Why AI Visibility Demands a Sector-Specific Approach: Lessons from Industry Data

Why AI Visibility Demands a Sector-Specific Approach: Lessons from Industry Data

AI visibility isn’t just a tech buzzword anymore , it’s a commercial battleground. If your brand isn’t getting picked up by generative AI systems, you’re invisible. And invisible doesn’t sell. But here’s the thing: treating AI visibility as a universal problem misses the entire point. Visibility doesn’t just depend on good SEO or chucking more money into paid media. It’s deeply tied to your industry, your audience, and critically, how AI interprets those things.

I’ve spent years watching businesses scramble to make sense of digital marketing. Now, with the explosion of generative AI systems from ChatGPT to Google AI Overview, we’re in a new chapter, but the same mistakes keep happening. Brands think if they optimise for one engine, they’ve cracked the code. Spoiler: they haven’t. And our data proves it.

One Size Doesn’t Fit All

Let me hit you with some numbers. Across our dataset of 2801 AI prompts, the overall brand mention rate sits at 62.8%. Not bad, right? But dig deeper. Break it down by provider and you’ll see just how messy things get. ChatGPT mentions brands 75.1% of the time, while Google AI Overview comes in at 45.6%. That’s a massive gap. The divergence between these systems? 38% of the time, they give conflicting recommendations.

So, let’s say you’re visible on ChatGPT. Great. But you can’t automatically assume you’re covered on Google AI or Perplexity. This matters more than you think, especially when you realise people aren’t loyal to just one platform. Users bounce around to find the answers or recommendations they trust most. Related reading: this piece about prompt engineering for visibility: how brands can influen.... Related reading: this piece about how b2b enterprises can dominate llm visibility in ai sys.... If you’re only optimised for one, you’re leaving pounds on the table and handing opportunities to your competitors.

And it’s not just about providers , it’s about context. Our buying-intent tests showed brands jump in and out of relevance depending on the type of query. A product-focused prompt might highlight Company X, but the same brand is nowhere to be found in a competitor comparison. Why? Because generative engines don’t rank brands the same way every time. They’re dynamic, contextual.. and painfully inconsistent.

Understanding Sector-Specific AI Visibility

Here’s where industries come into play. Not every sector faces the same generative visibility challenges. For example, brands in manufacturing and industrial automation (like Viking Pumps or Iconsys 2 from our network) saw wildly different visibility rates. Viking Pumps crushed it with a mention rate of 100%, while Iconsys 2 barely scraped 6.7%. Same sector, completely different outcomes.

Why? Part of it boils down to how AI systems recognise authority. Manufacturing brands with a strong library of technical content, highly cited case studies, and consistent industry mentions tend to perform better. If you’re in that space and haven’t mapped how your expertise shows up in knowledge panels or model citations, you’re missing a trick. Iconsys 2, for instance, might be invisible simply because the engines lack robust data to rank them against competitors. Related reading: our article on best practices for boosting llm visibility in b2b: strate.... Related reading: this piece about why 39% of ai systems disagree on brand recommendations (.... This is a solvable problem, but only if you spot it.

Contrast this with retail or entertainment, where personalisation and sentiment play bigger roles. TOCA Social, a sports entertainment brand in our dataset, had just 10% AI visibility. Sports tech companies often rely on brand-driven storytelling and partnerships, but generative AI might not see those narratives as "recommendable". Related reading: The Strategic Pivot: Why LLM Visibility Will Reshape Ente.... You can visibility tracking tools to see for yourself. If you're in this sector, the strategy needs to shift towards optimising for emotional language and customer-centric prompts.

In B2B, where I personally spend the bulk of my time, the stakes feel higher. Decision-makers are using AI to research vendors, compare tools, and even make big purchasing decisions. Yet visibility here is patchy. Across our tracked brands, Contxt itself (yes, my own baby) has a visibility rate of 13%. It stings, but the reality is generative engines don’t surface niche solutions consistently unless they’re aggressively optimised.

What This Tells Us About Strategy

My theory? AI visibility strategies by industry aren’t optional , they’re foundational. Here’s what I keep seeing:

1. Generative engines favour brands with clear, structured data. If you’re in a technical sector, like manufacturing or healthcare, all roads lead back to metadata. Labels, citations, definitions , these are the currencies engines trade in. Skip foundational optimisation here, and you might as well not exist.
2. In fast-moving sectors like retail, visibility depends on trends. Engines favour brands tied to current events, search trends, or viral moments. Timing matters more than businesses realise. If you’re pushing outdated keywords or last season’s search queries, you’re off the radar.
3. For B2B, it’s about evolving beyond just "visible". Decision-makers don’t just want to find you , they need to trust you. AI systems that recommend brands often pull authority signals from case studies, white papers, or even interviews. Being visible without credibility gets you nowhere.

And one big takeaway: don’t assume visibility equals recommendation. In our testing, some brands were mentioned in prompts but not actively recommended. Being "findable" is step one. You can how AI visibility monitoring works to see for yourself. Being positioned as the best choice? That’s where things get tricky.

The Cross-Provider Minefield

Let’s talk about provider divergence again. It’s one of the most under-discussed elements of AI visibility, but it’s a big problem. If generative engines can’t agree on which brands to recommend, neither can your potential customers.

Across our network, Claude (an AI system by Anthropic) had the highest mention rate at 79.1%, beating ChatGPT. But Google AI Overview lagged at 45.6%. If you’re optimising exclusively for Google AI, you’re essentially competing in a smaller pie. For brands, this isn’t just frustrating , it complicates strategy. You can’t define your visibility goals in terms of a single platform anymore.

This divergence also creates some messy user experiences. Imagine someone asks ChatGPT for the best industrial pumps. It recommends Viking Pumps. Then they try Google AI, and it mentions Viking in passing but pushes something else to the top. Which do they trust? More likely, they’ll cross-check both engines and figure it out themselves. If that’s your customer, it’s on you to dominate multiple generative spaces, not just one.

How Businesses Can Adapt

So, what’s the answer? I wish I could give you a handy checklist and magic solution, but this stuff isn’t simple. AI visibility demands testing, patience, and constant refinement. That said, there are strategies to help:

1. Audit your visibility now. Take a hard look at where your brand shows up across generative systems. Are you only visible on ChatGPT? Are there glaring gaps on Perplexity or Google AI? Use tools like Contxt to analyse visibility rates and cross-provider consistency.
2. Match optimisation to sector dynamics. If you’re in healthcare, focus on structured data and authority signals. In retail, push for trend alignment and sentiment-rich content. Tailor your AI visibility strategies by industry, not just by platform.
3. Experiment constantly. Across our testing lab, 58 experiments revealed insights no single piece of data could. Test buying intent prompts, knowledge-displacement queries, and even model updates. Visibility shifts over time , don’t let your strategies go stale.
4. Invest in long-tail content. Generative systems thrive on specifics. If your prompts are too generic, expect visibility to disappear. Detailed, niche content helps engines connect your brand to targeted queries.

The Future of AI Visibility

Here’s the good news: those who adapt now will win later. Most industries are still in the "figuring it out" stage when it comes to generative AI. Visibility gaps like the ones we’ve highlighted aren’t just opportunities , they’re warnings. Look at Viking Pumps sitting comfortably at 100% mention rates; they’re not just visible, they’re dominating their niche. Other brands barely scraping double-digit visibility? They’ve got work to do. And honestly, so do most of us.

AI systems will only get smarter. They’ll refine their algorithms, expand their databases, and become better at understanding context. The brands that succeed will be the ones who don’t just optimise blindly but who really understand how engines rank differently across industries.

The question is: will you be visible when it matters?

Your Challenge

If you’re reading this, you’re probably ahead of the curve already. But don’t sit on that. Get proactive. Audit your visibility. Test your prompts. Refine your approach. You don’t need to reinvent your entire marketing strategy overnight, but you do need to recognise where generative AI fits into your sector.

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

If you're ready to take control, we’ve built tools for exactly this. Let’s get your brand visible where it counts.