How to Optimise Your Website for Google AND AI Search Engines at the Same Time?

Most marketers I speak to in 2026 are stuck in a strange place. They keep asking me whether they should keep doing SEO or switch entirely to “AI SEO” or whatever the latest acronym is that week. My answer always disappoints them a little: you do not have to pick.

Google still drives massive amounts of traffic. AI tools like ChatGPT, Perplexity, Gemini, and Google’s own AI Overviews are growing fast. Optimising for both at the same time is not only possible, it is actually easier than running two separate strategies. The trick is knowing what they share, where they differ, and which moves give you wins on both fronts at once.

That single, unified approach is what I call HEO, or Hybrid Engine Optimisation. If you want the full background on the framework, my pillar guide covers it in depth: What is Hybrid Engine Optimisation (HEO) and Why SEO Has Changed in 2026. This article focuses on the practical “how to optimise website for Google and AI search engines” playbook, with the steps in the order I actually run them with clients.

Why You Cannot Pick Just One Anymore

A few years back, you could pour your energy into Google rankings and call it a day. That world is gone. Buyers now bounce between Google search, Google AI Overviews, ChatGPT, Perplexity, Gemini, and Claude, often within the same hour. If your brand only shows up in one of those places, you are missing the rest.

Picking one channel and ignoring the other is the kind of decision that looks safe today and costly twelve months from now.

What changed in search behaviour

Two things broke the old model. First, ChatGPT crossed 900 million weekly active users in February 2026, which means a huge share of search-style queries now happen outside Google entirely. Second, Google itself started answering questions directly through AI Overviews. The combined effect is fewer people clicking traditional results, and more people relying on AI-generated answers.

Users do not see this as a switch. They just want the answer, and they go wherever it shows up fastest. Your job is to be in both places.

The cost of optimising for only one

If you only do classic SEO, you keep your Google rankings but miss the growing share of buyers researching through AI tools. If you only chase AI visibility and ignore Google fundamentals, you end up with shaky technical foundations that hurt both your Google rankings AND your AI citations (since AI engines pull from Google’s index too). The smart move is to build one strategy that hits both at once.

What Google and AI Search Have in Common

Here is the part most articles skip. Before chasing what is different, it helps to see what overlaps. The good news is, a lot overlaps. If you have been doing good SEO already, you are closer to AI search optimisation than you think.

These four shared signals make up roughly 70% of what you need to do, and getting them right pays off on both channels.

Both want clear, helpful content

Google’s helpful content update and the way AI engines pick sources are surprisingly similar at the core. Both reward content that genuinely answers a user’s question instead of dancing around it. Both punish thin, padded, or rehashed content. If your page reads like it was written for a person who wanted a real answer, you are halfway there for both.

Both depend on technical health

Slow sites, broken pages, weird redirects, and crawl errors hurt you everywhere. Google bots need clean pages to index. AI crawlers (GPTBot, ClaudeBot, PerplexityBot, Google-Extended) need the same. Site speed, mobile responsiveness, clean HTML, and proper status codes are non-negotiable for both.

Both reward E-E-A-T signals

Experience, Expertise, Authoritativeness, and Trustworthiness sit at the heart of how Google ranks pages, and AI engines lean on similar signals when deciding what to cite. Real authors with credentials, original data, accurate sourcing, transparent business information, and consistent brand presence across the web all build trust on both fronts.

Both use schema to understand pages

Schema markup is one of the few things that helps Google AND AI engines in almost equal measure. Google uses it for rich snippets and to understand entities. AI engines use it to parse content cleanly and decide what to extract. If schema felt optional in 2022, it is mandatory in 2026.

Where Google and AI Search Differ

That said, they are not identical twins. Google and AI engines do diverge in a few important ways. Knowing where helps you stop wasting effort on tactics that work for one but not the other.

These are the four real differences worth memorising.

Different ranking signals and weights

Google still leans heavily on backlinks, on-page signals, and click data. AI engines weigh structured answer extractability, brand mention density across the web, and content clarity more than backlink count. A page with strong content but few backlinks can do better in AI citations than in Google rankings, and vice versa.

Different content formats they prefer

Google still rewards depth, sometimes 2,000 to 3,000 word pieces with rich sub-sections. AI engines reward chunks: short, self-contained answer blocks they can lift and quote. The fix is to write long content that is internally chunked, so each section works as a standalone quotable unit.

Different ways they discover content

Google crawls the web through Googlebot and indexes pages directly. AI engines mostly work from snapshots of training data plus real-time retrieval through tools like Bing search (for ChatGPT), their own crawlers, or direct partner data. This means timeliness shows up differently. A page can rank on Google in days but might take weeks or months to influence AI answers depending on the engine.

Different metrics for measuring success

The classic SEO dashboard tracks rankings, organic traffic, backlinks, and impressions. An AI search dashboard tracks brand citations across ChatGPT, Perplexity, Gemini, AI Overview appearances, branded search lift, and direct traffic from AI tools. They are related but not the same numbers.

The Foundation Layer for Both Engines

You cannot optimise content if the foundation is broken. Before touching anything fancy, get the basics right. Most websites I audit fail here, and they cannot understand why their AI visibility is weak even though they “do SEO”.

Run through these five before moving on. None of them are exciting, but they all matter.

Site speed and Core Web Vitals

Test every important page through PageSpeed Insights. Aim for LCP under 2.5 seconds, INP under 200ms, and CLS under 0.1. Slow pages hurt rankings and they also make it harder for AI crawlers to fetch and parse content cleanly.

Mobile-first responsive design

A meaningful share of your AI traffic and search traffic comes from mobile. If your site breaks on a mid-range Android phone, you are losing visitors on both channels at the same time. Mobile-first is not a 2018 buzzword anymore. It is table stakes.

Clean crawling and indexing setup

Open Google Search Console weekly. Watch for crawl errors, indexing gaps, and pages stuck in “Discovered, currently not indexed”. Same logic applies to AI crawlers: if they cannot reach the page, they cannot cite it.

HTTPS and clean URL structure

This one is basic but I still see violations. Every page on HTTPS. URLs that describe the content. No weird parameters where readable slugs should be. No mixed content warnings. Both Google and AI engines treat HTTPS as a baseline trust signal.

XML sitemap for both crawlers

Submit your XML sitemap to Google Search Console and to Bing Webmaster Tools (Bing powers some AI engines). Keep the sitemap clean, updated, and free of redirected or removed URLs. It helps every crawler find what matters.

Content Structure That Wins on Both

If I had to point to the single biggest lever in 2026, it would be content structure. Not the topic, not the word count, not even the keyword. The way you organise the page on the screen is what decides whether AI engines can cite it and humans can scan it.

Here is the structure I follow on every page I help write or rewrite. Each one of these moves helps both Google AND AI engines.

Answer-first paragraph under each heading

Right after every H2 or H3, write two to four sentences that directly answer what the heading promises. No throat-clearing intro. No “in today’s world” filler. Just the answer. AI engines can lift this paragraph as a standalone quote, and human readers get the point fast.

Use natural question-style H2s and H3s

When it makes sense, write headings the way people actually ask questions. “Why your website gets traffic but no leads” reads better and matches more search queries than “Website conversion issues”. Headings that match real questions get pulled into AI Overviews and Google’s People Also Ask more often.

Add FAQ blocks with schema

A clean FAQ section near the end of the article, with 8 to 12 questions and concise answers, gives AI engines easy fodder to quote. Wrap it in FAQPage schema so Google can show it as a rich result too. Two birds, one block of HTML.

Break content into quotable chunks

Long paragraphs are bad for AI extraction. Break thoughts into short paragraphs of 2 to 4 sentences. Use bold sparingly to highlight key terms. Add small definition boxes where helpful. Each chunk should make sense even if it is the only thing someone reads.

Use tables for comparisons and data

Tables are weirdly underused. AI engines love them because they are structured data hiding in plain sight. Whenever you have a comparison, a list of options with attributes, or a side-by-side of approaches, a table beats prose. Tables show up in AI Overviews more often than people realise.

Keep paragraphs short and scannable

Wall-of-text pages lose readers and confuse AI. Aim for 2 to 4 sentence paragraphs in most places. Whitespace is not wasted space. It is the breathing room that makes content actually get read.

Add original data and examples

This is the most underused E-E-A-T move. Original data (even small datasets from your own clients or research) makes your content uniquely citable. AI engines reward originality because it is hard to fake. Even one chart with your own numbers beats five generic ones from other sources.

Schema Markup for Google and AI

Schema is the layer that explicitly tells search engines and AI engines what your content is about. Most sites I audit either have no schema or have basic Article schema applied inconsistently. Fixing this is one of the highest-leverage moves you can make.

Below is the priority order I use. Implement them in this sequence and you will cover roughly 90% of what matters.

Article schema for every blog post

The baseline. Add Article (or BlogPosting) schema to every blog post and content page. Include headline, author, publish date, modified date, publisher, and image. This is the schema both Google and AI engines look at first when trying to understand who wrote what and when.

FAQPage schema for AI Overviews

If you only add one new schema this year, make it FAQPage on your FAQ blocks. It directly helps you appear in Google’s People Also Ask and feeds clean Q&A pairs to AI engines. It is also one of the easiest schemas to implement.

Organization schema for brand entity

On your homepage and About page, add Organization (or LocalBusiness if you serve locally) schema with your full business name, logo, address, contact details, and sameAs links to your social profiles. This helps AI engines build a clean entity profile for your brand, which is critical for citations.

Author schema for E-E-A-T trust

Add Person schema to author bios. Include credentials, sameAs links to LinkedIn and other profiles, and a clear description of expertise. Author authority is becoming a major signal for both Google and AI engines, and most sites still ignore it.

HowTo schema for step-by-step guides

If you publish tutorials, checklists, or step-by-step content, HowTo schema makes those pages eligible for special placements in search. AI engines also use HowTo data to surface clean step lists in their answers.

Technical Files That Matter in 2026

These are the files almost every “how to optimise for AI” article skips, which is strange because they are some of the most important moves you can make in 2026. They control whether AI engines can even access your content in the first place.

Get these right and you give yourself a real advantage over competitors still pretending it is 2019.

robots.txt for AI crawlers

Open your robots.txt file. Most sites have rules for Googlebot but nothing for AI crawlers like GPTBot, ClaudeBot, PerplexityBot, Google-Extended, or Bytespider. Decide consciously which ones you want to allow. Blocking them by default (which some CMS platforms do) means you are invisible to those engines.

Should you allow GPTBot and ClaudeBot

For most businesses chasing visibility, the answer is yes. Letting GPTBot, ClaudeBot, Google-Extended, and PerplexityBot crawl your site means your content can show up in ChatGPT, Claude, Google’s AI products, and Perplexity answers. The trade-off is that your content may be used to inform AI responses without a direct link back, but that exposure usually outweighs the cost. Sites that block these bots are quietly disappearing from AI search.

Adding llms.txt to your website

Llms.txt is a proposed standard that helps large language models understand your site’s structure and most important pages, similar to how XML sitemaps help search engines. Adoption is still early, but adding it now puts you ahead of competitors. The file lives at your root URL (yourdomain.com/llms.txt) and lists key resources in a clean markdown format.

Updating XML sitemap for AI

Make sure your XML sitemap is fresh and submitted to Google Search Console and Bing Webmaster Tools. Bing matters more than people realise because ChatGPT’s web search uses Bing as its primary source, so showing up in Bing’s index is part of your AI visibility strategy.

Entity and Brand Signals AI Engines Trust

Here is where AI search optimisation moves outside your website. AI engines build their understanding of your brand from how it shows up across the open web, not just from your own pages. If your brand is only visible on your own site, AI engines will not have enough signal to recommend you.

This is the off-page layer most teams forget, and it is where many of the biggest wins live.

Build a clean brand entity online

Make sure your brand name is consistent everywhere: your website, LinkedIn page, Google Business Profile, Wikipedia (if eligible), industry directories, Crunchbase, and your social handles. Inconsistencies confuse AI engines and weaken your entity profile. Pick a single brand spelling and stick with it everywhere.

Get mentioned on high-authority sources

Guest posts, podcast appearances, expert quotes in industry blogs, and roundup features all build the brand mention density that AI engines use as a trust signal. Mentions count even when they do not include a link. This is one of the few areas where PR and SEO have genuinely merged.

Be active in Reddit and Quora

AI engines pull heavily from Reddit, Quora, and other community platforms. Showing up in real conversations (not spammy self-promotion) helps your brand become part of the data AI engines learn from. Be useful, answer questions honestly, mention your work when it is genuinely relevant.

Add author bios and credentials

Every article and key page should have a visible author. The bio should include real credentials, a photo, a link to LinkedIn, and a short description of expertise. Anonymous content struggles to build trust with both Google’s E-E-A-T evaluation and AI engines that prefer cited sources with verifiable authors.

Use consistent NAP across the web

Name, Address, Phone (NAP) consistency across every listing matters even for non-local businesses. AI engines verify entity legitimacy partly through how consistently a brand presents itself across the web. Audit your top 20 listings and fix mismatches.

Platform-Specific Optimisation Tips

This is the section nobody else writes properly. Different AI engines reward slightly different things, and small adjustments per platform can move the needle.

You do not need to optimise separately for each one, but knowing where each puts its weight helps you prioritise.

What works for Google AI Overviews

Google AI Overviews lean heavily on pages that already rank well on Google itself, with FAQ schema, clean answer paragraphs near the top of sections, and high E-E-A-T signals. According to Google’s official AI optimisation guide, there is no special “AI Overview optimisation hack”. If you do good SEO and structure content for direct answers, you become eligible.

What ChatGPT looks for in citations

ChatGPT (when browsing the web) uses Bing as its primary search source, so being indexed in Bing matters. It tends to favour authoritative sites, well-structured content, and pages with clear, factual answers. Brand mention density across the web also matters because ChatGPT’s training data includes that broader context.

How Perplexity picks sources

Perplexity is the most transparent of the AI engines about how it cites. It tends to favour fresh content, authoritative domains, and pages with clean, well-structured answers. Pages with FAQ sections, comparison tables, and direct answers often get cited. Perplexity also shows the actual sources, which means a citation here often drives real traffic.

How Gemini selects content to surface

Gemini leans on Google’s own systems, so anything that helps you on Google generally helps you on Gemini too. The added layer is structured data and Knowledge Graph presence: brands with strong entity profiles in Google’s Knowledge Graph tend to show up more often in Gemini’s answers.

The Right Order to Implement Everything

This is where most marketers get stuck. They read a hundred tips and then freeze because they do not know where to start. Here is the exact order I run with clients. Follow it and you will build momentum without burning out the team.

Each phase compounds on the previous one, so do not skip.

Week 1: Audit and quick fixes

Run a full technical audit. Check Core Web Vitals, mobile usability, indexing status, robots.txt rules (including AI crawler permissions), and HTTPS. Fix anything obviously broken. This week is about cleaning up, not adding new things.

Weeks 2 to 4: Content restructuring

Take your top 10 traffic-driving pages and restructure them: answer-first paragraphs under each heading, FAQ blocks added, paragraphs broken up, tables added where useful. Do not write new content yet. Fix the highest-value pages first because that gives you the fastest visibility lift.

Month 2: Schema and technical files

Add Article, FAQPage, Organization, and Author schema across your site. Implement llms.txt. Update XML sitemap. Submit to both Google Search Console and Bing Webmaster Tools. This is the technical layer that supports everything else.

Month 3 onwards: Off-page authority

Start building brand mentions: pitch guest posts, get on podcasts, contribute to industry roundups, engage on Reddit and Quora. This is slow, long-term work but it is what eventually pushes you into AI citations.

How to Measure Success on Both Channels

You cannot improve what you do not measure. Most teams measure Google performance well but completely ignore AI visibility, which means they are flying half-blind. Here is a practical measurement framework.

You do not need every tool listed. Pick one from each category and start there.

Track Google rankings the usual way

Keep using Google Search Console, Ahrefs, SEMrush, or Surfer SEO for keyword rankings, impressions, clicks, and CTR. These tools are not going anywhere. They tell you how Google sees your site.

Track AI visibility with new tools

Add one AI visibility tracker like Profound, Otterly.AI, AthenaHQ, or Peec AI. These tools show you how often your brand is cited in ChatGPT, Perplexity, Gemini, and AI Overview responses. Most offer free trials so you can start small.

Filter AI traffic inside GA4

In Google Analytics 4, you can identify AI traffic by filtering referrals from chat.openai.com, perplexity.ai, gemini.google.com, and similar domains. Create a custom segment for “AI referral traffic” and watch it grow over time. This is a free way to start measuring AI impact.

Build a single HEO dashboard

Combine your Google metrics and AI visibility metrics into one dashboard (Google Looker Studio works well for this). Track them side by side weekly. This is your real-time view of how your dual strategy is performing.

Common Mistakes That Hurt Both Channels

When teams try to optimise for both Google and AI, a few mistakes show up over and over. Each one quietly costs visibility on both fronts at the same time.

If you can avoid these four, your strategy will move much faster.

Blocking AI crawlers by default

Some CMS platforms block AI crawlers in their default robots.txt configuration. Other site owners block them out of caution about content scraping. Either way, the result is the same: you are invisible to those engines. Check your robots.txt explicitly and make a conscious decision instead of leaving it to default.

Writing only for keywords or only for AI

Writing purely for keywords gives you stuffed, repetitive content that AI engines dislike. Writing purely for AI gives you clinical, fact-dump content that humans bounce from. The winning style sits in the middle: clear, natural, helpful writing with good structure. Write for humans first, structure for both.

Skipping schema because it feels old

Schema markup has been around for years, and some teams treat it like an outdated checkbox item. In 2026, it is one of the strongest signals you can send to both Google and AI engines. Not using schema in 2026 is like not using meta titles in 2010.

Ignoring brand mentions outside the website

Many teams treat their own site as the whole strategy. They polish the homepage endlessly while ignoring whether their brand shows up anywhere else on the web. AI engines learn about your brand from the wider web, not just from your domain. Off-page work is not optional anymore.

If you want help building the full plan (including the off-page authority work, schema implementation, and measurement dashboard), this is exactly the kind of work I do as an SEO Consultant. Feel free to reach out and we can map out the right strategy for your specific situation.