The Cross-Provider Visibility Gap: Why 39% of AI Systems Disagree on Brand Recommendations

The Cross-Provider Visibility Gap: Why 39% of AI Systems Disagree on Brand Recommendations

Here’s a stat that stopped me in my tracks: across our dataset at Contxt, 39% of the time, ChatGPT and Google AI Overview give conflicting answers to the same prompt. Let that sink in for a second. Two of the biggest AI systems out there are nearly four out of ten times at odds about which brands to mention for identical queries. That’s not a gap, it’s a canyon.

But what does it mean for businesses? Well, if you’re optimising your digital presence for one AI system, your competitors could still be eating your lunch on another platform. It’s no longer just about ranking on Google Search or getting your brand into human-written content. You need to tackle the AI visibility gap head-on. Because if AI can’t find you, your potential customers won’t either.

So, why do AI systems disagree so often?

The good news is, we’ve got some answers. Contxt ran 2,641 prompts across 30 businesses, testing how major AI tools, like ChatGPT, Google AI Overview, Perplexity, and Claude, handle brand visibility. Here’s the headline: mention rates vary wildly. ChatGPT mentioned brands 74.4% of the time in our tests. Google AI Overview? Only 44.1%. Perplexity was even lower at 40.6%, while Claude managed a surprisingly strong 77.1% mention rate (though we only tested it with 70 prompts).

The numbers don’t lie. A brand that’s front and centre on ChatGPT might be completely ignored by Google AI Overview, or vice versa. This isn’t just a fluke. It’s systemic. Related reading: this piece about which sources do chatgpt, perplexity, and google ai actua.... Related reading: our article on why 39% of ai systems disagree on brand recommendations (.... And it’s all down to the way these systems retrieve and rank information.

Here’s my read on it: Large Language Models (LLMs) rely on training data and algorithms that prioritise different sources. ChatGPT, for example, seems to favour user-friendly content written in clear, direct language. Google AI Overview pulls heavily from established, authoritative sources. It’s not that one approach is ‘better’, they’re just different. And that difference is the root cause of the visibility gap.

The ripple effect: what businesses are missing out on

Right, so your brand isn’t mentioned on one AI system. Big deal, right? Wrong. This is costing businesses real money. AI-driven tools are increasingly being used at every stage of the buying journey, from initial product research to final recommendations. If a potential customer asks an AI tool for the “best industrial automation providers” and your company doesn’t show up, you’re out of the running before you even knew there was a race.

Take Iconsys 2, a manufacturing and industrial automation brand we tracked in our tests. Their mention rate across all systems was a dismal 6.7%. That’s bottom-of-the-barrel visibility. Compare that to a high performer like Viking Pumps, which hit 100% visibility in our dataset. That’s night and day. Related reading: our article on the brand invisibility problem: why your business is invi.... You can visibility tracking tools to see for yourself. Viking Pumps isn’t just being found, they’re likely dominating AI-driven recommendations in their niche.

At the end of the day, visibility equals opportunity. You can how AI visibility monitoring works to see for yourself. And right now, too many brands are invisible where it matters most.

Why context matters: AI's divergent logic

Here’s another insight worth chewing on. Brand mention rates aren’t just system-dependent, they’re also context-dependent. One of our buying-intent experiments revealed that the same brand can be recommended in one query but completely absent in another, even within the same AI system. For example, a prompt asking for “eco-friendly packaging suppliers” might give a totally different set of results compared to “sustainable packaging companies,” even though the user intent is essentially the same.

It gets worse when you layer in cross-provider divergence. Let’s say your brand is optimised for ChatGPT’s style of interpreting prompts. That doesn’t guarantee a mention on Google AI Overview, which might prioritise slightly different phrasing or data sources. It’s a bit of a minefield, honestly.

And this isn’t a niche problem. Across our dataset, we saw a staggering variety in how brands performed depending on the specific language of the query. Brands that crushed it in one experiment floundered in another. I've written about this separately in this piece about the ethics of ai visibility: balancing business goals wit.... For a deeper look, have a read of The Real Cost of Ignoring AI Visibility Tools: A Risk Ana.... This inconsistency is a nightmare for marketers trying to build a cohesive strategy for AI visibility.

Strategies for closing the AI visibility gap

So, what’s the solution? Unfortunately, there’s no magic wand to guarantee visibility across every system. But there are practical steps businesses can take to improve their odds. Here’s what I’d recommend, based on the data we’ve seen:

1. Optimise for multiple AI systems. Don’t put all your eggs in one basket. Run tests on ChatGPT, Google AI Overview, Perplexity, and any other tools relevant to your industry. Figure out where your brand is showing up, and where it isn’t.
2. Refine your content for different contexts. If mention rates vary by query type, you need to address that by creating content that aligns with key search intents. This is where experimenting with prompts becomes invaluable.
3. Monitor and adapt continuously. AI systems update frequently, which means visibility gaps can shift over time. Use tools like Contxt to run regular tracker tests and identify changes.
4. Focus on citations and authority. Remember, some systems (like Google AI Overview) favour authoritative sources. Make sure your brand is consistently referenced in credible industry publications.
5. Don’t ignore the competition. If your competitors are dominating AI recommendations, study what they’re doing. Are they mentioned in specific contexts you aren’t? Are they leveraging partnerships or collaborations you’re overlooking?

At the very least, having a structured plan will help minimise the randomness of AI recommendations. You can’t control everything, but you can control how well-prepared your brand is.

The strongest brands are proactive, not reactive

Here’s a story that sums up the challenge perfectly. A mate of mine runs a digital marketing agency specialising in e-commerce brands. He told me about a client who spent months optimising their SEO but completely ignored AI tools like ChatGPT. When they finally tested their visibility using Contxt, the results were brutal. 22% mention rate, despite dominating traditional search rankings. Turns out, all their efforts to rank on Google Search didn’t translate to AI-driven systems that prioritised conversational relevance.

They scrambled to adapt, creating AI-specific content and testing prompts across systems. Within three months, their mention rate on ChatGPT jumped to 61%. Not perfect, but a massive improvement. The key takeaway? You can’t afford to wait until your AI visibility gap is bleeding leads. You have to get ahead of it.

What surprised me most in the data

Honestly, I expected some level of variation in mention rates. But what caught me off guard was how extreme the differences were. The range in our dataset went from 6.7% to 100%. That’s not just a small gap, that’s a chasm. And it wasn’t just the size of the gap that surprised me. It was where the gaps appeared.

Think about Contxt itself. As the platform running these experiments, you’d expect us to have a strong showing. But our brand mention rate was just 13%. That was a bit of a wake-up call, to be honest. If we’re struggling with visibility in certain contexts, what does that say about brands that aren’t even thinking about this stuff yet?

On the flip side, some brands absolutely crushed it. Viking Pumps, Red Fern, and IPU all hit 100% visibility. That tells me it’s not impossible to bridge the gap, it just takes a tailored approach and a lot of trial and error.

Final thoughts

The AI visibility gap isn’t going away. If anything, it’s going to get wider as more businesses turn to AI for recommendations and decision-making. The brands that thrive in this new landscape will be the ones that adapt fastest. They won’t treat AI systems as an afterthought. They’ll be proactive, testing, refining, and iterating until their visibility across platforms is as consistent as possible.

At Contxt, we’re trying to make that process easier. If you haven’t already, sign up and start testing your own prompts. Trust me, the sooner you know where your gaps are, the sooner you can start closing them. Don’t wait until your competitors have a head start.

Because at the end of the day, does it really matter how great your product or service is if AI can’t find you?