This article explores how AI is setting a new stage for e-commerce in 2025: the technologies driving it, the business benefits, the ethical concerns, and the specific opportunities for markets like Poland.

The evolution of product recommendation systems

Traditional recommender systems were based on collaborative filtering: if a customer bought X, they were likely to buy Y. By 2025, these systems have been replaced by hybrid AI engines that combine:

  • Collaborative filtering (user-to-user and item-to-item similarities)
  • Content-based filtering (product descriptions, attributes, reviews)
  • Contextual data (time of day, device, location, demographics)
  • Deep learning models that understand product relationships and customer intent

McKinsey reports that personalized recommendations drive 35% of Amazon’s revenue. Salesforce notes that 24% of all orders today originate from AI-driven product suggestions. The numbers prove that recommendation engines are no longer auxiliary—they are mission-critical.

Hyper-personalization at scale

By 2025, personalization has reached a new level of precision: hyper-personalization. Instead of offering broad category suggestions, AI predicts what a customer is most likely to buy in real time.

Key characteristics:

  • Dynamic session-based recommendations – AI modifies suggestions instantly as the shopper clicks.
  • Psychographic modeling – systems now understand why a user might prefer certain items.
  • Micro-segmentation – instead of grouping thousands of users, AI creates nearly unique user profiles.

According to Inbenta, 78% of customers are more likely to repurchase if they receive personalized offers.

This has changed the way businesses strategize: marketing teams now rely on AI-driven segmentation rather than broad advertising campaigns.

Multimodal discovery - the new standard

Product discovery in 2025 is visual, vocal, and immersive. Customers no longer just type in a search bar; they interact with platforms through multiple modes of input.

AI recognizes products from images uploaded by shoppers. Retailers like IKEA and Wayfair allow customers to see how furniture fits in their space via AR. This reduces return rates and increases purchase confidence.

Voice commerce

Smart speakers and in-app assistants are integrated with shopping platforms. Customers say:

“Find me a pair of running shoes under $120 that matches my last order.”
The AI instantly provides tailored results. Juniper Research estimates that by the end of 2025, 50% of households will use voice assistants for shopping.

Omnichannel continuity

AI ensures seamless recommendations across web, app, voice, and even physical stores. If you browse a coat online, your in-store screen or mobile notification continues the experience—no disconnection.

Advanced architectures behind recommendations

2025 has brought groundbreaking technical innovations:

  • Two-tower models (e.g., Allegro.com) unify structured product data and textual descriptions, enabling lightning-fast retrieval and ranking across millions of items.
  • LLM-powered recommenders show significant improvements in recall and diversity, thanks to their ability to process natural language and contextual data.
  • Style4Rec transformers add style-awareness and cart behavior into recommendations, leading to a measurable boost in metrics like NDCG (from 0.594 to 0.674).

These architectures allow e-commerce platforms to scale globally while still offering localized, context-aware experiences.

Agentic commerce - AI shopping assistants

One of the most disruptive shifts of 2025 is the emergence of AI shopping agents. Instead of browsing endlessly, customers delegate tasks:

  • “Order weekly groceries with healthy alternatives.”
  • “Find me a laptop with at least 16GB RAM under €1,200.”

Case studies

  • Walmart is testing “super agents” that curate products, compare offers, and make purchases with minimal user input.
  • Amazon’s Rufus assistant is projected to generate $700M in profits in 2025 through increased customer spending.

This agentic commerce means businesses must now optimize not just for human buyers, but for AI agents acting on their behalf.

Ethical AI - transparency and trust

With personalization comes responsibility. Shoppers demand clarity on how their data is used.

  • Transparency – retailers must disclose what data is collected and how recommendations are made.
  • Bias control – AI systems risk amplifying stereotypes; businesses need governance frameworks.
  • Data privacy – compliance with GDPR and local regulations is critical, especially in the EU.

Failing to address these issues risks not just reputational harm but also regulatory penalties.

Poland’s unique role in AI-driven e-commerce

Poland is emerging as a hub for AI innovation in Europe. Companies like Synerise provide advanced personalization engines capable of processing billions of user events per day. Their tools allow Polish retailers to compete with global giants, offering personalization both online and offline.

This positions Poland not only as a fast-growing e-commerce market but also as a technology exporter in AI-driven retail.

Strategic roadmap for retailers

To stay competitive in 2025, businesses should:

  1. Invest in modular AI architectures (LLMs, two-tower systems).
  2. Adopt multimodal discovery – visual, voice, and AR.
  3. Experiment with AI shopping agents to prepare for agentic commerce.
  4. Implement ethical frameworks to ensure trust and compliance.
  5. Leverage regional innovations – tap into local AI firms like Synerise for tailored solutions.