Search as we know it is undergoing a seismic transformation. For decades, traditional search engines like Google and Bing have been the cornerstone of how consumers discover, evaluate, and interact with brands online. But now, generative AI search, powered by large language models (LLMs) such as ChatGPT, Perplexity, Claude, and Google’s Search Generative Experience (SGE), is rapidly reshaping the user experience and redefining what it means to “search.”

Rather than presenting users with a list of blue links and paid ads, generative AI offers direct, conversational answers in real time – often without users ever needing to click through to a website. This shift from transactional search to contextual, AI-driven dialogue is not only improving accuracy and relevance, but also forcing digital marketers to confront a new reality: the rules of engagement have changed.

In this post, we’ll explore:

• Why generative AI search is trending more popular than traditional search
• The benefits and limitations of AI-powered discovery
• The implications for paid search and digital advertising
• What this evolution means for brands, and how to future-proof your strategy

The Rise of Generative AI Search

Generative AI search replaces static keyword matching with dynamic, contextual comprehension. It doesn’t just retrieve links – it synthesizes answers, drawing from massive datasets and inferring user intent in real time. Tools like ChatGPT, Google SGE, Microsoft Copilot, and Perplexity.ai are giving users direct, fluid responses that read more like a conversation with a subject-matter expert than a list of results.

This new approach is gaining momentum. According to Forbes, a growing number of users now trust AI-generated answers more than paid search ads, and nearly 50% of users in a Search Engine Land survey say traditional Google search results have become “less useful,” citing cluttered SERPs, excessive ads, and irrelevant content.

Bain & Company calls this shift the rise of “zero-click search,” where users increasingly get their answers without clicking on external links. AI assistants now answer questions, summarize reviews, compare products, and even recommend services – all within the search interface itself. The clickless experience, while convenient for users, poses profound challenges for brands that rely on paid or organic traffic.

Why Generative Search Is Gaining Ground

There are several structural advantages that explain the growing popularity of AI-powered search:

✅ Speed and Convenience

AI-generated responses are faster, more concise, and often eliminate the need to open multiple tabs. A single prompt “What’s the best wireless earbuds for under $150 with noise cancellation?” yields a clear, curated list, unlike traditional search which requires comparing results across several sites.

✅ Better Context and Personalization

Generative AI models use broader context from previous queries, user preferences, and known facts to refine answers. They excel at interpreting conversational intent and compound questions – something traditional search engines struggle with.

✅ Zero Friction = Higher Trust

As Forbes notes, users perceive AI summaries as more neutral than paid ads or heavily SEO-optimized content. They’re not being “sold to” – they’re being answered.

✅ Integrated Across Platforms

AI search isn’t confined to browsers. It’s embedded in productivity tools (Microsoft 365, Notion), messaging apps (Slack, Discord), and devices (Alexa, Siri), enabling search to become more ambient, visual, and voice-driven.

Limitations and Growing Pains of Generative AI Search

Despite its momentum, generative AI search isn’t flawless. There are real concerns to address:

⚠️ Misinformation and Hallucinations

AI can “hallucinate”, producing factually incorrect answers with confidence. For brands, this could mean misattribution, flawed comparisons, or the spread of outdated product information.

⚠️ Loss of Attribution and Clicks

Unlike traditional search which links users directly to your site, AI often summarizes your content without driving any traffic. That means less opportunity to convert users, collect data, or present your unique value proposition.

⚠️ Opaque Algorithms

AI platforms don’t provide transparent SEO playbooks like Google. Brands can’t easily optimize for “AI visibility,” and influence over how your brand is represented is limited.

⚠️ Measurement Challenges

With fewer clicks and on-site behaviors, marketing teams lose access to key performance indicators. Standard attribution models, conversion tracking, and retargeting may no longer work in a zero-click environment.

The Impact on Paid Search and SEM

Generative AI is disrupting one of the most profitable pillars of digital marketing: paid search. As more queries are resolved within AI interfaces, ad impressions, click volume, and conversion pathways are all being impacted.

🔻 Decline in Search Traffic

Zero-click answers reduce site visits. Users who once searched “best project management software,” clicked on a few links, and downloaded trials, now get all their info (and sometimes even product links) in one AI-generated paragraph.

🔻 Shrinking Ad Real Estate

Google’s SGE experience often places ads below the AI-generated answer – far less visible than in traditional search layouts. That affects CTR and raises the cost of acquiring attention.

🔻 AI Filters Out Paid Content

AI models often bypass paid promotions in favor of synthesizing what they consider objective or authoritative information. Marketers can no longer assume budget equals visibility.

🔻 Value of Keywords is Declining

Keyword-based targeting is less effective when AI interprets the user’s intent holistically. This diminishes the tactical power of traditional keyword bidding strategies.

What This Means for Brands: Implications and Best Practices

To thrive in this new paradigm, brands must reimagine how they create, distribute, and measure content. Here’s how.

1. Visibility Now Relies on AI Summarization

Generative AI prefers content that’s cleanly structured, clearly written, and easily extractable. Brands that fail to provide concise, factual, answer-rich content will be left out of the conversation – literally.

Best Practices:

• Use headers, bullet points, and short-form explanations
• Publish content that directly answers common user questions
• Apply schema markup to increase machine readability
• Avoid overly branded or jargon-heavy language that may be omitted

2. Owned Channels Are Now Mission-Critical

With declining reliance on search engines to drive discovery, brands need to strengthen their direct-to-consumer ecosystems.

Best Practices:

• Build email lists, SMS audiences, and loyalty programs
• Focus on content hubs (blogs, FAQs, video tutorials) to own your narrative
• Collect and activate first-party data to personalize user experiences

3. Shift From Keywords to Intent Modeling

AI search is based on user intent, not search volume. Your content should address real needs, not just rank for keywords.

Best Practices:

• Create content for each stage of the journey: awareness, consideration, decision
• Write for complex, long-form queries (e.g., “What’s the best CRM for small non-profits with limited budgets?”)
• Focus on topics, not just terms

4. Thought Leadership Drives Credibility and Citations

AI tools favor citing experts and reputable sources. Become one.

Best Practices:

• Publish original research, guides, and case studies
• Secure backlinks from authoritative media or academic sources
• Get your brand mentioned in public forums, roundups, and expert commentary

5. Attribution Must Shift to Influence-Based Models

Clicks and sessions are no longer reliable proxies for effectiveness. Influence – awareness, trust, recall – is the new north star.

Best Practices:

• Use brand lift studies, surveys, and social listening
• Track branded search growth and direct traffic over time
• Measure engagement within AI-integrated tools when available (e.g., Google SGE insights)

6. Meet Users in Multimodal Environments

Search is no longer text-only. Users now engage via voice, image, and video.

Best Practices:

• Produce visual explainers, demo videos, and how-to guides
• Optimize content for voice search with natural language structure
• Consider AR/VR content in categories like beauty, fitness, and fashion

7. Trust and Transparency Are Now Marketing Differentiators

AI platforms prioritize trustworthy sources. Brands that mislead, overpromise, or obfuscate risk being excluded.

Best Practices:

• Be transparent in product claims, pricing, and data use
• Fact-check your content rigorously
• Embrace ethical AI use and disclose when you’re using generative content

Brands Successfully Embracing Generative AI Search

As generative AI search accelerates, some brands are already adapting, and thriving, by reshaping their strategies to align with how AI platforms aggregate and present information. These early adopters provide practical models for what success looks like in this new ecosystem.

1. Expedia: Partnering with Generative AI for Personalized Travel Search

Expedia has leaned into generative AI by integrating directly with ChatGPT’s plugin ecosystem, allowing users to plan trips conversationally. Users can ask, “Plan me a 5-day trip to Italy with historical tours and mid-range hotels,” and receive personalized itineraries pulled from Expedia’s inventory, complete with booking links.

Why it works:

• Expedia is no longer waiting for users to search traditionally.
• It positions itself at the point of decision-making inside the AI interface.
• It builds loyalty through convenience and real-time personalization.

2. Mayo Clinic: Becoming a Trusted Source in AI Answers

Healthcare is a category where accuracy and authority are paramount. The Mayo Clinic has optimized its content for AI summarization by:

• Structuring content with clear subheadings, bullet points, and answer boxes.
• Ensuring every piece of information is medically reviewed and cited.
• Making its medical pages friendly to both users and machine-learning systems.

As a result, AI tools like Perplexity and Google’s SGE frequently cite Mayo Clinic as a top source for medical queries like “symptoms of dehydration” or “best treatments for migraines.”

Why it works:

• AI platforms prioritize trust, authority, and clarity.
• Mayo Clinic is effectively “zero-click SEO”-optimized to win citations.

3. Notion: Building Searchable, Multimodal Help Experiences

Notion, the popular productivity tool, has transformed its help documentation into a multimodal knowledge base optimized for both traditional and AI-based search. With a mix of:

• AI-friendly headers like “How to set up a team workspace”
• Short, actionable content chunks
• Embedded videos and infographics

…Notion ensures its content is easily digestible for users and generative AI alike. Notably, ChatGPT often cites Notion docs when users inquire about productivity tools or workflow automation.

Why it works:

• The content is designed for structured parsing and reuse.
• Visual and written materials enhance discoverability in multimodal AI environments.

4. L’Oréal: Voice and Visual Search Preparedness

As generative AI becomes multimodal, L’Oréal has invested in AI-optimized product visuals and voice-friendly search strategies. They’ve deployed:

• Detailed product descriptions that include key attributes (SPF, texture, use case)
• Visual assets tagged with structured metadata
• Content written in natural language to serve voice assistants like Google Assistant and Siri

AI search experiences like Google Lens and Amazon’s AI product descriptions pull directly from this kind of rich, structured content.

Why it works:

• L’Oréal prepares for non-textual search formats.
• Its product data is structured in a way AI can easily understand and reproduce.

5. HubSpot: Dominating AI-Searchable B2B Content

HubSpot continues to lead in B2B inbound marketing by producing AI-discoverable content at scale. Their blogs, templates, and tutorials dominate AI-powered queries like “best CRM for small businesses” or “how to automate email nurturing campaigns.”

Key tactics include:

• Answer-first formatting (“The best CRM for small teams is…”)
• Creating content for long-tail, intent-heavy searches
• Getting cited in industry publications and forums (which LLMs scrape and value)

Why it works:

• HubSpot anticipates high-intent questions and provides structured answers.
• It builds authority through consistency and volume.

Takeaway: AI Optimization Is the New SEO

These brand examples share a common DNA:

• Clear, structured, fact-based content
• Investment in multimodal assets (text, voice, video)
• Early participation in AI ecosystems like ChatGPT plugins or Google SGE
• A shift from chasing traffic to earning trust and citations in AI-generated summaries

Whether in travel, healthcare, B2B, or eCommerce, the message is clear: the brands that embrace the AI search experience, not just adapt to it, are the ones poised to win.

The New Role of Digital Advertising in the Age of Generative AI Search

As generative AI search becomes the primary gateway for online discovery, the traditional role of digital advertising, especially paid search, must be fundamentally redefined. In the past, ads competed for position at the top of a search engine results page. Today, with AI providing direct answers and fewer opportunities for clicks, digital advertising is shifting from a transactional, last-click driver to a strategic engine of brand visibility, contextual relevance, and AI influence.

Here’s how that shift is unfolding and what it means for brands:

1. From Click-Capture to Context Creation

Traditional paid search was largely about capturing demand: targeting users at the moment of intent with ads designed to convert quickly. In a generative AI environment, that moment of intent is often intercepted by AI, which offers summarized information, product comparisons, and recommendations — all before an ad can be served.

Now, the role of advertising is increasingly about influencing the content AI uses to build those answers. That means investing in:

• Brand awareness campaigns across channels (YouTube, social, display)

• Sponsored content and PR to seed brand mentions into authoritative sources

• Native advertising that blends seamlessly with informative content

Instead of bidding only on keywords, advertisers must shape the context in which their brand appears, both directly through ads and indirectly through owned and earned media.

2. Supporting AI Visibility Through Cross-Channel Reinforcement

AI models don’t just crawl web pages – they synthesize signals from multiple content types and channels, including social media, reviews, product metadata, news coverage, and even public forums like Reddit or Quora. Paid advertising can be used to amplify these signals, reinforcing a brand’s relevance and credibility in the places LLMs are most likely to mine for context.

Effective cross-channel ad strategies now include:

• Video content campaigns that demonstrate product use and social proof

• Influencer amplification that triggers organic discussions LLMs may cite

• Programmatic placements on publisher sites to gain visibility in content AI may scrape or summarize

The goal is to saturate high-quality, AI-visible ecosystems with your brand’s messaging, so that when users ask, “What’s the best solution for X?” your brand is part of the answer, even if no ad is shown.

3. Paid Search Still Matters — But Must Evolve

Paid search isn’t going away, but its role is changing. Google’s AI-enhanced SGE (Search Generative Experience) now includes ads beneath the AI-generated summary, which means:

• Lower visibility unless paired with strong organic/AI presence

• Higher CPCs as fewer clicks become more competitive

• A growing need for smart bidding and intent-driven creatives that align with what AI suggests

Advertisers should:

• Align search copy with likely AI-generated summaries (i.e., reinforce key differentiators)

• Monitor new SGE ad formats as they evolve (e.g., conversational ads, inline product cards)

• Treat paid search not just as a direct-response tool, but as a companion to organic AI discoverability

Additionally, Performance Max and other AI-enhanced campaign types are emerging as powerful ways to automate targeting and creative delivery, especially when paired with strong first-party data.

4. Retargeting and Journey Mapping Will Need Reinvention

One major consequence of zero-click and AI-led search is the disruption of traditional attribution and retargeting. If users never land on your site, traditional pixels and conversion tracking won’t work. That means digital advertising strategies must become:

• More channel-agnostic, focusing on journey orchestration, not just retargeting

• Dependent on first-party data collected via owned properties (e.g., email, loyalty apps)

• Aligned with brand lift and incremental impact studies rather than just ROAS

Emerging privacy standards, combined with AI-mediated browsing, mean that advertisers must find new ways to connect dots across platforms—likely via clean rooms, consent-based data sharing, and predictive modeling.

5. Experimentation Will Be Key in New AI-Integrated Ad Ecosystems

As generative AI search becomes integrated across platforms, inside voice assistants, smart devices, wearables, productivity tools, and even operating systems, ad opportunities will become embedded, conversational, and contextual.

We’re already seeing early signs of this:

• Amazon using Alexa to suggest product purchases based on previous behavior

• Microsoft integrating Copilot across its suite, with sponsored recommendations

• Google testing AI shopping assistants that offer curated options with built-in links

In this future, advertising becomes less interruptive and more assistive. Brands will need to:

• Test conversational ad formats and voice interactions

• Participate in AI plugin ecosystems (like ChatGPT’s marketplace)

• Build API connections that allow their inventory or content to be surfaced naturally within AI responses

Digital Advertising Isn’t Dying—It’s Becoming More Intelligent

In short, the role of digital advertising is no longer to simply chase conversions through isolated clicks. It’s now about orchestrating influence across a complex ecosystem where AI mediates the discovery journey.

Advertising must:

• Drive brand familiarity so AI knows who you are

• Support AI discoverability by feeding the right signals into the ecosystem

• Adapt to intent-rich, multi-platform environments where search is always on – even if it doesn’t look like search

This shift doesn’t eliminate the need for media investment – it magnifies its strategic importance. Budgets must be smarter. Creative must be more utility-driven. And performance must be measured not just in cost-per-click, but in share-of-mind, share-of-answer, and share-of-action.

Brands that get this right won’t just survive the AI revolution – they’ll be the ones defining it.

Emerging Opportunities in an AI-Dominant Landscape

While generative AI search disrupts traditional tactics, it opens new frontiers for innovation:

💡 AI-Specific Optimization

Brands can begin crafting content specifically for LLMs—anticipating likely questions and ensuring your responses align with AI model preferences.

📈 AI-Driven Ad Platforms

Tools like Google Performance Max and Meta Advantage+ are already using AI to enhance targeting, creative, and bidding. This is where traditional and generative AI worlds will increasingly merge.

🤖 Conversational Commerce

As AI assistants become gateways to commerce, expect integrations with eCommerce platforms. Imagine a user asking ChatGPT, “Buy me the best SPF moisturizer under $30,” and completing a purchase via plugin.

🌍 Localized and Multilingual AI Content

Generative models are increasingly multilingual and culturally adaptive. Brands can use AI to scale content into new regions without sacrificing nuance.

Final Thought: Don’t Chase Clicks — Build Context

The generative AI revolution isn’t just a shift in how users search – it’s a transformation of the entire digital advertising ecosystem. As search becomes more conversational, contextual, and closed-loop, the old models of impression-based buying and keyword-driven targeting are rapidly losing relevance. Brands can no longer rely solely on ad visibility to win attention; they must now compete for inclusion in the AI-generated answers that users trust most.

This means advertising strategies must evolve beyond the click. Winning in the AI search era requires blending performance with presence, delivering useful, structured content that earns AI citations, while simultaneously investing in full-funnel media strategies that build brand equity across emerging platforms. Success lies in creating value at the point of discovery, not just conversion.

For marketers, this is a call to action: optimize for intent over keywords, create content that serves both humans and machines, and reimagine paid media as part of a holistic, AI-integrated ecosystem. Whether it’s through conversational commerce, native integrations with LLMs, or intelligent retargeting based on inferred AI interactions, the brands that adapt fastest will define the next era of digital dominance.

Generative AI isn’t the end of digital marketing. It’s the beginning of something far more intelligent, more personalized, and more human.