What strategies improve brand visibility in AI search engines?

If you're investing in AI visibility tools like CrunchJunkie, you'll probably have access to more data than ever before. You know your retrieval rate. You know whether ChatGPT or Perplexity is citing your brand. You know your Share of Voice. You know how you compare against competitors. But then what? Being retrieved by an LLM isn't the end goal. The end goal is being recommended. They're not always the same thing. An AI search engine might retrieve your content as a source without ever mentioning your brand in its answer. Or it might recommend a competitor because their brand has stronger authority, even if your article covers the same topic. That's why AI visibility is about much more than publishing content. It's about becoming a brand that AI systems trust enough to recommend. Most marketing teams aren't struggling because they don't have enough data. They're struggling because they don't know which insights actually matter or how to turn them into a strategy. The good news is that many of the fundamentals aren't new. If you've done (good) SEO over the last decade, you'll recognise a lot of these principles. The difference is that AI search engines don't just rank pages — they generate answers. That changes what good content looks like and what signals they're likely to trust.

This is probably the easiest win. If your competitors are consistently being retrieved or cited for topics where your brand isn't, you've just found your content roadmap. Think of it like SEO keyword gap analysis, but for AI search. The mistake I see is marketers looking at AI visibility reports as a scorecard rather than a list of opportunities. AI visibility shouldn't become another KPI that gets reported on once a month and forgotten. It should help you decide what to write next, where to strengthen your authority and where your competitors are outperforming you. The goal isn't to collect more data. It's to make better decisions. Ask yourself: What questions are competitors answering that we aren't? Which products or services have low retrieval rates? Which customer problems are AI search engines solving without mentioning our brand? Does our customer services team have any insights about what customers need? Once you've identified those gaps, don't simply recreate what your competitors have written — use the gap as inspiration, then produce something genuinely more useful. Bring your own expertise. Add examples. Include first-hand experience. Challenge conventional thinking where you have evidence to do so. If every article says the same thing, there's very little reason for an AI system to prefer yours.

This is probably the biggest shift I see between traditional SEO content and content that's performing well in AI search. For years, marketers were told to write longer blog posts. More words. More headings. More keywords. That doesn't necessarily make content easier for an LLM to retrieve or cite. Instead, start with the question your customer is actually asking. Answer it immediately. Then spend the rest of the article proving why your answer is right. A structure that works for people and LLMs: a concise answer straight after the heading; a Key Facts section summarising the main points; clear headings based on related questions; short, self-contained sections that are easy to scan; evidence and examples plus expert opinion; and links to sources and original research. One question I always like to ask is: "If an AI model had already read the top ten articles on this topic, what would it learn from mine that it couldn't learn anywhere else?" That's your opportunity. Original research. First-party data. Customer insights. Real-world experience. Expert opinion. Don't just summarise what's already on the web. Add something to the conversation. People skim. AI systems retrieve answers. Your content needs to work well for both. Rather than hiding the answer halfway through a 3,000-word article, make it obvious within the first few paragraphs.

One of the biggest differences between Google and an LLM is how they decide what information to use. Traditional search is relatively deterministic. LLMs are probabilistic. They're effectively asking: "What's the most likely correct answer to this question?" That means trust becomes incredibly important. Anything you can do to prove your business is legitimate, knowledgeable and trustworthy makes it easier for AI systems to connect the dots. Ask yourself: Are your author profiles complete? Do you clearly explain who your experts are? Are professional accreditations visible? Are memberships of industry bodies easy to find? Is your company information consistent across your website and other platforms? This is where concepts like entity optimisation and entity linking come into play. Your website doesn't exist in isolation — your website, LinkedIn profiles, company listings, media mentions, podcasts, conference appearances and industry directories all contribute to your digital footprint. LLMs don't see webpages, they see entities and relationships. Your goal is to make it as easy as possible for them to connect your people, your products, your expertise and your brand across the web. The more consistent those signals are, the easier it becomes for AI systems to recognise your brand as a credible entity. You can find out more about entities and semantic SEO on this Search Engine Land guide.

This is one area I think is becoming even more important. AI systems don't just learn from your website, they learn from the wider web. If respected publications, experts and organisations are talking about your business, that's a much stronger trust signal than you saying you're great (I mean, you probably are great, but now we need to prove it!). Think about industry publications, podcasts, conference speaking, independent reviews, case studies, expert interviews and trusted media coverage. Good digital PR has always been valuable. Now it is non-negotiable. It helps build the external validation that makes your brand more likely to be cited in AI-generated answers. It's also why quality link building still matters. Not because you're chasing backlinks for the sake of SEO, but because trusted third-party websites reinforce your authority.

This is one of the least glamorous jobs, but it's one of the most important. Structured data gives search engines and AI systems machine-readable context about your website. If you're ecommerce, mark up products, prices, reviews, availability and FAQs. If you're a service business, describe your organisation, your services, your people and your locations. Structured data won't suddenly make ChatGPT recommend your business, but it removes ambiguity. The easier you make it for machines to understand your website, the more likely they are to retrieve the right information. As generative commerce continues to grow, this becomes an increasingly important foundation — which is also why we track AI shopping visibility at product level, not just brand level.

If I think there's one thing missing from a lot of AI content strategies, it's originality. Most brands still create content by looking at what everyone else has written and producing a slightly different version. That worked reasonably well for SEO. I'm not convinced it'll be enough for AI search. If you want your brand to become a source that AI systems cite, give them something worth citing. Publish original research. Then keep it up to date. AI search rewards sources that stay current, not just sources that publish once and move on. Share customer data. Create benchmarks. Write detailed case studies. Offer a different perspective — backed by evidence. The web doesn't need another generic "Top 10 Tips" article. It needs more sources that actually move the conversation forward.

We're entering a phase where every marketing team will have an AI visibility dashboard. Whether that's CrunchJunkie or another AI visibility platform, simply measuring your visibility isn't enough. The real value comes from understanding why you're being retrieved, where you're losing visibility to competitors and, most importantly, what to do next. That's exactly why we built CrunchJunkie. We didn't want another dashboard full of graphs. We wanted a platform that helps marketers turn AI visibility insights into action — identifying content gaps, uncovering competitor opportunities and understanding the factors that influence retrieval and brand recommendation. Because AI visibility reports aren't the strategy. They're the diagnosis. The strategy is what you do next. Use those insights to create better answers, strengthen your authority, earn more third-party validation and make it easier for AI systems to understand your business. AI search isn't rewarding the loudest brands, or the ones producing the most content. It's rewarding the brands that consistently provide the best evidence. They'll be the ones producing the most useful answers, demonstrating the strongest expertise and giving AI systems the confidence to recommend them.