What new AI search data reveals about visibility and trust

The digital marketing landscape is undergoing its most disruptive evolution since the advent of the commercial web. We are moving rapidly from a search engine economy built on link clicks to an answer-based economy dominated by artificial intelligence. In this transition, the mechanics of how brands get found, evaluated, and trusted are being completely rewritten.

Recent joint research conducted by Fractl and Search Engine Land, which was presented by Fractl cofounder Kelsey Libert at SMX Advanced, provides an invaluable benchmark for this new era. The findings paint a picture of a market in flux: consumer trust in AI-generated answers is declining, search behavior is fragmenting across multiple platforms, and AI visibility is becoming increasingly detached from traditional SEO metrics. To survive, organizations must fundamentally shift how they think about brand authority, governance, and content creation.

The Honeymoon Is Over: The Cratering of AI Search Trust

For the past few years, artificial intelligence was hailed as the ultimate friction-killer for search. Generative AI tools promised to deliver immediate, synthesized answers, bypassing the need to scroll through pages of ad-heavy, SEO-optimized search results. However, new consumer data indicates that the initial novelty of AI search has worn off, replaced by growing consumer skepticism.

The research reveals a stark year-over-year shift in user sentiment. In 2025, 82% of consumers reported that AI search was more helpful than traditional search engines. By 2026, that number plummeted to 54%—representing a dramatic 28-percentage-point decline in just twelve months. Over that same timeframe, the camp of outright AI search skeptics grew sixfold.

This erosion of trust is primarily driven by hallucinations and misinformation. When generative engines first appeared, they felt like magic. They offered instant answers. However, as users began encountering confidently delivered false facts, broken links, and outdated information, the friction returned. Users realized they could no longer accept AI-generated answers at face value; they had to manually verify the claims. Once a user has to double-check an AI’s output, the convenience of the instant answer vanishes.

Despite this drop in consumer confidence, the long-term outlook for generative search is not entirely bleak. AI is on an exponential improvement curve. Consumer trust is expected to restabilize as users become more adept at writing precise prompts and engineers roll out more robust retrieval models.

The acceleration of these technologies remains a double-edged sword. A June 5 CNN report highlighted warnings from Anthropic that artificial intelligence may soon reach a level of capability where it can improve its own systems without human intervention. While self-improving AI could drastically reduce hallucinations and boost accuracy, it may also deepen public anxiety regarding AI governance, making transparent brand communication more critical than ever.

The Multi-Platform Validation Loop

Because consumers can no longer rely blindly on a single AI-generated summary, their purchasing journeys have become highly fragmented. Modern buyers do not simply run a search, read an answer, and click “buy.” Instead, they engage in multi-platform validation.

The data shows that consumers now check an average of 2.4 platforms before finalizing a purchase decision. This cross-referencing behavior is not isolated to younger, tech-savvy cohorts; it is highly consistent across Gen Z, Millennials, and Baby Boomers alike. If your brand only has a presence on Google, you are missing the critical touchpoints where buyers go to verify your credibility.

When it comes to trusted product recommendations, traditional platforms still command a massive lead over standalone AI assistants. The research found the following distribution of consumer trust:

• Google: 39% of consumer trust (leading AI tools three to one)
• Reddit: 15% of consumer trust
• AI Tools (ChatGPT, Perplexity, etc.): 14% of consumer trust

The fact that Reddit ranks higher than all standalone AI search tools combined is a major indicator of current consumer psychology. In an era of automated content, human verification has become the ultimate premium asset. Buyers actively seek out raw, unfiltered forum discussions, peer reviews, and real human experiences to confirm that an AI-recommended product is actually worth their money.

To capture these cautious buyers, brands need to track their visibility across every touchpoint. Tools like Semrush One can help marketers monitor their multi-channel footprint, ensuring their brand shows up consistently whether a customer is searching on Google, checking Reddit, or querying an AI engine.

Organic Visibility Is Fragmenting, Not Disappearing

For search engine optimization professionals, the rise of AI-powered search features like Google’s AI Overviews has triggered widespread concern over organic traffic loss. The research confirms that the impact is real, but it also reveals a counterbalancing growth in other channels.

Approximately 50% of marketers report experiencing traffic declines since the launch of AI Overviews, with 61% pointing the finger directly at AI-driven search features. However, the loss of traditional search traffic is being offset by growth in alternative digital spaces:

• 57% of marketers report traffic growth from social and video platforms, including TikTok, Reddit, and YouTube.
• 40% of marketers are seeing increased traffic coming directly from AI assistants, such as ChatGPT and Perplexity.

Rather than destroying organic search, AI is fragmenting it. To survive this shift, brands must map their content strategy to the distinct user intents associated with each platform:

The Modern Digital Channel Map

• Google: Remains the undisputed king of web traffic at 84.8 billion visits. It serves primarily as an intent-capture engine where users go when they have immediate transactional or navigational needs.
• YouTube, TikTok, and Instagram: Serve as the primary platforms for brand discovery and visual demonstration.
• ChatGPT and Gemini: Used by consumers as research and learning hubs to digest complex topics or compare options.
• Facebook and Reddit: Function as human-validation networks where users seek real-world consensus and authentic peer feedback.

Marketers who continue to focus exclusively on optimizing for blue links are missing a vast web of touchpoints. A modern visibility strategy requires a presence across this entire ecosystem.

The GEO Hierarchy: Table Stakes, High Risk, and the Moat

As search engines evolve into answer engines, traditional SEO is giving way to Generative Engine Optimization (GEO). To help brands navigate this transition, the research categorizes common optimization tactics into a clear hierarchy based on their risk level, effort, and long-term defensibility.

High-Risk / Low-Defensibility Tactics: FAQ Optimization

Currently, the most popular GEO tactic among marketers is FAQ optimization, used by 49% of respondents. However, this strategy is highly vulnerable. Standard industry FAQs are incredibly easy for AI systems to crawl, scrape, and synthesize. If your content consists of simple questions and dry, factual answers, an AI search engine will gladly extract that information to answer a user’s query directly on the search results page, giving the user no reason to click through to your website.

While FAQ strategies are easy to implement, they offer almost no competitive barrier. To make FAQs work in the AI era, you must inject proprietary data, unique insights, and recognized subject matter expertise that an LLM cannot easily replicate without citing your brand.

Table Stakes: Basic Optimization

The foundational layer of GEO consists of tactics that are necessary for indexing but do not offer a strong competitive advantage on their own. These include:

• Building brand mentions (utilized by 43% of marketers)
• Establishing topical authority (utilized by 36% of marketers)
• Implementing structured schema data (utilized by 30% of marketers)

Think of these as your entry ticket. Without structured data and basic brand mentions, AI crawlers will struggle to understand your site’s entity structure. However, doing only this will not earn you top-tier placement in highly competitive AI queries.

The Moat: High-Defensibility Tactics

The ultimate goal for any brand is to build an intellectual property “moat” that AI search engines cannot easily bypass. This is achieved through:

• Original data and proprietary studies (utilized by 35% of marketers)
• Digital PR and earned media campaigns (utilized by 24% of marketers)

Large language models (LLMs) and Google’s Retrieval-Augmented Generation (RAG) systems have a constant hunger for fresh, authoritative, and timely data. When you publish a proprietary study with unique data points, AI models are forced to pull your data to answer timely user queries, citing your brand as the primary source.

Furthermore, digital PR campaigns earn high-authority brand mentions across major news outlets, niche publications, and industry blogs. These external mentions signal to AI algorithms that your brand is a trusted, verified entity within your market space.

Entity Authority vs. Legacy SEO Metrics

One of the most revealing aspects of the new search landscape is the shift in what AI engines value. Comprehensive data from Ahrefs, which analyzed roughly 75,000 brands across ChatGPT, AI Mode, and Google AI Overviews, reveals a major divergence from traditional SEO ranking factors.

When measuring the strength of various signals correlating with AI visibility on the Spearman scale (ranging from -1.0 to 1.0), a clear pattern emerged:

• Strongest Correlations (0.50 to 0.74): Branded web mentions and YouTube impressions.
• Weakest Correlations (Below 0.30): Backlink counts and digital advertising spend.

This data indicates a profound shift. Traditional SEO has long relied on backlink scale, often leading to link-building schemes and artificial domain authority. AI search engines, however, prioritize real-world brand presence and natural entity mentions. They look at where your brand is being talked about, how often it is mentioned alongside relevant industry keywords, and whether authoritative human sources are discussing your products.

For brands starting from scratch, building this entity authority requires a tactical, highly focused PR strategy:

• Targeted Media Pitching: Use tools like Semrush or Ahrefs to identify authoritative, niche-relevant publications that have covered your competitors. Reach out to these journalists with pitches that offer unique data or expert commentary to fill gaps in their existing coverage.
• Audience Mapping: Utilize platforms like SparkToro to pinpoint the exact publishers, YouTube channels, and podcasts your target audience actually consumes. Prioritize earned guest spots and mentions in these specific venues.
• Active Forum Contribution: Monitor relevant Subreddits and online communities. Contribute detailed, helpful, non-promotional answers to establish your brand’s expertise in spaces where real human verification occurs.

The core principle of modern earned media is simple: prioritize the creation of fresh, educational, and highly actionable industry insights, and distribute that value across the channels your target market trusts most.

The Trust Gap Brands Are Ignoring

As organizations rush to integrate artificial intelligence into their marketing pipelines, a massive disconnect has emerged between what consumers expect regarding transparency and how brands actually behave.

The research reveals a stark transparency gap:

• Between 84% and 91% of consumers state they want clear AI labeling across all content types, including written copy, video, audio, and imagery.
• Only 20% of organizations always disclose their use of AI.
• A striking 33% of brands never disclose their AI usage at all.

This lack of transparency is driving consumer skepticism. Buyers are not inherently opposed to companies using AI to streamline their marketing or creative processes; rather, they are deeply concerned when brands use AI to run their entire content engine with zero human oversight or quality control.

This concern is supported by marketer behavior. Nearly half of all surveyed marketers admit that they do not systematically fact-check their AI-produced content. Additionally, 48% of marketers acknowledge that while AI makes their content production faster, it ultimately makes the output more average.

A few forward-thinking brands, such as Dove, have actively positioned themselves against this wave of automated content by pledging not to use AI-generated imagery in their advertising. However, the vast majority of the industry continues to treat AI disclosure as a minor legal compliance checkbox rather than a critical opportunity to build brand trust.

The industry has yet to reach a consensus on where simple AI assistance ends and full AI generation begins. If an editor uses an AI tool to brainstorm headlines or outline an article, does that require a disclosure label? What if an AI drafts the first pass and a human rewrite follows? As these questions remain debated, brands that lean into radical transparency regarding their creative processes will stand out to increasingly skeptical consumers.

The Inverted Pyramid of Human Review

When marketers analyze how their teams spend time reviewing AI-generated content, a critical structural flaw becomes apparent. The study examined the allocation of human review time across various editorial tasks:

• Editorial review: 72% of attention
• Voice and tone review: 62% of attention
• Fact-checking: 54% of attention
• Plagiarism review: 42% of attention
• Legal review: 42% of attention
• Bias evaluation: 27% of attention

This represents an inverted pyramid of priorities. Organizations are spending the vast majority of their human resources on surface-level polish—making sure the content sounds pleasant and aligns with the brand voice. Meanwhile, critical risk mitigation tasks like fact-checking, legal compliance, and bias evaluation are left severely under-resourced.

This is a leadership crisis. When writing and marketing teams are pressured to scale content production without adequate training or updated workflows, they naturally focus on superficial edits because they lack the bandwidth to perform deep verification. Scaling up content output before fixing your internal review infrastructure is a direct path to damaging your brand’s credibility. AI itself does not destroy a brand’s reputation; unmonitored, untrained oversight does.

How Smaller Brands Can Compete and Win

Because AI visibility heavily rewards established brand equity, newer and smaller companies may worry that they will be completely shut out of generative search results. However, the rise of AI search actually levels the playing field in several unique ways.

AI search models do not simply surface the biggest websites; they surface the most authoritative answers for specific, highly detailed queries. This creates a massive opening for smaller, highly specialized brands.

• Dominate the Long Tail: Enterprise giants often focus on broad, high-volume keywords. Smaller brands can establish deep, unshakeable entity authority around long-tail, highly specific topics where actual customer conversions take place.
• Move Faster than Enterprise Bureaucracy: Large organizations are often slowed down by complex legal approvals, legacy workflows, and corporate bureaucracy. Smaller, agile brands can adopt new AI workflows, execute proprietary research, and publish timely, data-driven thought leadership far faster than their enterprise competitors.

The key to success for smaller brands is discipline. Do not try to use AI to churn out high-volume, generic blog posts. Instead, use AI to scale your research capabilities, uncover fresh insights, and build a highly focused library of expert content that solves real-world customer problems.

The 4-Step Playbook for Modern AI Visibility

Succeeding in the age of generative search requires a comprehensive rewrite of your marketing playbook. To ensure your brand remains visible, authoritative, and trusted, focus on these four core pillars:

1. Monitor Your Multi-Platform Presence: Track your brand’s visibility and sentiment across every channel where your buyers go to research and validate purchases, including Google, social platforms, Reddit, and major AI assistants.
2. Build Real Entity Authority: Focus your resources on creating original research, proprietary data studies, and deep subject matter expertise. These form the ultimate content moat that AI systems must cite.
3. Triangulate Your Brand Signals: Balance your marketing mix. Earned media mentions, active forum participation, and high-quality video content work together to signal authority to search engine algorithms and generative models alike.
4. Establish Rigorous AI Governance: Protect your brand’s reputation by setting up clear, structured human review workflows. Prioritize deep fact-checking and legal reviews over simple cosmetic edits, and maintain transparency with your audience through honest AI disclosure.

The mechanics of discovery may change, but the core foundation of marketing remains the same: trust is your most valuable asset. The brands that invest in transparency, original insight, and rigorous quality control will continue to win the clicks, the citations, and the customers.

To learn more about tracking your overall search presence and adapting to the future of digital visibility, view the terms of use and explore tools designed to help you navigate this shifting terrain.