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90 Day Plan to Get Cited by Generative AI in SEO for Marketers and SEOs

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Generative AI changes what counts as visible, so prioritise crawlability, clear answers and monitoring now. SEO is still essential, but it needs to be machine readable and brand centric to earn a place in AI answers. This week, verify your Search Console generative AI control, add direct answer sentences and FAQs to priority pages, and start basic synthetic prompt checks so you know where you stand.

TL;DR:

  • Ensuring your website’s content is crawlable, properly structured, and free of duplicates is crucial for AI citation accuracy and visibility.
  • Writing clear, direct answers and adding FAQ schema enhances machine understanding, with schema matching visible content for better AI recognition.
  • Regularly monitoring your AI presence through Search Console controls and synthetic prompt checks prevents outdated or incorrect citations from spreading.
  • Using AI as an editorial tool requires human verification to avoid factual errors and prevent content dilution from volume-driven approaches.
  • Future SEO success depends on treating AI visibility as part of a broader ecosystem, with ongoing content updates, intent-focused keyword strategies, and transparent governance.
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Table of Contents

Apply and adapt foundational SEO best practices for generative AI

The fundamentals haven’t changed, but the stakes have. If a large language model can’t read your page, it can’t cite your page. Google’s own guidance is blunt about this: crawlability, structured data that matches visible text, and high quality writing are the best preparation for generative AI features. No special AI sitemap, no secret markup. Just the basics, done properly.

Start with what’s hiding from crawlers. Content loaded only through JavaScript, text behind lazy loading that never renders in time, or copy stuffed into images without alt text all create blind spots. If your page’s answer lives in a component that needs a click to expand, an AI system may never see it.

Duplicate or conflicting pages are another quiet problem. When you have three versions of the same service page with slightly different claims, you’re not just confusing Google, you’re giving a language model three different “facts” to choose from. Consolidate down to one authoritative version per topic.

Your headings and lead sentences carry more weight than ever, because they’re what gets extracted first.

  • Write your H1 and opening sentence as a direct answer to the question the page targets.
  • Merge or redirect near duplicate pages so there’s one clear source of truth per topic.
  • Check that JavaScript rendered content, lazy loaded sections and image only text all appear in a plain HTML crawl.
  • Skip speculative fixes like unofficial “AI sitemaps” and stick to verified technical fundamentals.

None of this is exotic. It’s the same discipline good SEO has always demanded, just with a sharper reason to bother.

Authoring AI‑friendly content: plain English, structured answers and FAQ micro‑content

Writing for generative AI is really writing for a very literal reader. Plain English wins, because language models parse clear, direct sentences more reliably than clever ones. Government guidance on AI transparency reinforces the same point from a different angle: clear structure and plain language build trust with readers, and that same clarity happens to make content easier for machines to summarise accurately.

Here’s a practical sequence for reworking a page:

  1. Open each section with a one sentence answer to the implied question, before any context or backstory.
  2. Add a short FAQ block under the main content, phrased as real questions with two or three sentence answers.
  3. Match your schema markup to what’s actually on the page. If your FAQ schema lists five questions, all five need to be visible in the HTML.
  4. Add one unique fact, statistic or example per page that a competitor’s generic content doesn’t have.

Structured data does real work here. Google warns explicitly that when schema keys don’t mirror the visible headings and lead sentences, models tend to ignore the schema or misattribute the facts to the wrong entity. Treat your markup as a mirror of the page, not a wish list of what you’d like it to say.

Pro Tip: Pull your three most common customer questions from sales calls or support tickets and turn them into FAQ entries before writing anything new.

The pages that get cited aren’t always the longest ones. They’re the ones with an answer sitting in the first two sentences and a fact nobody else bothered to include.

Technical visibility and Search Console: controls, reports and immediate checks

Search Console now gives you a direct lever over generative AI visibility. The Search generative AI control lets you include or exclude your property from generative AI features, and a companion Generative AI performance report shows where your pages surface in AI responses. Opting out removes you from those features; it has no bearing on regular indexing or your standard organic rankings.

Run through this checklist before assuming anything is broken:

  • Confirm your generative AI control setting matches your intent. It’s easy to be opted out by default and not realise.
  • Open the Generative AI performance report and note which pages appear in overviews, and for which queries.
  • Crawl your site on both mobile and desktop rendering to catch content that only appears on one.
  • Check structured data parity: does every schema field match text a user (and a model) can actually see?
  • Test page speed and confirm images and video carry accessible alt text and captions, since unreadable media is invisible media.

Google announced new Search Console tools in 2026 specifically to help site owners navigate these generative features, so it’s worth checking the settings panel even if you audited it last year. A five minute check now can save weeks of wondering why a page that ranks well never gets cited in an AI answer.

Monitoring presence in LLMs: synthetic prompt monitoring and automated checks

Manual spot checks won’t cut it once you’re tracking presence across multiple AI systems. Synthetic prompt monitoring, running a fixed set of realistic queries against language models on a schedule, gives you a repeatable way to see whether your brand shows up, and whether what it says about you is correct. Practitioner reporting on AI search visibility describes this as a critical technique for catching outdated or incorrect citations before they calcify into the default answer a model gives.

A workable setup for most teams:

  1. Build a list of ten representative user intents covering your core services and common objections.
  2. Run each prompt across three models, say ChatGPT, Gemini and Perplexity, with two phrasing variants each.
  3. Log the results weekly: which brand names come up, which URLs get cited, and whether the facts stated are accurate.
  4. Flag anything that cites a stale or non canonical page, misstates a fact, or omits your brand where a competitor appears instead.

That structure, ten intents across three models with two phrasing variants, is deliberately minimal. It’s meant to be sustainable weekly, not a research project.

Pro Tip: Store every synthetic prompt result in a simple spreadsheet with date, model, query and citation, so you can spot a pattern instead of reacting to a single bad answer.

Teams that automate even this basic version of the process catch problems while they’re still small, rather than after a wrong fact has been repeated by three different models for two months.

Measurement and KPIs for generative‑AI visibility

Clicks and sessions no longer tell the whole story. A page can influence a buyer’s decision by being cited in an AI answer the buyer never clicks through from, which means your reporting needs a second lane sitting alongside traditional analytics.

  • Track AI impressions and citations from the Search Console Generative AI performance report separately from organic clicks and sessions.
  • Layer generative AI data against on-site conversion and assisted conversion reports to see if AI-sourced awareness eventually turns into a lead.
  • Set experiment style KPIs: share of citations across your monitored prompt set, citation accuracy rate, and conversion rate specifically for traffic that lands after an AI mention.
  • When reporting to a CMO or CRO, frame AI visibility as a leading indicator sitting upstream of pipeline, not a replacement for the numbers they already trust.

McKinsey’s analysis of AI search notes that AI answers draw on a mix of owned, third party and community sources, which is exactly why a single dashboard number won’t capture the full picture anymore.

Governance, transparency and risk management when using generative AI for content

Using generative AI to draft or edit content brings a responsibility to be upfront about it. Government guidance on AI transparency recommends clear labelling of AI-generated or AI-edited material, plain English writing and a regular review cycle, all in the name of keeping reader trust intact.

Assign a named owner to every page category, so two teams don’t publish conflicting answers to the same customer question. Set a review cadence, quarterly for evergreen pages, faster for anything time sensitive, and put it on a calendar rather than leaving it to memory. Governance works best as a genuine collaboration: marketing decides tone and priority, legal checks claims and disclosures, and whoever owns the tech stack keeps structured data and page templates in sync with what’s actually published.

Implementation playbook: a 90‑day plan for marketing teams and SEOs

This is the sequence that turns the sections above into a schedule rather than a wish list.

  1. Weeks 0 to 2, diagnostics. Crawl the full site for hidden or JavaScript-only content, build a content inventory flagging duplicates, run your first synthetic prompt set across three models, and confirm your Search Console generative AI control setting.
  2. Weeks 3 to 8, fixes. Consolidate duplicate pages, rewrite lead sentences on priority pages into direct answers, add FAQ blocks with matching schema, and refresh two or three high-value topics with a genuinely new fact or example.
  3. Weeks 9 to 12, monitoring and reporting. Automate the weekly synthetic prompt run, set your baseline KPIs for citation share and accuracy, run one small experiment (a rewritten FAQ, say) and measure the shift, then present findings to stakeholders in the format they already use for organic reporting.

Lean teams can run this whole cycle with a shared spreadsheet and existing crawl tools. The AI marketing action plan for small teams covers a lighter version of this same sequence if you’re doing it without dedicated headcount.

Pro Tip: If week two’s crawl turns up more than a handful of duplicate or conflicting pages, fix consolidation before touching anything else. Everything downstream depends on having one authoritative version of each answer.

Bring in outside help when the diagnostics phase surfaces problems your team doesn’t have the tooling or time to fix, structured data overhauls and large scale content consolidation are the two most common triggers.

Implementation playbook: a 90‑day plan for marketing teams and SEOs — overview diagram

Impact of generative AI on keyword research strategies

Keyword research used to mean finding the exact phrase people typed. Now it means understanding the question behind a conversational prompt, because that’s what’s feeding the model an answer. A search for “best CRM for a five person team” and a prompt asking an AI assistant the same thing can surface completely different sources, because the model is weighing intent and context rather than just matching terms.

This pushes keyword research toward intent clusters rather than single phrases. Instead of optimising one page per keyword, group the real questions a buyer asks across their decision process, initial research, comparison, objection handling, and write content that answers each cluster directly. Long tail, conversational phrasing matters more than it used to, because that’s closer to how people prompt an AI assistant than how they typed into a search box a few years ago.

Four-stage SEO intent cluster framework

It also means keyword tools built purely around search volume tell you less than they used to. A low volume phrase might still be the exact wording a language model paraphrases from your page, so pair volume data with a look at what questions actually appear in your synthetic prompt testing. The two data sets, traditional keyword research and prompt monitoring, are starting to answer the same question from different directions.

Role of AI in content personalization and user experience optimization

Generative AI is changing what a page shows a visitor, not just what it says to a crawler. Dynamic content blocks that adjust based on a visitor’s location, referral source or past behaviour are becoming easier to build and more common on service and product pages.

The risk is that personalisation can work against AI visibility if it’s not handled carefully. If a language model crawling your page sees a generic placeholder instead of the personalised content a real visitor gets, it’s grounding its answer on the wrong version. The fix is straightforward: make sure the default, crawlable version of any personalised section still contains a complete, accurate answer, with personalisation as an enhancement layered on top rather than a replacement for the base content.

On the user experience side, AI-assisted tools can speed up tasks like generating alt text, drafting meta descriptions, or flagging pages with thin content, freeing up time for the judgement calls that still need a person, deciding which facts matter, which examples to include, and where a claim needs a source. Used that way, AI supports the editorial process rather than substituting for it.

Ethical considerations specific to AI content generation beyond labeling

Labelling AI-generated content is the visible part of the job. The harder part is what happens before labelling ever comes into play: making sure the content is accurate and fairly represented in the first place.

Generative models can reproduce bias baked into their training data, skewing tone, examples or framing in ways that aren’t obvious on a quick read. A human editor checking for balance, not just grammar, is the practical safeguard here. Misinformation is the sharper risk: a model can state a fact confidently and be wrong, and if that draft goes live without a fact check against a primary source, the error becomes your published claim. Every statistic, price or claim in AI-assisted content needs the same verification standard you’d apply to a claim from a junior writer, checked against a source before it ships.

There’s also a fairness question in how AI-assisted content competes in the wider information ecosystem. Publishing thin, templated pages purely to catch AI citations crowds out more useful information and erodes the same trust that transparency labelling is meant to protect. The ethical bar isn’t just disclosure, it’s making sure what you disclose is actually worth reading.

Search algorithms are adjusting to reward exactly the qualities that help AI systems too: clear structure, verifiable facts and content that isn’t just a rehash of the top ten results. McKinsey’s research on AI search frames this as a shift from optimising a single owned website to influencing an entire ecosystem of sources an AI system might draw from, including third party mentions and community discussion.

Expect more granular controls to follow the pattern set by the Search generative AI control, giving site owners finer say over how and where their content appears in AI features. Expect measurement to keep splitting into two lanes too, traditional organic performance alongside generative visibility metrics, because the underlying discovery paths are genuinely different now.

The organisations that adapt fastest are treating this as an ecosystem problem rather than a single-website problem, tracking mentions and citations wherever they happen, not just on their own domain. That’s a meaningful shift in how a marketing team allocates its attention, and it’s likely to become the default way SEO performance gets reported within the next couple of years.

Case studies or examples showcasing successful integration of generative AI in SEO

The clearest pattern in successful integrations isn’t a single dramatic tactic, it’s disciplined repetition of the fundamentals covered above. Teams that consolidate duplicate content, rewrite lead sentences into direct answers, and add FAQ schema that matches visible text tend to see AI citations pick up on exactly those refreshed pages, because that’s precisely what the mechanism rewards.

Google’s own guidance notes that a small programme of topical refreshes and FAQ insertions often produces a disproportionate lift in AI citations compared with a broad campaign of brand new content. That’s a useful signal for prioritisation: fixing and sharpening ten existing pages tends to beat publishing thirty new ones, at least where AI visibility is the goal.

The common thread across teams doing this well is treating it as an iterative loop rather than a one-off project. Fix a page, run it through synthetic prompt monitoring, see whether the citation appears and whether it’s accurate, then move to the next page. It’s unglamorous work, but it’s the version of “AI SEO” that shows up in the reporting.

Risks and challenges associated with over‑reliance on generative AI for SEO content

The biggest risk isn’t using AI to help write content, it’s letting it write unsupervised. Language models can produce fluent, confident sentences that are factually wrong, and without a human fact check step, those errors go live and get cited as your brand’s position on something you never actually said.

Volume is a related trap. It’s tempting to use generative tools to produce far more pages than a team could write manually, but a flood of thin, similar pages dilutes your own authority and gives a language model multiple slightly different “sources” to choose from when it’s trying to cite you. Australian government guidance is explicit that transparency and regular review matter precisely because unchecked AI content erodes trust over time, not just accuracy.

There’s also a dependency risk worth naming: teams that stop doing original research, customer interviews or genuine data collection because AI can “fill the gap” end up with content that has nothing unique to offer. That’s the content most likely to get summarised and skipped rather than cited and trusted. Generative AI is a strong drafting and editing assistant. It’s a poor substitute for the judgement, verification and original insight that make content worth citing in the first place.

Publisher perspective: how MYBMC applies these methods for clients

The process runs audit, prioritise, implement, monitor, in that order, every time. Crawling for hidden content and duplicate pages first is important, because fixing those unlocks everything downstream. The main tradeoff is speed against governance: moving fast on content fixes is easy, but building a review cycle that catches errors takes longer to bed in properly. That patience is what protects the results.

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How MYBMC can help you act on this

You don’t need to run this whole playbook solo. Our AI Search Optimisation service picks up exactly where this article leaves off: auditing your priority pages for crawlability and structured data gaps, rewriting lead sentences into direct answers, and setting up a synthetic prompt monitoring routine so you know where you stand each week.

Mybusinessmarketingcoach

Many organisations currently do not systematically track generative AI visibility with dedicated KPIs, according to McKinsey’s research, which means most brands are flying blind on exactly the metric that matters here. We combine that monitoring with our core SEO services so the fixes and the tracking sit under one roof, not split across three vendors. If you’d rather have a second engagement priced for your monitoring stack, our creative testing partner is a useful resource for teams also refreshing ad creative alongside their content work.

Get in touch through our SEO services page to book an audit and see exactly where your priority pages stand today.

Sources

FAQ

How can AI be used in SEO?

AI can help draft and edit content, generate alt text, cluster keywords by intent and flag thin or duplicate pages faster than manual review. It’s most useful as an assistant inside an editorial process that still includes human fact checking and a clear content owner.

Can ChatGPT do SEO?

ChatGPT can support SEO tasks such as drafting FAQ answers, summarising research or suggesting heading structures, but it can’t crawl your site, read your Search Console data or verify facts against a primary source. Treat it as one tool in the workflow, not a replacement for technical audits or fact checking.

Is there an AI tool for SEO?

Search Console itself now includes AI-specific features: the Search generative AI control lets you opt a property in or out of generative AI features, and the Generative AI performance report shows where your pages appear in AI responses. Beyond that, synthetic prompt monitoring against models like ChatGPT, Gemini and Perplexity is a practical, tool-light way to track presence.

Is SEO still worth it in 2026?

Yes. The fundamentals that earn organic rankings, crawlable content, clear structure and genuine expertise, are the same fundamentals that get a page cited in AI answers, so SEO work now pays off in two visibility channels rather than one.

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