How AI Ranks Your Business and How to Get Mentioned in AI Search

You've been doing the work.

Publishing content. Updating your website. Maybe even seeing decent rankings in Google. But something's changed. Traffic isn't converting the way it used to. Or worse, traffic is dropping and you're not entirely sure why.

Here's what's happening: people are still searching, they're just not clicking through to websites the way they used to.

Instead, they're getting answers directly from AI. Tools like ChatGPT, Google's AI Overviews, and Bing Copilot now summarise, compare, and recommend businesses before anyone clicks a link. Some searches don't result in clicks at all.

This means visibility is no longer just about where your website ranks in a list of blue links. It's about whether AI systems understand your business well enough to mention you when someone asks a question you could answer.

Understanding how these systems decide which brands to mention, and which to ignore, requires a different mental model from traditional SEO.

This article explains that model.

Not tactics. Not shortcuts. But the underlying mechanisms AI systems use to decide when your business is relevant enough to surface.

The Shift From Google Rankings to AI Recommendations

Why "Ranking" No Longer Means Page One

Traditional search engines were built around links.

You typed a query, scanned a list of blue results, and chose which page to click. Visibility depended entirely on ranking position. If you weren't on page one, you were invisible.

AI search works differently.

Instead of listing pages for you to choose from, AI systems piece together answers on the spot. They pull information from multiple sources, predict what response best satisfies your question, and deliver it directly, often without showing you the full set of options they considered.

This changes everything:

• Pages don't "rank" in the traditional sense: There's no list. There's just the answer.

• Brands are selected, not just indexed: AI decides which businesses are relevant enough to mention.

• Visibility happens inside the answer, not in a list of links: If AI doesn't mention you, you're not part of the conversation.

This shift from "ranking" to "being recommended" is why traditional SEO alone isn't enough anymore. You're no longer competing for position on a page. You're competing to be understood well enough and mentioned consistently enough across the web that AI confidently includes you in its response.

How AI Search Actually Works (In Plain English)

AI systems don't browse the web in real time like humans do.

Think of it like this:

Imagine you're asking a knowledgeable friend for restaurant recommendations. They don't pull out their phone and start googling in front of you. Instead, they draw on what they already know, the places they've heard about, reviews they've read, patterns they've noticed over time. If they're unsure, they might quickly check a trusted source to confirm details, but mostly? They're working from memory and learned patterns.

That's essentially how AI search works.

AI systems operate through a combination of:

• Training data: What patterns the model has learned over time (like your friend's accumulated knowledge of restaurants)

• Retrieval systems: When external sources are pulled in to support an answer (like your friend double-checking a detail)

• Prediction: Generating the most likely useful response based on context (like your friend tailoring their recommendation based on what you've told them you like)

Rather than asking "Which page ranks highest?", AI systems are asking:

"Which concepts and entities best fit this question based on what I've learned and what I can safely reference?"

This distinction matters because it changes what gets surfaced.

If your business isn't clearly described, consistently mentioned, or easy to categorise, AI has nothing reliable to draw from. You're not just competing for a ranking position anymore. You're competing to be part of AI's learned understanding of your industry.

How AI Decides Which Businesses to Mention

This is where most confusion exists.

AI systems don't think in pages or keywords the way Google's traditional search does. They think in entities and relationships.

If that sounds abstract, here's what it means in practice.

Entity Recognition: The New Ranking System

An entity is a recognisable concept: a brand, a company, a tool, a place, a category.

When AI evaluates your business, it's not just looking at your website. It's asking: "What kind of thing is this? What does it do? Who is it for?"

AI systems treat businesses as entities with attributes:

• What problems they solve: Are you a CRM? A consultant? A plumber?

• Who they're for: Small businesses? Enterprise? A specific industry?

• How they're described elsewhere: What do other websites, reviews, or articles say about you?

• What category they belong to: Can AI confidently place you in a clear bucket?

Here's why this matters:

A brand with a clear, consistent identity is easier for AI to understand and therefore easier to recommend. If your positioning is vague or changes depending on where someone finds you, AI struggles to know when you're the right answer.

Semantic Association (Why Context Beats Keywords)

Remember how traditional SEO focused on keywords? AI search cares more about context.

What does "semantic" mean? Simply put, it's about meaning and relationships between concepts, not just matching exact words. If someone searches for "affordable accounting software for freelancers," AI doesn't just look for those exact words—it understands the underlying need and looks for brands associated with that type of solution.

Instead of matching exact phrases, AI systems look for contextual fit:

• Brand + problem alignment: Does your brand consistently appear when this specific problem is discussed?

• Brand + audience alignment: Are you mentioned in contexts relevant to the people asking the question?

• Brand + constraints: Do you fit the implied budget, geography, or industry requirements?

Here's what this looks like in practice:

If your brand consistently appears alongside a specific problem across multiple sources like blog posts, forums, comparison sites, reviews then AI becomes more confident about mentioning you when someone describes that problem, even if they never search for your name directly.

You might rank well for "project management software," but if AI never sees you mentioned in the context of "small teams with tight budgets," it won't recommend you to that audience.

This is why being mentioned in the right context matters. It's not just about backlinks anymore. It's about AI seeing your brand repeatedly associated with the problems you actually solve, for the people you actually help.

The AI "Confidence Score" (Why AI Mentions You or Doesn't)

While AI systems don't publish a literal confidence score, they behave as if one exists.

Every time AI considers mentioning your business, it's essentially asking: "How certain am I that this is a good recommendation?"

Signals that tend to increase AI's confidence include:

• Training data prevalence: How often your brand appears in relevant contexts across the web

• Third-party validation: Citations, comparisons, and expert mentions from sources AI trusts

• Community references: Independent commentary in forums, videos, reviews, and discussions

The key insight:

A business doesn't need to be the biggest to get mentioned in AI search. It needs to be the most understandable. Clear positioning, consistent messaging, and genuine mentions across the web matter more than scale.

Why AI Systems Ignore Good Businesses

Here's the frustrating reality: many capable businesses are invisible to AI systems, not because they're bad at what they do, but because they're unclear about how they present themselves.

You could be excellent at your work, have happy clients, and deliver real value. But if AI can't confidently understand what you do, who you're for, and when to recommend you, you won't get mentioned.

Category Confusion (AI Doesn't Know What You Do)

Vague positioning creates ambiguity, and ambiguity is AI's enemy.

If your business describes itself using broad labels like "digital marketing agency," "business solutions provider," or "IT consultant," AI struggles to determine when you should be recommended and when you shouldn't.

Why does this matter?

AI systems are risk-averse. If they can't confidently place you in a clear category, they'll choose a competitor they understand better. It's not personal, it's about reducing uncertainty.

Clear categories reduce risk for AI.

The more specific you are about what you do and who you help, the easier it is for AI to know when you're the right answer.

The Silo Problem (Only Existing on Your Own Website)

Self-published content alone is rarely enough to build AI confidence.

Think about it: if the only place AI can find information about your business is your own website, how does it know you're credible? Anyone can say anything about themselves online.

AI systems look for cross-confirmation:

• Independent mentions: Are other websites, blogs, or publications talking about you?

• Comparisons: Do comparison sites or review platforms include you alongside competitors?

• Descriptions written by others: What do customers, journalists, or industry experts say about you?

When all information about a brand comes from one source, you, confidence remains low. AI needs to see that others validate what you're claiming.

This is why being mentioned across the web matters. It's external proof that you exist, that you're active in your space, and that others find you worth referencing.

Technical Blind Spots That Block AI Crawlability

Even strong messaging can be undermined by technical issues that make your content hard for AI to read.

Common problems include:

• JavaScript-heavy rendering: If your content only loads through complex JavaScript, some AI systems may struggle to access it

• Missing or inconsistent structured data: Schema markup helps AI understand what your content is about; without it, you're harder to interpret

• Conflicting brand information across platforms: Different business names, addresses, or descriptions across Google, social media, and directories confuse AI about which version is correct

Machine readability matters - because AI systems depend on it.

You can have the best content in the world, but if AI can't reliably access and interpret it, you're invisible. This is where technical SEO and AI optimisation overlap.

Generic Content and "AI Slop"

Content created primarily to satisfy algorithms, rather than help real people, often lacks depth. And AI systems are getting better at detecting this.

AI systems are increasingly selective:

• Volume without substance is filtered out: Publishing ten shallow articles won't beat one genuinely useful piece

• Repetition without insight is ignored: Saying the same thing every competitor says adds no value

• Predictable templates are deprioritised: AI can recognise formulaic content that follows the same structure as thousands of other posts

What works instead?

Depth, clarity, and specificity are stronger signals than frequency. One well-researched, clearly explained article that addresses real questions will outperform a dozen generic posts stuffed with keywords.

AI search rewards content that demonstrates understanding, not just coverage.

Traditional SEO vs AI Optimisation (GEO / AEO)

Key Differences at a Glance

Traditional SEO	AI Optimisation
Pages rank	Brands are referenced
Keywords	Entities & intent
Backlinks	Mentions & consensus
Traffic	Share of AI visibility

Why SEO Still Matters (But Isn't Enough)

SEO isn't dead, it's foundational!

Traditional SEO provides the infrastructure that makes your content accessible:

• Crawlability: Search engines need to find your content

• Indexing: Your pages need to be stored and categorised

• Technical clarity: Clean code, fast loading, mobile optimisation

AI optimisation builds on top of this foundation. It acts as a selection layer, deciding which entities are relevant and trustworthy enough to include in an answer.

Think of it this way: SEO gets you into the conversation. AI optimisation determines whether you get mentioned in it.

You need both. Without SEO, AI systems can't access your content reliably. Without AI optimisation, they may access it but never reference you.

How AI Decides Which Businesses Make the Cut

AI doesn't show every relevant option, it picks the best fit for each person.

When someone asks a question, AI reads between the lines. It picks up on hints about budget, location, business size, and what stage they're at in making a decision. That's why two people asking similar questions can get different recommendations.

From there, AI shortlists entities that:

• Fit the specific context: Does this business align with the user's apparent situation?

• Have been associated with similar scenarios before: Has this brand appeared in relevant contexts repeatedly?

• Reduce risk through clarity and specificity: Is this recommendation defensible and clear?

Only a small subset of businesses ever makes it into the final answer.

Here's the pattern AI follows:

AI systems prefer familiar, clearly defined entities over ambiguous ones. They avoid vague recommendations because vagueness introduces risk.

Being clearly right for something specific beats being vaguely relevant to everything.

This is why category clarity and consistent mentions matter so much. If AI can't confidently place you, it won't risk recommending you.

How to Optimise Your Business for AI Search

This is not about gaming systems, it's about making yourself understandable.

Step 1: Lock In Category Clarity

If you can't describe your business in one precise sentence, AI likely can't either.

Clarity includes:

• Who you're for: Small businesses? Enterprise clients? A specific industry?

• What problem you solve: Not what you do, but what outcome you deliver

• Who you're not for: Being clear about boundaries helps AI know when not to recommend you

The more specific you are, the easier it is for AI to confidently include you when you're the right fit and exclude you when you're not.

Step 2: Build Trusted Mentions (Not Just Links)

Remember when we said AI looks for cross-confirmation? This is where that happens.

Unlinked mentions, comparisons, and expert references all contribute to AI confidence. These signals help AI understand how others describe you, not just how you describe yourself.

Where mentions matter:

• Industry blogs and publications

• Comparison sites and reviews

• Forums and community discussions

• Expert roundups and resource lists

• Case studies and testimonials on third-party sites

You can't control all of these, but you can create opportunities for them by being genuinely helpful, sharing expertise, and building relationships in your industry.

Step 3: Optimise for Entities, Not Just Keywords

AI cares less about exact keyword matches and more about how your brand is consistently described.

Strong entity signals often appear as:

• Brand + problem statements: "X helps small businesses with Y"

• Attribute-based explanations: "Known for," "specializes in," "focused on"

• Consistent descriptors across platforms: The same clear positioning everywhere

When AI sees your brand repeatedly associated with specific problems, audiences, or outcomes, it becomes confident about when to mention you.

Step 4: Use Structured Data Properly

Schema markup helps machines interpret meaning, not just read words.

Key schema types for businesses:

• Organisation: Who you are, where you're located

• Services: What you offer

• Reviews: Social proof and credibility

• LocalBusiness (if applicable): For location-based visibility

This isn't about gaming rankings. It's about making your information machine-readable so AI systems can accurately understand and reference you.

Step 5: Be the Best Answer for One Thing

AI systems favour predictability and clarity.

A brand that's clearly known for solving one specific problem is far easier to recommend than a brand trying to be everything to everyone.

This doesn't mean you can only do one thing. It means your positioning should make it crystal clear what you're known for, what problem you solve better than anyone else.

Ask yourself:

• If someone described our business to a friend, what would they say we do?

• What problem do people most commonly come to us for?

• If AI had to explain us in one sentence, what would be most accurate?

The clearer that answer, the more confidently AI can recommend you.

A Simple Self-Check: Is AI Likely to Mention You?

Before moving forward, ask yourself:

• Is our category unmistakable? Could someone quickly explain what we do?

• Are others describing us in their own words? Do we appear in independent content?

• Do we appear in comparisons? Are we listed alongside competitors anywhere?

• Would AI know when not to recommend us? Is our positioning specific enough that it's clear who we're not for?

If these answers feel unclear, AI's confidence will be too.

Want to know what successful businesses actually do with this knowledge? Read about the 7 foundational behaviors they apply over time to build lasting visibility in AI search.

Why Market Clarity Matters More Than Market Size

AI systems are often trained on datasets dominated by large, well-established brands with massive online footprints. This can make it feel like smaller or regional businesses whether you're in New Zealand, Australia, the United States, or operating globally are at a disadvantage.

Here's the reality:

You don't need to be the biggest to get mentioned in AI search. You need to be the clearest.

Local expertise, specialised focus, and contextual relevance often outperform scale. AI systems value fit more than size.

This works whether you're:

• A regional business serving a specific geographic market (Auckland, Sydney, Seattle)

• A niche specialist serving a specific industry globally

• A smaller player competing against enterprise-level brands

The key is making your positioning unmistakable to AI systems.

What this means in practice:

• Be explicit about who you serve and where (whether that's geographic, industry-specific, or both)

• Build mentions within the contexts that matter to your audience, local publications for regional focus, industry platforms for niche expertise, or both

• Use natural language that reflects how your customers describe their needs and location

• Establish authority within your specific market rather than diluting your message trying to be everything to everyone

Market positioning, whether geographic, industry-focused, or both isn't a limitation in AI search. It's your competitive advantage when you own it with clarity and consistency.

What This Means for Your Business

AI search isn't a trend you can wait out. It's already changing how potential customers discover businesses, compare options, and decide who to trust.

The businesses that will stay visible aren't the ones with the biggest budgets or the most content. They're the ones that make themselves understandable to both people and the systems helping people find answers.

If you're unsure whether AI can confidently explain what you do, who you're for, and when to recommend you, that uncertainty is costing you visibility right now.

The good news? This is fixable. It starts with understanding where you currently stand.

Find Out How AI Sees Your Business

Our AI Search Audit shows you exactly how your business appears in AI-driven search results and traditional search engines like Google.

You'll get:

• A clear assessment of how AI systems currently understand your business

• Specific visibility gaps that are holding you back

• Practical recommendations to improve your positioning for both human and AI search

• A roadmap that prioritizes what actually matters for your business

This isn't about chasing every new tactic. It's about building the clarity, consistency, and trust that make your business the answer AI reaches for.

Get in touch to discuss whether an audit is the right starting point for your business.