Why 39% of AI Systems Disagree on Brand Recommendations (and What It Means for GEO)

Why 39% of AI Systems Disagree on Brand Recommendations (and What It Means for GEO)

Here’s a scenario that’s way too familiar if you work in digital marketing. You optimise your content, polish your brand messaging, and invest hours testing keywords to hit all the right notes for search engines. Then you ask AI systems like ChatGPT and Google AI Overview simple recommendation prompts about your industry , and they give wildly different answers. Honestly, it’s enough to make you question if any of that hard graft made a difference.

My theory? It’s not that your efforts were wasted. It’s that AI systems recommendations operate in a fragmented ecosystem where data sources, algorithms, and context collide, often with bizarre results. And the 39% divergence rate between ChatGPT and Google AI recommendations from our data proves it. Brands visible on one platform are frequently invisible on another , for the same query.

So what on earth is going on here? And more importantly, if AI can’t even agree on which brands to recommend, how do we fix it? Buckle up, because we’re diving into this problem headfirst, with real examples, hard numbers, and actionable answers.

The 39% Divergence Problem: Why AI Systems Don’t See Brands the Same Way

Let’s start with the hard stats. Across our dataset of 2,641 prompts run across 30 businesses, 39% of the time ChatGPT and Google AI Overview gave conflicting recommendations. That’s not a small discrepancy. You’ve got brands that shine on ChatGPT with a mention rate of 74.4%, only to plummet to 44.1% when tested on Google’s AI. The gap widens further with providers like Perplexity, where mention rates drop to 40.6%, and Claude, which for all its niche uses, still gives consistently different answers from Google AI Overview.

Why does this divergence happen? In my experience, it’s down to three main factors:

1. Data Training Sources: Different AI systems are trained on different pools of data. Google’s AI integrates heavily with search engine behaviour and its own proprietary tools, while ChatGPT relies on its mixture of OpenAI training models and internet-based data up to its knowledge cutoff. If your brand isn’t a strong presence in those distinct ecosystems, it’s likely to fall through the cracks.
2. Query Context: AI systems don’t just blindly retrieve recommendations; they interpret the context of your prompt. A single word like “top-rated” versus “popular” can cause the AI to trigger entirely different prioritisation algorithms. Across our experiments testing buying intent, we found that the same brand might get recommended in one query type but be completely absent in another.
3. Provider Algorithms: AI providers have proprietary ways of ranking, filtering, and presenting data , and they don’t always agree on what counts as the strongest match. ChatGPT, for example, seems to favour brands that align with conversational tones and relatable use cases, while Google AI pivots toward established authority from its search landscape.

Let me make this practical. Say I’m trying to find recommendations for industrial automation brands. Iconsys 2 comes in at an abysmal 6.7% visibility across our network , but even then, ChatGPT and Google AI disagree on when (if ever) to mention it. If I was working for their marketing team, I’d be tearing my hair out. Related reading: this piece about the brand invisibility problem: why your business is invi.... Related reading: our article on the llm consolidated testing framework: 36 signals, 37,00.... How do you even begin bridging such a wide visibility gap?

Real Industry Examples: Who’s Winning and Losing in AI Visibility

Visibility isn’t equal , not by a long shot. Across the 30 brands we track, mention rates range from a painful 6.7% at the lowest to the golden 100% for some high performers. Here’s the breakdown:

• Low Visibility (<30%): This includes brands like TOCA Social (10%) and Contxt itself (just 13%). These brands are fighting an uphill battle to even get noticed across major AI platforms.
• Mid-range Visibility (30-70%): A mixed bag of brands that show up often enough to seem competitive but still miss out on a significant chunk of queries. This includes brands in sectors like entertainment, B2B tech, and smaller-scale manufacturing.
• High Visibility (70%+): The clear winners, including Viking Pumps, IPU, and Red Fern, all hitting 100% recommendation rates in our tests. These guys have obviously nailed their visibility strategy.

But here’s the kicker: even among high-visibility brands, the platform-specific behaviour can skew results dramatically. Related reading: our article on the search industry is collapsing: what the data actually.... Related reading: this piece about the hidden gap: why your brand is invisible to ai systems.... For example, Viking Pumps might dominate ChatGPT and Google AI Overview, but how sure are we that Perplexity or Claude would give it equal love? There’s always a potential blindspot.

GEO’s Role in Bridging the Gap

Now, let’s talk solutions. Because honestly, this divergence problem doesn’t just highlight where AI systems recommendations are falling short; it’s a massive opportunity for businesses to rethink their strategy. And this is where GEO (Global Entity Optimisation) comes into play.

GEO, in essence, is about ensuring your brand is visible across platforms, regions, and contexts. It’s not enough to have strong SEO anymore. You can visibility tracking tools to see for yourself. You can how AI visibility monitoring works to see for yourself. You need to optimise for AI-driven ecosystems that think differently , and that means re-engineering how your brand interacts with diverse AI algorithms.

Step 1: Cross-Platform Visibility Testing

First, you need to know where your brand stands across different AI systems. In our Contxt testing lab, we’ve run 58 experiments across 14 businesses, covering everything from prompt sensitivity to geographic visibility. One standout finding was just how inconsistent response rates are depending on provider. If 39% of prompts give conflicting answers, you want to know which AI systems are favouring your brand and where you’re invisible.

Step 2: Intent-Based Query Optimisation

This is pivotal. The way AI interprets intent affects whether your brand shows up at all. Our buying-intent experiments confirmed that a single tweak to language , shifting from questions like “What’s the best industrial automation tool?” to “Which brand is recommended for automated manufacturing?” , impacted visibility rates. GEO strategies need to focus on intent match optimisation across varied query styles.

Step 3: Multi-Language and Regional Optimisation

A mate of mine runs an agency specialising in European markets. He was blown away when I showed him our multi-language test results; brands he thought were globally recognised didn’t even register in certain languages. That’s a GEO blindspot too many people overlook. You’ve got to test regional queries and optimise content for multilingual AI interpretation.

Step 4: Competitive Displacement Strategies

Finally, let’s not forget the competitive angle. If an AI system is consistently recommending your rival over you, it’s a clear sign your GEO strategy isn’t cutting through. Our competitive displacement tests showed that tweaking brand keyword density and citation relevance in targeted prompts shifted recommendations , sometimes dramatically. The goal is simple: make your brand the obvious answer, even when the competition is in the mix.

The Bigger Picture: Why AI Visibility Matters

At the end of the day, the divergence problem isn’t just a technical quirk; it’s a signal that businesses need to rethink how they approach AI-driven marketing. Because let’s face it, AI’s influence on consumer decisions isn’t going away. It’s growing.

Here’s why this matters more than most people realise:

• AI systems are already the go-to for discovery. Whether it’s “best restaurants in Soho”, “top-rated sports tech brands”, or literally anything else, millions of consumers are relying on AI recommendations.
• If your brand isn’t visible on these platforms, you’re not just losing competitive edge , you’re basically invisible to an entire subset of decision-makers.
• AI visibility isn’t static. Algorithms update, providers evolve, and what works today may fail tomorrow. GEO isn’t a one-time fix; it’s an ongoing strategy.

And look, I get it. Fixing visibility gaps across AI systems feels like a bit of a minefield. We've covered this in more detail in this piece about emerging trends in ai visibility: what to expect in 2026. But from what we’ve seen in the data, brands willing to test, adapt, and optimise are already miles ahead.

What’s Next?

If I had to sum this up, here’s the takeaway: visibility divergence across AI systems is real, measurable, and fixable. Brands need to stop treating SEO as the holy grail and start thinking about GEO as the next frontier. Because when you’re optimised for AI systems recommendations across platforms, algorithms, and regions, you don’t just survive the divergence problem , you dominate it.

So here’s the challenge. Test your visibility. Run those multi-provider experiments. Find out where your blindspots are. I promise you’ll be shocked at the inconsistencies , but once you see the gaps clearly, you’ll know exactly where to focus your GEO strategy.

Need to get started? Sign up for Contxt today and let’s figure out where your brand stands. You’ll have the data, tools, and insights to optimise your presence (and stop tearing your hair out over conflicting AI recommendations). You’ve got this.