Has AI Broken Search?

For the past quarter century, the digital formula was straightforward: rank highly in Google Search, capture clicks, and convert demand. But the meaning of “search” has changed. We’ve moved from searching for “results” to expecting answers – and increasingly, those answers are delivered without a click.

Today, valuable conversations are happening in places many brands don’t yet measure or fully understand. AI-powered search experiences, large language models (LLMs), and answer engines are reshaping discovery while creating new blind spots in how impact is measured. The challenge isn’t simply adapting to new tools – it’s recognizing that traditional search metrics no longer tell the full story.

AI is Changing How People Search – and How Often They Click

In 2024, Gartner released a report predicting that traditional search engine volume could decline by 25% by 2026 as AI chatbots and virtual agents gained traction. While that prediction was forward-looking, early signals are already visible. Axios reported a 15%+ decline in traditional search referrals to major news publishers between mid-2024 and early 2025. Bain research further shows that 80% of consumers rely on zero-click results for at least 40% of their searches.

Zero-click behavior isn’t new – but its scale and consistency are. Google’s AI Overview, introduced in mid-2024, fundamentally changed how users navigate information. Instead of scanning multiple links, users receive synthesized answers that satisfy intent directly in the interface. As a result, more than 60% of Google Searches end without a click.

Under the hood, techniques like “query fan-out” run multiple searches across subtopics and sources simultaneously, combining them into a single response. The experience is faster, more comprehensive – and increasingly self-contained.

This shift extends beyond Google. Conversational platforms like ChatGPT, Gemini, and Copilot are taking center stage in their own right. Since its launch in 2022, ChatGPT alone has grown to process over 2 billion queries per day, with a significant share focused on general research and evaluation. Discovery is no longer confined to a single search box.

Discovery Before Intent: The Inclusion Battle

AI-driven discovery collapses the traditional funnel. Query, evaluation, and comparison often happen before a consumer ever reaches a brand’s website. By the time someone does click through, they’re already informed – sometimes highly informed.

In this environment, visibility is no longer about ranking first. It’s about being included at all.

When an LLM doesn’t surface your brand in response to a high-intent query, you don’t just lose position – you disappear from an entire class of consideration moments. Inclusion is the new competitive frontier.

Midway through 2025, PR platform Muck Rack published new research, What Is AI Reading?, which analyzed over one million links cited by AI tools. The findings were telling:

• 95% of AI citations come from non‑paid media
• 89% come from earned media
• Nearly half of the citations for recent queries originate from journalistic content

The implication is clear: AI visibility is driven less by keyword optimization and more by contextual authority – how often your brand appears in credible environments, how closely your content aligns to specific prompts, and whether AI systems “trust” you to answer a question accurately. Brands that consistently show up with relevance and authority across the ecosystem are far more likely to surface – not just in search results, but in AI-generated answers themselves.

Beyond Keywords: How AI Interprets Brand Confidence

AI systems don’t assess brand reputation in the traditional sense. They respond to prompts by assembling answers from sources they can confidently interpret. That confidence is shaped by context, relevance, and consistency across the digital ecosystem.

When a brand’s information is fragmented or conflicting — for example, mismatched descriptions across its website, LinkedIn, and third-party coverage — AI systems may surface alternative sources that appear clearer or more authoritative for the query. This is why high-performing brands are increasingly prioritizing structured data, such as Schema Markup (code that helps machines clearly understand and categorize content), to help LLMs accurately read, interpret, and reference their products and capabilities.

Look Beyond The Click: A Measurement and Budget Reset

Zero-click search didn’t begin with AI. Long before AI summaries appeared, many searches were already resolved without users clicking through to an external site. What changed is how frequently AI experiences satisfy intent entirely in-platform.

The implication isn’t that search has lost value – it’s that clicks are no longer the primary signal of impact.

As discovery shifts toward AI-mediated answers and conversational interfaces, marketers must expand their definition of success beyond CTR. Emerging KPIs focus on visibility, inclusion, and influence: how often your brand is surfaced in AI-generated answers, how consistently it appears in authoritative contexts, and whether it is recommended when buyers ask high-intent questions.

Comparison of Success Metrics: 2024 vs 2026

There is no shortcut for measurement. Treating all clicks as equal — or assuming search creates demand rather than reveals it — will become a strategic liability. In an AI-first discovery environment, growth belongs to brands that measure where they show up, how they’re framed, and whether they’re trusted in context, not just whether someone clicked.

Closing the Measurement Gap with Commercial Analytics

Complicating matters further, 60%–80% of the customer journey now occurs without brands’ knowledge. This creates an attribution void and widens existing measurement gaps, making it harder to understand what drives consumer action.

According to Bain & Company’s 2026 Marketing Outlook, traditional last-click attribution models overattribute value to branded search and direct traffic by 15–30%. This creates a ‘measurement blind spot’ where the impact of AI discovery tools—which introduce brands to new audiences in the first place—is significantly undervalued.

This is where Commercial Analytics and Marketing Mix Modeling have re-emerged as essential. As privacy constraints and AI fragmentation limit user-level tracking, leading brands are shifting from bottom-up methods such as MTA to outcome-based measurement. Incrementality testing and Commercial Analytics provide a clearer view of what truly drives growth – across channels, platforms, and increasing, AI-mediated discovery.

Commercial Analytics focuses on tracking patterns rather than individuals, using aggregated data—total spend and sales—to estimate relationships via statistical methods. It helps brands see how sales vary with spending on channels like TikTok or LLM optimization, running multiple KPIs with econometric factors, and scenario planning.

The transition to ‘AI Mode’ isn’t just a technical change in how we find information; it’s a cultural shift. Brands that can move beyond the click and embrace a top-down Commercial Analytics approach that proves incrementality can finally stop chasing ghosts and start driving real, measurable growth.