David Hayes, head of digital, Europe at Forsman & Bodenfors, notes a significant shift as brands realize that years of accumulated search equity are rapidly being transformed with AI’s evolution from search engines to answer engines. Consequently, AI search optimization has become a high-level discussion topic among many partners. McKinsey research supports this urgency, showing that half of consumers already use AI-powered search, with $750 billion in consumer spending predicted to flow through these platforms by 2028. Marketers are now rethinking SEO strategies, focusing on optimization not for humans but for algorithms. Savannah Bishop, group director of digital platforms at Fitzco, highlights that this shift demands new website structuring strategies, distinguishing traditional SEO from AI and generative search engine optimization (AEO and GEO). The landscape where shoppers find brands now extends beyond Google search results, with AI search prompting a “zero-click” environment where visibility measurement and strategy development are challenging. Agencies agree on tactics such as prioritizing clear Q&A formats via FAQs, structuring site content around questions and answers instead of keywords, adding alt text for visuals, and maintaining consistent brand messaging across all channels, from owned media to press releases and influencer campaigns. Reputation and credibility are paramount, as AI systems likely weigh these factors heavily when determining search results. Chris Rigas, VP of media at Markacy, asserts that perceived reputation and credibility increasingly outweigh having a direct answer. In practical terms, agencies like Go Fish Digital are reverse-engineering AI models through accessible APIs to understand how search platforms select content. Dan Hinckley, Go Fish’s chief product and AI officer, found that content positioned at the top of pages is more likely to be surfaced in AI search results. Forsman & Bodenfors has developed AI agents trained on brand tone, visibility standards, and SEO metrics to analyze client articles, speeding editorial processes while ensuring messaging consistency.

Software such as Syndigo facilitates maintaining uniform product data across retailers and digital platforms for better AI processing. Paid media strategies also adapt: Fitzco advises clients to leverage Google’s Performance Max campaigns, which can appear in AI-generated summaries even without direct user clicks. Hinckley emphasizes that AI language models seek to deliver information efficiently, similar to human users. Nevertheless, measuring and attributing success in AI search remains difficult due to the opaque nature of large language model (LLM) algorithms. Agencies track metrics like the frequency of client content appearing in zero-click results and monitor web traffic changes following AI strategy implementation. Rigas of Markacy points out that analytics gaps make it hard to pinpoint what truly drives performance improvements. Despite temptations, agency leaders caution against black-hat SEO tactics. Reddit, a major source for AI training data, recently sued Perplexity for unauthorized data scraping. Some brands reportedly create promotional posts specifically to attract LLM crawlers, while early tactics such as "cloaking"—embedding invisible text for algorithms—have emerged. Yet, experts warn these shortcuts could backfire as AI models may learn to detect and neutralize such attempts. Hayes stresses the importance of a measured, test-and-learn approach, affirming that there are no hacks for AI visibility and that attempts to game the system will likely fail similarly to early SEO manipulations.