QUICK ANSWER: Don't treat AIO as some separate workstream that needs its own strategy built from scratch. Get the SEO fundamentals right - architecture, trust signals, reviews, structured data, social presence, topical authority - and you've already done the vast majority of the work. What's actually left is mostly about how content gets packaged for extraction, and building the kind of off-site authority that gets you cited rather than just ranked.
There's a lot of panic in the air about AI Optimisation, or AEO (Answer Engine Optimisation), or GEO (Generative Engine Optimisation), depending which LinkedIn post you read this week - like it's some brand new discipline that's about to make everything we know about SEO obsolete. It isn't.
Strip away the acronyms and most of what makes a site "AI-ready" is exactly the same groundwork that's made sites rank well in Google for years. The destination has changed - you're aiming for a spot in a generated answer or an AI Overview instead of a blue link.
But the stuff that earns you trust to get there is largely identical.
And that's really the whole argument in a nutshell: whether it's a search engine crawling your site or an AI model retrieving from it, both are trying to answer the same underlying question - is this a real, credible, well-organised source worth trusting? Neither one cares about your marketing copy or how confidently you describe yourself. They're both looking for evidence: proof that a real business with real experience is behind the page, proof that other people and other sources vouch for you, proof that the information holds up. A search engine expresses its verdict as a ranking position. An AI model expresses its verdict as a citation, or a mention, or a recommendation buried inside an answer. Different output, same underlying test.
That's worth sitting with for a second, because it reframes the whole "AIO vs SEO" debate. It's not two competing systems evaluating you on different criteria - it's two different consumers reading the same signals and packaging their verdict differently. Once you see it that way, chasing AIO as some brand new checklist starts to look a bit backwards. The actual work is the same work: be a credible, well-structured, well-evidenced source. What changes is just who's reading it and how they choose to show it to someone.
Anyone who's spent years building a site's authority for Google hasn't been wasting their time; they've been building the exact foundation
AI systems now check before they'll cite or recommend you. So before you go rebuilding your strategy from scratch, it's worth being honest about how much of this you've already done. Here's what actually carries over.
1. Solid page architecture
This one hasn't changed at all, honestly. Search engines have always needed a clean, logical site structure to work out what a page is about - proper hierarchy, sensible internal linking, one clear topic per page, nothing orphaned or buried three clicks deep. AI systems need exactly the same thing, if anything they're less forgiving about it. When an LLM is trying to pull a clean answer out of a page via retrieval, it's leaning on the same structural cues: clear headings, logical flow, an unambiguous topic. A page that's a mess for a Googlebot crawler is just as useless to an AI trying to extract a citable answer from it. If anything, AIO raises the bar here - content needs to answer the question in the first couple of lines now, then build out the detail, rather than making people (or models) wade through three paragraphs of preamble first.
2. On-page testimonials and E-E-A-T signals
Google's E-E-A-T guidelines exist because search engines learned the hard way that they can't just take a page's word for it - they need proof. Testimonials, case studies, author bios, real credentials on the page. AI systems are running the exact same check, just for a different purpose. When a model's deciding whether to cite or recommend you, it's weighing up the same evidence of real experience and credibility before it treats you as trustworthy enough to reference. Trust signals were never really a "Google thing" - they're just what any system, human or machine, needs before it'll vouch for you.
On our own site, we pulled testimonials in through the Google Reviews API rather than manually copying text across. That mattered more than it might sound: hardcoded testimonials are just static text with no way for a search engine to verify they're real, current, or actually tied to a live review profile. Pulling them via the API means the reviews on-page are backed by structured, verifiable data - genuine star ratings, real reviewer counts, timestamps - which is exactly the kind of machine-readable trust signal algorithms favour. It's a small technical choice, but it's the difference between testimonials that look credible to a human and testimonials that are actually verifiable to a crawler.
3. Social media presence
This is the one I've swapped in for LinkedIn specifically, because it's bigger than one platform. An active, consistent social presence - LinkedIn, X, Instagram, YouTube, wherever your audience actually is - has always supported SEO indirectly. It builds brand signals, drives referral traffic, and helps search engines understand you as a proper entity rather than just a domain. That same entity recognition is exactly what AI models are doing when they build a picture of who you are and what you're known for. It's rarely about a direct ranking or citation boost from a single post - it's the accumulation of consistent, active signals across channels that both search engines and AI systems use to decide you're a real, credible business worth mentioning.
4. Review volume and velocity
Barely changes at all, this one. Review volume and velocity have always been a trust and ranking signal for local and service businesses - lots of reviews, coming in steadily, says "active, credible, trustworthy." AI systems weigh up exactly the same thing when deciding who to recommend. Ask an AI system for a supplier, agency, or product recommendation and it's drawing on the same aggregated sentiment and volume a person would check on a reviews site. A thin, stagnant review profile hurts you the same way in both worlds.
5. Directory presence
Consistent, accurate directory listings - NAP consistency, right categories, no half-finished profiles - have always mattered for local SEO and citation building. They matter just as much here, because directories are exactly the kind of structured, third-party-verified data AI systems lean on to cross-check facts about a business: what you do, where you're based, what you're known for. A directory listing is a trust anchor for a crawler and a data point for an AI's entity graph. Same input, same job, different reader.
6. Structured data and schema markup
This one's arguably more important for AIO than it ever was for SEO. Schema markup has always helped search engines understand context - what's a product, what's a review, what's a FAQ, what's an author. AI systems rely on that same structured data to extract facts accurately rather than guessing from unstructured prose. If your pages are marked up properly, you're handing both a search engine and an AI system the answer on a plate instead of making them work for it.
7. Site speed and technical health
Page speed, mobile usability, crawlability, no broken links or redirect chains - none of this is new for SEO, and none of it goes away for AIO either. AI crawlers and retrieval systems still need to be able to access, render, and process your pages without hitting friction. A slow, broken, or blocked site is invisible to an AI system in exactly the same way it's invisible to Google.
8. FAQ-style content
FAQs have been a solid SEO tactic for years - they map neatly onto how people actually search, and they're a natural fit for featured snippets and the "People Also Ask" box. That same clear, question-and-answer structure is basically a gift to AI systems, which are often trying to extract a direct, self-contained answer to lift into a response. Well-written FAQs do double duty: still good for SEO, and now genuinely one of the best formats for getting cited by AI.
As an example, we added FAQ sets across the service and industry pages. Each set was written to answer the real questions prospects search for in plain, self-contained language, rather than reworded service copy padded into a Q&A shape. That's the distinction that matters for both channels: Google and AI systems alike are looking for a direct answer they can lift cleanly, not a paragraph they have to parse to find one.
AEO techniques worth folding in
A few things I'd missed first time round that are more AEO-specific - genuinely newer tactics rather than SEO fundamentals in disguise, but worth having on the list:
- Question-first, concise answer blocks. Rather than just having an FAQ section, structure the actual body copy so each section opens with a tight, self-contained answer - a lot of AEO guidance points to keeping that opening answer to roughly 40 words or fewer - before expanding into supporting detail. It's the difference between writing for a scanning reader and writing for a model that's extracting a quotable chunk.
- Entity-first content structuring. Instead of organising content purely around keyword strings, structure it around clearly defined entities - your brand, your products, the concepts you want to own. This is what feeds knowledge graphs and helps AI systems understand not just that a page is relevant, but who or what it's actually about.
- Data-backed claims with source attribution. First-party data - your own benchmarks, case study numbers, customer outcomes - earns citations that generic third-party stats can't, because AI engines are increasingly citing back to original sources rather than whoever repeated the number last. If you've got proprietary data, put it front and centre and make clear it's yours.
- Answer freshness. Answer engines lean towards content that's demonstrably current - updated dates, refreshed stats, revised recommendations. Stale content doesn't just rank worse, it's less likely to be trusted as a live answer.
- AI crawler accessibility. Worth checking your robots.txt actually allows the AI crawlers (GPTBot, ClaudeBot, PerplexityBot, and so on) rather than blocking them by default or by accident. Some sites are now also publishing an llms.txt file - a plain-language summary of the site aimed at AI systems rather than search bots. None of this helps if the content itself is locked out before an AI ever gets to evaluate it.
- Multi-format, cross-platform consistency. The same core answer, description, or stat should read consistently whether it's on your site, your Google Business Profile, a directory listing, or a press mention. AI systems cross-reference sources, and inconsistent facts about your own business across the web undermine confidence in all of them.
Where AIO actually is different
All that said, AIO isn't just SEO wearing a new badge. A few things genuinely work differently, and it's worth being honest about them.
The goal itself has shifted. SEO's endpoint is a position on a results page and a click. AIO's endpoint is being selected as part of a generated answer, sometimes with no click at all - you're optimising for citation likelihood, not ranking position. That changes how you even measure success; your rank tracker won't tell you whether ChatGPT, Perplexity, or an AI Overview actually mentioned you, so citation tracking is becoming its own discipline alongside traditional rank tracking.
Content has to give up the answer immediately. Long-form SEO content could historically get away with a slow build before getting to the point. AI systems don't reward that - they're extracting and reusing whatever answers the query fastest and most clearly, so burying the good stuff costs you now in a way it didn't before.
Off-site trust plays out slightly differently too. Mentions on reputable third-party sites, podcast appearances, video transcripts, and news coverage all feed the signals that make an AI model more likely to cite you. It's not a million miles from digital PR and backlink building, but the emphasis shifts from "does this link pass authority" to "has this model encountered and trusted my brand across enough independent, third-party validation."
And you genuinely lose visibility into the output. With SEO you can check your rank any time you like. With AIO you're trying to influence a probabilistic answer from a large language model you don't control, built on training data and retrieval-augmented generation you can't fully audit. Visibility becomes something you infer from citation monitoring, not something you can just look up.
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