Decoding Brand Signals: How AI Systems Interpret Authority Across Industries

Decoding Brand Signals: How AI Systems Interpret Authority Across Industries

Here’s the uncomfortable truth: most brands are flying blind when it comes to understanding how generative AI systems see them. They’ve nailed SEO for Google search, tweaked their social algorithms for engagement, and optimised content for every conceivable platform. But ask them how their brand performs in ChatGPT, or whether Google AI Overview even recognises them, and the room goes quiet.

My theory? AI visibility is the next authority battleground. And the rules aren’t just new, they’re inconsistent. A brand might dominate in one system while being completely invisible in another. Worse, no one seems to know why. Yet this matters more than most businesses realise because generative AI isn’t just a trend , it’s rewiring how people discover and engage with brands.

Here’s what I keep seeing: the way AI platforms evaluate “authority” is nuanced, and it’s not one-size-fits-all. Context matters. Industry matters. The intent behind a query matters. And if you don’t understand how your brand is showing up (or not showing up), you’re already falling behind. Let’s break it down.

Is AI Authority the Same Across Industries?

Short answer: not even close. In fact, the same prompt can deliver wildly different outcomes depending on the industry you’re in. Our data at Contxt highlights this disparity clearly. Across 2641 AI prompts tested, we’ve seen a staggering 39% divergence rate between ChatGPT and Google AI Overview. That means more than a third of the time, these two systems recommend entirely different brands for the same query. If you’re visible on one but not the other, you’ve got a serious blind spot.

Take manufacturing, for instance. In that sector, we’ve tracked brands like Viking Pumps and Red Fern achieving a 100% mention rate across prompts. They’re crushing it. But Iconsys 2, another manufacturing player, only shows up 6.7% of the time. Same industry. Similar verticals. Completely different outcomes. Why? Because AI systems aren’t just scanning for “good” content or keyword density. Related reading: our article on the silent threat: why your brand is invisible in ai syst.... Related reading: The Cross-Provider Visibility Gap: Why Your Brand Might B.... They’re interpreting authority through a much more nuanced lens of citations, relevance, and specificity.

Now contrast that with entertainment. TOCA Social, an experiential sports brand, shows up in only 10% of prompts. That’s not a knock on their offering , it’s a reflection of how little AI systems see them as an authority in queries related to their niche. Related reading: this piece about from 0% to cited: what brands scoring 80%+ on ai actually.... If you’re not actively feeding these systems signals about your expertise, you’re effectively invisible.

The Three Pillars of AI Authority

So what exactly are these AI systems looking for? Based on the 996 unique prompt variations we’ve tested, three key factors seem to rise to the surface:

1. Domain-Specific Expertise

AI respects expertise, but only if it aligns with the query. This is where industry-specific nuances come into play. A brand like Viking Pumps thrives because its domain authority in industrial manufacturing is airtight , detailed product specs, real-world case studies, and a history of technical reliability. But throw Viking Pumps into a lifestyle-related query and they’ll be forgotten instantly. You can Contxt platform features to see for yourself. You can how Contxt works to see for yourself. Why? Because their authority doesn’t translate outside their niche.

This is where businesses often trip up. They assume general thought leadership will establish authority across AI systems. But if your expertise isn’t hyper-relevant to the query, the system will skip you. It’s not about casting a wide net; it’s about depth within your vertical.

2. Engagement and Signal Density

Here’s a killer stat from our dataset: brands with higher engagement signals (think citations, backlinks, mentions from reputable sources) consistently score better across generative AI. For example, in our buying-intent tests, brands recommended for purchase-related prompts had mention rates as high as 70-80%, compared to 10-20% for brands with weaker backlink profiles.

But engagement isn’t a single metric , it’s how signals connect. AI weighs not only how often you’re mentioned but also who’s mentioning you. If authoritative sources cite your brand, that creates a ripple effect. It’s similar to E-A-T (Expertise-Authoritativeness-Trustworthiness) in SEO but even more unforgiving. You can’t cheat your way into AI’s good graces.

3. Query Intent and Context

This one caught even me off guard. In our testing lab, we discovered that the same brand could be highly visible in one query type but completely absent in another. For example, buying-intent prompts heavily favour ecommerce-specific authority, while competitor-displacement prompts often prioritise brands with high consumer trust scores.

It’s not that your brand lacks authority , it’s that the system doesn’t see you as relevant to that particular question. That’s why understanding query intent is critical. Are you optimising your content for informational prompts, transactional ones, or both? If you’ve only focused on one, you’re probably underperforming across the board.

Cross-System Disparities: Why ChatGPT and Google AI Disagree

Let’s tackle this head-on. If ChatGPT references your brand but Google AI doesn’t, does that mean one platform is broken? Not at all. Each system is trained on different data and interprets authority uniquely. Our network data shows mention rates as follows: ChatGPT (74.4%), Google AI Overview (44.1%), Perplexity (40.6%), and Claude (77.1%). The gaps are massive.

It’s tempting to assume that if AI systems don’t agree, their algorithms must be flawed. But that’s not what’s happening. The divergence reflects differences in training data, priorities, and even the architecture of the models themselves. ChatGPT, for example, is hyper-reliant on large-scale web data and conversational patterns. Google AI Overview leans into structured data and broader knowledge graphs. Both have merit, but they’re focused on different outcomes.

This is why a multi-platform approach is non-negotiable. Optimising for one system won’t cut it. You need to understand where your brand is strongest and where you’re falling short. And if you’re not testing across platforms, you’re missing half the picture.

Actionable Steps to Improve AI Visibility

Alright, enough theory. How do you actually fix this? Here are four steps grounded in our findings:

1. Map Your Current AI Visibility

You can’t optimise what you can’t measure. Start by running experiments across different systems to see how often (and where) your brand shows up. Tools like Contxt (shameless plug, I know) can give you these insights. We've covered this in more detail in our article on why llm visibility tools are essential for b2b brands in .... I've written about this separately in Best Practices for Boosting LLM Visibility in B2B: Strate.... From our own experiments, we’ve seen brands go from 20% to 60% visibility within weeks simply by understanding where their gaps are.

2. Double-Down on Domain Content

If your content isn’t showing up in AI, it’s likely too general. Focus on creating high-quality, specific content tailored to your niche. Viking Pumps doesn’t just talk about pumps , they publish engineering guides, efficiency calculators, and maintenance checklists. That’s the level of specificity you need to dominate.

3. Build Stronger Relationships for Authority Signals

Remember, AI systems value not just what you say but who’s validating you. Partner with authoritative publications, industry blogs, or even influencers in your space. Build backlinks, get cited, and keep signalling that you’re the real deal in your sector.

4. Test Regularly and Iterate

AI visibility isn’t static. As models update, so do their preferences. We’ve run 58 completed experiments in our lab, testing everything from buying-intent prompts to sentiment analysis. The lesson? What works today might not work tomorrow. Treat AI visibility as an ongoing optimisation effort, not a one-and-done project.

The Future of Brand Authority in AI

Here’s where I think we’re headed. As generative AI becomes more embedded in daily life, its interpretation of authority will only get more nuanced. For brands, the challenge isn’t just about visibility , it’s about relevance. If you’re not aligning with the intent behind user queries, you’ll be invisible no matter how much content you create.

But it’s not all doom and gloom. Businesses that figure this out early will dominate. Imagine being the go-to recommendation in your vertical every time someone asks ChatGPT or Google AI a question. That’s not just good marketing; it’s owning the conversation in your industry.

So, the question is: are you ready to decode how AI sees you?