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An increasing part of the purchasing conversation may take place outside the store - in the assistant's response, product comparisons, or summaries of available offers. For e-commerce, this means a new task - ensuring that the customer not only finds the site but also that AI can accurately present the brand, its products, and purchasing conditions.
The Semly analysis connects trends European E-commerce Report 2026 with observations on visibility in AI responses and translates them into an action plan.
The most important change - competition for a place in the decision
LLMs, or large language models, are becoming part of the tools that help choose a product. The customer can describe the situation instead of entering the category name: what they need, how much they want to spend, and what they want to avoid. The system can respond with a comparison or a proposal of several solutions. In such a scenario, the visibility of the store starts to matter even before visiting its page.
The European E-commerce Report 2026 shows a market where the growth of online shopping coexists with price sensitivity, high expectations for convenience, and the development of AI at the stage of searching and comparing offers. Semly's conclusion is as follows: the quality of information about the offer becomes part of the ability to compete for the customer's choice.
Being visible in LLMs is important because it allows participation in the conversation where a shortlist of options is created. Equally important is how the brand is described.
A mention with incorrect information about the assortment or delivery may not help in the purchase. Therefore, the company should measure presence, accuracy, completeness, and sources of responses.
Three decisions for management
Incorporate AI responses into the observation of the purchasing path. Assign an owner for the quality of information about products and the company. Connect monitoring with the process of addressing specific gaps, and evaluate its effects together with conversion, service, and margin.
Another horizon is the readiness of the offer for evaluation and handling by an agent acting for the customer. This requires separate piloting; one should not equate appearing in the AI response with the ability to execute a purchase correctly.
A mature market shifts focus to the quality of choice
The European report forecasts €757.3 billion in B2C turnover in the EU-27 in 2026 and about 6% growth. At the same time, the share of online buyers in the population aged 16–74 is expected to remain at 72%. The growth of market value and the increase in the number of buyers are not the same process.
For the store, this means the need to work on why a customer would choose its offer: product fit, reliability of information, convenience of purchase, and post-sale relationship. An AI assistant can participate in the first part of this process - helping to evaluate options before the consumer proceeds to the transaction.
Figure 1. Source: European E-commerce Report 2026 Light, p. 11. Prepared by Semly. P = forecast. The indicators refer to the population, not just internet users.
Importance for visibility
Presence in AI responses should support the existing sales strategy.
The priority is the purchasing needs important for the company's profitability, not any questions that can generate content. Internet adoption does not measure the use of LLMs; the chart shows the maturity of the environment in which a new way of selection is developing.
AI shortens the path from need to product list
In the classic path, the customer assembles information from search engines, stores, comparison sites, and reviews. In the AI-supported path, part of this work can be taken over by the assistant: organizing criteria, explaining differences, and suggesting further questions. The final decision may still require checking price, availability, and terms with the seller.
The practical change concerns how to describe the offer. The phrase 'wide selection' says less than specific information about compatibility, dimensions, usage, or limitations. Such information helps both the buyer and the system preparing the response. It is a recommendation regarding the quality of the offer, not proof that a single feature causes an AI recommendation.
Figure 2. Semly analytical model; conceptual diagram. Paths may intersect. Influence on choice is not equivalent to a visit or purchase.
GEO and AEO refer in this report to work on finding and accurately presenting offers in AI responses. This includes assistants and generative search. Their sources and behaviors may vary; we do not treat them as a single channel with a common algorithm.
Europe - a common direction, different pace of change
Interviews in the European report indicate AI development primarily in inspiration, search, and comparison. The French organization FEVAD mentions the use of generative AI by nearly one-third of online buyers on the shopping path. In Denmark, 45% of consumers were noted to use AI for product research or purchases. These are separate studies and definitions - not a ranking of countries.
The German respondent emphasizes limited readiness to delegate purchases and payments. The Spanish market description places the nearest change primarily in offer discovery and decision-making. Luxembourg does not yet observe a significant impact from agentic AI. Together, this shows that assistance in choice is developing differently than autonomous transaction execution.
From these sources, there is no single reliable percentage of purchases in the EU 'made by LLMs'. However, there is a strategic signal: companies should already monitor the layer of information and recommendations, even if most transactions still close in the online store.
Implication for companies operating in multiple countries
In the Polish interview, the European report cites the Loyalty in e-Commerce 2025 study involving over 1600 respondents. According to this source, 66% use AI assistants in the shopping context, and 26% start searches with AI. These are answers to different questions, not two parts of a single market division.
For the seller, the difference between shopping assistance and the beginning of the search is crucial. The assistant can join the process after getting to know the brand, but can also participate in the initial selection. It is worth checking both scenarios: questions about a specific company and questions about a need without naming it.
Figure 3. Source: e-Chamber interview in European E-commerce Report 2026 Light, p. 65. Prepared by Semly. Two different questions. Using an assistant does not mean autonomous payment.
These percentages cannot be interpreted as the share of transactions completed by AI or a representative measure of the use of each model across Poland. They signal behavior in the cited consumer study. The own measurement of store visibility answers a different question: what does the assistant say about our offer?
The Polish store competes in an increasingly broader environment
Poland is approaching the EU level of online shopping adoption: in 2025, the report indicates 70% of the population aged 16–74 compared to 72% in the EU. The 2026 forecast assumes 72%. However, this does not mean identical baskets, preferences, or service costs.
From a GEO perspective, the local context of choice is important. A Polish customer may inquire about fast delivery, a parcel machine, a specific payment method, or available service. A store present in several markets needs reliable information about the actual offer in each of them. Simply translating the product description is not enough to present the purchase conditions.
Figure 4. Source: European E-commerce Report 2026 Light, pp. 11 and 63. Prepared by Semly. P = forecast. Aligning indicators does not mean identical spending per customer.
Semly's conclusion: the questions monitored in Poland should be based on customer needs and conversations with service staff. Only then is it worth expanding the panel to versions for additional markets, maintaining comparable intentions and local conditions.
Price and convenience must be understandable even outside the store
Interviews from France, Germany, Denmark, and the Netherlands describe cautious shopping and comparing the value of offers. The Italian market shows both price sensitivity and demand for quality and experience. These are the conditions in which the customer needs justification for their choice: what they get for the price and what compromise they are making.
In response, the AI product list can be organized by budget, application, or limitations. Therefore, the offer should explain the differences between variants and the total cost of purchase. Lack of information about required accessories, installation, or delivery costs can change the assessment of profitability.
This does not mean publishing thousands of nearly identical tips. More content does not automatically remove ambiguities. The starting point should be recurring customer questions: for whom the product is suitable, what it does not fit, what the price includes, and what the service conditions are.
From promise to verifiable fact
| General keyword | Information useful for selection |
|---|---|
| "Fast delivery" | Deadline for a given product, market, and method of receipt. |
| "High quality" | Material, parameters, documentation, and service conditions. |
| "Fits many models" | List of compatible models and exclusions. |
| "Good value for money" | Differences in variants and total cost of achieving the effect. |
Visibility arises in the entire information ecosystem
Own stores and marketplaces coexist. In Polish companies employing at least 10 people and selling online, the report indicates that 78% use their own website or app and 64% use platforms. The groups overlap. These are not channel shares in turnover.
For managing visibility, this means that product information can appear in many places: in the store, the manufacturer's catalog, on the platform, in guides, or reviews. Semly's research also shows that the set of cited domains is broader than the list of monitored stores.
Figure 5. Source: European E-commerce Report 2026 Light, p. 12; Eurostat. Semly's analysis. Companies can use both channels. These are the percentages of companies, not the share of channels in turnover.
Semly's conclusion: the audit of responses should include the origin of information. If the system uses an outdated description in a controlled channel, it is worth correcting that description. If it cites an independent source, facts should be checked and a substantive correction reported if necessary. The mere presence of a link does not prove that it caused a recommendation.
What follows from Semly's visibility observations?
Analysis of AI responses in Polish shopping categories shows several recurring patterns. Visibility depends on the context of the question, and a brand's position in a broad category does not describe all customer needs. In individual subcategories, the brands that most frequently appear in responses change.
The second pattern concerns sources: brand presence and domain citation are different phenomena. A store can be mentioned without a link to its own site, and the source of information can be a platform or a guide service. The third conclusion comes from the available comparison of one brand: responses and levels of presence varied between AI systems. This is not sufficient for ranking systems or generalizing across all brands.
The overall conclusion is not "every store must be first everywhere." It indicates the need to recognize under what shopping tasks the company is visible, whether its offer has been properly presented, and which important needs remain outside this picture. How this connects to European trends
The European report highlights the importance of relevance, price, and trust. Semly's observations indicate that presence in AI also depends on the specific context of choice. This is a consistent premise for working on the quality of information, but not proof that a specific content change led to increased visibility or sales.
Visibility depends on specific purchasing needs
Semly's research shows that the overall industry score may conceal a different picture of individual categories. The following observations present patterns from the analyzed responses, without indicating specific brands. They are not a ranking of industry attractiveness or an assessment of the effectiveness of their GEO actions.
The significance of this result is practical: a store does not need to be equally visible for every product application. It is worth determining whether it appears for those needs where its offer truly has an advantage.
What does this change in strategy?
| Industry | Pattern visible in Semly material |
|---|---|
| Zoology | A strong overall position coexisted with nearly equal results of leading brands in one category of food. |
| Health | The leading brand did not generally lead in every group of needs; the change occurred in the digestion category. |
| Children's products | Different brands led in travel, others in baby gear, toys, and room furnishings. |
| Beauty | Visibility varied between makeup, perfumes, and hair and face care. |
| Home and garden | The leader in furniture and kitchen was different from that in garden, lighting, and decorations. |
The European report emphasizes the importance of matching and product information. Semly's observations complement this picture: AI presence should be assessed at the level of the client's task, not just the entire brand. Priority should be given to significant purchasing needs where the offer is overlooked or described incompletely.
Mention, source, and recommendation describe different things
Monitoring just the company name provides an incomplete picture. Brand presence answers the question of whether it appears in the response. Citing the domain shows the source used. Context indicates whether the brand was presented as a matching option, example, comparison, or disclaimer. These events should not be treated as identical success.
In Semly's materials, sources also included platforms and services outside the monitored store set. The available comparison of systems for one brand showed differences in its presence. This justifies checking more than one contact point with AI, but does not allow determining which system is generally the 'best for e-commerce'.
How to interpret visibility share?
| Signal | Audit Request |
|---|---|
| Brand appears in the response | Check the context, description accuracy, and relevance to the need. |
| Custom domain is the source | Check the specific page and information that was used. |
| Information comes from external sources | Examine the currency of the description and any discrepancies. |
| Results vary between systems | Maintain separate measurement series, market, and comparable intentions. |
The percentage of responses with the brand has in the denominator responses to monitored questions. The brand's share of the total mentions of competitors has a different denominator: brand occurrences in the selected set. The second indicator can also increase when the presence of competitors decreases. Neither measures automatically the share of sales or reach among customers.
The set of questions should remain stable, and changes to the panel need to be documented. The absence of a brand or source is a signal for diagnosis, not evidence that it is enough to add content. The material does not allow determining whether a specific citation caused the recommendation.
Why is it worth being visible in LLMs?
In a scenario where a customer asks the assistant to choose a few options, the absence of a brand may mean losing the chance to consider its offer. Not every AI response leads to a purchase, but part of the decision about what to compare next may be made before entering the store. Visibility thus becomes one of the elements of brand availability for the customer.
The second reason is qualitative. Even when the assistant knows the company's name, it may not show the full range of products, skip local availability, or present outdated conditions. The company should know whether the image conveyed to the customer matches its actual offer. This task is similar to information control in other channels, extended to responses generated by AI.
The third reason concerns learning about the customer. An audit may reveal questions and criteria that the store has not previously explained well enough. Supplementing such information is valuable even when its impact on AI recommendations remains uncertain: it facilitates comparison and reduces misunderstandings.
Visibility as part of business performance
Presence in LLMs does not replace a good offer. The most frequently mentioned store may lose on price, availability, or service. Therefore, the goal should be to accurately present the offer based on important customer needs and measurable improvements in experience and sales, rather than just an increase in mentions.
How to check how AI sees your company?
A single question, "do you know our brand?" is not an audit. It may confirm name recognition, but it won't show whether the store appears when a customer searches for a solution without mentioning the brand. Questions about category, usage, comparison, purchase, and service conditions are needed. Examples: "What product to choose for a small apartment?", "What are the differences between variants A and B?" and "Does store X deliver this product to Poland?".
It is worth recording the full response, date, language, market, system and its mode, as well as visible sources. Repetitions help distinguish a single response from a more frequently observed pattern. It is also necessary to separate responses using current sources from situations where the system does not show where it draws information from.
Figure 6. Semly analytical model; conceptual diagram. Lack of mention does not prove the model's lack of knowledge. Every signal requires diagnosis.
An audit does not provide direct access to the model's internal knowledge. It shows its behavior under specific conditions. Lack of mention may result from question selection, sources, a limited list of suggestions, or other factors. Only analysis of responses and available information allows for formulating a hypothesis about the problem.
Lack of mention, error, and knowledge gap are different issues
If AI does not mention the store, it is not yet clear whether it needs new content. If it provides the wrong delivery date, the problem may lie in outdated sources. If it describes a category but omits an important variant, the cause may be a lack of clear product differentiation. Each situation requires a different intervention.
Before publication, the response must be compared with the confirmed state of the offer. The owner of the facts should be the product, sales, or support team, depending on the topic. The person conducting the monitoring is responsible for gathering evidence and priorities, not for independently adding product attributes.
| Audit Signal | What to Check | Possible Reaction |
|---|---|---|
| No Brand | Intent of the question, offer, repetitions, and sources. | Further diagnosis, not automatic content creation. |
| Incorrect information | Source of the error and current conditions. | Correction in the source and retest. |
| Incomplete description | Is the missing invoice published and readable. | Completion of the card or information page. |
| Conflicting data | Store, feed, platforms, and documentation. | Standardization of data and update process. |
Errors affecting product selection, cost, or usage should be prioritized. A high rate of incorrect responses is a more significant issue than a low number of mentions for a question of little importance to the company.
Example in action: the assistant cannot determine if an accessory fits a given device. The team confirms compatibility in the documentation, updates the model table on the product page, and refreshes the controlled offers. Then it repeats the compatibility questions.
This is an example of a process, not a description of a measured implementation.
How to supplement missing knowledge about the offer?
‘Supplementing LLM knowledge’ in practice primarily means improving the available, reliable information about the company and products. The store has no direct control over the memory of public models or which source they will choose. However, it can organize the information it publishes and distributes.
It's worth starting with one specific gap. If the question concerns product compatibility, a list of compatible models and limitations is needed. If it concerns international delivery - current countries, deadlines, and exclusions. The information should be where the customer and the system searching for sources have a reason to find it.
Figure 7. Semly analytical model; conceptual diagram. Publishing information does not guarantee its use by AI or the model's knowledge update.
Every change should have an owner, a date, and a basis in documentation. Re-measurement checks if the responses have changed but does not provide evidence of causality by itself. Updating the page does not equate to an immediate update of all models.
A consistent catalog is a common basis for sales and GEO
The European report combines better product information with product selection and reducing returns. Sweden emphasizes descriptions, size selection, and guidance; Italy highlights the importance of consistent, interoperable data for AI-supported commerce.
Semly's conclusion: the catalog should be treated as a source of facts used across multiple channels. Name, model, variant, parameters, purpose, and limitations should be consistent on the product card, in guidance materials, and in controlled distribution channels. Otherwise, even correct information competes with its contradictory version.
Content should explain differences that matter to the user. For size, power, compatibility, or material variants, simply changing the symbol in the name is not enough. Clear comparisons and descriptions of what need a given variant will meet are helpful.
The technical layer supports information availability
Google indicates that the basics of SEO remain essential for AI Overviews and AI Mode: indexing capability, helpful content, important information available in text, and compliance of structured data with the visible page. It does not require a special tag designated solely for AI. Meeting the requirements does not guarantee visibility. This applies to Google features, not universal principles of all LLMs.
The Product documentation describes the transmission of product information through structured data and Merchant Center. They should be treated as organized data channels for supported Google features; they do not guarantee recommendations across all assistants.
Delivery is part of the promise that AI can describe
In the Polish study Logistics and Delivery 2025 cited in the European report, 86% of respondents expect delivery within 48 hours, and 66% use parcel lockers. At the same time, 61% want AI assistance in choosing the delivery method. The study includes 1714 people; these are responses to separate questions.
For GEO, this is an important connection: the customer can inquire not only about the product but also about the possibility of obtaining it under specific conditions. If AI presents an inaccurate pickup date or method, the issue concerns the quality of the promise made before purchase.
Figure 8. Source: e-Chamber interview in European E-commerce Report 2026 Light, p. 66. Prepared by Semly. Various questions in the study; responses do not sum to 100%.
Semly Recommendation: delivery conditions should be current, market-specific, and verifiable with the product. Monitoring should also include questions about availability, time, and cost. Declared interest in AI assistance does not prove the effectiveness of the implemented assistant or consent to its autonomous decision-making.
Location includes payments and pickups
The importance of local conditions is also visible outside Poland. In Finland, nearly 44% of e-commerce packages go to lockers, and 41% to pickup points. This is the structure of packages, not the percentage of respondents. Polish and Finnish values should not be compared as identical indicators.
European interviews also describe payment differences: Bancontact in Belgium, iDEAL in the Netherlands, MobilePay in Denmark, or MB WAY and Multibanco references in Portugal. In Poland, BLIK and instant transfers are significant.
Figure 9. Source: European E-commerce Report 2026 Light, p. 46; Finnish Parcel Index cited in the interview. In the source, lockers: "nearly 44%". Values rounded.
Semly Conclusion: a store expanding should monitor whether AI responses distinguish its offer in different markets. This is not solely a linguistic task. The information "store delivers to the EU" may be insufficient if specific products, regions, or methods have restrictions.
A visit with AI must lead to a convenient purchase
In the Polish Omni-commerce 2025 study, referenced in the European report, smartphones surpass laptops in completing purchases. At the same time, the interview highlights the importance of a convenient mobile experience. The presence of AI responses does not eliminate form issues, unclear options, or lack of payment methods.
If the assistant directs the customer to an offer, the page should quickly confirm the features that justified the choice. When the question concerns compatibility or delivery, the user should not have to search the entire site again to verify this information.
Figure 10. Source: e-Chamber interview in the European E-commerce Report 2026 Light, p. 65. Prepared by Semly. Selected responses from the referenced national study; not the statistics for the entire EU.
Semly's conclusion: evaluate not only visibility but also the landing page. Check variant compliance, availability, readability of parameters, and the transition to the cart on mobile. This connects GEO with conversion optimization and helps avoid situations where interest increases, but the purchase process still fails.
Expansion requires checking the local brand image
The Polish interview in the European report indicates a rise in cross-border shopping in the referenced Omni-commerce study: from 15% of consumers in 2024 to 39% in 2025. This is a signal from a specific study, not an increase in the value of the entire EU cross-border trade.
For companies, this means expanding the set of alternatives with which the customer can compare the offer. The AI response can consider both the local store and the platform or foreign seller. The audit should identify actual alternatives that arise with important questions, rather than limiting itself to an internal list of competitors.
Figure 11. Source: European E-commerce Report 2026 Light, p. 65. Prepared by Semly. Change +24 percentage points. National study; not an increase in cross-border sales value in the EU.
Before entering the market, it is worth checking questions about category and purchasing conditions in the local language. Then confront the obtained picture with the margin after delivery, returns, payments, and service. Visibility indicates a potential contact point; it does not determine the profitability of expansion.
How to measure effects without confusing presence with sales?
The store needs several measures, as each answers a different question. The percentage of responses containing the brand describes presence in the panel. The correctness of the description and the number of significant errors indicate quality. Cited sources help diagnose the origin of information. None of these measures automatically equate to market share.
The panel should maintain consistent intentions, language, market, and repetition rules. It is worth separating questions with the brand name from those without it and recording changes in systems and content. Results from different tools should not be summed without clarifying their definitions.
| Layer | Sample Measure | Decision |
|---|---|---|
| Visibility | Presence for important needs. | Where to deepen the diagnosis. |
| Quality of Response | Errors, omissions, outdated conditions. | What information to correct. |
| Sources | Domains and pages used in the response. | Where to look for inconsistencies. |
| Experience | Conversion, questions, mismatches returns. | Does the client choose more easily. |
| Business | Order margin, retention, service cost. | Should scale operations. |
Traffic identified as coming from AI shows only part of the path. The impact of the previous conversation with the assistant may not be visible in the visit source. On the other hand, an increase in sales after changing content does not prove its causal effect. The pilot should be compared with similar products without changes and note other events.
First, the organization's readiness, then the scale
The European report shows a clear difference in digital intensity between large enterprises and SMEs. The index relates to a set of digitization conditions, not readiness for GEO, but it provides important context for implementation.
If product information is scattered and changes in purchasing conditions do not reach all channels, simply launching AI monitoring will not solve the problem. A process is needed: someone identifies the error, the data owner confirms the fact, the team publishes the change, and the next measurement checks the result.
Figure 12. Source: European E-commerce Report 2026 Light, p. 13; Eurostat. Prepared by Semly. SMEs: 10–249 people; large: 250+. The total for large companies is 101% due to rounding.
Semly's conclusion: it is worth starting with a limited scope for which the company can provide reliable data and accountability for corrections. Automation makes sense after establishing a quality standard. Faster publication of inconsistent information may exacerbate the problem rather than improve brand perception.
From presence in response to agent handling
Semly's research indicates that the development of agents acting for customers can expand marketing tasks: from brand monitoring in LLMs to preparing an offer that can be found, evaluated, and tested by the agent. This is a forecast for future competencies, not a measurement of adoption or sales.
This perspective complements the European E-commerce Report 2026. The report describes the use of AI in selection and comparison, but also limited readiness to delegate purchases and market differentiation. Therefore, Semly proposes to separate the current work on visibility from experiments with agents performing actions.
Figure 13. Semly analytical model; conceptual diagram. The diagram does not represent the level of implementation in the EU. Each stage requires separate verification.
Practical direction for the store
First, check if AI correctly recognizes the offer and its limitations. Then provide current data on variants, total price, availability, and delivery. If the company is testing agent support, it should separately verify the correctness of product selection, execution conditions, and customer purchase approval.
Such a pilot does not mean the necessity to implement every new protocol. It should arise from a specific scenario and the store's capabilities. Visibility in response and the ability to correctly execute the purchase are separate results to measure.
90-day plan - from diagnosis to a permanent process
The GEO program should start from customer needs and business goals. A good scope for the pilot is priority categories in one market, for which the company knows the margin, typical questions, and post-sale issues. The following plan is a proposal from Semly, not a result of an experiment.
What should be on the board meeting agenda?
A list of the most important issues for the customer, the state of their resolution, and the change in response quality. Alongside this, conversion, service cost, and margin in the pilot area. The monitoring report is the beginning of the decision, not its end.
The team should keep a change log: what was published, where, when, and on what basis. This facilitates the interpretation of subsequent measurements and distinguishes data improvement from model behavior change. A lack of rapid visibility growth does not negate the value of improved information for customers.
Semly Role - know how AI presents your offer
Semly helps monitor brand presence in AI responses, compare it with competitors, and analyze questions and sources. Such monitoring provides the team with material to assess when the brand appears in conversations and where to start further diagnosis. The range of functions depends on the chosen package.
The most important value of the process arises when observation leads to action: verifying incorrect descriptions, supplementing missing information, or standardizing data across channels. The e-commerce team retains responsibility for the accuracy of the offer and sales results; monitoring helps identify an additional point of contact with the customer.
In mature e-commerce, it is not enough to know how your own store looks. It is also important to know how it is presented in the responses that customers use. This connects European trade trends with GEO practice: better information, more accurate choices, and the ability to systematically assess where the company's image needs improvement.
Start with the questions customers ask before purchasing. Check if AI considers your company, what it says about the products, and on what sources it bases its response. Then, turn the most critical gaps into a specific action plan for information.
Methodology and scope of interpretation
The report is an original synthesis by Semly. It combines data and interviews from the European E-commerce Report 2026 Light with a qualitative interpretation of AI visibility research in Polish shopping categories. These layers describe different phenomena: the market, consumer declarations, and system behavior. They have not been combined into a single statistical indicator.
In the EU market section, it refers to 27 countries; Europe in the source includes 38 countries. The 2026 values marked with P are forecasts. The source report combines Eurostat data, national sources, and other databases. Definitions of turnover may include goods or goods and services. The studies referenced in interviews retain their own samples, questions, and periods.
Semly's observations cover zoology, health, children's products, beauty, and home and garden, based on measurements from August and September 2026. The selection of questions and competitors was intentional, and the time frames varied. The presented patterns are descriptions of the studied material, not a representative result for Poland or the EU. They do not allow for estimating the share of LLMs in sales.
The comparison of systems available in the material concerns one brand. In other comparisons, the breakdowns refer to categories. The brand presence score and domain citation score have different meanings. A lack of mention is not evidence of a lack of knowledge, and a mention does not necessarily imply a positive recommendation.
Business conclusions, the audit process, and the action plan are recommendations from Semly. The relationships between consumer trends, content quality, and visibility are presented as interpretations or hypotheses for testing. We do not attribute proven impact on revenue to them.
Sources
- Weltevreden, J.W.J. (2026), European E-commerce Report 2026 Light. Centre for Marketing Innovation, Amsterdam University of Applied Sciences; Ecommerce Europe and EuroCommerce. Main source, 109 pages. Tables: pp. 11–13, 63. Research and interviews: e.g., pp. 17, 20–24, 30, 34–35, 40–41, 46, 53, 65–66, 97, 101, 104. Methodology: p. 107.
- Semly, qualitative analysis of five visibility studies in AI responses in Polish e-commerce categories, August–September 2026. The proprietary material complements the interpretation of European trends.
- Google Search Central, AI features and your website.
- Google Search Central, Introduction to Product structured data.
- Semly, Brand visibility monitoring in AI.
Access to Semly documentation and site: September 28, 2026. The charts present source data; the process diagrams are Semly's original model. The analysis is not an official publication or recommendation from the authors of the European report.
Suggested citation: Semly (2026), E-commerce in the age of AI responses. How LLMs are changing product selection in the EU and Poland - and what it means for store visibility, September 28, 2026.
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