Milena Traikovich is a powerhouse in demand generation, renowned for her ability to transform raw analytics into high-performing campaigns that nurture premium leads. As global markets witness a seismic shift in brand value hierarchies, Milena’s insights into the intersection of performance optimization and customer-centric AI have never been more critical. Today, she joins us to dissect the strategies that propelled Zara to the top of the fashion world and what it means for the future of digital engagement in an era where data and human intuition must coexist.
Our discussion explores the evolving landscape of retail where AI has transitioned from a backend operational tool to a primary driver of brand relevance. We delve into the psychological gap between AI-driven product discovery and actual purchase conversion, the looming threat of “AI invisibility” for brands with poor data structures, and the strategic pivot toward first-party data in the wake of the “cookiepocalypse.” Milena shares her perspective on how brands can maintain a human touch while scaling automation and why structured data is now the foundation of modern brand awareness.
Zara recently reached a brand value of over $44 billion, surpassing traditional athletic leaders. How has their embrace of generative AI specifically contributed to this 18% growth over the past year?
Zara’s ascent to a value of $44.088 million is a masterclass in using technology to bridge the gap between digital convenience and physical reality. By launching virtual fitting rooms with generative AI, they allowed app users to create personalized avatars, effectively taking the guesswork out of the shopping experience by letting them preview clothes before a single dollar is spent. This tactile, visual connection reduces the friction that usually leads to abandoned carts, making that 18% growth a direct result of increased relevance rather than just more aggressive advertising. When a customer sees how a fabric drapes over an avatar that reflects their own body, it creates a sense of ownership and excitement that traditional static images simply cannot match. It’s about moving beyond mere operational efficiency and focusing on how the shopper feels during that critical moment of discovery, which is why Kantar now ranks them as the 66th largest brand globally.
While nearly 90% of shoppers find AI recommendations useful, only about half actually follow through with a purchase. What do you believe is causing this friction, and how can brands bridge the gap between discovery and conversion?
This gap—where 87% of shoppers find AI recommendations useful but only 55% complete the purchase—highlights a significant trust deficit that brands haven’t quite solved yet. While AI is fantastic at surfacing products and matching them to intent, it often fails to provide the emotional reassurance or “human” social proof that consumers crave before making a final commitment. To bridge this, brands must treat AI not just as a cold suggestion engine but as a reliable concierge that provides transparent, high-quality information that feels personalized but non-intrusive. We are seeing 20% of consumers starting their journeys on platforms like ChatGPT and Gemini, which means the discovery phase is already becoming automated. To turn those discoveries into sales, the transition from an AI recommendation to the checkout must feel seamless, grounded in trust, and backed by targeted offers that reward the customer for their engagement.
The concept of “AI invisibility” is becoming a genuine threat for modern retailers in the current ecosystem. Could you elaborate on why structured data is now as important as traditional brand awareness?
In our current landscape, having a beautiful brand and catchy ads isn’t enough if the machines that guide consumer choices can’t “read” your products effectively. If your inventory isn’t properly indexed with specific attributes like the “product_detail” tag, your brand effectively becomes invisible to the AI systems driving Search AI Modes. We are seeing a shift where the “attribute completeness score” in tools like the Merchant Center is becoming a critical KPI for merchandising teams who want to ensure their products appear in AI Overviews. It’s a sensory shift in marketing: we aren’t just designing for the human eye anymore, but for the digital “eyes” of crawlers that need structured data to understand a product’s context and match it to specific queries. Neglecting these technical details means losing out on high-intent traffic that is looking for exactly what you sell but simply can’t find you through the AI filter, regardless of how big your billboard is.
With the decline of third-party cookies, how should brands be leveraging their loyalty programs and internal data to stay competitive?
The “cookiepocalypse” has turned first-party data into the most valuable currency a retailer can hold, but the real challenge lies in organizing that vast, often disorganized collection of information. Retailers are sitting on a goldmine of purchase history and browsing behavior, yet many fail to use it effectively to inform their merchandising or marketing strategies. Loyalty programs are the perfect vehicle to solve this because they create a transparent exchange of value: the customer provides data, and the brand provides a better, more personalized experience. This data should then be used to power retail media and targeted ads that feel like a service rather than an interruption. When marketing, merchandising, and AI all feed off the same clean first-party data set, you can predict demand with incredible precision and shape your future assortments based on actual customer signals.
As automation scales at an unprecedented rate, there is a significant risk of losing the human touch. How can brands maintain a sense of restraint and relevance without becoming intrusive?
The key to successful AI integration is making sure it remains a silent, helpful partner in the background rather than an overbearing presence that makes the customer feel watched. Personalization at scale is a double-edged sword; if it becomes too automated or robotic, it loses the “human” spark that builds long-term loyalty and can even alienate shoppers who value their privacy. Brands need to focus on intent-based discovery—helping the shopper find what they need in that specific moment—rather than just hammering them with ads based on historical behavior. It requires a delicate balance of transparency and empathy, ensuring that the technology serves the user’s journey rather than the brand’s immediate sales targets. When a brand uses AI to simplify a complex choice or offer a genuinely helpful tip through a virtual assistant, it builds a foundation of trust that transcends the digital medium.
What is your forecast for the evolution of AI-driven brand building over the next two years?
I anticipate that by 2028, the distinction between “digital marketing” and “AI-driven customer experience” will completely disappear, as every touchpoint becomes inherently predictive and tailored to the individual. We will see a massive shift where brand value is determined not just by market share, but by “AI Share of Voice”—how often and how accurately a brand is recommended by autonomous assistants and generative search engines. Success will belong to the brands that master the technical side of structured data while simultaneously deepening their emotional connection with consumers through ethical and transparent first-party data use. Those who fail to adapt to this “new visibility” will find themselves sidelined, while innovators like Zara will continue to redefine what it means to be a global leader by making technology feel like a natural extension of the human shopping experience.
