04.02.2026

We’ve been talking for years about many trends that supposedly should have taken hold by now: shopping in virtual reality, ultra-granular personalization down to the individual customer, virtual product try-ons, or voice commerce.

7 trends in 7 sentences

Product search becomes dialog-based – AI understands intent instead of keywords.
AI agents will soon shop on behalf of humans.
Visibility is increasingly created outside the Google ecosystem.
Authentic, user-generated content becomes the counterweight to generative content.
Loyalty is based on value, not discounts.
Sustainability must be proven – product origin and attributes become transparent.
Flexible shop system architectures determine speed of execution.

All of this is technically possible, and some early adopters jump on board immediately. But how many retail companies actually have the prerequisites and resources to implement such innovations quickly? And which solutions are adopted rapidly by the broad mass of consumers?

So which technologies or behavioral changes are truly new, where are conditions changing radically, and which trends will really matter for e-commerce in 2026 and beyond?

Below is my personal overview of seven trends I consider strategically decisive in the coming years.

1. From keywords to intent: AI-powered product search

Artificial intelligence will fundamentally change online retail. People have talked about revolutions before, but now one is actually happening. One area particularly affected is product search.

Semantic, AI-powered search works differently from traditional keyword-based search. Vector-based search models don’t focus on individual terms but on the meaning and intent behind a query. As a consumer, I don’t have to enter the keyword “milk frother” to get relevant product suggestions – I can simply say, “I want to make coffee with fluffy milk.”

Consumers get used to this kind of intelligent search tool very quickly. That sets new expectations for future search behavior in AI-based systems – and for webshops, online marketplaces, and product information as well.

Large language models (LLMs) are AI models that can understand, process, and generate language by recognizing relationships of meaning in text.

AI-powered search uses artificial intelligence to interpret search queries based on meaning and intent and to deliver relevant results – not just matches for exact keywords.

AI-powered search relies on LLMs to interpret meaning, LLM-based interfaces make search dialog-driven, and AI-powered search front ends generate answers and summaries.

AI-based (product) search will very likely become the central discovery mechanism. A significant share of consumers say that AI recommendations influence their purchasing behavior, and trust in products suggested by generative AI is growing. Add to that new interfaces such as visual search using photos and videos, as well as conversational search that works through dialogue.

Anyone who doesn’t create a solid foundation today – in terms of technology and product data – for AI search and large language models isn’t just blocking conversion potential, but also future innovation.

Brief outlook: how LLMs are changing search and buying behavior

Consultation-heavy, everyday, or vaguely worded queries are increasingly shifting to LLM-based interfaces – such as chatbots, shopping assistants, or conversational search systems. In the medium term, this development is likely to noticeably change the balance of power in the search market and intensify competition. Alongside Google, which is expanding its search with features like AI Mode and AI Overviews, providers such as Perplexity, ChatGPT (OpenAI), Claude (Anthropic), or AI-powered assistants within e-commerce platforms are positioning themselves as alternative entry points for information and purchasing processes.

The key difference lies less in the provider and more in the underlying search and interaction mode. While classic search engines still rely on link lists and keywords, AI-powered search interfaces deliver contextual answers, recommendations, and summaries. Especially in the early stages of the customer journey, they provide orientation.

2. From searching to buying: when agentic AI takes over checkout

Virtual golden shopping cart in front of a dark, networked technology background with the red inscription “2026 +”, symbolic image for e-commerce trends

The second step of the AI revolution affects purchasing itself – the checkout. Agentic AI systems can take over the buying process rather than merely assisting with search. LLM-based shopping agents act as personal shopping assistants and actually complete the transaction – including ordering and payment. What sounds like a distant future vision is already being tested today.

For retailers, this creates a new sales channel that primarily works through machine-readable product data and integration with AI-powered shopping agents via APIs and structured interfaces. Product visibility will no longer depend solely on how well an item is presented in the shop, but also on how well AI agents can understand and recommend it.

The biggest hurdle on the path to agentic commerce is less about technology and more about trust. Many users hesitate to hand over payment processes entirely to AI systems. At the same time, many shops still treat automated purchasing processes as potential fraud. For e-commerce, this means: anyone who doesn’t engage today with machine-readable interfaces, clear data structures, and trustworthy transaction processes simply won’t exist in this new purchasing mode.

Learn more in our article series on Agentic AI and Agentic Commerce

3. User-generated content: authenticity as a strategic factor

In e-commerce – and especially in content marketing – generative AI is already widely used to automate processes and produce marketing content such as text, images, and videos. It’s hardly surprising that alongside enthusiasm for these capabilities, an opposing desire is growing: for real reviews and human experiences. Wherever GenAI produces content at scale, the value of what doesn’t feel synthetic increases.

User-generated content (UGC) thus becomes a central element of trust in digital commerce. Reviews, testimonials, unboxings, or short videos from real users complement classic product copy or glossy campaigns. People trust people.

Social media and social commerce amplify this effect. Platforms like TikTok, Instagram, Pinterest, or YouTube are no longer just sources of inspiration for many consumers – they are the starting point of product discovery. This shift is especially visible, and particularly among younger audiences, in video UGC. Short, authentic videos are gaining importance. They show products in use, answer unspoken questions, and reduce purchase uncertainty.

For retailers and brands, UGC is a long-term strategic lever in an intensely competitive environment. Actively involving customers, enabling co-creation, and making space for real voices builds trust and loyalty.

4. Rethinking loyalty: from discounts to real value propositions

Why do customers remain loyal to a brand over the long term? This question can’t be answered by content marketing alone – it goes to the core of loyalty management. When the next cheaper provider (think Temu and similar platforms) or a bigger discount is just one click away, the question becomes all the more pressing: what actually keeps consumers loyal to a company or a brand?

Traditional loyalty programs that focus exclusively on points, coupons, or discounts are increasingly reaching their limits. Loyalty is evolving – away from pure transactions and toward an exchange of value. Anyone who shares data, subscribes to newsletters, or allows push notifications today expects more than a few percentage points off. Relevance, personal benefit, and appreciation become the decisive factors.

Modern loyalty strategies therefore focus on personalized benefits, exclusive access, community elements, and experiences aligned with customers’ values and interests. Data is used not just to optimize offers, but to build emotional connections. Loyalty becomes a two-sided relationship: customers contribute attention, trust, and data – brands provide genuine value in return.

Brief outlook: data management as the foundation of AI-based personalization

Advances in AI make hyper-personalization in marketing possible. Modern systems go far beyond classic segmentation: content, product recommendations, or offers – even entire landing pages or in-app user experiences – can be adapted in real time to individual contexts.

AI-based hyper-personalization requires a robust data foundation. With the phase-out of third-party cookies and stricter data protection requirements, the focus clearly shifts to first-party and zero-party data – information that companies receive directly from their customers.

This puts trust and communication center stage. When retailers or brands are transparent about what data they collect, how it’s used, and what concrete benefits customers receive, people are more willing to share information.

For retailers, this means: anyone who wants to create relevant, personalized experiences in the future must build direct relationships and treat data protection as a strategic competitive advantage. Without a high-quality proprietary data base, AI remains largely ineffective.

First-party data is data a company collects directly through its own channels, such as purchase histories, website usage, customer accounts, or support interactions.

Zero-party data is information customers consciously and voluntarily share, for example through preference surveys or profile settings.

5. Subscription commerce: building long-term brand relationships through value

When loyalty is based on real value and the relationship between retailer and customer goes beyond a single transaction, subscription models gain relevance. The potential of subscription models extends far beyond streaming services. From replenishment subscriptions to meal kits to tool or sports equipment rentals, they offer customers convenience and predictability – and companies stable, recurring revenue.

Especially where products are needed regularly or where curation adds value, subscription commerce can reduce acquisition costs and significantly increase customer lifetime value.

The success of these models lies in the exchange of value. Customers benefit from convenience, personalized content, or exclusive perks, while brands can plan more reliably and build long-term relationships. Combined with loyalty programs, this effect can be amplified: loyalty is rewarded not just through individual purchases, but through ongoing engagement.

6. Transparency becomes mandatory – for example through digital product passports

Subscription models reflect the growing importance of reuse, renting, secondhand, and sustainable consumption. Sustainability is increasingly understood holistically and includes factors such as a product’s origin and production conditions. Material usage, recyclability, and durability come into focus, alongside green energy, short supply chains, and regional value creation.

These ESG criteria are no longer just an image factor – they are increasingly backed by regulation. In the EU, this expectation becomes binding from 2026 onward with the introduction of the Digital Product Passport (DPP). In certain categories, products will receive a digital proof of origin containing information on materials, production conditions, carbon footprint, and repair and recycling options. Greenwashing will be actively addressed – sustainability will have to be proven.

For retailers, product transparency gradually becomes a data and systems issue. At the same time, it creates an opportunity to tell credible, trustworthy stories.

7. Flexible e-commerce and webshop architectures as a success factor

Many of the developments described above – from AI-based personalization to loyalty models to digital product passports – share one thing: they are difficult and slow to implement with rigid, all-in-one systems. This is where composable commerce comes into play.

Instead of relying on monolithic platforms, companies increasingly build their e-commerce architectures from modular components connected via APIs. The underlying MACH logic (microservices, API-first, cloud-native, headless) makes it possible to replace, extend, or recombine individual functions as needed.

Headless architectures, in which the frontend and backend are decoupled, are becoming particularly important. They create freedom for customized customer experiences, faster experimentation, and consistent omnichannel setups. New touchpoints or AI services can be integrated much more quickly.

For retailers, this means: technological flexibility becomes a strategic prerequisite in an e-commerce world where both technologies and requirements are changing rapidly.

Headless? Huh?! Modern shop architectures explained simply

Composable commerce means that an online shop no longer consists of one large, rigid system where everything is tightly coupled. Instead, it’s built from many individual building blocks, each responsible for a specific task – for example, product search or payment. These components can be connected, swapped out, or adapted as needed, without rebuilding the entire shop.

In so-called headless architectures, the visible appearance of an online shop is separated from the underlying technical logic. Design, content, and user experience can be created and changed independently of the actual shop functions. This allows companies to test and add new ideas and features more quickly.

As a result, businesses can respond faster to new requirements and evolve their online retail step by step.

Conclusion: What these e-commerce trends have in common

All of these developments follow a shared logic: e-commerce is becoming more data-driven, more dialog-based, and more transparent. Purchase decisions are increasingly shifting into upstream, AI-supported interactions, while trust, relevance, and traceability gain importance. At the same time, demands on systems, data quality, and organizational flexibility are rising.

For online retailers, this means above all one thing: future success won’t belong to those who chase every new technology, but to those who create the structural foundations – clean data, adaptable architectures, and real value for customers. These trends are not short-term effects, but expressions of a fundamental shift that will shape e-commerce well beyond 2026.

Sources: Yotpo blog article, BigCommerce article, Akeneo blog article, DJUST blog article, norisk GmbH blog article, EuroShop.mag article on agentic AI & Agentic Commerce

Portrait of Julia Pott, member of the EuroShop editorial team, with shoulder-length brown hair, hoodie and open smile; copyright: beta-web GmbH

The portrait was AI-generated.

Author: Julia Pott | EuroShop.mag

Julia Pott has been working as an editor in the retail sector for eight years – initially at the retail magazine iXtenso, at times also as editor-in-chief. Today, she is responsible for the concept, editorial plan, content and design of the online magazine EuroShop.mag on behalf of the EuroShop and EuroCIS trade fairs. Her contributions combine editorial depth with a feel for trends and topics that drive the retail sector.

E-commerce trends 2026 and beyond: what online retailers really need to prepare for