Estimated reading time: 6 minutes
Table of contents
Key Takeaways
- Conversational AI tools like ChatGPT greatly influence the buyer journey, as shown by a tenfold increase in LLM mentions in 2025.
- Consumers appreciate personalized recommendations from LLMs due to their speed and tailored interactions.
- Brands must focus on LLM optimization to ensure visibility in AI-generated responses, unlike traditional SEO which focuses on search rankings.
- Strategies include auditing your brand presence, creating entity-rich content, earning reputable mentions, and using conversational language.
- Companies that optimize for LLMs now will likely dominate when consumers rely more on AI recommendations in the future.
A few years ago, asking a chatbot for a product recommendation might have sounded like science fiction. Today it’s commonplace. Conversational AI tools such as ChatGPT, Claude and Gemini tap enormous datasets to provide personalized suggestions. They digest complex requests, ask clarifying questions and speak in a human‑like tone. As consumers adopt these helpers, their influence on the buyer journey is growing quickly. Fairing’s Q2 2025 benchmark report tells the story: between January and mid‑July 2025, there was more than a tenfold increase in “How did you hear about us?” survey responses that mention a large language model platform (Fairing 2025). At the same time, roughly one in seven brands now reports at least one customer citing an LLM in attribution surveys (Fairing 2025). For marketers, this surge represents both an opportunity to engage customers in new spaces and a mandate to rethink visibility.
Why consumers ask LLMs for recommendations
The appeal of conversational AI goes far beyond novelty. Models like ChatGPT can conduct multi‑turn conversations, digest messy questions and suggest options that feel tailored to a user’s needs. Many people appreciate the speed and nuance of these interactions. According to IDC, over 45 percent of people now use generative AI weekly (IDC 2025), and adoption is accelerating. When someone asks a tool to plan a vacation itinerary, it’s natural to follow up with, “What’s the best luggage brand for frequent flyers?” These assistants remove friction from research: instead of sifting through dozens of links, you describe your preferences and receive instant, contextual responses.
There’s also an element of trust. Large language models synthesize information from reputable sources and present it in a conversational tone. When a prospect mentions that they heard about your company from ChatGPT, it signals that your content and reputation were strong enough to be incorporated into an AI’s knowledge base. Fairing’s data show that the average number of LLM mentions per brand is rising (Fairing 2025). Brands that fail to appear in these conversations risk falling off the radar entirely.
The data behind LLM‑driven discovery
Fairing’s benchmark study paints a picture of a nascent yet explosive channel. From January to July 2025 the number of respondents crediting an LLM skyrocketed more than ten times (Fairing 2025). While ChatGPT remains the most frequently mentioned assistant, competitors like Claude and Gemini are growing quickly. The distribution of mentions is wide but shallow: about 15 percent of brands in the dataset have at least one LLM‑attributed customer (Fairing 2025), but for many of those brands, mentions represent less than 0.5 percent of total responses. In other words, conversational discovery is still early, leaving plenty of room for first movers to build share of voice.
Industry forecasts underscore why optimization is critical. IDC projects that companies will spend up to five times more on LLM optimization than on traditional SEO by 2029 and that generative AI spending will grow at a 59 percent compound annual growth rate between 2023 and 2028 (IDC 2025). With over 45 percent of people using generative AI weekly (IDC 2025), discovery behaviour is clearly shifting. Marketers can’t afford to ignore channels that are drawing both consumer attention and corporate investment.
Why LLM optimization matters
If traditional SEO is about ranking high on a search results page, LLM optimization is about being included in the AI’s knowledge base and surfacing in its responses. Unlike search engines, which display dozens of results, a conversational assistant may mention only a handful of brands. When someone asks, “What are the best project management tools for small teams?” they might receive three suggestions. If your brand isn’t one of them, you’re invisible.
Optimizing for LLMs hinges on two factors: frequency and relevance. Frequency refers to how often your brand is mentioned across high‑quality sources. Relevance relates to how clearly those mentions tie your name to the problems you solve. Achieving this means earning citations on authoritative websites, ensuring consistent entity representation and providing structured, factual information that models can easily parse. The more signals an AI receives that your brand is credible and relevant, the more likely it is to include you in an answer.
Strategies for engaging consumers through LLMs
- Audit your presence. Start by asking popular assistants about your brand and your competitors. Note whether you’re mentioned, how you’re described and which sources are cited. This will reveal gaps you need to address.
- Create entity‑rich content. Publish articles, case studies and FAQs that clearly associate your brand with the challenges you solve. Include technical details, customer testimonials and comparisons. This gives models explicit data points to reference.
- Earn reputable mentions. Seek guest posts, interviews and citations on authoritative sites in your industry. Fairing’s research suggests that mentions across diverse platforms are fuelling the surge in LLM attribution (Fairing 2025). High‑quality backlinks have always mattered; now, high‑quality mentions do too.
- Use natural, conversational language. LLMs are trained on human conversation. Write in a clear, accessible style that mirrors the way people speak. Answer questions directly and anticipate follow‑ups. This improves the likelihood that your content will match conversational prompts.
- Monitor and iterate. Patterns will shift quickly as more consumers and assistants enter the market. Regularly revisit your audits, update your content and watch for emerging assistants or channels where your audience spends time.
Conclusion and next steps
Large language models are no longer just novelty chatbots; they are influential discovery engines. Data from Fairing shows explosive growth in customers citing LLMs, and IDC forecasts confirm that brands will pour resources into optimizing for these conversations. The companies that get ahead today—by auditing their presence, creating rich content, earning reputable mentions and speaking the language of their customers—will be the ones that show up when an AI assistant makes a recommendation.
Want to go deeper? Check out our guide on optimizing your content for LLMs to ensure your brand stays visible as conversational AI becomes the default discovery channel: How to Optimize Your Content for LLMs.
References
Fairing (2025) — https://fairing.co/resources/benchmarks/llm-product-discovery-benchmarks-q2-2025 IDC (2025) — https://blogs.idc.com/2025/09/12/marketings-new-imperative-the-shift-from-seo-to-llm-optimization/
