Answer Engine Optimisation (AEO) is the cross-functional discipline of structuring brand metadata, technical foundations, and external authority so Large Language Models (LLMs) cite a company during prompt-based queries. Unlike traditional search engine optimisation, which focuses on earning website visits, AEO secures early brand inclusion inside AI-synthesised recommendations. At our recent B2Breakfast roundtable in Copenhagen, senior marketing leaders gathered over coffee to break down how machine intelligence is shifting B2B buying behaviour off-site. Joined by my colleague Alexander Borgvall Björk alongside Ben Fröhlich from Peec AI, Henrik Fabrin from Dansk Industri, and Jennifer Montague from Cerivo, we discussed the strategic adjustments required to stay visible when search funnels go dark. Here is an editorial look at the core themes and practical realities discussed on stage.  

B2B discovery has shifted into closed answer engines

When sixty percent of individual buyers use tools like ChatGPT or Gemini during vendor evaluation, your target audience is being served synthesised answers long before they ever reach your website. Traditional Google search results now see zero-click rates pushing seventy percent, while direct click rates within AI tools sit closer to five percent. Research from G2 reveals that software buyers starting their initial research inside AI tools grew by over seventy percent in under a year. Furthermore, analysis across our B2B client portfolio shows that leads influenced by LLM recommendations convert four to six times higher and result in larger transaction sizes. As Alexander Borgvall Björk highlighted, eighty-two percent of buyers select a solution from their initial day-one shortlist. If an AI engine omits your brand during that first prompt, you are effectively excluded from the entire evaluation process before your sales team even knows an opportunity exists.  

Proprietary data and sentiment management dictate AI visibility

Large language models do not evaluate web pages based on keyword density; they parse schema markup, crawl accessibility, and external brand sentiment across neutral domains like Reddit, Wikipedia, and industry review sites. Non-Google web crawlers struggle when encountering heavy JavaScript setups, making server-side rendering and structured data essential for AI accessibility. Henrik Fabrin pointed out that generative engines heavily prioritise fresh, authoritative sources, with seventy percent of cited content having been updated within the past twelve months. This reality offers a practical entry point for mid-sized B2B brands without enterprise-level publishing budgets. Rather than generating vast quantities of generic articles, publishing a single piece of unique, proprietary research or a verified customer survey provides an exclusive data source that AI models naturally cite.  

Measuring prompt influence matters more than tracking direct clicks

Traditional attribution models miss the off-site research happening inside answer engines. During our panel discussion, Jennifer Montague introduced Share of Prompt as a critical tracking metric, advocating for regular audits of standard industry prompts across ChatGPT, Gemini, Perplexity, and Claude to monitor brand inclusion over time. Marketing teams must transition from relying purely on sourced attribution to evaluating influenced pipeline, using self-reported attribution on lead forms to uncover the dark research funnel. Ben Fröhlich demonstrated how inaccurate AI claims—such as a platform mistakenly reporting that a product lacks essential security integrations—can be corrected by updating the third-party editorial blogs and review pages that feed the models. When prospective buyers arrive pre-informed by AI recommendations, sales velocity improves noticeably as buyers transition from lead to pipeline stage much faster.  

Authentic human content is the primary defense against AI noise

An overreliance on automated tools has flooded digital channels with repetitive, low-value material. Jennifer Montague argued that mass-produced AI content creates excessive market noise that senior decision-makers actively filter out. The antidote is doubling down on human expertise through podcasts, live events, proprietary studies, and authentic community interactions. Henrik Fabrin advised business leaders against treating AI as an isolated initiative, recommending that teams apply machine learning to solve clear commercial problems instead. To prevent distraction, Jennifer Montague recommended implementing a three-month pilot framework for any new marketing technology, scaling tools that deliver clear business outcomes and abandoning those that only generate extra friction.  

Practical steps for your B2B search strategy

• Audit your category prompts monthly across ChatGPT, Gemini, Perplexity, and Claude to track brand inclusion on the day-one recommendation list.  
• Implement structured schema markup and clean crawl paths so non-Google AI agents can parse your domain without JavaScript obstructions.  
• Monitor external review platforms and editorial domains, as LLMs rely on third-party sentiment to form comparison summaries.  
• Introduce self-reported attribution on web forms to capture pipeline influenced by zero-click research inside AI engines.  
• Enforce a three-month evaluation period for all active AI tools to assess performance against commercial targets.  

If your brand disappeared from AI recommendations tomorrow, would your target audience ever find you?

To join our upcoming marketing roundtables, explore our events hub or subscribe to the Precis newsletter. You can also read our strategic AI overviews report.