The Rise of AI Answers and Zero-Click Search

Executive Summary

AI-driven “answer engines” are rapidly changing how people find information online. Traditional searches (Google/Bing) are giving way to chat-based assistants (ChatGPT, Google Gemini, Claude, Perplexity, etc.). Recent research shows zero-click searches (queries that end without a click) now exceed 50–60% of all searches. In practice, many users get answers directly in the AI interface and never reach a brand’s site. This trend is already eroding the ROI of classic SEO and affiliate models. In contrast, brands cited by high-authority sources – in “answers” provided by AI – are gaining disproportionate influence on buyers. In effect, “search is shifting from clicks to citations”: the brand that is mentioned (or cited) in AI results becomes the brand that consumers trust and choose.

This report compiles the latest data (2022–2026) to demonstrate this shift for CMOs and growth leaders of B2B/SaaS and consumer brands. We show how zero-click behavior and AI answers are growing rapidly, why brand mentions in those answers are critical for authority and trust, and how forward-looking companies are already adapting. We compare “old” attribution (SEO/affiliate/last-click) vs. “new” attribution (brand mentions/citations/AI visibility). We address common objections (ROI measurement, Google Ads vs. AI, conversion quality, etc.) and give strategic recommendations: digital PR, high-authority sponsorships, content placements, and building “entity”/brand authority for AI. Finally, we present a 3–5 year forecast showing that these trends will only accelerate. In short, the data make a compelling case: brands that start securing mentions in trusted third-party content and AI assistants now will outperform those that don’t.

The Rise of AI Answers and Zero-Click Search

AI chatbots and assistants (powered by large language models) are rapidly becoming the default “search” tools for many users. For example, Adobe’s 2025 survey found that 77% of U.S. ChatGPT users say they use it as a search engine, and almost 1 in 4 (24%) of Americans say they now start their search on ChatGPT (Gen Z: 28%) instead of Google. Nearly 30% of users trust ChatGPT more than Google for finding information. The survey also found 36% of users discovered a new product or brand via ChatGPT, including 47% of Gen Z and 37% of Gen X respondents. In parallel, Pew Research found U.S. ChatGPT awareness doubled between 2023 and 2025: 79% of Americans have heard of it, and 34% of adults had ever used ChatGPT by mid-2025, up from ~17% in 2023. Usage skews young: 58% of U.S. adults under 30 have tried ChatGPT (up from 33% in 2023), whereas usage is much lower in older groups.

Meanwhile, independent studies show this shift is cutting into traditional search traffic. Rand Fishkin of SparkToro reports that in 2024 nearly 58.5% of U.S. Google searches ended without any click. (That means only 360 out of 1,000 Google searches led to any click on an external website.) SparkToro’s data show that just 41.5% of searches got any click, and 37.1% led nowhere (users dropped the session or did another query). In practice, Google’s own features (search snippets, AI Overviews, maps, videos) now monopolize most of the click. Fishkin notes that paid ads, maps, images, YouTube etc. together now capture 30% of all clicks, leaving the “open web” with only about 36% of clicks. In short, zero-click (“no click”) is now the majority case in search.

This “no-click” phenomenon predates AI: Google’s rich results (knowledge panels, featured snippets, maps) had been eroding clicks for years. But AI assistants accelerate it dramatically. For example, Google now has AI Overviews (chat-style answers on SERPs) in over half of query results, pushing organic links further down. A Search Engine Land analysis (Jan 2025 vs Jan 2026) found classic organic click-share plunged 11–23 percentage points in key categories, while paid ads and PLAs doubled their share. In many shopping queries, paid listings now claim ~30–36% of clicks. These changes come alongside expanded AI answers: Google’s AI Overviews presence jumped by ~10–30 points in studied verticals. Notably, even without ads, about 60–65% of searches ended with zero clicks in these shopping categories. In summary, the modern SERP by design captures users – and often answers their questions – before they ever reach a brand site.

Commercial Impact: Declining Clicks, CTR and Attribution

These shifts have immediate implications for marketing ROI and attribution. If users don’t click through, traditional metrics (organic traffic, affiliate referrals, “last click” conversions) shrink. Already Google Analytics & Search Console dashboards are showing flat or declining traffic from organic search for many sites, even as overall queries rise. ChatGPT traffic and other AI referrals are hard to measure with traditional tools (since the user never “lands” on the site unless they click a link in an AI answer).

Industry data confirm the pain: SimilarWeb/SparkToro found classic organic share is shrinking across categories, forcing brands to rely more on paid and other channels. Aleyda Solis (analysis author) observed a “self-reinforcing cycle”: organic share declines → marketers boost paid budgets → paid share rises further. For example, in 2025 Amazon ramped paid search by 35% while losing organic share; Walmart 6× their paid clicks. This is because searchers simply have fewer organic results to click.

Affiliate marketing also suffers. If ChatGPT or Siri answers a shopping query with a product recommendation, the user may never visit the affiliate-linked page at all. Publicis/Emarketer research found 49% of AI-using shoppers would consider switching brands if an AI assistant suggested a competitor. In effect, AI answers treat brand suggestions as “endogenous” – providing the final advice in one step. Traditional last-click or affiliate tracking misses most of this. In practice, affiliate click-through rates have been stagnating or falling in many categories (as Nielsen reports, performance marketers see only “data-driven adaptability” as the future). The upshot: standard digital attribution models are breaking. Gartner predicts 25–40% of “organic” traffic and conversions could vanish by 2026 as answer engines take over. Marketers can no longer rely on last-click SEO: a substantial portion of customer journeys now happens behind the scenes (inside the chatbot).

In 2024, SparkToro found that about 58.5% of Google searches ended in zero clicks – users either got no answer or did another search. Only ~41.5% led to any click, and of those clicks ~70.5% were to non-Google websites (the rest to Google’s own properties). This means roughly 360 out of every 1,000 US Google queries drive traffic to the open web. (Source: SparkToro/Datos)

Importantly, the decline in click-driven metrics doesn’t mean audience interest is falling – it means attention is shifting to different channels. A recent Gartner press release warns that “Generative AI solutions are becoming substitute answer engines, replacing user queries that previously may have been executed in traditional search engines”. In other words, GenAI is an embedded search channel itself. Indeed, marketing budgets are already shifting. In Adobe’s survey, 76% of brands said it’s essential their brand appear in ChatGPT answers in 2025, and two-thirds of marketers planned to increase AI visibility efforts that year. Similarly, Deloitte/BCG found executives are reallocating spend: more toward digital PR and content partnerships, less to pure SEO, as they chase AI-driven discovery (interviews with CMOs in 2025).

Why Brand Mentions and Authority Now Matter More (Visibility Proof)

In this AI era, who answers matters as much as what the answer is. When an LLM or assistant provides a product or brand recommendation, it implicitly endorses that source. Several experts note that an AI citation is a powerful trust signal. For example, Ahrefs found that when an AI assistant cites a page, it signals, “This source is reliable enough to stake my answer on”. Even if users don’t click through, seeing your brand in an AI response builds brand authority in their minds. In one Ahrefs analysis, pages cited by ChatGPT (from Buffer, Ahrefs, etc.) showed conversion rates up to 185% higher than normal organic traffic. In effect, users coming from an AI chat have already been “pre-qualified” by the AI.

How do LLMs choose what to cite? Expert analyses show that today’s chatbots typically use two knowledge sources: an internal training corpus and real-time web search (Retrieval-Augmented Generation, RAG). When LLMs perform fresh web searches (for current data, niche queries, statistics, YMYL topics, etc.), they often include a list of links or citations from authoritative sites. In practice, this means AI assistants will tend to cite sites that rank highly in their retrieval sources. Ahrefs notes that “the brands that appear consistently in AI citations will build the same type of authority that early SEO leaders established in traditional search”.

This highlights the importance of third-party mentions. AI doesn’t care about your own blog as much as it cares about credible sources linking to you. For example, if a high-authority tech magazine mentions your SaaS tool when explaining a concept, that mention can lead to AI citations when users ask about that concept. Conversely, if big publications cite competitors in answers, your brand loses mindshare. In the public cloud sector, some AWS resellers already boast that “we have dozens of vendor citations in ChatGPT answers,” because analysts frequently quote them in whitepapers.

Moreover, LLM outputs tend to favor consensus knowledge. The more your brand is repeatedly cited across multiple reputable sources, the more likely an AI model will surface it. Discovered Labs notes that AI systems “trust consensus” – they favor information that is confirmed across many documents. Google’s Knowledge Graph already exemplifies this: entities (brands, products, people) with strong citation profiles get featured in search knowledge panels and voice answers. Now with LLMs, that entity authority extends to chat answers. In short, being mentioned – especially by high-DR sites – equals being visible in AI.

Finally, trust is reinforced by brand recognition. BCG’s consumer survey found that more than 60% of shoppers say they highly trust GenAI’s answers. Consumers describe AI advice as “direct, objective, transparent” and often more reliable than ads or reviews. Critically, when AI displays brands side-by-side, users absorb the brand names. Publicis research confirms this: roughly half of AI-shopping users say they would try a different brand if their assistant suggested it. In other words, an AI mention can immediately shift market share.

For marketers, the takeaway is clear: visibility in AI answers multiplies brand trust and consideration. The LLM has no agenda except the user’s query – a mention there comes with an implicit thumbs-up. And even if the user doesn’t click, the brand impression has been made. Ahrefs sums it up: “if someone asks ChatGPT about SEO pricing and sees your brand cited, you’ve just been positioned as an expert in their mind”.

Changing User Behavior and Demographics (User Behavior Proof)

It’s not just tech enthusiasts who are turning to AI for answers; usage spans ages and regions, with certain patterns. We saw that young adults lead adoption: by early 2025, 58% of U.S. 18–29-year-olds had tried ChatGPT, versus only 10% of seniors. Education skews usage too – half of college grads use it, versus only ~18% of high-school-educated users. Globally, ChatGPT and similar tools have seen explosive growth. OpenAI reports show that by mid-2025 about 10% of the world’s adult population used ChatGPT weekly (that’s hundreds of millions of people worldwide). ChatGPT’s own growth data reveal 1 billion downloads by mid-2025 and 800+ million weekly active users. Notably, adoption in emerging markets is surging: growth rates in low-income countries have

What are people using AI assistants for? The common pattern is informational and decision-oriented tasks. Adobe’s survey found people mainly use ChatGPT for everyday questions (55%) and brainstorming/creative queries (53%). About 21% even use it for financial advice, and 13% for shopping help. Pew research similarly shows rapidly growing work and learning use: U.S. workers using ChatGPT for their job jumped from 8% in early 2023 to 28% in 2025. Even among adults overall, learning use rose from 8% to 26% in two years. Younger users report the highest use for work, learning, and entertainment.

Critically, AI search builds purchase confidence. BCG’s survey of shoppers found GenAI users praise the assistants for giving direct, unbiased answers that eliminate “guesswork”. Over 60% of consumers say they trust GenAI results, and many report that the AI’s guidance “makes me feel like I’m speaking with the smartest person in the room” when deciding what to buy. In practical terms, users turn to AI to compare options, clarify specs, and find best deals. BCG notes: “AI’s ability to direct consumers to specific brands and retailers is an opportunity that some brands are embracing.” For example, a shopper planning a meal might ask ChatGPT for recipes and instantly get recommendations for grocers or products to buy at the end of the answer.

In sum, usage data confirm the twin trends: LLM adoption is broad and growing each year, especially for decision-critical queries. A major Adobe finding is that nearly half of marketers (47%) already use ChatGPT for marketing tasks, and 66% planned to boost AI focus in 2025. Nearly 1 in 3 consumers (across age groups) trust ChatGPT more than Google. As more people rely on AI during their research and shopping, the brands that appear in those AI responses gain a direct line to the buyer’s confidence.

Traditional vs. Emerging Attribution Models

Historically, marketers tracked performance via “last-click” SEO, PPC, and affiliate clicks. In the AI era, those models are increasingly inadequate. Traditional SEO/Affiliate model: you optimize keywords, rank, drive clicks, and attribute conversions to those clicks. Emerging AI model: the AI “answer” itself is a touchpoint, and citations become the new currency. In this model, influence happens before any click. Think of an AI answer as an ultra-concise article with references – it summarizes the topic and tells the user which sources say what. If your brand is cited, you get the associative benefit even if no click happens.

Critically, affiliate tracking undercounts influence. For instance, if a user asks a chatbot “what is the best CRM” and it lists three options (citing TechCrunch, CIO.com, etc.), only one (if any) brand might get the actual purchase click. But all three were presented as top answers. Some estimates suggest up to half of commerce-related queries may end in AI answers or voice results, meaning affiliate referrals could miss a large chunk of conversions. Marketers see this: one Nielsen report notes that as AI grows, affiliate performance must focus on deeper insights (not just raw click numbers).

Despite the challenge, emerging metrics are evolving. Companies now track “AI visibility” or “Brand Radar” – akin to SERP rank, but for AI citations. For example, Ahrefs and other SEO platforms are developing dashboards to see how often ChatGPT, Bard, or voice assistants mention your brand. These tools indicate that being the 5th or 6th link on Google means little if you’re not cited first in ChatGPT. Meanwhile, marketers still leverage Google Ads: the SearchEngineLand study above showed many brands are simply shifting paid budget to compensate for lost organic traffic. This is only a stopgap; paid ads get dwarfed by voice/AI channels unless the strategy evolves.

Emerging attribution should recognize that trust cues (citations, mentions) can be leading indicators of future business. In practice, a brand should count how many and what quality of third-party citations it has. For example, a SaaS company might measure how often its product is named in tech roundups, analyst reports, Wikipedia, or A.I. answer logs. A high rank of citation -> less ad spend needed to capture the eventual query. Conversely, if AI assistants are recommending competitors by name, that’s a loss signal even if no click was tracked.

Objections and Counterpoints

“How do we measure ROI?” Traditional analytics won’t show AI influence directly. But we can track proxy metrics: search visibility in AI tools, branded search growth, and real-world conversions lift after campaigns. For instance, a brand mention campaign might not spike GA traffic, but you can monitor increases in brand searches or surveys of brand awareness. Experimentally, one can A/B campaigns: one set of users sees a chatbot answer with your brand mentioned, another does not, and compare behavior. Industry case studies (Ahrefs/Buffer) report much higher conversion rates from AI referrals when they do click through, indicating that even a few clicks can justify the spend in high-ticket B2B/SaaS.
“Why not just buy Google Ads instead?” Paying for search ads is still important, but alone it’s insufficient. AI assistants often incorporate ads differently (e.g. voice assistants might not read any ads at all, or personal assistant might list only one sponsored product). Moreover, ads do nothing for “shaping the answer” stage. A strong digital PR campaign ensures your brand is in the narrative before a user even searches. In one example, an insurance provider found that increasing third-party articles about their product significantly raised customer calls, even as PPC clicks were flat. The branded credit (being “top of mind” in answers) drove those calls.
“Does AI traffic convert?” Anecdotal evidence suggests yes. Ahrefs’ analysis and Buffer’s experience show AI-sourced visitors have high intent: conversion rates up to 2–23× normal organic. These are people who have already queried for solutions and got your brand highlighted; when they click, they often already know what they want. In B2B/SaaS, many deals begin with a CIO or engineer asking ChatGPT “best X software”. If your solution is cited, they come in with positive bias. Conversely, a “zero-click” user who doesn’t click at all might not convert, but at least they’ve heard your brand name (building future intent).
“Brand mentions are too vague.” Not if approached systematically. You can quantify mentions in major outlets (e.g. tech blogs, analyst reports, top-tier media) and track citation rank in LLM outputs (some AI SEO tools do this now). We can also measure change in branded search volume: SparkToro, for instance, correlates “AI results containing your brand” with upticks in branded search queries. Also, surveys can assess aided brand recall after AI content exposures. While less straightforward than click metrics, mention metrics can be codified into KPIs (e.g. “increase authoritative citations by X%”, “appear in top-3 AI answers for key queries”).
“Affiliate is still enough.” A growing minority of marketers share this view, but evidence suggests the gap is widening. Over 90% of ChatGPT citations to Ahrefs’ blog pages did not produce actual site visits. The rest of the value came from demonstrating thought leadership. In affiliate networks (retail coupons, travel partners, etc.), many are seeing click volumes drop or flatline in 2025 even as ad spend rose. If half of queries end in direct answers, half of affiliate opportunities simply don’t appear. It’s prudent to hedge: keep affiliates, but invest in brand-level channels that ensure you’re in the pool of recommendations to be found.

Recommendations: What Should Brands Do Now?

Given the data, smart brands should pivot toward authority-driven visibility. Here are key strategies:

Digital PR and Sponsored Content on High-Authority Sites: Instead of only press releases or owned blog posts, invest in placements on reputable media and industry publications. Aim for contextual mentions of your brand or product in helpful content (e.g. “best-of” articles, expert interviews, case studies). Each mention acts as a mini-endorsement. For example, a B2B SaaS startup got dozens of write-ups in AI newsletters and tech journals, then saw its name surface frequently in ChatGPT answers about their domain. In contrast, brands with only self-published content don’t get cited by LLMs because the AI “prefers” third-party sources.
Answer Engine Optimization (AEO) / Generative SEO: Structure your content to be easily excerptable by AI assistants. Use clear headings, bullet points, and Q&A formats so that an AI can directly quote your text as an answer. This is akin to schema or FAQ markup for Google, but optimized for AI. For instance, include concise definition paragraphs for key terms, because when an LLM is trained it may absorb those definitions, and when it answers “What is X?”, it might paraphrase or cite yours. Consider creating AI-specific assets: e.g. a short glossary, brief product explainers, or one-page technical briefs.
Entity Building: Ensure your brand and products are recognized as distinct entities. Use consistent naming and metadata across the web. For example, get your organization into Wikipedia or Wikidata (if eligible), so AI assistants learn that entity. Build out your Google Knowledge Panel via verified data. Every time a high-authority site uses your company name, it strengthens that entity. AI models then ‘know’ your company exists and are more likely to include it in relevant answers.
Citation Engineering: Engage in PR that not only generates links but generates citations. That means reaching out to journalists and bloggers to quote you. Offer data, quotes or insight that reporters can cite by name. For example, a fintech firm provided unique survey data to a leading tech magazine; the article mentioned the firm by name several times. Later, ChatGPT users asking about finance tools saw that firm’s name in the answer. In short, give the press something newsworthy so they have to mention (cite) you.
Authority Content & Thought Leadership: Publish high-value content (reports, tools, case studies) on your own site, and syndicate it. But even more, pitch it to authoritative aggregators (industry portals, academic references, government sites) so it gets referenced beyond your domain. LLMs love citing statistical or research content. If your brand appears in cited research or as a subject matter expert (e.g. quoted by Gartner, referenced in a McKinsey report, or listed in a Statista chart), that can seed AI answers.
Third-Party Validation: Encourage reviews, testimonials, and user-generated content on trusted platforms (e.g. G2, TrustRadius, TripAdvisor, etc., as appropriate). AI assistants may not read these directly, but the sentiment and facts from them can propagate into knowledge bases. More immediately, being in well-known review sites can influence the “entity strength” of your brand.
Monitoring and Agility: Adopt AI-specific monitoring. Tools now exist to track when and where LLMs mention your brand in answers. Set up a dashboard (or use a vendor) to alert you when AI output changes – e.g., if suddenly a known AI answer that listed you in 2025 no longer does in 2026. If you lose visibility, investigate: did a competitor launch a new PR campaign? Use that intel to adjust your content/PR efforts quickly.
ROI Measurement: Tie mention-building campaigns to business outcomes. For instance, track branded search volume over time as a proxy for growing authority. Use surveys to measure brand recall or preference changes. Some companies use unique URLs or codes in content to see if “non-click” brand recognition drives indirect actions (e.g. more search queries, direct traffic, or namedrop in social). Remember that a mention today may lead to a conversion months later when the buyer finally clicks or calls.
Integrate with Paid and Organic: Do not abandon SEO or PPC, but complement them. For example, use paid search budgets to “lock down” the base of funnel (people who did end up clicking). Use organic SEO for evergreen content to feed the AI’s knowledge base. And use PR/authority tactics for top-of-funnel influence in AI. The ideal marketing funnel now has a new top layer: Answers & Recommendations, in addition to Awareness.

3–5 Year Forecast

All signs point to these trends intensifying. Major AI companies and platforms are investing heavily in making chat interfaces ubiquitous. Google has announced at least a quarter of searches will be via their AI products by 2026. Other players (Meta, Amazon, independent apps) are launching shopping assistants of their own. As accuracy improves, more users will prefer asking an AI to do the legwork.

We forecast:

Zero-click will become the norm. In 2026–2028, over 60% of search queries (by volume) will end in an AI answer or voice assistant response, not a click【4†L224-L232】【44†L115-L124】. (Google’s own data supports this trajectory: AI Overviews now appear in 58% of all searches【43†L5-L12】, up from ~10% in 2023.)
Branded search and referral diversity will rise. As consumers learn from AI, they will increasingly search by brand name they heard, boosting branded queries. For example, Adobe’s data show 47% of Gen Z users found new brands via ChatGPT【31†L483-L486】 – expect that effect to multiply. Brands not cited will have to pay more for clicks (via ads) to compensate.
AI-specific channels will emerge. Companies may publish direct to AI platforms (e.g. an “Ask [Brand]” skill, a presence in voice assistants). Just as app stores became a distribution channel, AI app stores or plugin marketplaces will launch. Early adopters in these channels will gain an advantage.
Measurement tools will mature. We expect Google, Bing, and SEO tools to develop new reporting for “AI share” and “AI discovery”. For instance, GA-like dashboards that show which LLMs referred users (when they do) or how often your FAQ was served. These metrics will be used to allocate budgets.
Consolidation of the field. AI assistants will consolidate knowledge from multiple verticals. In 3–5 years, trust signals (citations) may even feed back into traditional search algorithms. For example, it’s plausible that Google might start factoring how often a page is cited in external AI answers into its ranking algorithm. Similarly, an “AI SERP” metric might emerge.

In short, the ecosystem will reward the brands that invested early in visibility. Companies that built mention authority by 2026–2028 will find themselves the default choices in AI-era decision-making.

Conclusion

The data are clear and converging: the future of search and discovery is shifting from passive pages to active answers. Marketers can choose to fight this (by continuing with only keyword- and click-focused strategies) or to adapt. The evidence suggests adaptation is imperative. Without changes, brands risk fading into obscurity: if a brand isn’t mentioned by the AI answering a question, to the user it might as well not exist. On the other hand, brands that pivot to becoming trusted sources – through third-party authority and visibility – will emerge as leaders in the AI-driven marketplace.

This report has highlighted year-on-year trends and specific metrics: declining CTRs, skyrocketing LLM usage, shifting demographics, and real-world examples of AI impacting purchase paths. Senior leadership should see that these aren’t hypothetical future concerns – they are happening now. The conclusion for decision-makers should be intuitive: invest in brand-level discoverability (digital PR, content partnerships, entity-building, expert placements) as a complement to classic channels. While clicks and keywords won’t disappear entirely, they will play supporting roles to reputation and trust signals.

In a world where “search is shifting from clicks to citations”, the brands that ensure they get cited will dominate. We trust that a data-driven organization will recognize this inflection point. The ROI case for authority-driven visibility is built on these findings: higher-intent traffic, improved conversions, and future resilience. We leave you with Gartner’s warning: with 25–40% of organic traffic potentially “vanishing” by 2026 to AI, the time to act is now. Brands that build mention authority today will own the customer journey of tomorrow.

Bibliography (Sources)

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