Agenda-setting intelligence, analysis and advice for the global fashion community.
As artificial intelligence (AI) embeds itself across the entire customer journey, large language models (LLMs) and AI agents play an increasingly critical role in brand discovery and paths to purchase. Concurrently, the proliferation of these technologies is evolving how products are surfaced — and which brands are recommended to consumers.
According to a CI&T report, 61 percent of consumers are using or have used AI when shopping, with 53 percent doing so often. As these tools increasingly influence purchasing decisions, traditional channels are being disrupted — affecting how consumers discover and consider a product before check-out.
The impact extends beyond product discovery. As AI becomes more autonomous and increasingly browses on behalf of consumers, retailers must provide accurate and structured information across every touchpoint. Stretching from delivery estimates and returns policies to availability and pricing, operational excellence is now directly impacting discovery and conversion.
AI agents can access and analyse the available information on price, availability, delivery and returns to rank and recommend what they perceive as the most reliable brands. If information is opaque or unreliable, brands risk being filtered out.
In its latest report titled “Loyalty in the Agentic Era,” Zeos — the Zalando-owned logistics provider which counts On, Mango, Next and Pepe Jeans as partners — examines why brands need operational excellence to earn recommendations from AI agents.
Below, The Business of Fashion shares extracts from Zeos’ report alongside analysis anchored in BoF’s latest reporting.
Ensure Owned Platforms Are AI-Ready
Zeos:
According to Precedence Research, the global AI-enabled e-commerce market is expected to grow from US$7.25 billion in 2024 to US$64.03 billion by 2034. Meanwhile, PwC estimates that agentic commerce could account for 15 percent of online retail market share by 2030.
Consumers are already adapting to this new model. Today, 45 percent of shoppers use AI agents when buying products — particularly for interpreting reviews or identifying better deals, according to CI&T.
Shoppers are increasingly relying on AI to evaluate options and determine which products enter their consideration set. Zeos’ report highlights that 77 percent of consumers now treat generative AI tools such as ChatGPT as search engines, while nearly 40 percent use them to filter their initial product discovery.
BoF:
The transition from traditional search engines to AI agents marks a significant shift in online shopping. This means focusing on ensuring products are indexed and easily discoverable by AI, with rich and structured content becoming increasingly important to gain a competitive edge over traditional SEO strategies.
Today, retailers are adjusting their online presence to optimise for AI search engines through generative engine optimisation (GEO) and answer engine optimisation (AEO).
Between 2024 and 2025, shopping-related searches on generative AI platforms grew 4,700 percent, according to BoF and McKinsey’s The State of Fashion 2026 report. “Generative” inputs — general queries from users seeking recommendations — accounted for 37 percent of ChatGPT searches, according to AI marketing consultancy Profound.
With approximately 41 percent of consumers trusting generative AI search results more than paid ads, according to The State of Fashion 2026, brands have more incentive to ensure their AI-driven recommendations are accurate.
Brands like Zara and H&M have seen considerable referral traffic from AI platforms, accounting for 15 percent and 8 percent of their traffic respectively between early 2025 and early 2026.
Optimise Logistics Data For Better AI Recommendations
Zeos:
While AI shopping agents consider traditional search terms, they also delve deeper into a website’s interface, surfacing recommendations and weighing factors such as inventory availability, shipping costs, delivery speed and return policies alongside the specs of a product itself.
AI systems favour retailers whose operational data is structured and verifiable. If product or logistics data remains inaccessible, brands risk being excluded before consumers ever encounter them. API-forward (application programming interfaces) — structures that enable brands to connect systems and share real-time data across the customer journey — permit AI agents to validate delivery outcomes with greater confidence and increase the likelihood that products are recommended in the first place.
Consumer expectations continue to reinforce the need for operational excellence. According to Zeos, 54 percent of shoppers define fast delivery as same-day or next-day, while perceptions of speed decline sharply after 48 hours and by five days, four in five consumers consider delivery too slow.
Zeos draws on a case study from Pepe Jeans to illustrate the commercial implications of a tightly orchestrated logistics framework. By consolidating fragmented inventory into a unified fulfilment network, the retailer can now allocate stock dynamically across markets, allowing inventory in one region to satisfy demand in another — strengthening the brand’s competitiveness in an increasingly AI-forward retail environment.
BoF:
As brands compete for visibility in a saturated landscape, operational excellence is becoming central to winning customer attention.
With 79 percent of fashion executives expecting customer acquisition costs to rise, according to The State of Fashion 2026, retailers are placing greater emphasis on achieving retention via logistical excellence.
Inventory accuracy has become equally important. McKinsey estimates that poor inventory allocation across sizes can reduce profits by as much as 20 percent on average, while Lululemon cited insufficient inventory in smaller women’s sizes as a contributing factor to slower US growth in the first quarter of 2024.
New AI-powered tools are also optimising inventory management. Seventy-five percent of fashion executives told BoF and McKinsey in 2025 they planned to prioritise data-driven tooling. Kering reported a 20 percent improvement in the accuracy of its inventory forecasting using AI demand planning.
Redefine Loyalty to Strengthen Retention
Zeos:
Once a brand is discoverable and recommended by AI as a reliable solution to a customer’s inquiry, the next step is to maintain the momentum and retain these new clients.
According to 2026 research of over 50 million active Zalando customers from Zalando Insights, price (35 percent), assortment (27 percent) and logistics (22 percent) remain the primary drivers of purchase, but they are no longer enough to create lasting preference.
Increasingly, a brand’s ability to foster an emotional connection with its customer is an important lever to pull. Twenty-one percent of customers now choose a retailer simply because they like it, while thirteen percent browse primarily for inspiration or entertainment.
The report also dissects the limitations of traditional customer loyalty programmes. Eighty-eight percent of customers use loyalty programmes only to access discounts on purchases they already intended to make, while 86 percent say programme membership does not make them loyal to a brand.
With customer acquisition costs rising, brands relying on discounts alone risk eroding margins without strengthening long-term retention.
The commercial opportunity is significant. Customers with a genuine emotional connection to a brand spend, on average, 20 percent more than non-members, while more than half of consumers say they are more likely to purchase from a retailer with a loyalty programme. Zalando Plus’ members — the retailer’s free loyalty and reward programme — visit the platform twice as often, spend three times more and order twice as much as non-members.
BoF:
Personalisation, convenience and customer experience remain among the strongest drivers of customer loyalty. According to PwC, 73 percent of consumers consider customer experience an important factor in their purchasing decisions, reinforcing the idea that long-term loyalty depends on more than efficiency.
As previously analysed by BoF, effective loyalty programmes combine tangible and emotional incentives to keep shoppers engaged and brands need to find the right balance of rewards for their unique customer base. What’s more, popular loyalty programmes with unique perks have become a subject of online buzz for consumers, driving user-generated content about unique experiences designed for loyal clients.
Another BoF case study titled “How Brands Build Genuine Communities” unpacks how brands like Bandit Running are fostering emotional connections beyond transactional relationships.
The brand forges deep emotional ties with runners during major race weekends to create higher customer lifetime value. It hosts community pop-ups at world marathons that elevate the emotional experience with race-day essentials, social events and celebratory portraits. Additionally, Bandit supports runners year-round by offering structured training programmes for tentpole New York City races to foster progression and community growth under the label’s guidance. “It creates a really deep emotional tie,” co-founder and chief executive Nick West told BoF.
By building loyal followings through events and a sense of shared identity, brands can extend customer relationships beyond purchase and give consumers reasons to return that are difficult for competitors — or AI shopping assistants — to replicate.
For further insights, download Zeos’ “Loyalty in the Agentic Era” report here.
This is a sponsored feature paid for by Zeos as part of a BoF partnership.
