The Future of AI in U.S. Advertising: A $25 Billion Opportunity
According to EMARKETER, U.S. advertisers are set to invest over $25 billion into AI-driven search advertising by 2029, representing around 14% of total search budgets. This represents a significant increase from just $1.04 billion this year. The shift isn’t merely speculative; substantial infrastructure changes are already taking place behind the scenes, with platforms linking live, structured product feeds directly to large language model (LLM) interfaces, making it easier for consumers to engage with products.
Transformative Changes in E-commerce
Shopify has recently unveiled its Catalog API, allowing agents such as Perplexity to access real-time product information—including titles, prices, and inventory levels—without resorting to scraping. This is particularly important as various AI shopping experiences, from ChatGPT to Amazon’s “Buy for Me,” are showing that conversational agents can efficiently research, refine, and finalize purchases in a seamless flow. This newfound capability is effectively transforming informative responses into shoppable advertisements, which aligns well with the projected spending surge detailed by EMARKETER.
Redefining Search Advertising
AI-driven search advertising differs fundamentally from traditional formats that typically present alongside blue links. EMARKETER characterizes this new model as “sponsored answers, brand mentions, and affiliate links embedded within AI-generated summaries or conversational responses.” This requires that AI agents be enriched with detailed product titles, comprehensive attributes, and real-time inventory updates to effectively surface “sponsored” suggestions.
Infrastructure Advancements
The growing infrastructure underpinning this transformation is crucial. According to Gavin McKew, Director at Shero Commerce, the Shopify Catalog API provides a machine-readable feed of every product available on Shopify. This allows discovery applications like ChatGPT, Perplexity, or Copilot to pull real-time data on product titles, prices, stock levels, and enriched attributes—all without delays associated with scraping.
The Shift in Brand Strategy
This rapid evolution presents an optimization challenge for brands. Success will depend on treating Shopify metafields, product descriptions, and structured data not as mere technical tasks but as vital components for media performance. McKew advises merchants to consider each product field as a piece of advertising copy and to optimize descriptions and standard metafields accordingly.
Retail expert Scot Wingo stresses the need for a credible agentic-commerce experience. To satisfy consumer needs, customers must be enabled to view and purchase products within the chat environment itself. This demands a robust real-time feed system combined with either secure payment processing embedded within the AI platform or a streamlined integration that redirects the consumer to the retailer’s checkout. Accurate product data is essential for either pathway to function effectively.
The Accelerating Growth of Media
EMARKETER’s projection of growth—shooting from $1 billion to $25 billion over five years—mirrors historical trends in digital advertising. Retail media, for example, managed to reach similar scale within a five-year span, while traditional search and social advertising took longer to evolve. AI search advertising seems well-positioned to replicate retail media’s rapid adoption trajectory.
A survey conducted by Adobe among 5,000 U.S. consumers revealed that 39% have utilized generative AI for online shopping, with 53% intending to do so within the year. Shoppers are increasingly deploying AI for a range of tasks, such as:
- Researching products (55% of respondents)
- Receiving product recommendations (47%)
- Hunting for deals (43%)
- Discovering gift ideas (35%)
- Finding unique items (35%)
- Creating shopping lists (33%)
The rapid growth in retail media began when merchants enabled sponsored listings within their platforms, providing immediate returns on advertising spend. This sector, which started expanding nine years ago, has reached over $60 billion in ad revenue, while AI search is only beginning its journey.
However, before advertisers can fully commit larger budgets to AI search, more intricate systems must be in place, including clean, real-time product data feeds integrated with checkout capabilities. EMARKETER forecasts an inflection point between 2027 and 2028 when spending is expected to surge from $4.77 billion to $12.65 billion. This growth will likely correspond with the launch of premium placement options alongside these free catalog feeds.
Consequently, brands and agencies should adopt a trial-and-error approach with initial modest budgets, progressively ramping up investment as technology infrastructure matures. Brands that actively engage with AI platforms and refine their product data now will be in an advantageous position once advertising inventory becomes readily available.
Diverse Strategies Among Retailers
The rise of AI shopping agents is compelling retailers to make strategic decisions regarding their participation in conversational commerce. Two distinct approaches are already emerging, influencing how brands should strategize their AI search advertising efforts.
For instance, Walmart is pursuing a hybrid strategy that incorporates developing its own shopping agents and enabling consumers to shop its assortment using their preferred AI tools. In stark contrast, Amazon has adopted a more enclosed approach by developing proprietary AI systems like Rufus and Alexa+, while limiting access to external agents.
On the other hand, multi-vendor platforms like Shopify’s collaboration with Perplexity exemplify a third model that promotes a unified, cross-retailer shopping experience. Users can leverage AI search to discover products, compare various offers from multiple Shopify stores, and finalize purchases via Shop Pay in a single conversational flow.
Such diverging retailer strategies pose complex challenges for brands in terms of prioritizing platforms and allocating budgets effectively across differing AI shopping environments.
Complex Media Attribution Challenges
AI-enhanced shopping experiences introduce new complexities around measurement that exacerbate existing challenges in retail media attribution. When transactions begin in conversational interfaces and are completed via various checkout systems, the conventional categorization of budgets tends to falter.
For example, if a shopper employs Perplexity to research “wireless headphones under $200,” receives AI-based recommendations, and ultimately completes a purchase through Shop Pay, which budget category does that sale belong to: “search,” “retail media,” or “affiliate”? Existing attribution frameworks were not designed for scenarios where transactions commence in a conversational format and finalize in retailer wallets.
This complexity isn’t merely about budget classification. Marketing teams will require new frameworks to monitor and optimize spending across conversational interfaces, especially as these platforms begin to develop their own advertising inventory. The intricacy increases further when an individual AI agent may source product details from various retailers, compare prices across different platforms, and facilitate transactions through entirely separate systems.
Even with EMARKETER’s projection hitting $25 billion by 2029, AI search advertising is expected to remain smaller than retail media in terms of absolute dollars. However, incremental growth in advertising—which is likely to trend upward post-2027—may increasingly favor AI-powered search as conversational interfaces emerge as primary discovery channels.
Preparing for the AI Search Advertising Revolution
The convergence of product-data feeds with AI search advertising presents first-mover advantages for brands willing to consider these systems as interrelated challenges rather than isolated issues. To thrive in this evolving landscape, there must be an alignment across teams traditionally siloed in product management, media buying, and technology systems.
The necessary technical infrastructure is quickly maturing beyond current offerings, with standards like Anthropic’s Model Context Protocol (MCP) emerging as what one might call a “USB-C for AI agents.” This protocol standardizes connections that expose products, inventory, loyalty programs, and transactional capabilities to compliant AI models. This innovation permits retailers to expand their participation across AI marketplaces without sacrificing customer data integrity or control over pricing.
As highlighted by Jason Goldberg, Chief Commerce Strategy Officer at Publicis Commerce, adaptability will be crucial. In his recent white paper, he states that the companies that succeed in the new AI search landscape will likely not be the loudest but the most agile. His analysis of disruptions in AI commerce suggests that brands should neither neglect optimizing their products for LLMs nor avoid engaging in pilot projects with emerging AI commerce partnerships.
The rationale behind EMARKETER’s $25 billion forecast becomes evident when considering the developments in infrastructure. Product feeds that once only catered to comparison shopping engines or retailer websites are now enabling conversational AIs to autonomously research, recommend, and finalize transactions. Coupled with increasing consumer adoption, the stage is set for the explosive growth predicted by EMARKETER.
Brands that integrate their product data management with their media strategy will be poised to capitalize as AI search advertising continues to scale. In contrast, those that choose to keep their AI initiatives apart from core commerce operations may find themselves at a competitive disadvantage as conversational search gains traction.