A new phase of the European Union’s Artificial Intelligence Act has begun, bringing important transparency obligations into effect for companies using chatbots, generative AI and synthetic content.

From 2 August 2026, Article 50 of the EU AI Act applies across the European Union. The rules are designed to ensure that consumers understand when they are interacting with an AI system and when apparently authentic content has been artificially generated or manipulated.

For e-commerce companies, the changes may affect customer-service bots, virtual shopping assistants, AI-generated advertising, product imagery, synthetic influencers and automated editorial content.

However, the regulation does not require retailers to label every product description or marketing image created with AI. The obligations depend on the type of system, the nature of the content and whether the company is acting as an AI provider or simply using a third-party tool.

E-commerce chatbots must identify themselves as AI

One of the clearest requirements concerns AI systems that interact directly with consumers.

Providers of these systems must ensure that users are informed that they are communicating with AI, unless this is already obvious to a reasonably informed person.

For online retailers, this can include:

• Customer-service chatbots
• Virtual shopping assistants
• Automated returns agents
• Conversational recommendation tools
• AI-powered order-tracking systems
• Voice-based shopping assistants

The disclosure should appear no later than the first interaction. A message such as “Hello, I’m an AI-powered shopping assistant” or a persistent “AI Assistant” label within the interface may satisfy the requirement.

Retailers should not assume that a robot icon, unusual bot name or automated writing style makes the artificial nature of the system sufficiently clear.

Although the legal design obligation falls primarily on the system provider, e-commerce companies should verify that the disclosure is displayed properly across their websites, applications and customer-service channels.

Vendor contracts should also clarify who is responsible for implementing, testing and maintaining the notice.

Not all AI-generated content needs a visible label

Article 50 also covers AI-generated and manipulated audio, images, video and text.

Providers of generative AI systems must ensure that covered outputs can be identified in a machine-readable format. This may involve metadata, watermarking, content credentials or another technical method.

This technical marking requirement does not mean that every retailer must place a visible “AI-generated” label on every piece of AI-assisted content.

The visible disclosure duties placed on businesses using the content are narrower. They primarily concern:

• Deepfakes
• Certain AI-generated texts dealing with matters of public interest
• Emotion-recognition and biometric-categorization systems

A standard product description, category page or promotional email does not automatically need a visible AI label simply because generative AI helped produce it.

What this means for product descriptions

Retailers increasingly use AI to create:

• Product titles and descriptions
• Marketplace listings
• Category pages
• Buying guides
• Marketing emails
• Translations
• Frequently asked questions

Most ordinary commercial copy will not require a visible AI-generated label.

The specific disclosure obligation for AI-generated text applies when the material is published to inform the public about a matter of public interest.

This may become relevant when an e-commerce company publishes AI-generated content involving:

• Product safety
• Public health
• Environmental claims
• Sustainability
• Financial services
• Consumer rights
• Legal or regulatory developments

For example, a simple description of a product’s dimensions is unlikely to trigger the rule. An AI-written article claiming that the product provides medical benefits or complies with environmental regulations may require closer scrutiny.

Human editorial review remains important

Even when AI-generated text addresses a matter of public interest, a visible label may not be required if the content has undergone meaningful human review and a person or company assumes editorial responsibility.

The review must be more than a basic spelling or grammar check.

A qualified editor should assess the substance of the content, verify its sources, correct inaccurate claims, and have the authority to approve or reject the final version.

Retailers should document:

• Who reviewed the material
• Which claims were checked
• What sources were used
• Who approved publication
• Who assumes editorial responsibility

This is particularly relevant for company blogs, sustainability reports, regulatory explainers and product-safety content.

When AI-generated images may need disclosure

AI-generated advertising and product imagery do not automatically require a visible label.

The main visible-disclosure obligation applies when the content qualifies as a deepfake.

Under the AI Act, a deepfake is an AI-generated or manipulated image, audio or video content that resembles a real person, object, place, organization or event and could falsely appear authentic.

In e-commerce, this could include:

• A synthetic celebrity endorsement
• An artificial customer testimonial
• A fake video of a real person using a product
• A fabricated representation of a warehouse or factory
• A manipulated demonstration that exaggerates product performance
• A realistic virtual influencer presented as a genuine person

When content qualifies as a deepfake, its artificial nature must be disclosed clearly when the viewer first encounters it.

Machine-readable metadata alone is not enough. Consumers must be able to understand that the material is artificial without using specialized software.

Virtual models and synthetic influencers

Not every virtual model will necessarily qualify as a deepfake.

A clearly fictional animated character or obvious digital mascot is less likely to mislead consumers than a photorealistic virtual influencer presented as a real customer.

Retailers should consider:

• How realistic the content appears
• Whether it resembles a real person
• Whether viewers are likely to believe it is authentic
• Whether it imitates a testimonial or endorsement
• Whether the artificial nature is clear from the context

Even when the AI Act does not require a label, EU advertising and consumer-protection rules may still apply.

Companies should not create the false impression that a genuine customer used, reviewed or recommended a product when the person is entirely synthetic.

AI editing of product photography

Routine image editing does not automatically trigger the AI Act’s transparency requirements.

Basic adjustments such as resizing, color correction, sharpening and noise removal are less likely to be treated as synthetic-content generation.

The risk increases when AI is used to make substantial changes, including:

• Altering the size or proportions of a product
• Simulating features the product does not have
• Changing materials or colors inaccurately
• Adding accessories that are not included
• Manipulating how clothing fits a model
• Creating fabricated before-and-after results

These practices may violate consumer-protection rules even when they fall outside the strict deepfake definition.

AI-generated reviews remain a major risk

The new transparency rules should not be interpreted as permission to create fake customer reviews.

Publishing fabricated reviews, endorsements or testimonials can already breach EU consumer-protection law.

An AI-generated testimonial may also qualify as a deepfake when it depicts a realistic person describing an experience that never occurred.

Retailers should clearly distinguish between:

• A genuine customer review
• A paid endorsement
• A fictional demonstration
• A virtual spokesperson
• A simulated use case

The safest policy is to prohibit synthetic customer reviews altogether.

Marketplaces face additional challenges

Online marketplaces may need to manage AI-generated content produced by thousands of third-party sellers.

They should review whether their systems:

• Preserve machine-readable AI markings
• Allow sellers to disclose deepfakes
• Prohibit synthetic reviews
• Prevent misleading AI-generated product demonstrations
• Offer reporting tools for deceptive content
• Retain disclosures when listings are reformatted or syndicated

Marketplaces that provide their own AI listing generators should also assess whether they are acting only as deployers or may assume additional responsibilities as providers.

A practical compliance checklist

E-commerce companies should now take several immediate steps.

Audit customer-facing AI

Identify every chatbot, shopping assistant, voice agent and automated support tool that interacts with customers.

Confirm that each system clearly identifies itself as AI.

Define legal responsibilities

Establish whether the company is acting as a provider, deployer, distributor or importer for each AI system.

Do not rely only on the terminology used by the vendor.

Review synthetic media

Identify where AI is used to generate or substantially alter images, audio or video.

Assess whether any material could qualify as a deepfake.

Protect technical markings

Check whether website optimization, image compression, marketplace uploads or content-management systems remove metadata or watermarks added by the AI provider.

Introduce editorial controls

Create a documented human-review process for AI-generated content involving safety, health, sustainability, regulation or other public-interest topics.

Update supplier contracts

Require AI vendors to explain:

• How chatbot disclosures are implemented
• How synthetic content is marked
• Whether the marking survives export and compression
• What compliance documentation is available
• How future regulatory changes will be communicated

Train relevant teams

Marketing, legal, customer service, e-commerce, content and technology teams should all understand the transparency requirements.

Compliance is not solely an IT responsibility.

Penalties and commercial risk

Breaches of the relevant EU AI Act obligations can result in fines of up to €15 million or 3% of the company’s worldwide annual turnover, whichever is higher.

Authorities must consider proportionality, including the size of the business and the seriousness of the infringement.

However, financial penalties are not the only concern.

Misleading AI-generated content may also lead to:

• Consumer complaints
• Advertising investigations
• Platform sanctions
• Product-information disputes
• Reputational damage
• Loss of customer trust

Transparency as part of the customer experience

The EU AI Act does not prevent retailers from using chatbots or generative AI.

Its central principle is that consumers should not be misled about whether they are interacting with a person or viewing content that appears authentic but is artificially generated.

A clearly labeled chatbot can still provide fast and effective service. AI-assisted content can still improve productivity when its claims are properly reviewed. Virtual models and synthetic media can still be used when their presentation is transparent and not deceptive.

For e-commerce businesses, the priority should be to identify where AI interacts directly with consumers, where synthetic content could be mistaken for reality, and where human editorial responsibility is required.

As AI becomes more deeply integrated into online retail, transparency will become both a legal obligation and a competitive trust signal.

The post EU AI Act enforcement begins: What e-commerce businesses must disclose about chatbots and AI-generated content appeared first on Cross-Border Magazine.

ESW has launched a new agentic commerce solution designed to help international brands make their products discoverable, purchasable and operationally supported inside artificial intelligence platforms.

The cross-border e-commerce provider announced that Microsoft Copilot will be the first integration for ESW Agentic Commerce, allowing participating brands to connect AI-powered product discovery directly with secure checkout and payment capabilities.

The launch represents another important step in the development of agentic commerce: a model in which AI assistants do more than recommend products. They can increasingly compare options, guide purchasing decisions and complete transactions on behalf of shoppers.

For international retailers, however, connecting an AI recommendation to a completed order is considerably more complicated than adding a buy button to a chatbot. Payments, localisation, fraud prevention, import duties, compliance, fulfilment and returns must still operate correctly behind the interface.

ESW is positioning its new Agentic Commerce infrastructure as the layer that manages that complexity.

What is ESW Agentic Commerce?

ESW Agentic Commerce is a new infrastructure solution intended to connect retailers’ existing e-commerce operations with AI-powered shopping environments.

The system allows brands to integrate and optimise their product catalogues for discovery by AI platforms. Once a shopper finds a suitable product, the transaction can be completed securely within the AI experience rather than requiring the customer to move to a separate retailer website.

According to ESW, the solution works alongside a retailer’s existing e-commerce infrastructure. Brands therefore do not need to replace their current commerce platform to participate in AI-assisted shopping channels.

The technology is supported by the ESW Agentic Hub, which acts as an infrastructure layer connecting AI agents with the company’s international commerce capabilities.

Through this layer, AI platforms can potentially access the product, checkout and operational services required to move a shopper from initial discovery to a completed cross-border order.

ESW says Microsoft Copilot will be the first integration, with support for additional agents and platforms expected in the coming months. The service is immediately available to brands and customers in the United States, while broader availability is planned.

Microsoft Copilot becomes the first integration

Microsoft has been developing Copilot into a transactional shopping environment rather than limiting it to search, recommendations and product comparisons.

Copilot Checkout allows shoppers to discover, evaluate and purchase products without leaving the conversational interface. Microsoft says the merchant remains the merchant of record and retains ownership of the transaction, customer data and commercial relationship.

The ESW integration extends this concept towards international commerce.

A customer could theoretically ask Copilot for a product recommendation, compare several options, select an item and complete the purchase inside the same conversation. ESW would then provide the underlying international commerce infrastructure required to process and support the transaction.

That infrastructure can include:

• Localised checkout experiences
• International payments
• Fraud management
• Duties and tax calculations
• Regulatory compliance
• Cross-border shipping
• Fulfilment
• Customer service
• International returns

The customer may experience the journey as a simple conversation, but the transaction still depends on a complex network of commercial and logistical systems.

This operational layer is particularly important when the shopper, merchant, payment provider and delivery destination are located in different countries.

From conversational search to conversational checkout

Generative AI has already changed how some consumers research products. Shoppers can ask an AI assistant to compare specifications, interpret reviews, recommend suitable products or narrow a large catalogue according to highly specific requirements.

Until recently, however, most AI-assisted shopping journeys ended with a link.

The AI could recommend a product, but the shopper would usually have to leave the conversation, visit the merchant’s website, find the correct product again and proceed through a conventional checkout.

Each additional step created an opportunity for the customer to abandon the purchase.

Agentic commerce attempts to remove that separation between discovery and transaction. Instead of acting primarily as a search interface, the AI assistant becomes an active participant throughout the purchasing journey.

Microsoft reported that shopping journeys involving Copilot produced 53% more purchases within 30 minutes of an interaction than journeys without Copilot. When clear shopping intent was present, journeys involving Copilot were reportedly 194% more likely to result in a purchase.

These figures were published by Microsoft and should be interpreted as early platform data rather than independent measurements of the wider e-commerce market. Nevertheless, they suggest that reducing the distance between product recommendation and checkout could have a measurable effect on conversion.

Why agentic commerce is especially relevant to cross-border retail

Domestic transactions are already complicated. International orders add multiple layers of friction and risk.

A product recommended by an AI assistant may be available in one country but restricted in another. The displayed price may need to include local taxes, import duties or customs charges. Payment preferences, consumer regulations, delivery options and return procedures may also differ by market.

An AI assistant cannot reliably complete an international order simply by identifying the right product. It must also connect with systems capable of answering operational questions in real time.

These may include:

• Is the product available in the shopper’s country?
• What is the final landed cost?
• Which currency and payment methods should be offered?
• Are import restrictions applicable?
• How long will delivery take?
• Who is responsible for collecting taxes and duties?
• Can the item be returned locally?
• How will fraud and payment risk be managed?

ESW already provides international commerce services covering payments, compliance, localisation, shipping and returns across more than 200 markets. The company says it works with more than 150 global brands and processes more than eight million cross-border orders annually.

Agentic Commerce applies those capabilities to a new customer interface.

Rather than building a separate international transaction system for every AI platform, ESW’s proposed model gives brands a central infrastructure layer that can be extended across multiple agents.

Product data becomes a commercial priority

The emergence of AI-powered shopping will also increase the importance of product data.

Traditional e-commerce search is often built around keywords, categories, filters and paid placements. AI discovery is more conversational and contextual.

A shopper may not search for a particular product name. Instead, the shopper might ask:

“Find me a waterproof jacket suitable for a week in Iceland that fits in hand luggage and costs less than €200.”

To respond accurately, the AI must understand far more than a product title and category. It may need structured information about materials, sizing, climate suitability, weight, delivery availability, price, inventory and return conditions.

Brands whose product information is incomplete, inconsistent or inaccessible to AI systems may become less visible even when their products would otherwise meet the customer’s requirements.

This creates a new optimisation discipline for merchants. In addition to search engine optimisation, marketplace optimisation and feed management, brands will increasingly need to consider how products are interpreted and selected by AI agents.

Important areas will include:

• Accurate structured product attributes
• Consistent descriptions across channels
• Real-time pricing and inventory
• Clear shipping eligibility
• Detailed sizing and compatibility information
• Transparent return conditions
• Verified brand and product information
• Machine-readable commercial policies

In agentic commerce, product data is not merely descriptive content. It becomes part of the decision-making infrastructure.

ESW says retailers can retain their existing platforms

One of the most significant aspects of the ESW proposition is that retailers are not expected to replace their existing e-commerce platforms.

ESW Agentic Commerce is designed to operate alongside the merchant’s current technology stack. The retailer can therefore extend its catalogue and transaction capabilities into AI environments while continuing to use its existing platform for core commerce operations.

This reduces one of the most obvious barriers to agentic commerce adoption.

Large international retailers often operate highly customised systems involving commerce platforms, enterprise resource planning software, product information management, order management, warehouse systems and multiple payment providers.

Replacing that infrastructure solely to participate in a new AI sales channel would be costly and risky.

An integration layer allows retailers to test agentic commerce without treating it as a complete platform migration.

Nevertheless, brands will still need to determine how orders generated through AI interfaces are attributed, managed and measured. Questions around customer identity, consent, marketing permissions, returns and post-purchase communication will become increasingly important as the channel develops.

The retailer must remain visible

Agentic commerce could reduce purchasing friction, but it may also weaken the direct relationship between shoppers and retailers.

When a customer shops through an AI assistant, the assistant may control much of the experience:

• The initial recommendation
• The comparison criteria
• The products displayed
• The explanation of advantages and disadvantages
• The checkout interface
• The post-purchase conversation

This creates a strategic risk for brands. The AI platform could become the primary interface, while the retailer becomes an invisible fulfilment provider.

Microsoft has attempted to address this concern by emphasising that merchants participating in Copilot Checkout remain the merchant of record and retain control of their transaction data and customer relationship.

However, formal ownership of the transaction does not necessarily guarantee control over the customer experience.

Retailers will need to ensure that AI commerce integrations preserve brand identity, communicate accurate product information and create opportunities for continued customer engagement after the purchase.

Otherwise, brands may become increasingly dependent on AI platforms in much the same way that many merchants became dependent on search engines, marketplaces and social networks.

Agentic commerce will create new compliance questions

The ability of an AI system to complete a purchase introduces legal and regulatory questions that conventional product recommendation tools do not face.

Retailers must be able to demonstrate that customers were shown accurate prices, material product information, delivery conditions and return rights before the transaction was completed.

For cross-border orders, the process may also involve:

• Consumer protection requirements
• Data protection rules
• Product safety obligations
• Customs declarations
• Sanctions screening
• Import restrictions
• Tax collection
• Payment authentication
• Accessibility requirements
• Records of customer consent

There is also the question of responsibility when an AI assistant recommends an unsuitable product, presents incorrect information or completes a purchase based on an ambiguous instruction.

The commercial agreement between the retailer, commerce provider and AI platform will need to define how responsibility is distributed.

These issues will become more significant as AI agents gain greater autonomy. Recommending a product is different from selecting quantities, accepting terms, choosing delivery services or authorising payment.

Agentic commerce infrastructure must therefore provide both convenience and traceability.

AI platforms are becoming a new distribution channel

ESW’s launch should be understood as part of a broader shift in e-commerce distribution.

Retailers have previously adapted their businesses for successive generations of digital channels:

• Desktop websites
• Mobile commerce
• Online marketplaces
• Social commerce
• Retail media
• Voice commerce
• Conversational commerce

AI assistants may now become another major entry point.

ESW cites McKinsey research estimating that agentic commerce could represent a global opportunity of between $3 trillion and $5 trillion by 2030. The company also references a Gartner prediction that traditional search engine volume could decline by 25% as consumers increasingly use AI chatbots and virtual agents.

These projections should not be treated as guarantees that AI agents will replace conventional e-commerce journeys. Consumer trust, technical reliability, product coverage and regulatory oversight will all influence adoption.

However, the direction of investment is increasingly clear.

Microsoft, Google, OpenAI, payment companies, commerce platforms and major retailers are all developing systems that allow AI assistants to participate more directly in shopping and transactions.

The strategic question for retailers is therefore shifting from whether AI will influence product discovery to how much of the commercial journey AI platforms will ultimately control.

What retailers should do now

Most brands do not need to rebuild their entire commerce strategy around autonomous AI agents immediately. However, waiting until the channel is mature could leave retailers with significant product-data and infrastructure work to complete.

Retailers preparing for agentic commerce should begin with several practical steps.

Audit product information

Product catalogues should contain detailed, consistent and structured information that AI systems can interpret accurately.

Missing attributes, inconsistent naming and vague descriptions could reduce both visibility and recommendation quality.

Improve real-time data access

AI shopping systems require current information about prices, promotions, inventory, delivery options and geographical availability.

Static or frequently outdated feeds will not be sufficient for transactional experiences.

Review international checkout capabilities

Retailers should assess whether their existing systems can calculate local prices, duties, taxes and delivery conditions in real time.

Agentic discovery will not generate sustainable revenue if customers encounter inaccurate landed costs or failed international orders.

Establish clear governance

Brands should define which actions an AI platform can perform, what information it can access and when customer confirmation is required.

Higher-risk actions should include appropriate human approval or explicit shopper consent.

Protect the customer relationship

Retailers should understand what customer information they receive, how post-purchase communication works and whether they can continue serving the customer outside the AI platform.

Measure the channel separately

Agent-driven traffic and transactions should be distinguishable from conventional search, marketplace and direct website sales.

Without clear attribution, retailers will struggle to evaluate conversion, acquisition costs, repeat purchases and platform dependency.

A significant step, but not the finished model

ESW Agentic Commerce provides a clearer view of how AI-assisted shopping could become operational at an international scale.

The announcement moves the conversation beyond experimental chatbots and product recommendations. It focuses on the less visible infrastructure required to turn an AI interaction into a compliant, fulfilled and serviceable transaction.

Microsoft Copilot gives ESW an established AI interface through which to launch the solution. ESW contributes the international checkout, payment and operational capabilities required behind that interface.

The initial availability is currently limited to the United States, and broader adoption will depend on how quickly additional markets, retailers and AI platforms are integrated. Questions also remain regarding customer ownership, compliance, attribution and the influence AI assistants will have over product selection.

Even so, the direction is important.

E-commerce is entering a stage in which the storefront may no longer be a website, app or marketplace page. It may be a conversation.

For international brands, success in that environment will depend not only on whether an AI assistant can find their products, but whether the entire cross-border transaction can operate correctly after the recommendation is made.

The post ESW Launches Agentic Commerce with Microsoft Copilot, Connecting AI Discovery to Checkout appeared first on Cross-Border Magazine.

London-based live commerce platform Tilt has raised $26 million in fresh funding, marking one of the most notable recent funding rounds in Europe’s e-commerce startup ecosystem. The investment comes as live shopping, social commerce and AI-powered seller tools move from experimental formats to serious strategic priorities for online retailers, marketplaces and recommerce platforms.

The round includes Vinted Ventures, the investment arm of second-hand marketplace Vinted, as a new investor, alongside existing backers TQ Ventures, Balderton Capital, Earlybird, Seedcamp and others. According to Tilt, the new capital brings the company’s total funding raised to over $50 million.

Founded in 2021 by former Revolut employees Abhi Thanendran and Neil Shah, Tilt is building a live auction app where sellers showcase products in real time and buyers can bid, interact and purchase through livestreams. The company positions itself as a European answer to the rise of entertainment-driven commerce, combining video, community, auctions and AI-powered discovery into one shopping experience.

Why Tilt’s funding matters for European e-commerce

For years, European e-commerce has been defined by search, static listings, price comparison and increasingly efficient checkout flows. Tilt’s model points in a different direction: one where online shopping becomes more interactive, social and content-led.

That shift is already visible across the wider market. TikTok Shop has been expanding across Europe, joining existing markets including France, Germany, Ireland, Italy, Spain and the UK. TikTok has also reported that more than 100,000 European businesses had already joined TikTok Shop across France, Germany, Italy, Spain and Ireland, with strong growth in daily gross merchandise value across those markets.

Tilt operates in the same broader movement, but with a more focused live auction and community-commerce model. Instead of simply adding shoppable videos to a social network, Tilt is building a dedicated marketplace around real-time selling. This makes the company especially relevant for fashion, sneakers, collectibles, second-hand items and categories where scarcity, trust, personal presentation and community can influence conversion.

From static listings to live interaction

Traditional e-commerce is built around product pages. Tilt is built around live interaction.

Sellers go live, show items on camera, answer questions and create a sense of urgency through auctions. Buyers do not only browse; they watch, interact, bid and return to sellers or communities they trust. This is why live commerce can create a very different type of retention from conventional marketplace shopping.

Tilt says it has grown 8x since its 2024 Series A. The company also reports that buyers spend over an hour a day on the app, that 70% return week-on-week, and that 70% of monthly GMV comes from repeat buyers. The platform is currently live across the UK, Italy, Spain and Poland.

These figures matter because they suggest Tilt is not only acquiring occasional buyers. It is trying to build a repeat shopping habit around live discovery, seller personality and community engagement.

AI is becoming central to Tilt’s live commerce model

Tilt’s funding round is not just a live shopping story. It is also an AI e-commerce story.

The company has developed a set of AI tools designed to reduce friction for sellers and improve product discovery for buyers. These include Snap, a tool that can turn an item shown on camera into a listing within seconds; a real-time AI copilot that supports sellers with pricing and responses during livestreams; natural-language search across live sessions; and AI-generated clips that can repurpose livestream content for social media distribution.

This is strategically important. One of the challenges of live commerce is operational intensity. Sellers need to source products, present them, answer questions, price items, manage auctions, create content and maintain community engagement. AI can help reduce that workload and make live selling more scalable for smaller merchants.

For buyers, AI also helps solve a discovery problem. In a live shopping environment, inventory is fluid and time-sensitive. Traditional search is less effective when the best product may be inside a livestream happening right now. Tilt’s AI-powered matching and search tools are designed to connect users with relevant live sessions in real time.

Vinted Ventures’ participation signals a recommerce opportunity

The participation of Vinted Ventures is one of the most significant elements of the round. Vinted is one of Europe’s best-known second-hand marketplaces, and its venture arm’s investment in Tilt suggests that live commerce is increasingly relevant to the future of recommerce.

Second-hand and resale categories are particularly well suited to live selling. Items are often unique, condition varies, trust matters, and buyers may want to see products from different angles before purchasing. A livestream can make resale feel more transparent and more engaging than a static listing.

Tilt itself framed Vinted’s backing as a strategic signal. In the company’s announcement, CEO and co-founder Abhi Thanendran said that the next generation will not browse static listings in the same way, but will discover and buy through video, conversation and live interaction. Vinted’s Martijn van Heeswijk also highlighted Tilt’s differentiated experience in live selling, with potential in fashion and beyond.

For European recommerce, this could be an important development. If resale marketplaces increasingly move toward video-led discovery, the competitive landscape could shift from who has the largest catalogue to who can create the strongest buyer-seller interaction.

Europe is becoming a live commerce battleground

Live commerce has already become a major retail format in Asia. Tilt says the category is a $370 billion-plus industry in Asia, while Western markets are still at an earlier stage of adoption.

In Europe, the format is now becoming more competitive. TikTok Shop is expanding its European footprint. Whatnot has established itself as a major live auction player globally. At the same time, local platforms such as Tilt are trying to build a European model adapted to regional consumer behaviour, seller communities and recommerce habits.

The wider e-commerce context supports this shift. Eurostat data shows that in 2024, EU enterprises generated 19.49% of total turnover from e-sales, with websites and apps accounting for 8.39% and EDI-type sales accounting for 11.07%. Web sales remain the dominant e-sales method among enterprises, but the next layer of competition is increasingly about discovery, attention and conversion rather than simply having an online sales channel.

This is where live commerce becomes strategically relevant. As more businesses sell online, standing out through static listings becomes harder. Content-led and community-led formats offer a new way to reach buyers, especially younger audiences already accustomed to discovering products through short video and creator recommendations.

What Tilt will use the funding for

Tilt says the new funding will be used to accelerate its AI roadmap, expand its seller base across new markets and continue scaling the team. The company currently has a team of around 60 people.

The expansion strategy appears to focus on three areas.

First, Tilt needs to continue improving the seller experience. Live commerce only works at scale if sellers can list products quickly, manage livestreams efficiently and reach relevant audiences without excessive manual work.

Second, the company needs to expand geographically. Tilt is already active in the UK, Italy, Spain and Poland, but Europe remains fragmented by language, payment habits, delivery expectations and local marketplace preferences.

Third, Tilt needs to defend its position as larger players move into the same space. TikTok Shop has enormous distribution. Whatnot has significant funding and category experience. Traditional marketplaces could also add more livestreaming and auction features over time.

Why retailers and marketplaces should pay attention

Tilt’s raise is relevant beyond the startup itself. It reflects a broader direction of travel in e-commerce.

Retailers, brands and marketplaces are under pressure to reduce customer acquisition costs, increase retention and create more engaging shopping experiences. Live commerce offers a possible answer by combining product discovery, entertainment, community and conversion in the same environment.

For fashion and second-hand sellers, the opportunity is particularly clear. Products can be demonstrated in real time, condition can be shown more transparently, and scarcity can create urgency. For niche categories such as sneakers, collectibles or limited drops, live auctions can turn shopping into an event rather than a transaction.

However, the model also creates new operational requirements. Sellers need presentation skills, inventory discipline, fast fulfilment, clear returns policies and strong customer service. Platforms need trust and safety systems, payment infrastructure, moderation, buyer protection and logistics partnerships. In that sense, live commerce is not simply a marketing format. It is a full e-commerce operating model.

The bigger picture: AI, video and recommerce are converging

Tilt’s $26 million funding round sits at the intersection of three major e-commerce trends.

The first is AI automation. E-commerce companies are increasingly using AI to support product listing, discovery, search, content generation and customer interaction.

The second is video-led shopping. TikTok Shop’s expansion across Europe shows that major platforms believe content-driven commerce can become a mainstream sales channel.

The third is recommerce. Vinted Ventures’ participation highlights the relevance of live selling for second-hand and resale categories, where trust, uniqueness and product condition are especially important.

Together, these trends suggest that the future of online shopping in Europe may not be defined only by faster delivery or lower prices. It may also be defined by richer interaction, stronger communities and smarter discovery.

Conclusion: Tilt’s round is a signal for Europe’s next e-commerce phase

Tilt’s $26 million raise shows that investor interest in European e-commerce is moving beyond traditional marketplace models. The new capital, combined with Vinted Ventures’ participation, positions Tilt as one of the most closely watched European startups in live commerce.

The company still faces major challenges. Live shopping adoption in Europe remains less mature than in Asia, competition from TikTok Shop and Whatnot is intensifying, and scaling across Europe requires strong localization. But Tilt’s growth metrics, AI tooling and recommerce relevance make the company an important indicator of where the market may be heading.

For European e-commerce, the message is clear: the next wave of competition will not only be about who sells online. It will be about who can turn shopping into discovery, interaction and repeat engagement.

The post Tilt raises $26M as Europe’s live commerce race accelerates appeared first on Cross-Border Magazine.

Artificial intelligence is often presented as a software revolution. But the numbers tell a more physical story.

AI does not run in the abstract. It runs inside data centers. Those data centers require electricity, water, cooling systems, chips, land, substations, transformers, fiber connections, backup power and skilled workers. As AI moves from experimentation to global implementation, these physical requirements are becoming one of the technology’s biggest constraints.

The issue is not whether AI can scale in theory. The issue is whether the world can, in practice, build enough infrastructure to support it.

According to the International Energy Agency, global data center electricity consumption was around 415 TWh in 2024, equal to roughly 1.5% of total global electricity consumption. In the IEA’s base case, this figure is projected to more than double to around 945 TWh by 2030, reaching just under 3% of total global electricity demand. That means data center electricity use is expected to grow by around 15% per year between 2024 and 2030, more than four times faster than growth in overall electricity consumption from other sectors.

These figures reveal the central problem: AI is not only a digital technology. It is becoming a major physical infrastructure challenge.

415 TWh in 2024: The Starting Point of the AI Infrastructure Problem

The first number to understand is 415 TWh.

That is the IEA’s estimate for global data center electricity consumption in 2024. To put this in perspective, data centers already consumed around 1.5% of the world’s electricity before the full global implementation of AI.

This matters because the AI boom is arriving atop an existing digital infrastructure. Cloud computing, streaming, enterprise software, financial systems, e-commerce, online advertising, and social media already require large data center networks. AI adds a new and more intensive layer of demand.

Traditional data center workloads were already significant, but AI workloads are different. They require high-performance GPUs, dense server clusters, advanced networking, and more powerful cooling systems. This means that AI does not simply increase the number of servers. It changes the type of infrastructure required.

The key point is that AI begins from a high baseline. The world is not moving from zero to AI infrastructure. It is adding AI to an already energy-intensive digital economy.

945 TWh by 2030: Data Center Electricity Demand Could More Than Double

The IEA projects global data center electricity consumption to reach around 945 TWh by 2030 in its base case. That is more than double the 2024 level.

This is one of the most important numbers in the AI infrastructure debate.

A rise from 415 TWh to 945 TWh means an increase of roughly 530 TWh in just six years. That additional demand exceeds the total annual electricity consumption of many countries.

The number also explains why governments, utilities, and energy companies are becoming increasingly involved in AI strategy. AI expansion is no longer only a question for technology companies. It is also a question for power markets, grid planners, and national energy policy.

The challenge is not only total consumption. It is also speed. A 15% annual growth rate in data center electricity consumption is extremely fast for an infrastructure-heavy sector. Electricity grids, substations, transmission lines, and power generation assets are not built at software speed.

This creates one of the main physical walls of AI: demand can grow faster than infrastructure can respond.

Just Under 3% of Global Electricity Demand: Why the Global Figure Can Be Misleading

By 2030, data centers could account for just under 3% of global electricity consumption, according to the IEA base case.

At first glance, 3% may not sound overwhelming. But this global percentage can be misleading for three reasons.

First, data center demand is highly concentrated. AI infrastructure is not evenly distributed across the world. It tends to cluster around major cloud regions, fiber routes, energy hubs, financial centers, and technology markets.

Second, data centers require reliable, continuous power. A large AI facility cannot depend on an intermittent or uncertain electricity supply. It needs firm capacity, high uptime, and stable grid connections.

Third, local grids can become stressed even if the global electricity share remains modest. A national or global average hides local bottlenecks. One region may have enough electricity on paper but lack the transmission capacity, substations or transformers needed to connect a new AI data center campus.

This is why the data center energy debate should not focus only on the global percentage. The more important question is whether the right amount of power can be delivered to the right locations at the right time.

176 TWh in the United States in 2023: The US Shows the Scale of the Challenge

The United States provides one of the clearest examples of how quickly data center electricity demand is growing.

A Lawrence Berkeley National Laboratory report, backed by the US Department of Energy, estimated that US data centers consumed 176 TWh of electricity in 2023. That represented around 4.4% of total US electricity consumption.

The report also estimated that US data center electricity use could rise to between 325 TWh and 580 TWh by 2028. That would represent between 6.7% and 12.0% of total US electricity consumption.

This range is important because it shows how uncertain the future still is. The lower and upper estimates depend on factors such as AI adoption, server shipments, hardware efficiency, cooling choices, and utilization rates.

But even the lower end of the projection is significant. Moving from 176 TWh in 2023 to 325 TWh in 2028 would mean a major increase in just five years. The upper estimate of 580 TWh would imply a much more dramatic transformation of US electricity demand.

This is why AI infrastructure has become a strategic issue in the US. It affects grid planning, power generation, corporate energy procurement, environmental policy, and regional development.

325 TWh to 580 TWh by 2028: Why Forecast Ranges Matter

The US forecast range of 325 TWh to 580 TWh by 2028 should not be treated as a single guaranteed outcome. It is a scenario range.

This distinction matters.

AI energy demand is difficult to forecast because several forces act simultaneously. Demand for AI services may grow faster than expected. Hardware may become more efficient. Models may become smaller and more specialized. Inference may become cheaper. Or, on the other hand, AI may be embedded in almost every digital workflow, creating much higher demand.

The wide range indicates that the future of AI infrastructure remains open. But it also shows that even conservative scenarios require serious planning.

If the lower scenario materializes, the US still needs major investment in power infrastructure. If the upper scenario materializes, the country faces a much larger energy planning challenge.

The safe conclusion is not that one exact number will happen. The safe conclusion is that AI has made data center electricity demand too large to ignore.

11x Growth in AI Server Power Density: The Rack-Level Problem

AI’s physical challenge is not only about total electricity use. It is also about power density.

The IEA reports that the power density of AI servers increased by a factor of 11 between 2020 and 2025. By 2027, it could rise by another four times.

This is one of the most important technical constraints in AI infrastructure.

A traditional data center may have enough physical space but not enough power and cooling capacity per rack. AI servers concentrate huge amounts of computing power in a small area. That creates intense local heat and requires more advanced electrical and cooling systems.

The IEA also notes that an individual server rack in an advanced data center could, by 2027, have peak power demand equivalent to that of 65 households.

This number illustrates why AI data centers are different from older facilities. The challenge is not only building more square meters of data center space. It is building facilities capable of supporting extremely dense computing environments.

In practical terms, this means more pressure on transformers, power electronics, cooling systems, backup power, and facility design.

65 Households per Rack: Why Cooling Becomes a Core Constraint

The image of an AI rack drawing peak power equivalent to that of 65 households helps explain the cooling problem.

Every watt of power a server uses eventually becomes heat. The more power concentrated in each rack, the more difficult it becomes to remove that heat safely and efficiently.

Cooling already accounts for a significant share of data center energy use. The IEA notes that cooling can account for around 7% of electricity use in efficient hyperscale data centers, but more than 30% in less efficient enterprise data centers.

AI increases the importance of cooling by altering the facility's thermal profile. High-density GPU clusters often require liquid cooling, direct-to-chip cooling or other advanced thermal management systems.

This creates several consequences:

• Older data centers may not be able to host advanced AI workloads without expensive retrofits.
• New AI data centers must be designed around cooling from the beginning.
• Operators need specialized expertise to manage liquid cooling systems.
• Cooling choices can shift pressure between electricity and water consumption.

The more AI scales, the more the data centers become a thermal engineering challenge.

7% to Over 30%: Cooling Efficiency Can Change the Energy Equation

The range from 7% to over 30% of electricity used for cooling shows how much facility design matters.

Efficient hyperscale data centers can reduce the cooling share through advanced design, better airflow, liquid cooling, climate-aware siting and operational optimization. Less efficient facilities may spend a much larger share of their electricity simply removing heat.

This is critical for AI because cooling inefficiency multiplies the energy burden. If compute demand rises quickly and cooling systems are inefficient, total electricity consumption grows faster.

However, cooling optimization is not straightforward. A system that reduces electricity use may require more water. A system that reduces water use may consume more electricity. A facility in a cooler climate may need less cooling but may still face grid or connectivity constraints.

This is why AI sustainability cannot be judged by one number alone. Electricity, water, carbon intensity, and location all matter.

Water Consumption: The Less Visible AI Resource Problem

Water is becoming one of the most politically sensitive physical constraints for AI data centers.

The reason is simple: data centers often need water for cooling, especially when evaporative systems are used. Water consumption varies widely depending on location, design, climate, and cooling technology.

United Nations University researchers have warned that AI infrastructure creates pressure not only on electricity systems but also on water, land and e-waste systems. A recent Reuters report on UNU research stated that data centers could double their power and water consumption by 2030 due to growing AI demand.

The water issue is highly local. A data center in a water-rich region does not create the same risk as a facility in a drought-prone or water-stressed region. This is why public opposition can emerge even when a company claims to have made global sustainability progress.

For communities, the question is not only whether the data center is efficient. The question is whether local water resources are being used to support global AI services.

This is one of the clearest examples of AI's physical nature. A digital service can create very real local resource conflicts.

2.5 Million Tonnes of E-Waste per Year: The Hardware Lifecycle Problem

AI infrastructure also creates an e-waste challenge.

United Nations University researchers have warned that AI infrastructure could generate up to 2.5 million tonnes of e-waste per year by 2030.

This matters because AI hardware evolves quickly. GPUs, servers, networking equipment, batteries, cooling systems, and power components may be replaced frequently as newer generations become more efficient or more powerful.

The environmental impact of AI is therefore not limited to electricity use during operation. It also includes the manufacturing, transportation, replacement, and disposal of hardware.

This creates a lifecycle problem.

A model may become more efficient over time, but if the infrastructure supporting it requires rapid hardware turnover, the environmental burden does not disappear. It shifts into supply chains, mining, manufacturing, and waste processing.

Responsible AI infrastructure must therefore include recycling, reuse, material recovery and longer hardware lifecycle planning.

More Than 800 Operators Surveyed: The Industry Itself Sees the Bottlenecks

The physical constraints are being raised not only by environmental groups or external critics. Data center operators themselves are reporting them.

Uptime Institute’s 2025 Global Data Center Survey, based on responses from more than 800 data center owners and operators, highlights rising costs, worsening power constraints, AI density challenges, staffing issues, supply chain delays and technological uncertainty.

This is important because it shows that the bottlenecks are operational rather than theoretical.

Operators are facing real constraints in securing power, upgrading facilities, hiring staff, managing supply chains and adapting to AI workloads. The industry is resilient, but it is also under pressure.

This is another reason why AI implementation may be slower or more uneven than the hype suggests. Demand for AI can grow quickly, but the infrastructure required to deliver it depends on slower physical systems.

165% by 2030: Market Forecasts Point in the Same Direction

Financial sector forecasts also point to significant growth in data center power demand.

Goldman Sachs Research has forecast that global data center power demand could increase by around 165% from 2023 levels by 2030.

This type of forecast should be treated differently from IEA or DOE-backed estimates. It is a market outlook, not a public-agency baseline. But it remains useful because it shows that energy demand growth is now central to investor expectations for AI.

The direction of travel is consistent across multiple sources: AI is increasing demand for data center capacity, and that capacity requires large amounts of power.

The exact number may vary by methodology. The trend is clear.

The Chip Constraint: AI Hardware Cannot Scale Instantly

The physical walls of AI are not limited to energy and water. AI also depends on specialized hardware.

Advanced AI systems require GPUs, AI accelerators, high-bandwidth memory, networking equipment, optical components, power electronics, and advanced packaging. These supply chains are complex and concentrated.

A shortage in any one of these components can slow deployment.

This is why AI infrastructure is vulnerable to semiconductor supply constraints, export controls, manufacturing bottlenecks, geopolitical tensions and competition for advanced memory and packaging capacity.

The key issue is that software demand can scale almost instantly, but chip supply cannot. Semiconductor manufacturing requires years of planning, enormous capital investment, and highly specialized production capacity.

AI’s global implementation therefore depends not only on model development but on the physical ability to manufacture and deliver the hardware required to run those models.

The Land Constraint: Data Centers Need Space, Permits and Local Acceptance

AI also requires land. Large data center campuses need server halls, cooling systems, substations, backup generators, batteries, security perimeters, access roads, and sometimes adjacent energy infrastructure.

This creates pressure in regions where suitable land, grid access, and connectivity overlap. The best data center locations are not just empty plots. They must combine power availability, fiber access, regulatory approval, water or cooling resources, construction capacity, and political acceptance.

This is where local resistance can become a barrier. Communities may question whether data centers consume too much electricity or water, whether they create enough permanent jobs, whether they increase local energy costs, or whether they place too much pressure on public infrastructure.

This means AI companies need more than technical capacity. They need a social license to build.

The Core Equation: AI Growth Is Now Physical Growth

The numbers point to one conclusion: AI growth is becoming physical growth.

• The global data center sector consumed around 415 TWh in 2024.
• It could reach around 945 TWh by 2030.
• Data center electricity demand could grow around 15% per year.
• US data centers used around 176 TWh in 2023.
• US data center demand could rise to 325 TWh to 580 TWh by 2028.
• AI server power density increased 11 times between 2020 and 2025.

By 2027, an advanced AI rack could draw peak power equivalent to 65 households.

Cooling can represent around 7% of electricity use in efficient hyperscale facilities but more than 30% in less efficient enterprise data centers.

AI infrastructure could generate up to 2.5 million tonnes of e-waste per year by 2030.

These numbers show that AI is no longer only a model race. It is an infrastructure race.

What the Numbers Mean for Global AI Implementation

The data suggests that AI’s global implementation will be shaped by six physical bottlenecks. The first is electricity. AI requires large volumes of reliable power.

The second is grid capacity. Electricity must be delivered to specific locations, not just generated somewhere in the system. The third is cooling. Higher power density creates more heat and requires more advanced thermal management.

The fourth is water. Cooling choices can create local water stress and political opposition. The fifth is hardware. AI depends on complex semiconductor and component supply chains.

The sixth is land and permitting. Data centers need physical space, public acceptance, and regulatory approval. These bottlenecks do not mean AI will stop growing. They mean AI growth will be more uneven, more expensive, and more dependent on infrastructure planning than many early expectations suggested.

AI Is Digital in Experience, Industrial in Reality

AI feels digital because users experience it through software. But its foundations are industrial. It runs on electricity. It depends on the chips. It produces heat. It may consume water. It requires land. It generates hardware waste. It needs power grids, cooling systems, fiber networks, transformers, substations, and skilled workers.

The central question for AI is therefore changing. The first question was: Can AI become intelligent enough to transform business and society? The next question is: can the physical world support AI at a global scale?

The answer will depend not only on better models, but on energy systems, data center design, chip manufacturing, cooling technology, water management, regulation, and public trust. 

AI may be the defining digital technology of the decade, but its limits are increasingly physical.

The post The Physical Walls of AI: The Numbers Behind the Energy, Water, and Infrastructure Challenge appeared first on Cross-Border Magazine.

Return Helper has raised US$4 million in Series A funding to expand its AI-powered cross-border e-commerce returns infrastructure. The round highlights the growing importance of reverse logistics in international online retail, where returns are no longer seen as a simple operational cost but as a strategic part of profitability, customer experience, and inventory recovery.

The funding round brought together a mix of financial and strategic investors, including Cathay Venture, MLC Ventures, Jun Yue Investment, Pegatron Venture Capital, and returning investor Colopl Next. MLC Ventures, the corporate venture arm of Mitsubishi Logistics Corporation, is especially relevant to the company’s next phase, as Return Helper looks to deepen its logistics capabilities and expand further across Japan and other international markets.

Founded as a technology-first provider of global cross-border e-commerce returns solutions, Return Helper supports merchants selling through major platforms such as Shopify, Amazon, TikTok and eBay. The company operates more than 20 overseas warehouses, works with over 30 carriers and logistics partners, and has offices across Asia, including China, Hong Kong, Japan, Singapore, and Taiwan.

The new capital will support three areas: international expansion, AI development and recommerce. Together, these priorities point to a larger shift in e-commerce logistics. As cross-border selling becomes more common, merchants increasingly need infrastructure that can manage not only outbound fulfillment but also returns, resale, recycling, refurbishment, and local recovery of returned inventory.

Why cross-border returns are becoming a strategic issue

Returns are among the most complex aspects of e-commerce. In domestic retail, they already pose challenges for customer service, warehouse operations, refunds, fraud prevention, stock visibility, and resale. In cross-border e-commerce, those problems multiply.

International returns can involve customs declarations, local regulations, fragmented carrier networks, long transit times, high shipping costs, inspection delays, and uncertain resale options. A returned product may be worth less than the cost of shipping it back to the original warehouse. For many merchants, especially those selling low-margin products internationally, the financial logic of cross-border returns can quickly break down.

That is the problem Return Helper is trying to solve. Instead of treating international returns as a linear process in which products are simply sent back to the point of origin, the company focuses on local handling and asset recovery. Returned items can be inspected, processed, resold, recycled, or redirected through local networks, helping merchants recover more value and reduce waste.

This approach is increasingly important in Europe, where e-commerce growth, sustainability expectations and regulatory pressure are all changing how retailers think about returns. Customers expect easy return options, but merchants need to control costs. Regulators and consumers are also paying more attention to waste, product destruction, and the environmental footprint of online retail. For cross-border sellers, that creates demand for smarter returns infrastructure.

AI as the next layer of returns management

Return Helper’s funding round also reflects a wider trend: the use of AI in operational e-commerce workflows. While much of the public conversation around AI in retail has focused on product recommendations, chatbots and content generation, the next wave of adoption is moving deeper into logistics and back-office operations.

Returns management is well-suited to AI because it involves repetitive yet decision-heavy processes. Every return can require a judgment: should the item be accepted, refunded, exchanged, repaired, resold, recycled, or routed to a specific warehouse? Should the merchant pay for return shipping? Is the product still suitable for resale? Is there a fraud risk? Is local recovery more profitable than international shipment?

AI agents and automated decision systems can help merchants process these decisions faster and more consistently. In Return Helper’s case, the company has stated that part of the Series A funding will go toward developing AI agents to automate return decisions. This suggests that the company is not only building logistics infrastructure but also decision intelligence for cross-border reverse logistics.

For merchants, the value is clear. Faster returns processing can improve customer satisfaction. Better routing decisions can reduce costs. Automated inspection and resale workflows can increase recovery rates. More accurate data can help teams understand why products are being returned and how to reduce future return volumes.

Recommerce moves to the center of the business model

One of the most interesting aspects of Return Helper’s positioning is its emphasis on recommerce. The company is not only helping merchants manage returns; it is helping them turn returned goods back into recoverable value.

Recommerce refers to the resale, refurbishment, or redistribution of returned, used, or excess inventory. In the context of cross-border e-commerce, recommerce can be especially valuable because returning products to the original market is often inefficient. A product sold into Europe from Asia, for example, may be too expensive to ship back. Local resale or recycling may be the better financial and environmental option.

This matters because e-commerce returns have traditionally been treated as a margin drain. A customer returns a product, the merchant loses the sale, shipping costs increase, warehouse work increases and the product may lose resale value. If returns can instead be converted into local resale opportunities, the economics change.

Return Helper’s model fits into this shift. By combining overseas warehouses, carrier partnerships, returns software, warehouse management systems and recommerce operations, the company aims to help merchants recover value from products that might otherwise become stranded, discounted or written off.

For European e-commerce, this is particularly relevant. As marketplaces, direct-to-consumer brands and cross-border sellers compete on convenience, return policies remain a major part of the customer experience. However, generous return policies are only sustainable if the operational model behind them is efficient. Recommerce gives retailers a way to balance customer expectations with profitability and sustainability.

Europe as a key growth opportunity

Although Return Helper has strong roots in Asia, Europe appears to be an increasingly important part of its growth story. The company has reported strong momentum in Europe, including significant revenue growth and a growing base of European merchant partners.

This is not surprising. Europe is one of the world’s most attractive e-commerce regions, but it is also highly fragmented. Different languages, consumer expectations, carrier networks, tax rules, and market structures make cross-border operations complex. For merchants selling into multiple European countries, managing returns through a single centralized workflow can be difficult.

At the same time, European consumers are accustomed to strong consumer protection standards and convenient post-purchase experiences. A poor returns experience can damage trust and reduce repeat purchases. For international sellers, especially those entering Europe from Asia or other regions, returns infrastructure can become a competitive barrier.

Return Helper’s European opportunity lies in helping those sellers localize the post-purchase experience. Instead of forcing every returned product through a lengthy international reverse-logistics chain, merchants can use local processing, local resale, and more flexible warehouse routing. That can reduce cost, shorten processing times, and improve customer satisfaction.

The role of strategic investors

The investor mix in Return Helper’s Series A is also significant. This is not only a software funding story. The participation of logistics-linked and corporate investors suggests that cross-border returns are becoming a priority area for the broader supply chain industry.

MLC Ventures, the corporate venture capital arm of Mitsubishi Logistics Corporation, brings a clear logistics angle to the round. Mitsubishi Logistics has deep experience in warehousing, transportation and supply chain infrastructure, which could support Return Helper’s expansion in Japan and beyond. Pegatron Venture Capital also adds a strategic dimension, given Pegatron’s wider role in technology manufacturing and supply chain ecosystems.

This type of investor base is important because returns management requires more than a software dashboard. It depends on physical infrastructure, carrier relationships, warehouse processes, local market knowledge, and operational execution. By combining software and logistics capabilities, Return Helper is positioning itself in a category where pure SaaS tools may not be enough.

What this means for merchants

For e-commerce merchants, the Return Helper funding round is part of a broader signal: returns are becoming a core part of international growth strategy. Brands that expand across borders without a clear returns model may face rising costs, slower refunds, poor customer reviews, and inventory losses.

A stronger returns strategy can help merchants in several ways. It can reduce unnecessary international shipping, improve refund speed, support exchanges, recover more value from returned items, and provide better data on product quality, sizing, customer expectations, and fraud patterns.

For marketplace sellers, the pressure is even higher. Platforms such as Amazon, TikTok Shop, eBay, and Shopify-based storefronts all create different operational requirements. Sellers that operate across multiple channels need systems that can centralize returns and connect the process with warehouses, carriers and resale channels.

Return Helper’s focus on multi-platform support is therefore important. By serving merchants who sell through Shopify, Amazon, TikTok, eBay, and other channels, the company addresses the reality of modern e-commerce: sellers rarely operate through a single storefront.

The wider market trend: from fulfillment to reverse logistics

For years, the e-commerce logistics conversation focused heavily on fulfillment speed. Retailers competed on faster delivery, better tracking, distributed warehouses, and last-mile convenience. That remains important, but the next competitive layer is reverse logistics.

As online sales volumes grow, return volumes grow with them. In categories such as fashion, footwear, electronics, and home goods, returns can represent a major operational burden. Cross-border returns add even more friction. This is why investors are paying closer attention to companies that can deliver returns more cheaply, smarter, and more profitably.

The Return Helper round shows that reverse logistics is becoming part of the core e-commerce technology stack. Returns management, warehouse software, AI decision-making, carrier orchestration, and recommerce are no longer separate functions. They are converging into a single post-purchase infrastructure layer.

This is also where AI can create measurable value. Unlike more experimental AI use cases, returns management has direct operational metrics: processing time, cost per return, resale recovery rate, refund time, customer satisfaction, fraud reduction, and inventory recovery. That makes it easier for merchants to connect technology investment with business outcomes.

Return Helper’s next challenge

The opportunity is clear, but the challenge is also significant. Cross-border returns are operationally complex, and scaling them across markets requires consistency, local knowledge, and strong execution. Return Helper will need to prove that its model can work across different regions, product categories, merchant sizes, and regulatory environments.

Its ability to integrate AI into real logistics workflows will also be important. AI-powered returns decisions can create value, but only if they are supported by accurate data, reliable warehouse processes and clear merchant rules. Automation in returns cannot be generic; it must account for product condition, category, value, customer history, regional regulation, and resale potential.

If Return Helper can combine AI decision-making with strong physical infrastructure, it could become an important player in the next phase of cross-border e-commerce logistics.

Return Helper’s US$4 million Series A funding round reflects a larger shift in global e-commerce. Cross-border returns are no longer a secondary operational issue. They are becoming a strategic battleground for profitability, customer experience, and sustainability.

By focusing on AI-powered returns management, recommerce and international logistics infrastructure, Return Helper is addressing one of the hardest problems in online retail: how to make global selling work after the sale has already happened.

For European merchants and international sellers entering Europe, the message is clear. Growth is not only about acquiring customers and shipping products across borders. It is also about building the infrastructure to manage what comes back. As return volumes rise and margins remain under pressure, companies that can turn reverse logistics into recoverable value may become increasingly important to the future of cross-border e-commerce.

The post Return Helper Raises US$4M Series A to Scale AI-Powered Cross-Border E-Commerce Returns appeared first on Cross-Border Magazine.

Smart Robotics gets €10M funding in a Series A round designed to accelerate the company’s European expansion and strengthen its AI-powered robotic picking technology for logistics and warehouse operations. The Netherlands-based company, headquartered in Best, develops robotic pick-and-place systems for intralogistics environments, helping warehouses automate high-volume, repetitive, and labor-intensive handling tasks.

The round was led by Rotterdamse Havendraken, with participation from Innovation Industries and Dutch family office Ernij Next. The investment will be used to expand Smart Robotics’ commercial presence across Europe, support product development, and further improve its AI-driven control layer for warehouse automation.

Smart Robotics gets €10M funding as warehouse automation demand accelerates

The funding comes at a moment when European logistics operators, fulfillment centers, and e-commerce warehouses are under pressure to increase throughput while managing persistent labor shortages, higher operating costs, and greater SKU complexity. As online retail and cross-border commerce become increasingly operationally demanding, automation is shifting from a long-term innovation project to a practical necessity in many warehouse environments.

Smart Robotics focuses on one of the most difficult areas of automation: robotic picking. In real warehouse settings, robots must identify, grip, move, and place a wide variety of items that differ in shape, size, packaging, weight, and fragility. This is significantly more complex than automating repetitive tasks with predictable objects.

The company says it has already deployed more than 120 systems across 15 countries and five industries, completing more than one billion successful robotic picks in live production environments. This operational history is central to Smart Robotics’ positioning, because every robotic pick generates data that can be used to improve the company’s AI systems over time.

A Series A round focused on European expansion

The €10 million Series A round is intended to help Smart Robotics scale its technology across Europe. According to funding reports, the company will use the fresh capital to strengthen commercial operations, expand into additional European markets, and continue advancing its AI-driven pick-and-place platform.

This is particularly relevant for the e-commerce and logistics sectors, where automation adoption is increasingly tied to speed, reliability, and cost control. Warehouses serving online retail must manage fast order cycles, volatile demand, seasonal peaks, and returns flows. In that context, robotic picking can help operators reduce dependency on manual labor while improving consistency in repetitive processes.

Smart Robotics’ model also reflects a broader shift in warehouse automation. Instead of offering only standard hardware, the company combines robotic cells, software, and AI-based control systems. Its solutions are designed for tasks such as item picking, palletizing, and depalletizing, making automation more adaptable to real-world logistics environments.

Why robotic picking matters for e-commerce fulfilment

For e-commerce businesses, warehouse performance directly impacts the customer experience. Fast delivery promises, accurate order preparation, and reliable fulfillment are now essential parts of online retail competitiveness. As a result, the warehouse is no longer just a back-end operational space; it has become a strategic layer of the e-commerce value chain.

Robotic picking addresses a key bottleneck. In many fulfillment centers, picking remains one of the most labor-intensive and difficult-to-scale tasks. Unlike pallet movement or conveyor-based sorting, item picking often requires flexibility, perception, and decision-making. A robot must understand what it is looking at, decide how to grip the item, and place it correctly without damaging the product or disrupting the flow.

Smart Robotics claims its systems can achieve up to 1,000 picks per hour and maintain 99.5 percent uptime. Those figures, if consistently achieved in live warehouse conditions, help explain why investors are paying closer attention to AI-powered intralogistics automation.

The role of AI in Smart Robotics’ warehouse automation platform

The most important part of the Smart Robotics story is not only the robot arm, but the intelligence layer behind it. The company’s AI-based software is designed to help robots operate in environments where product variety and workflow conditions change constantly.

That matters because e-commerce warehouses rarely handle a narrow and predictable set of items. A single fulfillment operation may process thousands of SKUs, from boxes and bags to fragile or irregularly shaped goods. Traditional automation can struggle in these conditions because it is often built around repeatability. AI-driven robotics aims to add adaptability.

Smart Robotics’ reported milestone of more than one billion successful real-world picks gives the company a potentially valuable operational dataset. In practical terms, this means the company can use data from deployed systems to improve object recognition, gripping strategies, and robotic decision-making.

Investors back physical AI for logistics

The participation of Rotterdamse Havendraken, Innovation Industries, and Ernij Next shows continued investor interest in companies that apply AI to physical operations, not only software-based workflows. In logistics, this trend is often described through concepts such as embodied AI, physical AI, or intelligent automation.

The difference is important. While many AI tools focus on digital tasks, warehouse robotics must perform reliably in the physical world. That means dealing with uncertainty, movement, object variation, safety requirements, and integration with existing warehouse systems.

Smart Robotics’ funding round, therefore, fits into a larger investment pattern: capital is flowing into technologies that make supply chains more resilient, automated, and data-driven. For cross-border e-commerce, this is especially relevant because international growth increases operational complexity. More markets, more SKUs, more delivery promises, and more returns all add pressure to fulfillment networks.

What the funding means for European logistics

Smart Robotics gets €10M funding at a time when European logistics companies are looking for ways to increase capacity without simply adding more labor. In many markets, recruiting warehouse workers remains difficult, especially for physically repetitive roles. At the same time, e-commerce customers expect faster deliveries, better tracking, and fewer errors.

Automation can help address these challenges, but only when it is flexible enough to operate in real warehouse conditions. This is where Smart Robotics is trying to differentiate itself. Its approach combines AI software, operational data, and robotic hardware to automate tasks that have traditionally been hard to mechanize.

The company’s expansion could also support a more competitive European logistics technology ecosystem. While much of the robotics investment conversation is often dominated by US and Asian players, Smart Robotics is positioning itself as a European specialist in AI-driven intralogistics.

Smart Robotics and the future of intralogistics

The Series A round gives Smart Robotics additional resources to scale its platform and expand its reach, but the company will still operate in a competitive and technically demanding market. Warehouse automation buyers typically need proof that systems can deliver reliable returns on investment, integrate with existing workflows, and handle the operational complexity of live fulfillment environments.

That makes Smart Robotics’ existing deployment base an important part of its commercial argument. The company reports more than 120 deployed systems, more than 1 billion completed picks and operations, and operations across 15 countries. These figures suggest that the company is not only developing experimental robotics but also deploying systems in production environments.

For logistics operators and e-commerce businesses, the key question will be whether AI-driven robotic picking can scale across different warehouse formats, product categories, and market conditions. If it can, robotic picking could become a central part of the next-generation fulfillment infrastructure.

A funding round that reflects the new priorities of e-commerce logistics

Smart Robotics gets €10M funding not simply because warehouse robotics is a promising technology category, but because the operational demands of e-commerce are changing. Fulfillment networks must now support faster order cycles, higher product variety, cross-border expansion, and tighter cost control.

In that environment, automation is becoming a competitive requirement. Companies that can combine robotics, AI, and real-world warehouse data are well-positioned to benefit from this shift.

Smart Robotics’ latest funding round shows that investors see AI-powered intralogistics as a key growth area in Europe. For e-commerce and logistics companies, the message is clear: the future of fulfillment will increasingly depend on intelligent automation capable of operating in complex, high-volume, and fast-changing warehouse environments.

The post Smart Robotics gets €10M funding to scale AI-driven warehouse automation across Europe appeared first on Cross-Border Magazine.

Artificial intelligence is becoming one of the most important forces shaping digital retail across Europe. What began as a tool for automation and analytics is now influencing how online stores attract customers, personalize shopping journeys, optimize pricing, forecast demand, and improve profitability. For retailers competing in a fast-changing market, AI is no longer a side project. It is increasingly central to growth.

The data behind Europe’s online economy helps explain why this matters. European B2C e-commerce turnover reached €887.4 billion in 2023, and the market continues to expand as more consumers shift purchasing habits online. At the same time, AI adoption among businesses is accelerating, creating new opportunities for retailers that move early and execute well.

This combination of strong market demand and rising technology adoption is driving AI-driven e-commerce growth in the EU.

The Size of the Opportunity in Europe

Europe is one of the world’s largest and most diverse e-commerce regions. It combines mature digital markets such as Germany, the Netherlands, and the Nordics with high-growth opportunities across Southern and Eastern Europe.

According to European market data:

• European B2C e-commerce turnover reached €887.4 billion in 2023
• The market grew year over year from the previous period
• 77% of EU internet users aged 16–74 shopped online in 2024
• 67% of online purchases were made on mobile devices
• Cross-border B2C e-commerce sales in the EU reached €246 billion

These figures show a market with both scale and momentum. They also show why efficiency, relevance, and customer experience are becoming more important than simple traffic growth.

AI Adoption Is Accelerating Across EU Businesses

The strongest signal that AI will shape the future of retail in Europe is the speed of business adoption.

Recent EU data shows:

• 20.0% of enterprises in the EU used AI technologies in 2025
• This was an increase of 6.5 percentage points versus 2024
• AI adoption is significantly higher among large enterprises, but growing across all company sizes

This matters because once adoption reaches critical mass, AI shifts from competitive advantage to competitive necessity. Businesses that fail to modernize risk slower growth, lower margins, and weaker customer experiences.

Why AI Is Driving E-Commerce Growth in the EU

European retailers face a familiar challenge: customer acquisition is expensive, competition is intense, and shoppers expect seamless experiences across devices and channels.

AI helps solve these challenges by improving the economics of growth.

Instead of relying only on larger ad budgets or heavier discounting, retailers can use AI to increase the value of every visit, every transaction, and every customer relationship.

The most important growth levers include:

• Higher conversion rates
• Better average order value
• Lower acquisition costs
• Stronger retention and loyalty
• Faster fulfillment
• Better inventory efficiency
• Lower support costs

That is why AI is becoming essential for sustainable e-commerce expansion across the EU.

Smarter Search and Product Discovery

One of the fastest ways AI improves e-commerce performance is by helping shoppers find the right products faster.

Traditional search tools rely heavily on keywords. AI-powered search goes further by understanding context, intent, and behavior.

For example, instead of only matching exact terms, AI can interpret searches such as:

• Sustainable running shoes under €100
• Modern desk for small apartment
• Gift ideas for a 10-year-old who likes science

This reduces friction and shortens the path to purchase.

In large product catalogs, better discovery can significantly improve conversion rates while lowering bounce rates.

Personalization That Increases Revenue

Consumers increasingly expect relevant experiences. Generic storefronts often underperform in competitive markets.

AI allows retailers to personalize:

• Homepage content
• Product recommendations
• Promotions
• Search results
• Email timing
• Loyalty offers
• Cross-sell suggestions

The result is a shopping journey that feels more useful and less transactional.

Many retailers see personalization as one of the highest-return AI use cases because even modest improvements in conversion or basket size can scale quickly across thousands of orders.

Dynamic Pricing and Promotion Optimization

Price sensitivity remains high across many European markets. Consumers compare offers quickly, especially in categories such as electronics, fashion, and home goods.

AI helps retailers adapt prices and promotions in real time based on factors such as:

• Demand levels
• Inventory position
• Competitor activity
• Seasonality
• Customer behavior
• Margin goals

This allows businesses to stay competitive without relying on unnecessary blanket discounts.

For retailers managing multiple countries and currencies, pricing intelligence can be especially valuable.

Better Demand Forecasting and Inventory Management

Inventory problems damage growth in two ways:

• Stockouts lead to lost sales
• Overstock ties up working capital and increases markdown risk

AI forecasting models use historical sales, seasonality, promotions, and customer trends to improve planning accuracy.

For European retailers operating across multiple warehouses, markets, and languages, better forecasting can create a major operational advantage.

Benefits often include:

• Fewer stockouts
• Lower excess inventory
• Faster replenishment
• Better cash flow
• Improved fulfillment performance

AI-Powered Customer Service at Scale

Customer support remains expensive and difficult to scale, especially for brands selling across multiple countries and languages.

AI assistants and chat tools can now handle many common tasks such as:

• Order tracking
• Returns questions
• Product recommendations
• Sizing guidance
• Delivery updates
• Policy clarification

This improves response speed while reducing support costs. It also helps smaller retailers offer service levels that previously required larger teams.

How Leading EU Retailers Are Using AI

The impact of AI is visible across major European e-commerce players.

Zalando

Zalando uses AI to improve fashion recommendations, search relevance, and customer engagement. In a category where inspiration and fit matter, personalization can directly influence revenue.

OTTO

OTTO has invested in AI for demand forecasting and inventory optimization, helping improve planning efficiency in a large retail operation.

Allegro

Allegro applies intelligent systems to search relevance, marketplace optimization, and fraud prevention.

ASOS

ASOS has explored AI-driven personalization and trend analysis to improve digital shopping experiences.

bol.com

bol.com uses automation, recommendations, and service enhancements to improve conversion and customer satisfaction.

These examples show that AI is not limited to one country or one retail model. It is becoming a pan-European growth engine.

The Role of Mobile and Cross-Border Commerce

The EU market has unique characteristics that make AI especially valuable.

Mobile Commerce

With 67% of online purchases made on mobile devices, retailers must optimize smaller screens, faster decisions, and shorter attention spans.

AI helps by improving:

• Mobile search relevance
• Faster recommendations
• Simpler checkout journeys
• Personalized product ranking

Cross-Border Commerce

Cross-border B2C sales worth €246 billion show that consumers are increasingly willing to buy from sellers in other EU markets.

AI can support cross-border growth through:

• Language localization
• Currency adaptation
• Smarter recommendations
• Fraud detection
• Dynamic logistics decisions

Challenges Retailers Still Need to Solve

Although AI offers major opportunities, success is not automatic.

Data Quality

Poor product data weakens recommendations, search, and forecasting.

Integration Complexity

Disconnected systems limit automation and slow execution.

Compliance and Trust

European businesses must align AI use with privacy expectations and regulatory requirements.

Clear ROI Measurement

The best programs focus on measurable commercial outcomes rather than technology hype.

What Smaller EU Businesses Should Prioritize First

Not every company needs enterprise-scale budgets to benefit from AI.

Strong starting points include:

• AI search tools
• Product recommendation engines
• Automated email personalization
• Support assistants
• Demand forecasting tools
• Pricing optimization
• Fraud prevention systems

The goal should be practical wins that compound over time.

The Future of AI and E-Commerce in Europe

As adoption rises, AI will likely become embedded across the full retail value chain.

The next phase may include:

• Autonomous shopping assistants
• More predictive merchandising
• Deeper personalization
• Smarter marketplaces
• Faster cross-border operations
• More efficient supply chains

Retailers that build strong data foundations and implement high-value use cases early will be best positioned to lead.

The evidence is clear that AI is driving e-commerce growth in the EU. Europe already has a massive digital retail market, strong consumer adoption, and rapidly increasing business investment in AI. Companies are using intelligent systems to improve discovery, increase conversions, optimize operations, and create better customer experiences.

The next chapter of European e-commerce growth will not be defined only by who has the biggest catalog or the largest ad budget. It will be defined by who applies AI most effectively.

The post How AI Is Driving E-Commerce Growth in the EU appeared first on Cross-Border Magazine.

Artificial intelligence is no longer a future trend in online retail. It is already reshaping how American companies attract shoppers, convert traffic, fulfill orders, and build long-term customer loyalty. Across the United States, businesses are using AI not simply to automate tasks, but to grow faster in a market where competition is intense, and consumer expectations continue to rise.

That makes AI driving e-commerce growth in the USA one of the most important stories in modern retail. The U.S. remains one of the world’s largest digital commerce markets, with online sales reaching $1.233 trillion in 2025, up 5.4% year over year, according to the U.S. Census Bureau. E-commerce also accounted for 16.4% of total retail sales, continuing its steady share gains across the broader economy.

The key question is no longer whether AI matters. It is how much of future growth will belong to the companies that implement it best.

Why AI Matters More in the U.S. Market

Growth in American e-commerce has become harder to achieve. Paid acquisition costs have increased, customer loyalty is weaker, and shoppers can compare prices, reviews, and alternatives in seconds.

In that environment, AI gives retailers an edge by improving the economics of growth. Instead of relying only on more traffic or bigger discounts, companies can use data and automation to make every customer interaction more valuable.

AI is helping U.S. retailers improve:

• Conversion rates
• Average order value
• Retention and repeat purchases
• Marketing efficiency
• Inventory accuracy
• Fulfillment speed
• Customer service costs

For many brands, that combination is more powerful than traffic growth alone.

The Data Behind AI-Driven E-Commerce Growth

The strongest argument for AI in retail is measurable performance. Several recent studies show that AI is already influencing online shopping behavior in the United States.

Adobe Analytics reported that AI-driven visits to U.S. retail sites grew 4,700% year over year in July 2025. While AI traffic still represents a relatively small share of total visits, the growth rate suggests a new discovery channel is emerging.

During Black Friday 2025, U.S. online spending reached a record $11.8 billion, up 9.1% from the previous year. Adobe linked part of that momentum to AI-powered shopping tools that helped consumers find deals faster and compare products more efficiently. AI-driven retail traffic during the event surged 805% year over year.

Additional research found that traffic referred by AI tools converted 31% higher than other traffic sources and produced 27% lower bounce rates, suggesting AI-assisted shoppers may arrive with stronger purchase intent.

Those figures do not prove that AI alone created all the growth, but they strongly suggest that AI is becoming a meaningful commercial lever.

Amazon: AI at Scale

Amazon has integrated artificial intelligence across nearly every layer of its e-commerce model. Customers experience it through smarter search results, personalized recommendations, and conversational tools such as Rufus. Behind the scenes, Amazon uses AI in pricing, forecasting, warehouse robotics, and delivery routing.

This matters because growth at Amazon’s scale depends on efficiency as much as demand. Faster recommendations, better search relevance, and lower fulfillment costs can create billions in incremental value over time.

Amazon’s strategy also reflects a broader U.S. trend: AI works best when it improves both customer experience and operations simultaneously.

Walmart: Turning Physical Scale into Digital Growth

Walmart shows how AI can help a legacy retailer compete aggressively online. Rather than separating stores from digital commerce, Walmart uses AI to connect them.

That includes:

• Real-time inventory visibility
• Smarter replenishment
• Faster local delivery decisions
• Personalized shopping experiences
• Automated supply chain workflows

Because Walmart stores are close to most American households, AI helps transform physical proximity into an e-commerce advantage. Faster pickup, same-day delivery, and more accurate stock availability all contribute to digital growth.

Shopify and the Democratization of AI

Not every U.S. retailer is Amazon or Walmart. Millions of smaller brands rely on platforms like Shopify, and AI is making advanced capabilities accessible to them.

Instead of hiring large technical teams, merchants can now use AI for:

• Product content creation
• Personalized offers
• Merchandising suggestions
• Automated email flows
• Customer support assistants
• Campaign optimization

This is important because AI-driven growth in the USA is not limited to enterprise retailers. Smaller businesses can now compete with tools that were once available only to major corporations.

Consumers Are Adopting AI Shopping Tools

Consumer behavior is changing alongside retailer adoption. One 2026 industry report found that 51% of consumers had used generative AI for online shopping in 2025, up from 38% in 2024. It is also estimated that roughly 45 to 50 million U.S. consumers regularly use AI for shopping-related tasks.

That does not mean shoppers want AI to replace human decision-making entirely. In many categories, people still value trust, brand reputation, and human recommendations. But AI is increasingly becoming part of the shopping journey, especially in product research, comparisons, and discovery.

Where AI Delivers the Fastest Growth

Not every AI use case creates the same commercial impact. In the U.S. market, several areas consistently stand out.

Smarter Search and Product Discovery

Many online stores lose sales when shoppers cannot quickly find the right product. AI-powered search understands intent rather than matching only keywords, which can improve relevance and shorten the path to purchase.

Personalization That Actually Converts

Modern AI can adapt product rankings, homepage content, promotions, and recommendations in real time. More relevant experiences often lead to higher conversion rates and larger baskets.

Some industry estimates suggest personalization leaders can see revenue lifts of up to 40%, though results vary widely depending on execution and data quality.

Better Marketing Efficiency

As paid media becomes more expensive, AI helps brands target audiences more accurately, test creatives faster, and allocate budgets more effectively. This can lower acquisition costs while improving return on ad spend.

Forecasting and Inventory Planning

Stockouts and overstock both damage profitability. AI helps predict demand using historical sales, seasonality, and customer trends, leading to better inventory decisions and stronger margins.

Customer Support at Scale

AI assistants can answer common questions instantly, recommend products, and resolve routine issues 24/7. That improves customer satisfaction while reducing support costs.

What the Statistics Do Not Prove

AI is powerful, but it is not magic. Many companies still struggle to move from pilots to scaled results.

One 2026 analysis found that while AI adoption is widespread, only a small share of retailers have fully scaled their programs across the organization. In other words, many companies are experimenting, but fewer are capturing the full growth opportunity.

That gap usually comes down to three issues:

Poor Data Quality

AI systems are only as good as the data behind them.

Weak Integration

Disconnected platforms reduce the value of automation.

No Clear ROI Focus

Businesses that chase trends without measurable use cases often underperform.

Why the USA Will Continue Leading AI Commerce

The United States is especially well-positioned for AI-led e-commerce growth because it combines:

• Massive consumer demand
• Advanced logistics networks
• Strong software ecosystems
• High venture and corporate investment
• Intense retail competition
• Rapid adoption of new technology

When innovation creates even small percentage gains in a market worth more than a trillion dollars, the impact can be enormous.

The evidence is increasingly clear: AI is becoming a real driver of e-commerce growth in the USA.

It is helping retailers attract higher-intent traffic, convert more shoppers, personalize experiences, optimize supply chains, and operate more profitably. The strongest companies are not using AI as a gimmick. They are embedding it into the foundations of their growth.

As competition increases and margins tighten, the next chapter of American e-commerce will likely be shaped less by who spends the most on ads and more by who applies artificial intelligence most effectively.

The post The AI is Driving E-Commerce Growth in the USA appeared first on Cross-Border Magazine.

Amsterdam-based Wonderful has secured $150 million in Series B funding, reaching a valuation of around $2 billion and confirming its position as one of Europe’s fastest-growing AI startups.

The round was led by Insight Partners, with participation from major global investors including Index Ventures, IVP, Bessemer Venture Partners, and Vine Ventures.

While the company is relatively young—founded in 2025—it has already raised approximately $286 million in total funding, scaling rapidly across global markets.

What Wonderful actually does

From AI hype to real enterprise deployment

Wonderful operates an enterprise AI agent platform designed to automate complex workflows, particularly in customer-facing and operational processes.

Its core offering includes:

• AI agents handling customer service across voice, chat, and email
• Automation of internal processes like IT support, onboarding, and compliance
• A model-agnostic architecture, selecting the best AI model per use case

This is a crucial distinction. Instead of building a single AI model, Wonderful focuses on orchestrating multiple models and embedding them into real business environments.

The key differentiator: “hyper-local AI”

Why this matters for Europe

One of Wonderful’s most important innovations is its “hyper-local deployment model”.

Rather than offering a one-size-fits-all AI solution, the company:

• Deploys teams directly inside client organizations
• Adapts AI systems to local languages, regulations, and cultural nuances
• Operates across 30+ countries worldwide

This is particularly relevant in Europe, where:

• Markets are fragmented
• Regulations differ country by country
• Multilingual customer interactions are the norm

Why investors are betting big

The shift from AI experimentation to execution

The funding reflects a broader market shift:

Enterprises are moving from testing AI to deploying it at scale

Wonderful is positioned at the center of this transition because it solves one of the biggest problems in AI adoption:

• AI demos are easy
• Production deployment is hard

Challenges include:

• Integration with legacy systems
• Regulatory compliance
• Language localization
• Reliability in real-world scenarios

Wonderful’s approach directly targets this “execution gap”.

Direct implications for e-commerce and logistics

Although Wonderful is not a logistics company, its impact on the sector is significant.

Customer experience automation

E-commerce companies can use AI agents to:

• Handle returns, refunds, and delivery queries
• Provide 24/7 multilingual support
• Reduce customer service costs

This is especially valuable for cross-border e-commerce, where language and time zones create friction.

Operations and back-office efficiency

In logistics and supply chain environments, AI agents can:

• Automate shipment tracking communications
• Manage exceptions and delays
• Support warehouse and IT workflows

This reduces operational overhead and improves responsiveness.

Localization at scale

For companies operating across Europe, Wonderful’s model enables:

• Market-specific customer interactions
• Compliance with local regulations
• Better conversion rates in non-English markets

This is a major advantage in regions where US-centric AI tools often struggle.

Growth trajectory and expansion plans

Wonderful’s expansion is aggressive:

• Active in 30+ global markets
• Workforce expected to grow from ~350 to ~900 employees
• Increasing focus on industries like telecom, finance, manufacturing, and healthcare

Revenue is already in the tens of millions, with growing demand for broader enterprise use cases beyond customer support.

Competitive landscape

Wonderful operates in a highly competitive space that includes:

• AI customer support platforms
• Enterprise automation tools
• Emerging AI agent startups

However, its differentiation lies in:

• Execution-first approach
• Global deployment capability
• Localization strategy

Many competitors focus on English-speaking markets, while Wonderful is explicitly targeting global, multilingual enterprises.

What this signals for the European tech ecosystem

Europe is building applied AI leaders

Wonderful’s funding highlights an important trend:

• Europe is not just producing AI research
• It is now building commercial AI platforms at scale

This aligns with broader investment patterns favoring:

• Infrastructure-heavy startups
• Enterprise-focused AI
• Real-world deployment capabilities

The $150M funding round for Wonderful is more than just another AI investment. It signals a shift toward practical, deployable AI systems that solve real operational challenges.

For e-commerce and logistics companies, the implications are clear:

• AI is moving from experimentation to core infrastructure
• Localization and execution are becoming competitive advantages
• Automation of customer and operational workflows will accelerate rapidly

Wonderful represents a new category of European startups:
AI companies built not just to innovate—but to operationalize AI at scale.

The post Dutch AI company Wonderful raises $150M appeared first on Cross-Border Magazine.

Artificial Intelligence has rapidly become a foundational technology in e-commerce, from product recommendations and dynamic pricing to autonomous agents and generative content. However, regulation is evolving unevenly across regions. The European Union, the United States, and China have each adopted distinct legal frameworks that directly impact how AI can be developed and used in digital commerce.

This article provides a comprehensive, SEO-optimized analysis of current AI laws in the EU, compares them with those in the U.S. and China, and explores how these frameworks influence real-world e-commerce innovation and competitiveness.

The EU AI Act: The World’s First Comprehensive AI Law

A Risk-Based Regulatory Framework

The EU Artificial Intelligence Act (Regulation EU 2024/1689) is the most advanced and comprehensive AI legislation globally. It introduces a risk-based classification system that determines compliance requirements for AI systems.

Unacceptable Risk includes prohibited systems such as social scoring.
High Risk covers strictly regulated use cases like credit scoring or hiring tools.
Limited Risk includes systems that require transparency obligations such as chatbots.
Minimal Risk applies to largely unregulated systems, such as recommendation engines.

In e-commerce, most AI applications, such as recommendation engines, personalization, and chatbots, fall into limited-risk categories, while use cases like credit scoring or fraud detection may qualify as high-risk systems.

Key Legal Obligations for Businesses

The EU AI Act imposes several obligations on both AI developers and deployers:

• Businesses must ensure transparency, including disclosure of AI-generated content.
• Human oversight mechanisms must be implemented.
• Technical documentation and auditability are required.
• Data governance and bias mitigation must be addressed.
• Employees must have sufficient AI literacy.
• General-purpose AI models, such as large language models, must also provide summaries of training data and comply with copyright laws.

Timeline and Enforcement

The regulation entered into force in August 2024. Partial enforcement is taking place between 2025 and 2026, with full applicability expected by August 2026.

Penalties for non-compliance can reach up to 7 percent of global annual revenue, placing it on a similar level of impact as GDPR.

Interaction with Other EU Digital Laws

The AI Act operates alongside GDPR, the Data Act, and the Digital Services Act. This creates a complex but highly structured compliance environment, especially for e-commerce platforms that rely heavily on personal data and automated decision-making.

Impact of EU Regulation on E-commerce

Slower Innovation, Higher Trust

The EU’s regulatory framework prioritizes consumer protection, transparency, and ethical AI. While this increases user trust, it can also slow AI adoption due to increased compliance burdens.

Practical Implications for E-commerce

Companies operating in the EU must label AI-generated product descriptions or reviews. They need to ensure explainability in recommendation systems when those systems significantly influence user decisions. AI-driven pricing or personalization tools must be auditable. Algorithms must be designed to avoid discrimination.

These requirements increase operational complexity and costs, but they also strengthen consumer trust and long-term brand credibility.

The United States: A Fragmented, Innovation-First Approach

The United States does not have a single comprehensive AI law. Instead, regulation is shaped by a mix of sector-specific rules, state-level legislation, and federal guidelines.

As of 2026, dozens of AI-related laws exist across multiple states, resulting in a fragmented regulatory landscape.

Regulatory Philosophy

The U.S. approach is market-driven, innovation-focused, and less restrictive than the EU. This allows faster experimentation but introduces legal uncertainty for companies operating across states.

Impact on AI Development

Companies benefit from faster deployment of generative AI models and fewer restrictions on the use of training data. This flexibility has encouraged innovation among startups and large technology firms alike.

Major platforms have rapidly integrated AI into product recommendations, logistics optimization, and marketing content generation.

E-commerce Use Cases in the U.S.

AI is deeply embedded in U.S. e-commerce operations. Common applications include hyper-personalized recommendations, AI-driven pricing optimization, conversational commerce through virtual assistants, and automated supply chain forecasting.

The relatively light regulatory environment has enabled large-scale commercial deployment of AI technologies.

China: State-Controlled AI with Aggressive Commercial Deployment

Centralized and Strategic Regulation

China’s approach to AI regulation is centralized, state-driven, and aligned with national strategic priorities. The government plays an active role in shaping AI development while enforcing strict oversight on content, data usage, and digital platforms.

Key Characteristics

AI systems and algorithms often require registration with authorities. Content moderation rules are strict, and platforms are held accountable for outputs. AI development is closely aligned with national economic and technological goals.

AI in E-commerce: A Global Benchmark

China is one of the most advanced markets for AI-powered e-commerce.

Live Commerce and AI Avatars

AI is widely used in live-stream shopping. Virtual influencers and AI-generated hosts present products in real time, interact with audiences, and operate continuously without human limitations. This model has significantly reduced costs and increased scalability.

Hyper-Automation

Major Chinese platforms leverage AI across the entire commerce value chain. This includes automated supply chains, visual search capabilities, predictive inventory management, and real-time personalization at scale.

Regulatory Trade-off

China combines rapid innovation and deployment with strong government control. This enables rapid scaling of AI technologies but places less emphasis on individual privacy and transparency than the EU.

EU vs USA vs China: Strategic Comparison

Regulatory Approach

The EU follows a strict, risk-based, rights-focused model. The United States adopts a flexible, fragmented, innovation-driven approach. China uses a centralized, state-controlled, and strategically coordinated model.

Impact on AI Development

In the EU, innovation is slower but safer and more structured. In the United States, innovation is fast but comes with legal uncertainty. In China, innovation is both rapid and coordinated under government direction.

E-commerce Competitiveness

The EU emphasizes trust and compliance but lags in AI deployment speed.  The United States leads in AI innovation and platform development. China leads in execution and large-scale AI-driven commerce models.

Data Usage

The EU enforces strict data protection rules.  The United States maintains moderate regulation, varying by sector and state.  China allows extensive data use under state supervision.

The Future of AI in Global E-commerce

Convergence vs Fragmentation

The global regulatory landscape remains fragmented. The EU is setting compliance benchmarks, the United States is driving innovation, and China is building a state-driven AI economy.

This divergence requires companies to adapt their AI strategies to the markets in which they operate.

Strategic Implications for Businesses

Organizations must design AI systems that can be adapted to different regulatory environments. Internal AI governance frameworks are becoming essential. Businesses must carefully balance innovation with compliance and risk management.

Many global companies are beginning to use EU standards as a baseline for compliance while deploying more advanced AI capabilities in less restrictive markets.

AI regulation has become a central factor in e-commerce strategy. The European Union leads in regulation and ethical standards.  The United States leads in innovation and rapid commercialization.  China leads in execution and large-scale implementation.

Success in global e-commerce will depend on the ability to navigate these three regulatory ecosystems while leveraging AI effectively and responsibly.

The post AI Regulation and E-commerce: EU vs USA vs China (2026) appeared first on Cross-Border Magazine.