AI Search Rewrites Brand Visibility and Marketing Strategy

The seismic shift in digital marketing is here: AI search is reshaping how brands connect with consumers, moving beyond human-centric content to machine-optimized experiences. This conversation with James Cadwallader, CEO of Profound, and Daniel Shin Un Kang, Head of Organic and Agentic Search at Expedia, reveals the hidden consequences of this transition. It's not just a new user interface; it's a fundamental redefinition of visibility, content strategy, and competitive advantage. Marketers who grasp this paradigm shift now will gain a significant lead in navigating the AI-driven information landscape. This analysis is crucial for CMOs and marketing leaders aiming to future-proof their strategies and secure their brand's relevance in the evolving digital ecosystem.

The Unseen Battleground: How AI Search Rewrites the Rules of Visibility

The internet, as we know it, is undergoing its most significant transformation since its inception. The shift from content built for human eyes to content optimized for machine consumption, on behalf of humans, is not merely an evolution; it's a revolution. This fundamental change, driven by AI search and answer engines, renders many traditional marketing metrics and strategies obsolete. As James Cadwallader, CEO of Profound, astutely observes, "search engines, i.e., Google Search, is the biggest outcome really in the history of capitalism. And that outcome is predicated on this idea of, we will give you links and you will click them..." This model, built on the click, is now being fundamentally challenged. The advent of generative AI, exemplified by tools like ChatGPT, allows users to "just talk to the internet and it talks back to you," bypassing the need for link clicks altogether. This seismic shift creates a new, often invisible, battleground: how brands appear and are represented within AI systems.

Daniel Shin Un Kang, Head of Organic and Agentic Search at Expedia, highlights the immense difficulty in even measuring this new landscape: "Unlike traditional search with a mature ecosystem of data and players, you don't actually get a lot of data services or ground truth." The industry is grappling with a lack of established metrics and reliable data, forcing reliance on "synthetic prompts" to approximate what users might ask AI models. This complexity is compounded by the sheer volume of content generated by major platforms like Expedia, with "hundreds of millions of assets." Profound's role, as explained by Cadwallader, is to provide the crucial "visibility into not just how they show up, but also the why." This involves understanding how AI models, such as ChatGPT or Gemini, pull information from the open web, and how content needs to be structured and distributed to be discoverable by these bots. The implication is stark: the focus must shift from optimizing for human search queries to optimizing for machine interpretation and retrieval.

"We're not building content anymore for human beings, right? We're building content for user agents on behalf of consumers or human beings."

-- James Cadwallader

This statement encapsulates the core challenge. Content designed for human readability may not be effectively parsed or prioritized by AI. The consequence of ignoring this is a gradual erosion of visibility, not through a sudden drop in rankings, but through a subtle disappearance from the AI's synthesized answers. This creates a delayed but significant competitive disadvantage. Brands that continue to operate under the old paradigm risk becoming irrelevant in the new AI-driven information ecosystem. The immediate payoff of traditional SEO or content marketing might feel productive, but it fails to account for the downstream effects of AI's ascendance.

The Hidden Cost of "Obvious" Solutions

The transition to AI search presents a deceptive landscape where seemingly obvious solutions can lead to significant downstream problems. Many companies, accustomed to the established metrics of traditional search, are attempting to apply the same logic to AI-driven platforms. However, as Daniel Shin Un Kang points out, the metrics themselves are problematic. The reliance on "synthetic prompts" to gauge visibility is a necessary workaround, but it introduces variance. Different methodologies--whether using APIs or headless browsers, or whether memory is enabled--can lead to vastly different outcomes. This lack of a consistent "ground truth" means that optimizing for the wrong proxy can lead teams down unproductive paths, consuming resources without achieving genuine visibility in AI outputs.

Furthermore, the very nature of AI search--synthesizing information rather than simply linking to it--means that the "click" is no longer the primary goal. Profound's insight, as articulated by Cadwallader, is that "in a world where you can talk to the internet and it talks back to you... the only thing that will matter to marketers is how it, how AI talks about your brand." This requires a fundamental rethinking of content strategy. Simply producing more content, without considering its machine-readability and its ability to influence AI-generated answers, is akin to shouting into the void. The "slop" that some fear from AI content generation is, in fact, a consequence of providing AI with insufficient or poorly structured context. When AI models are fed rich, well-organized data, their outputs can be remarkably insightful. The challenge for marketers is to create that rich context.

"The scale problem is theoretical. The debugging hell is immediate."

-- Daniel Shin Un Kang

This quote underscores the critical distinction between the perceived problems and the actual operational challenges. While the "scale" of AI search might seem like a future concern, the immediate difficulty lies in "debugging" how content is being interpreted and presented by AI. This involves understanding technical aspects like file types and bot recognition, as well as the "worthiness" of the content itself--its relevance, trustworthiness, and utility. Teams that fail to address these immediate, albeit complex, issues will find their content increasingly sidelined, creating a competitive disadvantage that compounds over time.

The Long Game: Building Brand Moats in the Age of Agents

The rise of AI agents and answer engines introduces a new dimension to competitive advantage: the ability to build lasting relevance in a constantly evolving information landscape. Profound's work with Expedia exemplifies a strategic approach that prioritizes long-term positioning over short-term gains. As Daniel Shin Un Kang explains, the goal is to ensure Expedia's brand and offerings are discoverable within AI outputs, whether through direct brand mentions, inclusion in retrieved snippets, or by influencing user queries. This involves optimizing for four key inputs: training data, retrieval processes, user-driven brand queries, and consolidated user memory.

The complexity of this task is immense. It requires not only technical enhancements to content discoverability but also a deep understanding of "worthiness"--ensuring content is relevant, trustworthy, and useful. Profound's methodology, which prioritizes accuracy through more resource-intensive methods like direct API interaction over less reflective methods, demonstrates a commitment to this long-term view. This approach acknowledges that while immediate cost savings might be tempting, they can compromise the accuracy needed to build a sustainable advantage in AI search.

Furthermore, the concept of "sentiment" is emerging as a critical, yet previously overlooked, factor. Unlike traditional search, where links were neutral, AI answers can carry opinions and qualitative assessments. James Cadwallader notes, "If I ask ChatGPT, 'What are the best marketing podcasts in the world?' It will say something about The CMO Podcast, but it will give an opinion as well." Monitoring and influencing this sentiment in AI outputs is a new frontier for marketers. Profound's focus on this area, alongside content orchestration and automation, signals a strategic foresight. The ability to "re-earn your right to play in the category every six months," as Winston, founder of Harvey AI, puts it, highlights the dynamic nature of this new ecosystem. Brands that invest in understanding and shaping their AI presence now, even when it requires effort and patience, will build durable competitive moats that are difficult for others to replicate. This requires a willingness to embrace discomfort in the present for significant advantage in the future.

Actionable Strategies for the AI-Driven Future

• Empower Your SEO/AEO Team: Recognize that the function formerly considered a "black sheep" of the marketing stack may now be the most critical. Provide these teams with increased resources, agency, and strategic importance. This function is no longer just about ranking; it's about ensuring your brand's voice is heard by machines.

  • Immediate Action: Conduct an audit of your current SEO team's capabilities and resources.
  • Longer-Term Investment: Integrate SEO/AEO strategy into core business objectives, not just marketing.

• Invest in Machine-Readable Content: Prioritize creating and structuring content that AI bots can easily parse and understand. This includes technical considerations like file types and bot recognition, alongside ensuring content is relevant, trustworthy, and valuable.

  • Immediate Action: Review your website's technical SEO and content formatting for bot accessibility.
  • This Pays Off in 6-12 Months: Develop a content strategy that explicitly considers AI consumption alongside human readability.

• Develop a Multi-Platform AI Visibility Strategy: Understand that AI search is not a monolithic entity. Different AI models (ChatGPT, Gemini, Claude, etc.) and emerging platforms (AI ads, agent commerce) will require distinct optimization strategies.

  • Immediate Action: Identify the primary AI platforms your target audience uses for information retrieval.
  • This Pays Off in 12-18 Months: Build a portfolio approach to AI visibility, addressing AEO, AI ads, and agentic commerce.

• Embrace Persona-Based Prompting: Experiment with simulating user personas to understand how AI outputs vary based on different user characteristics. This provides a more nuanced understanding of your brand's potential visibility.

  • Immediate Action: Identify 2-3 key customer personas for your brand.
  • This Pays Off in 6 Months: Begin testing prompts through these personas to analyze AI responses.

• Monitor AI Sentiment: Begin tracking how AI models describe your brand and offerings. This qualitative data is becoming increasingly important and can reveal subtle shifts in perception that traditional metrics miss.

  • Immediate Action: Assign a team member to periodically monitor AI responses for brand mentions and sentiment.
  • This Pays Off in 9-12 Months: Integrate sentiment analysis into your ongoing brand monitoring efforts.

• Prepare for Agentic Commerce: As AI agents become more capable of completing tasks, understand how your products and services will be discovered and transacted within these agentic frameworks. This requires a proactive approach to ensure your offerings are discoverable and purchasable through AI agents.

  • Immediate Action: Research current developments in agentic commerce and AI-driven purchasing.
  • This Pays Off in 18-24 Months: Develop a strategy for how your brand will participate in agentic commerce ecosystems.

• Foster Internal Expertise: Recognize that navigating this new landscape requires specialized knowledge. Empower internal teams or partner with experts who can dedicate themselves to understanding and executing AI-driven marketing strategies.

  • Immediate Action: Designate an individual or small team to become the internal subject matter expert on AI search and marketing.
  • This Pays Off in 6-12 Months: Establish clear reporting lines and decision-making authority for your AI marketing initiatives.