AI and SEO: What AI means for the future of SEO [Expert Tips & Interview]

This transformation is not a gradual shift but a significant paradigm change. The introduction of accessible AI tools like ChatGPT over three years ago marked a pivotal moment, democratizing advanced AI capabilities and accelerating their adoption by the general public. This widespread embrace has, in turn, driven search engines to integrate AI-powered features at an unprecedented pace. Data from McKinsey indicates that AI-powered results already constitute half of Google’s search outputs, with projections suggesting this figure will climb to 75% by 2028. This trend underscores the urgency for marketers to understand and adapt to the converging forces of AI and SEO.

The Cascading Impact of AI on Consumer Behavior and Search Engines

The impact of AI on SEO is a multifaceted phenomenon, originating with a fundamental shift in how consumers interact with search engines. No longer are users solely reliant on traditional keyword-based searches. The advent of sophisticated voice assistants like Alexa and Siri, and advanced chatbots such as ChatGPT, Perplexity, and Gemini, has fostered a more conversational and direct approach to information retrieval.

Research from GWI highlights a significant generational shift, with 31% of Gen Z individuals now preferring AI platforms or chatbots for online information discovery. This preference is not isolated to younger demographics; broader trends suggest a substantial migration towards AI-driven search. Semrush research forecasts that traffic generated by Large Language Models (LLMs) could surpass traditional Google search by the end of 2027. Further underscoring this preference, HubSpot research reveals that 79% of individuals already utilizing AI for search believe it offers a superior user experience compared to conventional search engines. This evolution in consumer search behavior and preferences is a direct consequence of AI’s growing influence.

In response to these evolving consumer demands and the burgeoning popularity of AI interfaces, search engines themselves have undergone a significant transformation. Google, a dominant player in the search landscape, has proactively integrated generative AI features into its core search experience. The rollout of "AI Overviews" and an "AI Mode" offers users a more interactive, chatbot-like experience directly within the search results page (SERP).

This integration has profound implications for content visibility and website traffic. Google reports that over 27% of searches now conclude without a user clicking through to a website, as the AI-generated overviews provide sufficient answers. This trend has been exacerbated by data from SimilarWeb’s Annual Digital 100 Report, which noted a decline in search referral traffic to major web domains. Between May 2024 and May 2025, zero-click searches increased from 56% to nearly 69% of queries, while search referral traffic to 1,000 tracked web domains fell from 12 billion visits to 11.2 billion visits during the same period.

The spatial dominance of AI overviews on SERPs—occupying approximately 42% of desktop screens and 48% on mobile—further pushes traditional organic listings further down the page. This diminished visibility poses a significant challenge for high-quality content that previously enjoyed substantial traffic and engagement.

AI’s Restructuring of SEO Strategies

The convergence of AI and SEO has necessitated a fundamental rethinking of established optimization practices. A comprehensive analysis by Semrush of over 200,000 keywords revealed that nearly 95% of queries triggering AI Overviews lacked paid advertisements or had minimal commercial intent. This observation suggests that AI summaries are primarily deployed for informational searches, while transactional queries may continue to be routed through traditional SERPs.

This strategic deployment by search engines has a direct impact on marketers, particularly concerning top-of-funnel educational content, which typically drives significant traffic and builds brand awareness. The potential reduction in clicks for such content raises concerns about its effectiveness in lead generation and customer acquisition.

In response to these shifts, marketers are increasingly adopting new strategies, including Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO). These approaches focus on making content more appealing and accessible to AI features within search engines and generative AI models, respectively.

Evolving Keyword Research and Topic Discovery for AI-Powered Search

Traditional keyword research, once centered on exact phrase matching and metrics like search volume and keyword difficulty, is now being augmented by AI-driven methodologies. The new frontier of keyword research for AI search emphasizes intent mapping, topic clustering, and, crucially, conversational query analysis.

Users interacting with AI systems often employ a more natural, conversational style compared to the concise queries typical of traditional search engines. For instance, instead of searching for "ice cream shop NYC," a user might ask, "What’s the best ice cream shop near me?" This shift towards longer, question-based queries is a key driver of AI Overview responses, as confirmed by Pew Research Center findings.

Consequently, marketers must reorient their keyword strategies to encompass a broader range of question-based queries, focusing on "what," "how," "why," and "best" formulations. To facilitate this, several strategies are proving effective:

• Audience Question Inventory: Compiling a comprehensive list of questions frequently asked by the target audience throughout their buyer’s journey. This can be achieved by collaborating with sales and customer service teams who are on the front lines of customer inquiries.
• Leveraging Public Data: Utilizing tools like AnswerThePublic and Google’s "People Also Ask" (PAA) boxes to identify common user questions and ascertain Google’s algorithmic understanding of relevant topics.
• AI-Powered SEO Tools: Emerging AI tools are proving invaluable for optimizing for AI search. Platforms such as HubSpot’s Breeze, Semrush’s Copilot, and Ahrefs’ AI Content Helper offer features for large-scale search intent analysis, content gap identification, and the generation of topic clusters aligned with the buyer journey, including conversational long-tail queries. HubSpot’s Content Hub, in particular, excels at building topic clusters that map keywords to buyer intent and facilitate the creation of content that earns citations across both traditional and AI search environments.

Content Optimization for Machine Learning and E-E-A-T

The quality of content remains a paramount factor in achieving success in the AI-SEO landscape. Google’s E-E-A-T framework—Experience, Expertise, Authoritativeness, and Trustworthiness—continues to be a critical benchmark, especially as AI models draw heavily from reputable sources. AI systems strive to generate the most accurate and helpful answers possible, making content that references credible sources, showcases original research, and presents data compellingly more likely to be favored.

Studies, such as one by the Digital Marketing Institute, indicate that content enriched with credible citations and statistics can improve AI visibility by 30-40% compared to content lacking these elements. AI tools themselves can assist in enhancing content quality and structure. Tools like ChatGPT can provide feedback on drafts, suggest improvements for audience targeting, brainstorm topics, identify knowledge gaps, generate metadata, source data, create visual aids, and even perform proofreading. For existing content, AI can help identify stale information, suggest updated statistics, and recommend structural changes to bolster E-E-A-T signals.

Furthermore, AI tools like HubSpot’s Content Remix facilitate content repurposing and optimization across various media formats, maximizing the reach and impact of existing assets. While human oversight and editing remain crucial for AI-generated content, integration of AI into SEO and content workflows has been reported to yield better returns by nearly 70% of companies.

Technical SEO and the Importance of Machine-Readability

Technical SEO plays a crucial role in ensuring content is accessible and understandable to AI systems. Machine learning algorithms, whether powering Google’s search index or LLMs for AI answers, favor content with specific structural characteristics. This includes proper schema markup, clear heading hierarchies, concise paragraphs that directly address user queries, and dedicated FAQ sections. These elements enhance a page’s "extractability" for AI.

Consequently, marketers are advised to place a greater emphasis on structured data implementation, header optimization, and overall page formatting. Advanced SEO platforms, including Screaming Frog, Semrush, and Ahrefs, leverage machine learning to automate website crawling, identify technical issues (such as broken links, duplicate content, slow page speeds, and missing schema), and prioritize fixes based on their estimated impact. What once required extensive manual audits can now be flagged, triaged, and assigned in a fraction of the time.

A critical aspect of technical SEO for AI is ensuring content is accessible to AI crawlers. Inadvertent blocking of AI bots through robots.txt rules or JavaScript rendering issues can prevent content from being indexed and, therefore, from appearing in AI-generated answers. Adherence to Generative Engine Optimization (GEO) principles, which emphasize technical accessibility and machine readability, is paramount.

Adapting SEO Strategies for the AI Era

To effectively navigate the evolving SEO landscape shaped by AI, marketers are advised to adopt a multi-pronged approach. Amanda Sellers, Manager of EN Blog Growth at HubSpot, offers several key takeaways for adapting SEO strategies to the AI era:

1. Prioritizing Original, Comprehensive Data

Sellers emphasizes that merely producing evergreen, factual content is no longer sufficient, as AI can readily generate such information. The focus must shift towards creating "citation-worthy" content. This involves providing comprehensive answers that demonstrate deep knowledge and credibility. By offering original data and thought leadership, brands can position themselves as authoritative sources that AI models are more likely to cite. The more frequently a brand’s information is cited by other reputable sources, the stronger its authority becomes in the eyes of both search engines and AI systems.

2. Emphasizing Structure and Context

Content design must prioritize structure to facilitate AI’s processing of information. AI systems retrieve content in discrete "chunks" and do not possess human-like comprehension. Therefore, writing content in semantically rich sections and strengthening semantic associations increases the likelihood of accurate retrieval and, consequently, visibility. Semantic richness is achieved through clear, direct statements that explicitly define correlations and relationships. Tools like HubSpot Content Hub can aid in creating structured templates at scale, enabling teams to produce AEO-optimized content more efficiently.

3. Expanding Brand Presence Across Multiple Channels

The principle of repeated exposure is also applicable to AI and LLMs. The more a brand is mentioned or present across authoritative contexts online, the more likely AI systems are to trust and cite it. This extends beyond traditional backlinks to include mentions in reputable industry publications, discussions in high-quality forums, and references in academic or government sources. Establishing this multi-channel presence validates a brand’s subject-matter expertise across diverse online territories, including other publications, forums, review sites, and social media platforms. Tools like HubSpot’s Content Remix can streamline the repurposing of content for broader distribution.

4. Establishing and Showcasing Credibility

Beyond broad online presence, actively showcasing a brand’s credibility is essential. This includes displaying awards, accolades, and social proof directly on the website. Features such as customer testimonials, positive reviews, case studies, awards, and certifications serve as tangible evidence of a brand’s value and trustworthiness. This accumulated "lore" reinforces a brand’s status as a valuable resource for its target audience, search engines, and AI systems.

5. Maintaining Foundational SEO Practices

Despite the rise of AI, fundamental SEO principles remain crucial. Sellers advises a "two birds, one scone" approach: investing in strategies that enhance traditional search rankings will also improve AI visibility. This involves positioning content to offer unique value that AI cannot easily replicate, such as expert insights, proprietary data, or a distinct brand voice. While AI systems may not inherently emphasize differentiation, traditional SEO does. Therefore, creating content with unique value benefits both arenas.

Frequently Asked Questions About AI and SEO

What is SEO for AI?

SEO for AI, also known as Generative Engine Optimization (GEO) or Answer Engine Optimization (AEO), is the practice of optimizing content to appear within AI-generated answers from platforms like Google AI Overviews, ChatGPT, Perplexity, and Gemini. While traditional SEO focuses on ranking in search results, AI SEO aims for inclusion or citation within AI-generated summaries. Both require accurate, up-to-date, and comprehensive content, clear structure, and technical accessibility, but differ in their specific emphasis. AI SEO particularly favors structured data, modular content architecture for easy extraction, and a presence on authoritative third-party sources.

Is SEO still worth it with AI?

Absolutely. Traditional SEO remains highly relevant, even with the rise of AI. As of September 2025, Google continues to be a dominant source of traffic, delivering significantly more volume than ChatGPT, Gemini, and Perplexity combined. While organic traffic may become more challenging to acquire, brand visibility, authority, and citations within AI answers are becoming increasingly critical throughout the buyer’s journey. Furthermore, SEO forms the foundational layer upon which AI search visibility is built. AI systems rely heavily on content that has established authority and trust through traditional SEO signals, with over 99% of AI Overview sources originating from pages ranking in the top 10 organic results. The objective now is to optimize for both traditional search results and AI-generated answers concurrently.

Can SEO be done with AI?

Yes, AI can significantly enhance SEO efforts. AI tools can assist with keyword research, content ideation and generation, content optimization for readability and E-E-A-T, technical SEO audits, and competitive analysis. AI provides scale and efficiency, but human expertise remains indispensable for strategic differentiation and nuanced understanding. The optimal approach combines AI for efficiency with human insight for expertise.

What is the relationship between AI and SEO?

The relationship is symbiotic and multifaceted. AI is influencing consumer search behavior, reshaping how search engines function through machine learning algorithms and generative AI summaries, and has become an integral tool within SEO practices for automation and analysis. AI acts as both the environment in which SEO operates and a powerful tool for its execution.

Are recent SEO shifts due to AI?

Many recent SEO shifts, including Google’s "Helpful Content" update and the increased emphasis on E-E-A-T, are direct responses to the proliferation of AI-generated content. These updates aim to promote content that prioritizes human value over algorithmic manipulation. While generative AI can produce vast amounts of content, it often excels at objective information and regurgitation, while human creators can offer unique opinions, emotional reflection, and first-party research—qualities that are increasingly valued in search rankings.

Is AI-powered search changing SEO?

The evidence suggests a significant impact. Google’s search market share has seen a slight decline, partly attributed to the rise of AI search. While many AI-satisfiable searches might have historically resulted in zero-click outcomes, the integration of AI Overviews by Google and the emergence of AI-specific platforms are fundamentally altering user interaction patterns. The goal of SEO is evolving to encompass visibility not just in traditional SERPs but also within AI-generated summaries and citations.

Has AI changed search behavior?

Quantifying macro behavioral changes is challenging, but AI is undoubtedly influencing how users seek information. A noticeable increase in zero-click searches, largely driven by AI Overviews, indicates that users are obtaining answers directly from search results. While this may reduce raw traffic numbers, optimizing for AI search can still enhance brand visibility and awareness, with early data suggesting AI-referred visitors may convert at higher rates.

Does AI shift the balance of organic vs. non-organic marketing strategies?

A diversified marketing strategy remains paramount. While Google’s effectiveness as a channel has faced challenges from featured snippets, increased ad real estate, and zero-click searches, it continues to be a powerful source of demand. The introduction of AI is another evolutionary step, requiring adaptation rather than abandonment. The core principle of not placing all marketing efforts in a single basket holds true, and marketers must adapt their strategies to leverage AI while maintaining a robust mix of organic and non-organic channels.

The Future of SEO in an AI-Dominated World

AI is undeniably rewriting the playbook for SEO, but it has not rendered the fundamental principles obsolete. The core tenets of creating accurate, clear, and valuable content for the reader remain paramount. The key difference lies in the evolving landscape – the objective is no longer solely to conquer a search engine results page but to navigate AI systems that synthesize, summarize, and cite information.

The current era of AI represents the next evolution in search, building upon previous transformations like featured snippets, algorithm updates, and the rise of zero-click searches. Marketers who embrace these changes and strategically adapt their approaches will be best positioned to shape the future of search and maintain their competitive edge in an increasingly intelligent digital ecosystem.

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Jia Lissa

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