A recent study found that Google’s AI overviews returned negative brand information 44%*1 more often than ChatGPT, potentially surfacing old criticisms to the top of the search engine results pages (SERPs).
AI engines now compress a brand’s entire historical digital footprint into one single response at the top of the SERPs, which includes everything from where your brand has been mentioned (negatively or positively) and company announcements to product recalls and previous partnerships. In the past, this may have been harder to find or required deeper research, with results appearing over two or three pages.
In addition to this, another study found that over a third (35%)*2 of respondents identified reliability as their biggest concern with Large Language Models (LLMs), with many noting that AI tools often produce inconsistent or inaccurate results.
The rise of AI Overviews has accelerated the shift toward a zero-click reality. In 2026, data suggests that between 60% and 65% of searches*3now end without a single click to an external website, creating a dangerous reputation gap. If an AI generates a negative or outdated summary of your brand, the user has already received their answer and may never click through to your site to see the corrected information. This means your website is no longer the first impression; the AI overview is.
What does this mean for businesses?
LLMs are generally reliable when it comes to providing basic business information, so long as on-site content is kept up-to-date and is accurate and consistent. However, they can be less reliable for niche, real-time, or highly specific data, often generating plausible-sounding but completely false statements (known as hallucinations).
Moreover, training data in LLMs is often outdated or not updated regularly enough, making them unreliable for real-time market trends or current news. Outputs can often reflect bias in their training data, too, which could lead to issues around recruitment or market demographics.
For businesses looking to maximise their visibility in LLMs, this means that understanding how AI platforms describe your brand and measuring share of voice across search and AI should be a critical metric for companies seeking to focus their teams and resources where it matters most. It’s also important to ensure on-site content is constantly refreshed and updated, to ensure accuracy, and so that LLMs aren’t accidentally sharing inaccurate or out-of-date information.
Contrary to popular belief, LLM seeding is not just about posting more content. Crucially, it’s the strategic engineering of your brand’s digital footprint to ensure machine-readability and authoritative distribution. This means ensuring that your on-site content is organised efficiently, making it easier for LLM crawlers to find the information to pull through to AI Overviews, and prioritising placements on high domain authority sites, such as Reddit and Quora, and top-tier news outlets.
How to boost business visibility in LLMs
If these tools aren’t referencing your brand or content accurately or at all, then you’re missing out on a growing share of visibility, and this is where LLM seeding comes in. LLM seeding usually involves publishing content in various places and formats that LLMs are most likely to crawl, understand and cite.
You can utilise this strategy to ensure your content appears in AI-generated answers, even if no one clicks through to your website. It’s all about boosting brand awareness and sharing positive news about your business.
Here’s how you can use SEO, digital PR and AI-aware content strategies to boost your business’s visibility on LLMs and make sure it’s citing your business accurately and positively, stopping out-of-date negative material from harming your visibility:
Audit and ensure brand consistency
Ensure that any website mentioning your business is not only accurate, but consistent too. You can do this by doing a quick search online to see where you’re mentioned. Go through sites such as LinkedIn, Wikipedia, and your About Us page, and ensure your brand’s mission, leadership, and core services are described identically.
Inconsistent data signals unreliability to an LLM, leading to exclusions or hallucinations. Ensuring consistency across all platforms means that LLMs will see the information as reliable and will be more likely to pull it through for AI Overviews.
Boost digital PR efforts
LLMs verify facts and information by looking across multiple sources. Don’t just publish content on your blog; boost your authority through Digital PR and link building. This helps to seed information across authoritative third-party trade journals, industry niches, comment boards, and news outlets to build a verified footprint.
If you want you or your brand to be known for a specific topic, you’ll need to secure placements across a variety of sources. It can be a mix of brand mentions and links, but the key is that you and your brand are mentioned on a high-authority site, in the space you want to be an authority in.
To do this, you could set up alerts on sites such as ResponseSource, Qwoted and X to receive journalist requests in your sector and issue reactive statements on trending topics. You could also use a site like AHRefs to look for unlinked mentions, and reach out to each, asking if they need any further expert commentary for anything they’re currently working on and ask if they’re able to provide a link where they’ve mentioned you previously.
All of this builds the E-E-A-T (Experience, Expertise, Authoritativeness, and Trustworthiness) signals that LLMs use to verify who is a true authority in a specific niche.
Front-load citable chunks
LLMs are pattern-matchers that favour the first 30% of a page when looking for information to cite about a brand. Place clear, 50-word definitions or key takeaways at the top of your on-site content and articles to make them easy for AI to lift and cite.
Weaponise structured data (listicles, FAQs and comparison tables)
LLMs prioritise ranking-style articles and listicles, especially when they match user intent, such as “best tools for Digital PRs” or “top SEO agencies for startups”, for example. Adding transparent criteria boosts trust and means your business is more likely to show up when this topic is searched.
Use comparison-ready tables within your content and ensure you have an up-to-date FAQ section, too. AI models love structured data and information, as it makes it easier to crawl and pull information, especially if it’s consistent and verifiable across multiple platforms. By using HTML tables for product specifications or pricing, you increase the likelihood of being cited in ‘best of’ or ‘Versus’ AI responses.
Monitor Share of Model (SOM)
Shift your KPIs from keyword rankings to AI Share of Voice. Track how often your brand is mentioned, as well as the sentiment of those mentions, across multiple AI tools, such as ChatGPT, Gemini, and Perplexity, and make sure you’re up to date with how they see your brand.
This will give you up-to-date visibility on how your brand looks across multiple tools, and gives you the option to dilute any negative information with fresh, high-authority positive stories if necessary. The quicker you do this, the better your brand will look in AI Overviews in the long term.
The search era as we knew it is over, and we’ve entered the era of AI Synthesis. In this new landscape, a brand’s website is no longer the definitive source of truth; the LLM’s summary is.
If your digital footprint is fragmented, outdated, or plagued by historical negatives, Google’s AI Overviews will amplify those flaws to 65% of searchers who will never click through to hear your side of the story. By treating LLM seeding and Share of Model (SOM) with the same rigour as traditional SEO, businesses can stop being victims of AI hallucinations and start engineering their own digital narrative.
In 2026, you don’t just want to be found, you want to be the version of the truth that the AI trusts.
