How Reliable Logistics Data Optimises AI Discoverability
With agentic commerce growing, brands need infrastructure that machines can trust and experiences that customers remember.
The rise of AI and large language models (LLMs) is transforming the way brands are discovered and recommended to consumers, with a significant impact on the retail landscape. According to a CI&T report, 61% of consumers have used AI when shopping, with 53% doing so frequently. This shift in consumer behavior presents both opportunities and challenges for retailers, as AI agents evaluate products based on information such as delivery estimates, returns policies, availability, and pricing to rank and recommend brands.
In their latest report titled "Loyalty in the Agentic Era," Zeos — Zalando-owned logistics provider — emphasizes the importance of operational excellence for brands to gain recommendations from AI agents. With the global AI-enabled e-commerce market projected to grow from $7.25 billion in 2024 to $64.03 billion by 2034, retailers must adapt to ensure their products are AI-ready by providing accurate, structured information across all touchpoints.
This includes optimizing for generative AI platforms through techniques like generative engine optimization (GEO) and answer engine optimization (AEO), as consumer searches on these platforms have surged by 4,700% between 2024 and 2025.
Brief written by urgent.news from Business of Fashion's own syndicated text. Machine-written — may contain errors; check the original before relying on it.
