Traversing New Terrain: Considerations for Brands in the Face of a Changing Search Landscape

By Ray Yu , Peter Dewey , Robert Derow , Joe Simon , Meredith Finnerty , and Ryne Landers

The recent surge in generative AI and the growing trust in knowledge engines are transforming search dynamics. AI enhancements now play a pivotal role in shaping user journeys, blurring the lines between search engine marketing (SEM) and search engine optimization (SEO), and making search pathways more specific and focused. With the introduction of multimodal AI to assist in complex decision-making and improve ad targeting, the user’s search experience is becoming increasingly sophisticated. To stay ahead of these early-stage developments, organizations must intensify their analytics to decipher customer search behaviors and recalibrate their content strategies both on- and off-site to increase their testing velocity and determine what works and what doesn’t as the landscape evolves.

Adapting to AI-Enhanced Search

AI is reshaping the search landscape by personalizing and enhancing the user experience. Google’s rollout of AI features such as multistep reasoning and AI-organized search results makes it imperative for brands to develop content that extracts value from these capabilities. This AI-driven environment demands a more profound understanding of customer behavior and a dynamic approach to content creation that goes beyond traditional SEO tactics, focusing on depth, relevancy, and engagement.

• Rollout of GenAI capabilities to search: Google’s new search GenAI enhancements streamline user interactions by providing synthesized, highly relevant information and richer, more personalized search results. The introduction of multistep reasoning caters to complex queries, such as trip planning or product research, potentially reducing outbound clicks and increasing the quality of traffic within websites. These innovations are poised to transform search dynamics significantly, offering a competitive edge to brands that quickly embrace them and adapt.
• Changing nature of queries: As users become more adept at interacting with the new search experience, the nature of queries will shift toward more engaged, conversational styles. In tandem, there will be a rise in navigational and discovery-oriented searches and more detailed, long-form queries. This evolution underscores the importance of understanding the difference in commercial, transactional, informational, and navigational queries, as the search experience will look and feel very different.
• Amplified importance of SEO: The new era of search demands greater sophistication in content creation and SEO practices. Brands must not only continue to strategize for traditional organic search results but must now also incorporate additional analytics that take into account the branching logic native to GenAI-driven answers. This branching logic begins in a similar way to traditional SEO but further enhances to generate relevant responses.
• Continuous evolution and blurred lines between search tactics: Search engine results pages are evolving, becoming richer and more complex with new features, such as AI-driven answers, innovative ad formats, and enhanced local results; as a result, the SERP layout will likely continue to evolve from the traditional structure with top paid results consistently above organic results. Both SEO and SEM content will need to be updated not only to target standard result placements but also to seamlessly integrate with AI responses.

Strategic Actions for Brands

To gain competitive advantage in a landscape increasingly influenced by AI, brands need to focus on enhancing analytical capabilities, accelerating testing cycles, scaling up content creation, and expanding their brand presence. In the same way that companies such as Amazon and Booking Holdings (Priceline) capitalized on Yahoo and Google’s new search engines 20 years ago, organizations must now move quickly to capture outsized gains in the face of a new search landscape. Here are four ways brands can act now.

Sharpen analytics on new customer behaviors

As AI complicates the search landscape, brands need to improve their data analytics to understand and address new customer behaviors and search patterns. Robust analytics help tailor content strategies more precisely, aligning with both AI-based search engine algorithms and user expectations.

Mine AI answer paths to build a content pipeline.

• Research AI prompts generated for your core search terms to understand the key questions asked and build content tailored to those queries.
• Use semantic query tools like AlsoAsked and AnswerThePublic to build comprehensive topical maps to expand on the AI answer paths. Incorporate customer inputs for the human touch.
• As AI-produced content has become more pervasive online, search engines have quietly added qualitative human signals to the search algorithms to separate content with unique, valuable insights from derivative works. These real human insights provide value AI content can’t replicate. Develop social listening and trend detection capabilities, utilize your CRM for customer language, and solicit detailed reviews for authenticity and increased relevancy.

Ensure accountability through measurement.

• Utilize AI position tracking tools like Semrush’s AI Overviews tracking or BrightEdge’s Generative Parser to monitor your brand’s performance in AI answers and adjust your strategy as needed.

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Reshape search testing with GenAI-specific tests

The rapid advancement of AI-driven search is pushing brands to accelerate their testing and learning cycles to maintain pace. By increasing the frequency and scope of these cycles, brands can quickly adapt to new platform and algorithm changes as well as evolving user behaviors.

Tactical steps for performance marketers:

• Review the AI Overview text fragments (the highlighted passage of text in the answer’s cited website link), test improvements, and submit your updated content via Webmaster Tools. Test AI answers format types and content to influence the results. Use short, concise passages designed for the answers and test data points, expert quotes, and user inputs.
• Sharpen in-house testing capabilities for more rigorous learning while establishing ambitious objectives to ensure your organization maintains accountability.
• Meaningfully increase test-and-learn loops to cut through signal noise and understand algorithm levers as they evolve.
• Pilot the latest AI ad formats to determine which work best with your business.

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Tailor content based on conversational responses with AI

As AI systems increasingly do heavy lifting for context and personalization, leading to deeper conversational engagements, we expect to see an increasing longtail of terms driving engagement. Brands will need to respond with a greater number of content pieces across the funnel at a scale never seen before. Leveraging AI tools for content development will saturate AI-generated search results while ensuring the content meets the specific needs of the target audience.

Tactical steps for performance marketers:

• Analyze AI answers and related semantic queries and utilize GenAI to produce a large index of deep content designed for specific segments and users. Large brands should run machine learning analyses of user engagement across their digital presence to develop segments, understand goals, and build content at scale. Utilize AI and NLP tools for SEO to optimize content AI systems. Use the testing approach above to adjust content for optimal performance with AI.
• The latest search updates favor short, concise answers in AI Overviews. Use APIs and data pipelines to provide up-to-date specific answers with dynamic variables, such as pricing, shipping, and product features.
• Expand the depth of your content index with highly optimized, dynamic pages to appear in the cited sources of AI answers.
• Accelerate multimedia assets such as video content and infographics to tap into multimodal capabilities of AI-assisted search.

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Re-imagine Brand Amplification

Revise your content partnership strategy and content mediums to maintain a strong brand presence where large language models (LLMs) source their data. Expand into new channels where rising generations are spending time and monitor discussions of your brand in offline channels to respond proactively.

Tactical steps for performance marketers:

• Investigate the primary citation sources that language models use within your industry and target these platforms with PR and outreach campaigns; by shaping the content on these influential sites, your brand’s messages become part of the language models’ answers, enhancing visibility and driving referral traffic.
• Deploy the AI answers testing above to ensure the most success.

Conclusion

The future of search is here with the ongoing integration of AI, making the user experience more native, direct, and personalized. For brands, staying ahead means embracing these changes, innovating their approaches, and continually adapting to the rapidly evolving landscape to figure out what works for them. By strategically leveraging AI as a tool within analytics and testing—for both content and branding—organizations can not only survive but thrive in this new era of search.