"Zero-click" search is disrupting inbound B2B marketing
B2B marketers are witnessing a fundamental shift in how buyers discover and engage with content. The familiar marketing funnel—where prospects move from awareness to consideration to decision through trackable touchpoints—seems to be vanishing.
AI-driven search hasn't destroyed the B2B funnel so much as made large parts of it invisible. In this new landscape, success relies on adapting to reputation economics, grappling with zero-click journeys and lost attribution, and building a durable content moat rooted in trust and unique insights.
According to Profound, an AI visibility platform optimising brand presence, nearly 80% of buyers rely on answer engines for at least half of their decision-making process. This leads to an "invisible funnel" where most of the early touchpoints and research steps leave no trace in your web analytics. When the first interaction seen is a demo request or contact form, it might feel like leads are appearing from thin air, the result of an invisible funnel that started and progressed entirely within AI-driven channels.
Buyers emerging from this invisible funnel may already be far down the path to a decision. Bain & Company reports that 85% of B2B buyers ultimately purchase from one of the vendors they had on their radar from the very start of their research. If AI assistants are front-loading that research, buyers might form a "day one list" without ever engaging with broader content or sales outreach. This makes it harder for marketers to insert new brands or influence consideration mid-funnel. If you weren't in that initial AI-fed answer, you might not exist in the buyer's mind at all.
AI systems don’t present ten blue links; they deliver one synthesised answer drawn from multiple sources. There is no first page of results to dominate. You’re either in the AI’s answer box, or you’re invisible. This paradigm shift is what is called “reputation economics” over traditional SEO tactics. In essence, AI search algorithms curate sources based on trust, semantic authority, and consensus rather than just keyword relevance. A brand’s overall digital reputation, the consistency of its message, the credibility of its content, and the frequency of third-party mentions, becomes the deciding factors in whether the AI will trust and cite it. In Simple words -
| Traditional SEO | Reputation Economics |
|---|---|
| Optimizes for Keywords & Clicks | Optimizes for Trust & Citations |
| Focus on Traffic Volume | Focus on Share of Voice |
| Goal: Rank #1 on Page 1 | Goal: Be Cited in the Answer |
A brand's digital reputation and frequency of third-party mentions are now the deciding factors for AI visibility. The consistency of its message, the credibility of its content, and the frequency of third-party mentions becomes the deciding factor in whether AI will trust and cite it.
If thousands of independent sources describe your company as the best option, the AI internalises that association as truth.
The convergence of messaging across all these channels increases "entity authority"—the likelihood that a brand is recognised as a legitimate actor in a category.
High-quality research, case studies, and proprietary insights signal expertise to AI models, increasing the likelihood of citations in synthesized answers. On the other hand, documentation and tutorials provide precise, reusable explanations for high-intent “how-to” queries, positioning the brand as the authoritative source during evaluation and implementation stages.
Earned media and influencer content act as distributed evidence layers that AI systems use to shape category narratives, prioritizing high-trust editorial coverage and credible expert commentary. Together, they generate quotable differentiation and reinforce consensus signals boosting inclusion in AI-driven “top vendor” and “compare options” responses.
AI prioritizes these platforms because they represent authentic, practitioner-led conversations. Reddit has become one of the most cited domains on LLMs. LinkedIn captures how practitioners talk about problems in natural language, aligning with how users prompt AI tools.
List-style rankings and high review volumes are easily parsed by AI to generate "top vendor" lists and comparison tables. High category placement increases the chances of mentions in AI-generated answers.
Metrics like website traffic, click-through rates, lead conversions, and attribution models that credit each channel for its role in a sale are either declining or evaporating. Early touchpoints like clicks on a search ad, blog visits, webinar sign-ups, and demo requests are essentially invisible to the company’s tracking tools. Instead of making “traffic from AI” the north-star metric, since traffic may undercount the impact, focus on AI visibility. This means optimising content to appear in AI answers and tracking the share of voice in AI channels. An interesting flip side to fewer clicks is that the clicks you do get from AI-informed buyers tend to be higher quality.
G2's inaugural AEO category Grid names these as leading platforms in Answer Engine Optimization:
The B2B marketing funnel isn't broken. It is evolving into something more complex and less visible. Those marketers who cling to the old playbook of easy clicks and traditional SEO may indeed feel like their funnel has collapsed.
Those who embrace the invisible funnel and are investing in reputation, rich content, and new measures of success will find that buyers are still flowing through, even if the path they took doesn't show up on Google Analytics.
In the end, what seems invisible is made visible in the outcomes: higher-intent leads, faster trust-building, and brand preference.