Why 39% of AI Systems Disagree on Brand Recommendations (And How GEO Bridges the Gap)

Why 39% of AI Systems Disagree on Brand Recommendations (And How GEO Bridges the Gap)

Here’s a scenario: you’re relying on AI systems to boost your brand’s visibility. You’ve tested on ChatGPT, Google AI Overview, Claude, maybe even Perplexity. One AI recommends your company left, right, and centre, while another barely acknowledges your existence. Frustrating? Absolutely. But it’s no fluke, according to our data at Contxt, this kind of cross-provider divergence happens a staggering 39% of the time.

Think about that for a second. Nearly four in ten prompts give conflicting answers based purely on which AI system generates them. If you’re banking on one platform to put your brand in front of potential customers, you might be missing out on opportunities elsewhere. That’s a problem. And it’s why Generative Engine Optimisation (GEO), our methodology for improving brand visibility across multiple AI systems, is gaining traction. Let’s break it down.

What’s Really Going On Here?

When we started testing AI visibility tools across different platforms, the penny dropped quickly: these systems aren’t mirrors of each other. ChatGPT and Google AI Overview, for example, don’t just interpret prompts differently, they operate on fundamentally distinct architectures, data sources, and training strategies. So the brands that perform well on one might struggle, or outright disappear, on the other.

Our dataset confirms this disparity. Across 2,641 prompts tested across 30 businesses, ChatGPT had a stellar mention rate of 74.4%, while Google AI Overview lagged at 44.1%, and Perplexity came in even lower at 40.6%. Claude, though tested less frequently, came out surprisingly strong at 77.1%. Related reading: this piece about emerging trends in ai visibility: what to expect in 2026. Related reading: Why LLM Visibility Becomes a Revenue Lever in the Agent Era. Statistically, if your brand is visible on ChatGPT, it’s far from guaranteed to show up on Google AI, and vice versa.

Why does this matter? In commercial terms, inconsistent visibility can result in missed sales, muddled branding, and wasted marketing budgets. If an AI system doesn’t recognise your business, there’s zero chance it’ll recommend you to users searching for products or services, and those users will go elsewhere.

The Commercial Impact of Cross-Provider Divergence

You might be wondering, “Does this really affect my bottom line?” Honestly, yes. Consider this: for brands in the entertainment and sports tech sector, we saw visibility rates as low as 10% (looking at you, TOCA Social). That’s shocking when you think about how competitive that space is. If someone’s asking an AI system for the best interactive sports venues and your brand isn’t even mentioned, you’re losing potential customers before the conversation starts.

Compare that with manufacturing companies like Viking Pumps and Red Fern, which achieved perfect visibility across all systems. Industrial brands aren’t typically the most cutting-edge marketers, but their visibility strategy clearly works. Why? They’ve nailed the key principle: consistency matters more in AI than anywhere else. Related reading: our article on why b2b brands struggle with llm visibility (and how to g.... Related reading: The Brand Invisibility Problem: Why Your Business is Invi.... If your branding is scattered or your information fragmented across systems, you’ll never hit those 100% figures.

Another issue is intent. In our buying-intent tests (across 12 businesses), we discovered that mention rates varied dramatically based on query type. A brand might appear in response to “best CRM software for e-commerce” but vanish when asked, “What should I use to track customer activity?” Same brand, same purpose, completely different visibility outcomes. You can AI visibility tracking features to see for yourself. You can how AI visibility monitoring works to see for yourself. That inconsistency erodes user trust and narrows your chances of being chosen.

Why AI Systems Disagree

Let’s get technical for a moment. Cross-provider divergence isn’t just bad luck. It stems from how each AI system processes, sources, and interprets information. For example:

1. Data Sources: ChatGPT pulls from pre-trained datasets and user inputs, while Google AI Overview integrates search engine results and contextual web data. Different sources mean different priorities.
2. Algorithms: Some systems (like Claude) optimise for natural language fluency, while others (like Perplexity) prioritise fast, citation-heavy responses. Your brand needs to cater to those quirks.
3. Prompt Sensitivity: According to our sensitivity tests, subtle wording changes, a “best” versus “recommended”, for instance, can flip the switch on whether a brand is mentioned at all.

At the end of the day, it boils down to this: AI systems don’t see your brand the same way humans do. If your messaging isn’t tailored to their specific quirks, you’ll be ghosted more often than not.

The GEO Solution: Bridging the Gap

In my experience, the key to solving this AI visibility headache is consistency. That’s where Generative Engine Optimisation (GEO) comes in. GEO isn’t rocket science, it’s a systematic way of ensuring your brand appears reliably across AI platforms, regardless of their differences.

Here’s what actually works:

1. Unified Messaging: Your brand’s core identifiers, name, product descriptions, unique selling points, must be structured clearly and uniformly across all AI-friendly platforms. If your messaging is inconsistent, AI won’t know what to prioritise.
2. Prompt Strategy: GEO involves testing and refining prompts to maximise mention rates. For instance, instead of generic questions like “What’s the best e-commerce platform?”, try more intentional queries: “Which UK-based e-commerce solution supports sustainable businesses?” This approach helps train the AI to recognise your niche strength.
3. Cross-provider Visibility: Through tools like Contxt, you can systematically track how your brand performs on ChatGPT, Google AI Overview, and beyond. Knowing where you’re strong (and weak) allows real-time adjustments.
4. Intent Alignment: GEO analyses user intent behind queries to help you optimise not just for keywords, but for the context in which those keywords appear.

Real Examples from Contxt

Let me give you an actual example. One client of ours, Iconsys 2 (manufacturing/industrial automation), was stuck in the “low visibility” bucket, 6.7% mention rate across the systems we track. Their messaging was all over the place: different product descriptions on different platforms, conflicting brand tones, and zero alignment with AI-driven query patterns.

After applying GEO principles, their visibility started climbing. We restructured their data inputs, aligned their messaging across systems, and ran hundreds of prompt tests to ensure better mention rates for industry-relevant queries. The result? They moved into the “mid-range” visibility band within three months and are on track for higher performance next quarter. It’s still early days, but the improvement speaks volumes.

Contrast that with Viking Pumps, which was already hitting 100% visibility across our tests. They didn’t need a complete overhaul, they just needed minor tweaks to reinforce their presence across buying-intent queries. That’s the beauty of GEO; it’s tailored to where your brand currently stands.

Next Steps: Getting Started with GEO

If you’re dealing with low visibility across AI systems, or worse, conflicting recommendations, it’s time to act. Here’s how:

1. Audit Your Current Visibility: Start by tracking how often your brand is mentioned across platforms. Are you a household name on ChatGPT but invisible on Google AI Overview? That discrepancy is your starting point.
2. Optimise for Prompts: Craft queries that highlight your strengths while aligning with each system’s quirks. Experiment, refine, repeat. (Contxt’s prompt trackers are a lifesaver here.)
3. Align Your Messaging: Ensure your branding is unified across sources, websites, product descriptions, social platforms. AI systems thrive on reliable data. If yours is scattered, you’re toast.
4. Test, Test, Test: Run experiments tailored to your industry. Our testing lab has templates for buying-intent prompts, competitor displacement tests, and even multi-language visibility, use them to refine your output.

And most importantly, don’t tackle this alone. AI visibility tools like Contxt are designed to simplify this process and give you the actionable insights you need to close those visibility gaps. We've covered this in more detail in this piece about ai visibility metrics that matter: a practical framework .... You can try Contxt here, trust me, it’ll save you weeks of trial and error.

Final Thoughts

So, does it matter that 39% of AI systems disagree on brand recommendations? Absolutely. Inconsistent visibility costs businesses sales and erodes trust in an era where AI is often the first touchpoint with customers. But the good news is, this gap isn’t insurmountable. GEO gives you the tools and strategies to bridge it.

If you’re sceptical, I get it, I was too, at first. But after seeing results like Iconsys improving their visibility and clients reaching 100% mention rates, I’ve veered firmly into the “this works” camp. AI systems are here to stay, and the sooner brands start optimising for them, the better.

Ask yourself: is your brand leaving opportunities on the table? If the answer’s yes, it’s time to dive into GEO and start turning that visibility inconsistency into a competitive advantage.