It's not an unusual scene at all: it's half past ten in the evening, and in your webshop a visitor is trying to decide between two similar products. They have one simple question - and if they don't get a quick answer, they'll close the page, search for the same product elsewhere, and a few minutes later complete the purchase in a competitor's webshop.
A few years ago, the best you could say to this was that customer service has working hours too. Today, however, more and more shoppers expect immediate help regardless of whether they're browsing in the morning or late at night. AI-based chatbots offer a solution to exactly this. Modern systems no longer work from pre-written questions and canned answers: they can interpret the customer's intent, look up information in the product range, help with comparisons, answer common customer service questions, and hand the conversation over to a colleague when needed.
In a webshop, the chatbot is less and less an extra or a fancy indulgence; set up well, it can become a part of sales and customer service that keeps working even when the support team is no longer available.
Customers no longer want to wait until tomorrow
One of e-commerce's greatest advantages is that the webshop never closes. But this creates a peculiar situation: while you can shop at any time of day, traditional customer service is mostly still available eight to ten hours a day. The gap in the customer experience opens up exactly when the visitor would need help.
According to Zendesk's 2026 Customer Experience Trends research, 74% of consumers now expect 24-hour customer service availability as a result of AI's advance, and 88% want faster response times than a year earlier.
The twist is that in a webshop, customers most often turn to support not with complex questions but with simple problems - and they want the answer immediately. The customer wants to know, for example, when their package will arrive, whether a larger size is available, whether two products are compatible, how much shipping costs, which variant would suit them better, or how returns work.
A significant share of such questions can be automated, given well-structured product and customer service data. Not necessarily to replace customer service, but so that simple questions get answered instantly, while the team can concentrate on the genuinely complex cases.
There's a huge difference between the old chatbot and an AI assistant
Many people still identify this technology with the chatbots that spread a few years ago. Those typically worked with pre-written menu options: the visitor could choose whether they wanted information about shipping, payment, warranty or contact details, but as soon as they stepped outside the predefined script, they often got the familiar reply: 'Sorry, I don't understand the question.'
That wasn't really intelligent customer service - it was a menu system disguised as a conversation. Assistants built on generative AI, by contrast, can interpret more complex questions phrased in natural language. If someone writes, for example, that they're looking for running shoes for asphalt, for three runs a week of roughly ten kilometres, and good cushioning matters to them, a system with access to proper product data no longer just looks up the 'running shoes' category. It can interpret the use case, match it against the characteristics of the product range, and then recommend a few genuinely relevant products.
It's worth noting here that this kind of customer service can do more than just supply information - it can also effectively support sales. That's exactly why an AI chatbot can be far more than a plain question-and-answer automation.
The chatbot effectively becomes a digital salesperson
In a physical store it's natural for the salesperson to ask questions, clarify, and then help you choose based on your answers. In webshops, by contrast, we long tended to leave the customer alone with hundreds of products, countless filters and technical specs, trusting them to piece together the right decision on their own. An AI-based commerce assistant can change that. With proper product data it may be able to compare the options, explain the key differences, suggest complementary products, or refine the need with follow-up questions. It can ask, for example, about the size of the room, the device they intend to use, or whether price, performance or a longer warranty matters more to the customer.
This is what makes a chatbot truly valuable in a webshop. Not simply because fewer questions reach customer service, but because it can actively help the visitor get from uncertainty to a purchase decision.
The numbers show this isn't just a technology fad either
The business impact of chatbots is hard to capture in a single universal conversion figure, since much depends on the webshop's size, product range, the quality of the rollout, and the data the system can access. But analyses based on larger samples clearly show that automated customer handling is far more than a technological curiosity by now.
Smartsupp's analysis published in 2026 examined data from more than 175,000 accounts and nearly 5 billion website visits measured during 2024. Within their own customer base, webshops using a chatbot handled 89.2% of enquiries, while without a chatbot the rate was 71.2%. Sites using such a system were also able to handle roughly six times more customer conversations on average.
Since these are figures from one provider's own platform, they shouldn't be applied unchanged to every webshop, but the direction is clear: with automation, far more customer interactions can be handled without having to grow the support headcount at the same rate.
According to Salesforce's 2025 State of Service research, conducted with 6,500 customer service professionals, companies expect that by 2027 AI could handle roughly half of all customer service cases, compared with about 30% measured at the time of the research. So the question is less and less whether AI will appear in customer service, and more how to build it in so that it genuinely makes the customer experience faster and better.
What does all this mean in a Sellvio webshop?
An AI assistant becomes truly useful when it doesn't operate as a completely separate system, but knows the environment in which it's helping the customer. Generic answers aren't enough: it needs to know what products the webshop offers, how the categories are structured, which attributes belong to each product, and, where applicable, what order information is available.
Sellvio offers several options in this respect. Its integrations include customer communication services such as Dialogity, which provides live chat, a chatbot and an AI-based sales assistant, or Smartsupp, which complements chat and chatbot features with customer behaviour analytics. LiveChat and Tawk.to integrations are also available.
A big advantage of this setup is that you don't have to develop your own customer communication system to add chat or chatbot functionality to the webshop. An existing service can be slotted into your operation quickly, while the more advanced solutions can go well beyond simple message exchanges. And this is where the really interesting part begins...
When the chatbot works from the webshop's own data
It's very important to understand that an AI assistant's real knowledge largely depends on what data it can access. If it only works from general information, it can only give general answers. But if it connects to the webshop's own system, it can provide help on an entirely different level.
Through Sellvio's REST API, external systems can access product, category, brand, product parameter, filter and order data, among other things, with the appropriate permissions. From a customer service perspective, this can be especially interesting for a large webshop with a complex product range. If the customer asks, for example, 'Which 18-volt drill is compatible with battery X?', a general AI model can at best try to infer from its general knowledge. A properly integrated assistant, however, can search the webshop's actual product data and answer based on that.
The same logic applies to order-related questions. Through the Sellvio API, orders can be queried by various data points, so with the right integration the assistant can even take part in handling specific customer situations. This doesn't happen automatically, of course: the data connection, permissions and privacy rules need to be set up deliberately. But the technical possibility is there for the chatbot to be far more than a simple FAQ window, and to become a customer service tool genuinely built on the webshop's own data.
A good chatbot doesn't answer everything
It may sound strange at first, but the quality of an AI assistant isn't shown by its ability to produce some answer to every question. Far more important is that it also recognises when it doesn't have enough, or sufficiently reliable, information to answer.
If, for example, there's no reliable data on when a product is expected to arrive, it must not invent a delivery date. If the warranty terms are ambiguous, it mustn't improvise its own rules, and it has to be able to pass a complex complaint or dispute on to a real customer service colleague. That's why it's worth treating separately the simple, automatically answerable questions; the cases that require webshop or order data; and the situations that call for human judgement. Shipping terms, payment methods and basic product information can typically be automated, while compatibility, stock levels or order status may already require a data connection. A complaint, a custom discount or a special warranty case, on the other hand, may still need human handling.
So a good chatbot's job is not to replace customer service at any cost, but to recognise which tasks it can perform safely and which ones it needs to hand over.
The AI assistant also produces valuable data
Chatbots have a less flashy but commercially very valuable side: they show precisely where customers get stuck in the webshop. If, for example, dozens of people ask the same question about a product every week, it may not be the chatbot's answer that needs refining, but the product page itself. The same goes for shipping information, comparable products or missing filter options. When the same questions keep coming back, they're no longer individual support cases - they're signals pointing to content, UX or even assortment problems. The conversations thus generate continuous qualitative market research. Customers are essentially showing you, in their own words, what information they're missing, what they don't understand, and what makes their decision harder.
With all this in mind, our usual advice is that customer service data must not be isolated from marketing, SEO or product development. Used well, this information can directly help you build better product pages, more useful content and more transparent buying processes.
Round-the-clock customer service no longer necessarily means a night shift
One of e-commerce's old contradictions is that while the webshop can sell 24 hours a day, for a long time it could typically only offer real help during business hours. Generative AI is gradually starting to erase that difference.
It's a basic truth that a well-built chatbot no longer just takes a few simple questions off customer service's shoulders; it can help choose a product, reduce uncertainty, serve the visitor even when no colleague is online, and meanwhile gather valuable information about which questions occupy customers the most.
In Sellvio's case, several ready-made chat integrations are available for this, and the API makes it possible to connect more advanced solutions built on the webshop's actual product and order data.
Next-generation customer service will therefore probably not simply mean more staff, but a well-constructed collaboration between automation and human agents. The system handles what it can do quickly and reliably, and the human steps in where experience, judgement or an individual decision is genuinely needed. So when someone asks in the webshop at half past eleven at night which product to choose, they no longer necessarily have to wait until the next morning for an answer.