Something fundamental has shifted in how customers discover and evaluate brands. And most Australian marketers haven't caught up yet.

Here's what's happening: before a potential customer visits your website, before they pick up the phone, before they even type your brand name into Google, they're asking an AI.

According to recent McKinsey research, half of all consumers now intentionally seek out AI-powered search engines, with the majority saying it's become their top digital source for making buying decisions. This spans all age groups. Even most baby boomers are using AI search. A separate study found that 37% of consumers now begin their searches with AI tools rather than traditional search engines. They want one clear answer they can act on immediately, not a page of links to wade through.

The question Australian CMOs need to be asking isn't whether AI will affect their brand perception. It already has. The question is: what is AI actually saying about your brand right now, and is any of it wrong?

What AI Misunderstandings Cost You

Large language models don't understand truth the way humans do. They predict what text should come next based on patterns they've learned. When information about your brand is incomplete, outdated, or simply wrong in their training data, they'll confidently present that misinformation as fact.

NewsGuard research found that false information in AI responses to prompts on news topics nearly doubled from 18% to 35% across 2025. When it comes to brand and company information, the potential for errors compounds. AI models hallucinate. They make things up. And they do it while sounding completely confident.

Lost demand you never see in analytics

Picture this scenario. A procurement manager at a mid-sized Melbourne company asks ChatGPT to compare software providers in your category. The AI responds with pricing information that's eighteen months out of date, making you look 40% more expensive than you actually are. The manager moves on to your competitor without ever visiting your website.

You'll never see this in your analytics. There's no bounce rate to measure because they never bounced. There's no lost lead in your CRM because they never became a lead. This is invisible demand destruction, and it's happening to Australian brands every day.

AI-powered search is influencing purchasing decisions across every major category. McKinsey projects that by 2028, AI-powered search will influence $750 billion in consumer spending. Brands that aren't visible and accurate in AI responses could lose up to half their traffic from traditional search channels.

Competitive misattribution that hands wins to your rivals

Here's something even more frustrating than AI getting your information wrong: AI crediting your innovations to your competitors.

When a potential customer asks an AI to compare your brand against competitors, the response draws from whatever information is most prevalent and well-structured online. If your competitor has done a better job of publishing clear, crawlable content about their features and capabilities, the AI might attribute similar features to them even if you pioneered the innovation.

Research on retail brands found that AI tools tend to recommend more established brands over newer ones, even when the upstart has better offerings for the specific query. Your carefully cultivated market position can be undermined by an AI that simply hasn't ingested enough information about what makes you different.

Customer service load from AI misinformation

The customers who do make it through to you often arrive with expectations set by AI that don't match reality. They've been told you offer features you don't have, pricing that doesn't exist, or availability in regions you don't service.

Your customer service team then spends valuable time correcting misconceptions that started in an AI conversation you had no visibility into. These aren't complaints about your service. They're complaints about a fictional version of your service that an AI invented.

The operational cost is real. The brand damage from disappointed expectations is harder to quantify but potentially more significant. Every customer who feels misled, even if the misleading came from AI rather than you, walks away with a diminished view of your brand.

The Quarterly AI Brand Audit

The good news is that AI brand accuracy is something you can measure and improve. You just need a systematic approach.

We recommend running a structured AI brand audit every quarter, with monthly spot checks on critical claims. Here's how to build one that actually works.

The 15-prompt test bank

Start by building a bank of prompts that cover the most critical aspects of how customers might ask about your brand. Test these across at least three AI platforms: ChatGPT, Google Gemini, and Perplexity. Their responses will differ, and you need to know where each one is getting things right or wrong.

Positioning and brand identity prompts: Ask variations of "What does [Brand] do?" and "What is [Brand] known for?" Check whether the AI captures your actual value proposition or defaults to generic category descriptors. Does it mention your key differentiators? Does it position you correctly in the market?

Competitive comparison prompts: Frame queries like "Compare [Brand] vs [Competitor 1] vs [Competitor 2]" and "What are the alternatives to [Brand]?" These reveal how AI positions you against competitors. You'll often find surprising misattributions or outdated competitive dynamics.

Price and value prompts: Test "How much does [Brand] cost?" and "Is [Brand] expensive?" Pricing information gets stale quickly in AI training data, and incorrect pricing is one of the fastest ways to lose potential customers.

Use case and industry prompts: Include questions like "Is [Brand] suitable for [specific industry]?" and "Can [Brand] be used for [specific use case]?" with particular attention to Australian-specific scenarios. Does the AI know you service the Australian market? Does it understand local compliance requirements?

Regional and compliance prompts: Ask "Does [Brand] operate in Australia?" and "Does [Brand] meet Australian [relevant regulation] requirements?" For many categories, understanding local availability and compliance is critical to the purchase decision.

Proof and credibility prompts: Test "What certifications does [Brand] have?" and "Who are [Brand's] notable clients?" AI often struggles with up-to-date proof points, case studies, and accreditations.

Scoring rubric for evaluation

Score each AI response across four dimensions on a 0-3 scale.

Accuracy (0-3): Is the information factually correct? A score of 3 means completely accurate. A 2 indicates minor errors or omissions. A 1 means significant inaccuracies. A 0 means the response is fundamentally wrong or includes damaging misinformation.

Distinctiveness (0-3): Does the response capture what makes your brand different? A 3 means your key differentiators are clearly communicated. A 2 means some differentiation is present. A 1 means you're described generically. A 0 means you're confused with competitors or described in ways that could apply to anyone.

Recency (0-3): Is the information current? A 3 means fully up to date. A 2 indicates slightly outdated but not materially wrong. A 1 means noticeably outdated information. A 0 means the response relies on significantly old information that misrepresents your current offering.

Commercial Impact (0-3): Would this response help or hurt a purchase decision? A 3 means the response would encourage purchase. A 2 means neutral impact. A 1 means it creates unnecessary friction. A 0 means the response would actively discourage purchase or drive customers to competitors.

Building your evidence log

For every issue you identify, document it systematically. Record the exact prompt used, the AI platform, the date, and the problematic response. Note what the correct information should be and where that correct information currently exists online. Assign an owner and a due date for the fix.

This evidence log becomes your working document for improving AI brand accuracy over time. It also provides the data you need to report on progress to leadership.

Fixing the Record

Once you know where AI is getting your brand wrong, you can start fixing it. This requires a multi-channel approach because AI models draw information from diverse sources.

Owned content: Your website is the primary source of truth for AI. Update product pages, FAQs, and documentation with clear, specific, crawlable text. Avoid burying critical information in images, PDFs, or interactive elements that AI can't easily parse. Use structured data markup (schema) to help AI systems understand your content.

PR and communications: Media coverage influences AI training data significantly. Align your PR narratives with the proof points you want AI to surface. When you issue press releases, include the specific claims, features, and positioning you want associated with your brand.

Partner and marketplace listings: If you sell through partners or marketplaces, ensure your brand information is consistent and current across all listings. Inconsistent information across channels confuses AI models and can result in blended, inaccurate responses.

Technical foundations: Make sure your sitemap is current and your robots.txt doesn't block AI crawlers from accessing key pages. Some organisations are implementing retrieval-augmented generation (RAG) approaches that allow AI systems to pull directly from verified brand content rather than relying solely on training data.

Governance and Reporting

AI brand accuracy needs to become an ongoing governance function, not a one-off project. Here's how to structure it.

Roles and responsibilities: Brand owns the strategy and accuracy standards. Digital executes the content updates and technical fixes. Customer experience provides the feedback loop on what misinformation is causing real-world problems. Legal reviews any claims-related issues.

Cadence: Run the full audit quarterly. Conduct monthly spot checks on your highest-impact prompts. Review significant changes (new products, pricing updates, acquisitions) against AI responses within two weeks of announcement.

Board-level view: Create an AI Brand Accuracy Score that rolls up your audit results into a single metric. Track this against lead quality, branded search volume, and customer service ticket deflection. The correlation between improved AI accuracy and these downstream metrics will build the business case for ongoing investment.

What This Means for Brand Research

Here's where this connects to the broader brand health picture. Traditional brand tracking measures what customers think after they've engaged with your brand. AI brand auditing measures what they're being told before they ever reach you.

Both matter. But if AI is actively misrepresenting your brand at the top of the funnel, even the best brand tracking will only show you the consequences, not the cause.

The most comprehensive approach combines regular AI brand auditing with deeper brand health studies that explore how perceptions are formed and why. When you see a disconnect between what AI says about you and what customers believe, you've identified both a problem and an opportunity.

Research from G2 found that AI search-driven leads convert 40% better than traditional search leads. These are high-value prospects arriving with intent to purchase. Making sure they arrive with accurate expectations is worth significant investment.

Time to Look Under the Hood

Most Australian brands have spent years optimising for Google. They've invested in SEO, built content strategies around search visibility, and tracked their rankings religiously.

AI is now creating a parallel discovery channel that operates by different rules. You can't buy your way to the top of an AI response. You can't game the algorithm with keyword stuffing. What you can do is ensure that the information AI systems have access to about your brand is accurate, current, distinctive, and commercially helpful.

The brands that start auditing and improving their AI presence now will have a significant advantage as AI search continues to grow. The brands that wait will find themselves explaining why their lead quality is declining despite stable search rankings.

Open ChatGPT right now and ask it about your brand. You might be surprised by what it says. And what it doesn't.

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Want to understand how your brand is really perceived, both by AI and by your actual customers? We help Australian brands build comprehensive brand health studies that reveal not just what customers think, but why they think it. Book a conversation today!