention or a citation. The visible content still needs to be accurate, useful and eligible for discovery.
How quickly will results change?
There is no fixed timeline. Changes depend on technical access, indexing, the platform, the prompt, competing sources and how often information is refreshed. Use repeatable tests and page-level business metrics to evaluate progress.
Build a system, not a collection of AI search tricks
The completed outcome is a focused AI search optimisation workflow: your priority information is accessible, evidence-led, clearly structured and monitored against the questions that matter to customers.
Start with a baseline, choose one commercially meaningful topic cluster and make the next three to five page improvements measurable. Then use the findings to decide what deserves further investment. If you need a starting point, run an AI visibility check to see how your business is currently represented in a small set of category questions.