Observed Signal · Feb 12, 2026 · Strategy / Playbook · Source: Toms Marketing Ideas · Impact: 2/5 · Sentiment: Neutral
How to Get AI Chatbots to Recommend Your Brand
The piece describes a practical playbook for getting large language model chatbots (e.g., ChatGPT, Gemini, Claude, Perplexity) to recommend a brand organically. The author uses HiBob as a case study: after applying the described SEO/AI tactics, HiBob—a $2.7 billion HR software company—appeared in ChatGPT recommendations for relevant prompts rising from 17% to 51% over six months, and in Gemini results from 9% to 61%. The newsletter argues that 89% of B2B buyers now use AI chatbots to research products, that many marketers only buy “AI visibility” tracking tools without actionable steps, and that practical AI SEO/GEO tactics are needed to capture AI-driven demand and demo bookings.
Highlights that LLM chatbots are a growing discovery channel for B2B buyers and that brands can materially increase inclusion in AI recommendations—relevant to SEO, martech and demand-generation strategies but not a platform-level policy or technical release.
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Key Takeaways & Evidence Grounding
- HiBob ranked #1 in repeated ChatGPT recommendation tests conducted by the author.
- HiBob is described as a $2.7 billion company.
- The author cites that 89% of B2B buyers use ChatGPT, Claude, and Perplexity for product research.
- HiBob’s share of relevant ChatGPT mentions rose from 17% to 51% over six months; Gemini mentions rose from 9% to 61% in the same period.
- Oz Tollman Goodman is Head of SEO at HiBob and provided the playbook described.
Connected Companies & Entities
2 Entities mappedOntology Mapping & Concepts
Related Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Unlocking AI: Transform Your Content for New Search Trends
The newsletter explains that search discovery has shifted from traditional Google-first SEO to AI-powered search driven by large language models (LLMs). Research cited includes Limy’s analysis of 80 million clickstream lines showing most AI-cited sources appear well beyond Google page one, and studies from Ahrefs, Adobe and Microsoft showing low overlap with Google top results and materially higher conversion rates from AI-driven traffic. The piece outlines specific content and technical tactics to appear in AI answers: prioritize semantic, problem-solving content formatted as Question → Direct Answer → Evidence → Follow-ups; include FAQ schema; ensure GPTBot/ClaudeBot/PerplexityBot access in robots.txt; submit sitemaps to Bing; adopt the emerging llms.txt standard; and use server-side rendering so critical content is in HTML. Case studies (Tastewise) and metrics are used to show fast visibility gains for startups that adapt.
Marketers' playbook for brand visibility in AI agents
Jon Williams responds to Mark Ritson’s warning by offering a practical playbook for brands to remain visible and recommended by generative AI agents. He argues that fame functions as training data for large language models: brands that surface frequently become the model’s named recommendations, while weaker brands may be ignored. Williams recommends measuring a "share of model" metric (brand mention rate, recommendation rate, prompt coverage, model-specific visibility, volatility), using tools such as Semrush AI Visibility Toolkit, Profound and Peec AI, and optimizing content for citations rather than just search position. He highlights that paid AI discovery products are emerging (Google AI Mode, Perplexity, ChatGPT/OpenAI) and stresses the value of third-party validation channels like Reddit, review sites and editorial coverage to shape AI recommendations.
B2B PR to Influence AI Buying Recommendations
MarTech published guidance on how B2B brands must adapt public relations and content strategies to appear in AI-generated answers that increasingly drive software buying decisions. Citing a March 2026 G2 survey of 1,000+ B2B buyers (71% use AI chatbots for vendor research; over half start buying with an AI query) and research from Magenta Associates showing five brands capture 80% of top AI responses in a category, the article argues brands need a "dual-path" PR approach. One path preserves earned media and backlinks for human discovery; the other focuses on structured content, consistent entity signals, and measurement of AI-driven decision outcomes so AI systems can parse, verify, and recommend vendors accurately. The piece also notes AI shortlists are often four to seven brands versus ten blue links on a Google results page and reminds readers MarTech is owned by Semrush.
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