Many transactions, many systems
In retail and e-commerce, many small transactions arise every day: customers ask about their order, returns arrive, product data needs maintaining, suppliers send invoices, business customers order by email. Each one is simple on its own. Taken together, however, they keep an entire team busy.
On top of that, the information sits in different systems: the shop, the inventory management system, the shipping provider, the inbox. Answering a single question often means looking in three programs. This is exactly where AI agents can help, because they bring together information from several systems and act on it.
Customer service inbox and order status
A large share of customer service enquiries revolves around a few topics: where is my order, how do returns work, can I still change the address? An agent can recognise such requests, even when the customer does not give an order number, and find the order by name or email address.
How a status enquiry works
- The request, customer and order are identified.
- The status is retrieved from the shop, inventory management and shipping provider.
- A reply in your tone of voice is drafted, with the tracking link and the next step.
- Complaints and special cases go straight to your team.
You decide whether replies go out directly or are first prepared as drafts. Many retailers start with drafts and only release individual request types for automatic replies after a review period.
Returns and complaints
Returns and complaints require care, but they often follow a fixed process. An agent can record the reason for the return, check whether the deadline has been met and prepare the next steps: return label, credit note or replacement delivery.
With complaints, the human element is especially important. An annoyed customer does not want to feel they are talking to a machine that does not understand their problem. A sensible approach: the agent records the complaint in full, with photos and a link to the order, and hands it over to a person who decides and replies.
Product data and supplier invoices
Maintaining product data
New products need descriptions, attributes and categories. Manufacturers often supply this data in different formats, as a PDF, spreadsheet or data sheet. AI can extract the relevant details, bring them into your format and suggest description texts. Before a text appears in the shop, someone should check it, especially technical details that matter for the purchasing decision.
Supplier invoices
In purchasing, invoices arrive from many suppliers in different forms. An agent can read them, match them against the purchase order and goods receipt, flag discrepancies in quantities or prices and pass the document on to accounting. Invoices above a set threshold go to a person for approval.
B2B orders from emails
In wholesale and B2B, orders often do not come through the shop but by email, as a PDF or as an informal message. Someone reads them and creates the order manually in the inventory management system. An agent can recognise items, quantities, delivery address and requested date, match the item numbers to your product range and create a draft order.
- Unclear items are flagged rather than guessed.
- Customer-specific prices and terms are taken into account if they are stored in the system.
- An order confirmation can be prepared and sent after review.
- Unusual quantities or new customers go for approval.
Systems that can be connected include Shopify, WooCommerce, Odoo and SAP, as well as the common email inboxes. We clarify which interfaces your system offers before we build anything.
Transparency in AI replies
When customers communicate with an AI system, they should know about it. The EU AI Act sets out transparency obligations for certain AI systems that interact with people. Regardless of the exact legal classification, openness is also a question of trust: a short note that a reply was generated automatically, and an easy way to reach a person, create clarity.
Which obligations apply to your use case can be assessed case by case. Our guide to the EU AI Act gives an overview. At the Scalient Group, every use case is classified by risk before it is built, and the obligations are documented.
Conclusion
In retail, the value of AI lies where many similar transactions meet several systems: status enquiries, returns, product data, invoices and orders by email. An agent brings the information together and handles the routine, while your team takes care of complaints, special cases and decisions. If you are open about when an AI is replying, you gain time without losing trust.