The reflex, when a marketing team notices its brand missing from AI answers, is to treat it like a ranking problem. Get to the top. Beat position one. That instinct is aimed at the wrong target, because AI search stopped behaving like a ranked index a while ago. It doesn't surface the single best page. It recommends the brand it is most confident fits the specific person asking.

Your buyers have already made the switch. B2B software buyers are now more likely to open their research with an AI chatbot than with Google, according to G2 research fielded in March 2026 among 1,076 decision-makers: 51%, up from 29% the previous April. So the question for a growth-stage B2B marketer is no longer how to rank a URL. It's how to make AI systems confident enough about who you serve, and prove it across the sources they already trust, that they name you to the right buyer.

Traditional SEO asked a simple question: which page deserves the top slot for this query? Generative search asks a different one. Search is moving from "an information retrieval tool to a recommendation tool", as one Search Engine Land analysis of AI-era discovery puts it, and to earn the recommendation your brand has to make the system confident about "who you are, what you offer, who you serve, and when your brand is relevant." (Worth flagging: Search Engine Land is owned by Semrush, so read its GEO coverage as informed industry commentary rather than neutral scripture.)

Think of it less like winning a race and more like being the vendor a well-briefed colleague names when a peer asks them for a recommendation. The colleague isn't reading out a ranked list. They're making a judgement about fit.

None of this is settled doctrine. Google has pushed back on the idea that "generative engine optimisation" is a separate discipline, arguing that good SEO is still good GEO, so treat the terminology loosely. The behaviour underneath it is already live in this market, though. Google AI Mode rolled out to Australians from October 2025, and the recommend-then-transact loop is visible locally: Bunnings is becoming one of the first Australian retailers to let customers select recommended catalogue items and check out inside Google AI Mode. That example is B2C retail, but the mechanism is the point. If an AI answer can carry a shopper from question to purchase without a blue link, it can just as easily carry a procurement lead from "best vendor for X" to a shortlist that either does or doesn't include you.

The personalisation trap: the same query, a different brand for each buyer

This is where the ranking mindset breaks down completely. Two buyers can type the identical query and get different brands back. In a Search Engine Land walkthrough of retrieval versus citation, the author notes that he and a client's CEO are near-identical on paper (same age, same region, same executive title, both red-wine drinkers), yet an LLM would likely recommend each of them a different wine, because it remembers individual preferences where Google does not. Retrieval-augmented generation pulls from trusted sources, then weights them by what it knows about the person asking.

For a B2B buyer, substitute wine for vendor. Two heads of operations at similar companies can ask the same tool to compare platforms in your category and see different shortlists, shaped by their earlier chats, their stated priorities, and the platform they happen to use.

And platform matters more than most Australian marketers assume. Australian sites receiving any AI-referred traffic jumped from 13.5% in 2023 to 88.5% in 2025, a first-party study of 115 Australian brands by Optimising found, with ChatGPT driving 90.2% of identifiable AI visits, well ahead of Perplexity at 4.25% and Gemini at 2.5%. One tool dominates the volume here, but each system holds its own memory of each buyer, so the same question can return a different brand on ChatGPT than it does on Perplexity. (US research suggests buyers now validate across about 2.4 platforms before purchase; no verified Australian or UK equivalent of that figure exists yet, so treat it as directional.)

What actually earns the recommendation: consistency and citation, not clever markup

So what moves the needle? Two things, and neither is a schema trick.

The first is entity consistency. AI systems build confidence by cross-checking what they find about you across sources, and conflicting information (a different description here, an outdated category there) is exactly the kind of ambiguity these systems struggle with, per that same Search Engine Land travel analysis. For an Australian or UK B2B brand, that means your own site, your Google Business Profile, your LinkedIn, relevant industry-association listings and local review or comparison directories should all tell one coherent story about who you serve. Inconsistency doesn't just confuse buyers. It teaches the model to hedge.

The second is third-party citation. A Fractl and Search Engine Land study tied AI visibility far more to brand authority than to SEO scale. Drawing on Ahrefs analysis of roughly 75,000 brands, it found branded web mentions and YouTube impressions showed the strongest correlations with AI visibility (around 0.50 to 0.74 on the Spearman scale), while backlink count and ad spend fell in the weakest tier, below 0.30. Neil Patel's team frames the consequence bluntly: in AI outputs, the most repeated claim, not the most accurate one, rises to the top, and earned media and credible reviews are treated as high-trust signals.

Let's be clear about what doesn't work. Satellite pages, hidden copy and stuffing content into schema were bad SEO for years, and they don't earn generative visibility either. The LLMS.txt file is a useful test case: Google confirmed it neither helps nor hurts search rankings and isn't used by Search. A tidy file you can generate in an afternoon was never going to substitute for being genuinely, repeatedly cited by sources the model already trusts.

Measuring it when the clicks fall

If AI recommends you without sending a click, your dashboard can show a decline while your actual visibility grows. That gap is real, and it is local. Australian news sites have reported organic-search drops of around 35% since AI Overviews arrived, SmartCompany reports, as more answers resolve on the results page itself.

So change what you count. The same travel-brand analysis suggests treating branded-search growth, AI mentions and citations, and assisted conversions as visibility signals, rather than assuming fewer clicks means less demand. In GA4, you can watch assisted conversions through the conversion-path reports to see where an AI-influenced buyer first entered the journey.

The measurement layer is becoming its own funded category, on both sides of this market. Sydney startup Hall raised $2 million pre-seed to measure how often a brand is mentioned across ChatGPT, Perplexity, Claude, Gemini and Copilot, and whether those mentions convert; founder Kai Forsyth notes that companies now ask "How often does ChatGPT mention our brand?" In the UK, Searchable raised £3.1 million for a tool that shows how LLMs interpret a brand's content, with founder Chris Donnelly arguing that visibility "is earned by how AI interprets and prioritises your brand."

One caveat on scope. US keyword panels suggest AI answers now trigger on a large share of commercial queries, with B2B technology especially high (BrightEdge data reported via an aggregator put it at 82% in early 2026). That is a US panel, single-sourced and directional, not an Australian or UK coverage rate. What is confirmed for this market is simpler: AI answers are live in Search here, so buying-intent queries in your category are already meeting them.

A first-90-days plan when you're flying blind

None of this requires a big budget. It requires sequence. If you're starting from zero visibility into how AI describes you, a simple 90-day arc keeps the work honest.

Days 1 to 30: establish the baseline. Prompt ChatGPT, Perplexity and Google AI Mode with the questions a real buyer asks in your category, and capture what comes back, including who gets recommended and which sources the answer leans on. Neil Patel's team recommends tracing each narrative back to its sources rather than reacting to the summary alone. In parallel, audit your entity consistency across your site, Google Business Profile, LinkedIn and the main directories in your sector.

Days 31 to 60: fix the source of truth, then feed it outward. Reconcile the conflicting descriptions so every profile agrees on who you serve. Then invest where the correlations actually point, in earned mentions and third-party citation across the publications, association sites and comparison platforms your buyers and the models both trust. This is slow, compounding work, closer to digital PR than to technical SEO.

Days 61 to 90: measure and set a cadence. Track branded search and assisted conversions as your leading indicators, and make AI monitoring a standing habit rather than a one-off audit, since AI outputs shift as the web shifts. A brand that checks monthly will catch a hardening narrative while it's still soft.

Key takeaways

  • AI search recommends brands to specific buyers instead of just ranking pages, so aim to be the confident-fit answer, not position one.
  • Personalisation means the same query returns different brands to different buyers and across different tools. ChatGPT dominates AI traffic volume in Australia (90.2% of identifiable AI visits), yet each platform keeps its own memory of each buyer.
  • Entity consistency plus third-party citation are what move AI visibility. Branded mentions correlate far more strongly than backlinks or ad spend, and neither a schema trick nor an LLMS.txt file will substitute.
  • When clicks fall, measure branded search, AI mentions and assisted conversions instead. Local tools such as Hall in Australia and Searchable in the UK now track AI mentions directly.
  • Run a 90-day arc: baseline what AI says, fix entity consistency, earn citations, then monitor on a standing monthly cadence.

Frequently asked questions

What does it mean to earn an AI recommendation?

It means making AI systems confident enough about who you serve, and having that confirmed across the third-party sources they trust, that they name your brand to a relevant buyer. Unlike ranking, which surfaces a "best" page, a recommendation is a judgement about fit for the specific person asking.

Is AI search visibility, or GEO, a separate discipline from SEO?

This is not settled. Google argues that good SEO is still good GEO and has pushed back on treating generative engine optimisation as a distinct field. The practical signals that help (entity consistency, third-party citation, crawlable and clear content) overlap heavily with strong SEO, so treat GEO as an emphasis rather than a separate rulebook.

Are B2B buyers really using AI to research vendors?

Increasingly, yes. B2B software buyers are now more likely to start research with an AI chatbot than with Google, per a March 2026 G2 survey of 1,076 decision-makers (51%, up from 29% the prior April). That respondent pool is global with no published Australian or UK breakdown, so read it as an international signal with a local read-across.

How do I measure AI visibility when my clicks are falling?

Shift from click counts to branded-search growth, AI mentions and citations, and assisted conversions in GA4. Falling clicks can coincide with rising AI visibility, because an AI answer may recommend you without sending a visit, so treating fewer clicks as less demand can mislead you.

If you don't yet know what ChatGPT, Perplexity or Google AI Mode say when a buyer asks about your category, that is the first gap to close. M2.0's AI Visibility Check is a free snapshot of how your brand currently shows up in AI answers for your own category, a baseline you can act on before you invest a dollar in fixing it.

Amina
Editorial Team