Zero-click effects hit informational queries hardest; in-answer commerce for B2B is still nascent. The B2B buyer journey is multi-stakeholder and non-linear. Make sales and marketing facts LLM-readable and add reach-back options on canonical pages.
Large language models (LLMs) and AI discovery layers now surface concise answers for early research. That amplifies zero-click outcomes for informational queries but does not replace multi-stage B2B buying. Immediate priorities are practical - you need to make facts machine-readable, publish role-specific evidence, and expose low-friction reach-back options. These changes increase the chance that AI-led discovery becomes a sales conversation.
AI layers lift answers from authoritative pages, so early-stage research may not need a click. Visibility now depends on structured facts, consistent entities, and provable outcomes. This favors pages that speak directly to specific buyer roles.
Discovery is answer-first, such as explainers, comparisons, and decision checklists that an AI can quote. Shortlist signals include entity-rich solution pages, case proofs, and return on investment (ROI) calculators. Consensus forms via stakeholder kits, such as one-pagers for finance, information technology (IT), legal/procurement, and operations, are marked up so facts are retrievable.
1. Start by making your company, product, and industry information easy for machines to understand. This is done through JSON-LD (JavaScript Object Notation for Linked Data), which is a way of tagging content on your website so that search engines and large language models (LLMs) can recognise and reuse it. Using standard types like “Organization,” “Service,” or “FAQ” ensures that when someone searches for information, your facts are more likely to be surfaced in AI-generated answers. Consistent naming across all pages helps prevent confusion and strengthens your authority.
2. Once buyers find you through AI-led discovery, provide them with a clear next step that requires no additional effort. Place obvious Calls To Action (CTAs) at the top of your solution and industry pages. Keeping these CTAs visible and simple allows stakeholders to move directly from research to engagement, without having to scroll or search for how to contact you. For a CEO, this translates to reducing friction at a critical stage of the buying journey.
3. Finally, do not only track traditional website clicks. Instead, create a fixed set of key buyer questions and monitor how often your company is being mentioned or cited in AI-generated answers. This approach shifts the focus from pure traffic to visibility in the AI layer - an early indicator of influence. By reviewing which of your pages are picked up and quoted, you can identify content gaps and decide where to strengthen your proof points. For leadership teams, this gives a clearer view of whether marketing efforts are reaching decision-makers before they ever arrive at your site.
Show evidence: case proofs, short ROI snapshots, and role-specific one-pagers. Tell the AI how to cite you with schema markup, consistent entity names, and clear metadata. The purpose should be to make it easy for an AI to lift a fact and for a human buyer to continue the journey via a low-friction reach-back.
Make facts LLM-readable, surface stakeholder evidence, and add obvious reach-back CTAs. These practical steps improve the odds that AI discovery converts into a sales conversation.
Canonical page: The authoritative page you control for a topic.
Buying jobs: Gartner’s framing of discrete tasks in B2B purchasing.
Reach-back CTA: An on-page action to bring prospects from AI discovery back to you.
JSON-LD: JavaScript Object Notation for Linked Data.
LLM: Large language model.