Artificial intelligence can play a role in many areas of a store: sales, inventory, catalog, suppliers, documents, customers or online store.
But its value is not simply about adding an “AI” feature to each of these tools.
When it can access the necessary data and functions, it above all lets you start from a request written in everyday language to look up information, analyze a situation or prepare an action.
Here are seven practical uses.
1. Analyzing sales
“Compare my sales this week with last week's and show me the main differences.”
POS software already produces a lot of data. AI can make it easier to explore without forcing the merchant to build each report manually.
For example, it can compare:
- revenue;
- number of tickets;
- average basket;
- quantities sold;
- sales by category;
- sales by product;
- locations.
But noticing a change is not always enough. If revenue drops by 8%, the next question becomes:
“What is mainly contributing to this drop?”
The analysis can then look for the categories, products or other metrics that changed the most. You do need to distinguish statistical contribution from causality: the data can show which elements accompany a change without necessarily proving why customers changed their behavior.
This use is covered in detail in our article on in-store sales analysis.
2. Anticipating inventory and preparing restocking
“Which products are likely to fall below their stock threshold this week?”
Inventory management is particularly well suited to cross-analysis. Available stock alone does not tell you whether a product is really at risk.
In particular, you need to be able to take into account:
- sales rate;
- available stock;
- orders already placed;
- supplier lead time;
- safety stock;
- possibly seasonality.
AI can help bring out the products that deserve particular attention. The merchant can then continue:
“Prepare a restocking proposal for these products.”
The quantity to order should not, however, be deduced from the sales rate alone. It can also depend on packaging, the minimum order, the target stock, orders in progress or supplier constraints.
The value is therefore in moving from detecting the risk to a verifiable order proposal, and not in treating the calculation as automatically right. Conversely, the same logic makes it possible to bring out dead stock or overstocked products.
3. Managing the catalog
Creating and maintaining product records can become tedious when the catalog contains hundreds or thousands of products. AI can help prepare certain operations. For example:
“Prepare a new record for this product: T65 flour, 25 kg bag, supplier X.”
From the information provided and the data model used by the store, it can prepare the necessary fields. It can also help to:
- look up a product;
- fill in certain information;
- rewrite a description;
- classify products;
- spot incomplete records;
- prepare bulk changes.
Whether the product is actually created then depends on the system the AI is connected to and the permissions it has. This distinction is important: generating the information for a record and actually modifying the catalog are two different operations.
4. Handling suppliers and commercial documents
Part of day-to-day management relies on information that moves between several documents:
- quotes;
- orders;
- delivery notes;
- invoices;
- supplier price lists.
AI can help find these documents, extract certain information from them or prepare the next document in the process. For example:
“Prepare the quote for this customer request.”
Then, once the quote is accepted:
“Prepare the invoice from the approved quote.”
The value is in avoiding needlessly re-entering the same information. For purchasing, a request can also concern suppliers:
“Which suppliers have raised their prices on these products?”
or:
“Find the last order placed with this supplier.”
Here again, preparing, creating and sending are not necessarily the same operation. A binding action can keep a validation step.
5. Segmenting, building loyalty with and following up with customers
AI can also make customer analysis easier when transactions are linked to identified customers. A request such as:
“Which loyal customers haven't bought anything for two months?”
seems simple, but the term “loyal” must first be defined. For example:
“Identify customers who made at least six purchases over the last twelve months and haven't bought anything for 60 days.”
The segment then becomes measurable. AI can then help to:
- analyze recency;
- measure purchase frequency;
- find the customers who generated the most revenue;
- identify drop-off behavior;
- prepare a message for a segment.
For example:
“Prepare a win-back message for this group.”
The quality of the action depends first on the quality of the segment. AI does not automatically turn a customer base into a relevant campaign: the criteria chosen remain decisive.
To go deeper on this case, see our article on following up with inactive customers, and our loyalty capabilities.
6. Managing the online store
E-commerce also generates repetitive tasks. AI can help to:
- prepare a product record;
- rewrite a description;
- classify products;
- look for missing information;
- analyze online sales;
- compare behavior across channels.
For example:
“Rewrite this product description so it is clearer without changing the technical specifications.”
Content generation is a direct use here. On the other hand, syncing inventory between the store and the online store is not done by the AI itself. It depends on the architecture of the management system and the integrations between channels. AI can query this data or look for certain inconsistencies, but it does not replace the technical mechanism that keeps inventory in sync.
7. Automating recurring tasks
Some requests come back every day or every week. For example:
“Every Monday, summarize last week's sales and highlight the significant gaps.”
Or:
“Every morning, check the products close to their restocking threshold.”
When an analysis always follows the same rules, it can become a routine. This spares the merchant from manually repeating the same search.
But not every automation has to produce an action. You need to distinguish:
An information routine
“Send me the summary every Monday.”
From conditional monitoring
“Only flag the products that fall below their threshold.”
And from an operational action
“Create a supplier order.”
These three levels do not have the same consequences or the same need for control. A question that is useful once in a while can thus become an automated task when it meets a recurring need.
An eighth, cross-cutting lever: AI vision
Some uses start not from data already recorded, but from a document or an image. For example:
- photo of a delivery note;
- supplier invoice;
- paper catalog;
- price list;
- menu;
- product label.
An AI able to analyze images can help extract and structure the visible information. A photo of a delivery note can, for example, be used to pick out the product references and quantities written on the document.
But an important distinction must be kept: reading “12 units” on a document does not physically prove that 12 units were actually delivered. Likewise, extracting the information from a catalog does not mean it should be imported without checking.
AI vision reduces re-entry; it does not remove the need to check the information when the stakes justify it.
From question to action
All these uses can be grouped into several levels:
- Search“Find the last invoice from this supplier.” The AI looks for existing information.
- Understand“How have my sales changed this week?” It reports and connects the available data.
- Analyze“Which categories contribute most to the drop?” It looks for the elements that help interpret the situation.
- Prepare“Prepare the restocking for these products.” It builds a proposal from the available data and rules.
- Act“Create the matching supplier order.” If it has the necessary tools and permissions, an agent can carry out an operation in the system.
- Automate“Run this check again every Monday.” A recurring request becomes a routine.
This progression helps explain what distinguishes a simple conversational interface from a system able to use tools. Answering a question and acting in the information system are not the same capability. It is this ability to chain understanding, analysis and action that distinguishes an AI agent from an assistant limited to producing an answer.
AI does not replace business software
This is an important point. AI is not meant to replace:
- the POS that records sales;
- the inventory system that records stock movements;
- the CRM that stores customer information;
- the invoicing system;
- the e-commerce engine;
- supplier tools.
It can be an access and action layer on top of these systems, provided they are connected and the necessary data is available.
This distinction helps avoid a misleading idea: AI does not spontaneously “know” what is happening in the store. It must have the data or tools that let it find out.
Not all data is equal
The quality of the analysis depends directly on the quality of the data.
A stockout forecast will be unreliable if inventory is not properly updated. A customer analysis will be limited if most transactions are not linked to any customer. A comparison between stores can be misleading if the locations do not use the same rules or scopes.
A very powerful AI does not automatically fix inconsistent data. The more significant the planned action, the more the quality of the data used should be checked.
What level of control should you keep?
Not all actions require the same level of validation.
Displaying revenue carries little risk. Preparing a product record carries more. Sending a customer communication, changing a price or creating a supplier order can have a direct impact on the business.
The level of control can therefore be adapted to:
- how reversible the action is;
- its financial impact;
- its impact on a customer;
- the confidence placed in the data;
- the store's internal rules.
The goal is not to prevent automation. It is to apply it where it truly saves time without creating a disproportionate risk.
How does Gillia apply these uses?
Gillia brings together several management functions and can also use the tools and data it is connected to.
The merchant can start with a question:
“Which products are at risk of a stockout this week?”
then continue:
“Prepare the matching restocking.”
Or start from a customer analysis:
“Which regular customers are starting to stop coming back?”
then:
“Prepare a win-back action for this segment.”
The value is in keeping the context between question, analysis and preparing the action, rather than starting from scratch in several tools. Some requests that are useful day to day can also become recurring routines. Depending on the action and the permissions granted, a validation can be kept before execution.
These uses cover only part of what is possible. The Gillia use case catalog brings together detailed use cases by area.
AI for retail: start with the problem, not with the AI
A relevant use does not start with:
“Where can I put AI?”
but rather with:
“Which task wastes my time?”
“Which information is hard to find?”
“Which analysis do I have to redo regularly?”
“Which operation involves too much re-entry?”
From there, you can determine whether AI really adds something. For a perfectly fixed rule, classic automation can sometimes be simpler. For a question that requires understanding language, connecting several pieces of data or adapting the analysis to the context, AI can become far more interesting.
The right use of AI is not the one that looks most spectacular. It is the one that genuinely improves a task in the store.