Retail automation: repetitive tasks to simplify

In a store, many tasks aren't difficult. They're simply repetitive.

Checking sales. Checking inventory. Spotting overdue invoices. Preparing a report. Identifying customers to follow up with.

A few minutes here, a few minutes there: repeated every day or every week, these operations add up to a significant amount of time.

Automation lets you hand some of these tasks over to a system so they run at a set frequency or when a specific condition is met. But not all tasks are automated the same way — and artificial intelligence isn't always necessary.

What is automation in a store?

Automating a task means defining the conditions under which it should run, without having to trigger it manually each time.

A very simple example:

If a product's stock falls below 10 units, generate an alert.

Another example:

Every Monday morning, produce the previous week's sales summary.

In both cases, the task is repetitive and its trigger can be defined in advance.

Automation becomes particularly useful when the task:

• comes up regularly;
• relies on available data;
• follows sufficiently clear rules;
• takes time when done manually;
• or is simply at risk of being forgotten.

Classic automation or AI automation?

The two approaches don't meet exactly the same need.

Classic automation

It follows predefined rules:

If available stock is below 10, send an alert.

The condition is perfectly explicit. There's no need for an AI to interpret the situation.

For many simple tasks, this approach is even preferable: it's predictable, fast and easy to control.

AI automation

It becomes useful when the task requires more interpretation. For example:

Every Monday, analyze the previous week's sales and highlight unusual variations.

Testing a fixed value is no longer enough. The system has to determine which data to examine, compare it and highlight the elements that match the instruction.

AI can then be used to interpret the objective and analyze the context, while classic automation mechanisms can still handle the triggering of the task. The two approaches are therefore often complementary.

1. Automate sales tracking

A merchant regularly checks the same metrics:

• revenue;
• number of receipts;
• average basket;
• sales by category;
• change compared with a previous period.

Rather than redoing the same sales analysis every week, a routine can automatically produce a summary. For example:

Every Monday, compare last week's sales with those of the week before.

The analysis can then highlight the main variations observed. You can also ask:

Only flag differences greater than 15%.

The merchant then no longer necessarily receives a full report: they can focus their attention on the exceptions that match the rules defined.

2. Monitor inventory

Inventory is another area particularly well suited to automation.

The simplest version is to monitor a threshold:

Alert me when an item falls below its minimum stock.

This task doesn't necessarily require AI.

A more advanced analysis can take several pieces of information into account:

• available stock;
• recent sales pace;
• orders in progress;
• restocking lead time;
• safety stock.

The request can then become:

Every morning, highlight the items likely to run out before the next restocking.

The result is a risk estimate, not a certainty that a stockout will occur.

3. Prepare restocking

Inventory monitoring can be extended by preparing the restock. For example:

Every Thursday, prepare the items to reorder for the following week.

To be relevant, this supplier order preparation may need to take into account:

• sales;
• available stock;
• orders already placed;
• supplier lead time;
• packaging units;
• minimum order quantities;
• target stock.

The routine can prepare a proposal. That doesn't mean a quantity calculated from recent sales alone is automatically the right quantity to order. Preparing a restock and actually placing the order are two different steps.

4. Track overdue invoices

Monitoring due dates is a good example of a repetitive task that can be automated without complex analysis.

Every morning, show the invoices that have reached their due date and are still unpaid.

The routine can also sort the results:

• recently due;
• more than 15 days overdue;
• more than 30 days overdue;
• amount to watch.

The merchant can then decide which reminders to send. If the system is allowed to prepare the messages:

Prepare a reminder for invoices more than 15 days overdue.

Sending is then an additional step, which can keep a validation depending on the rules chosen.

5. Identify customers to follow up with

Another routine can monitor how certain customer segments evolve. For example:

Every month, identify customers who used to buy regularly but haven't bought anything in 90 days.

The term “regularly” does need to be defined, however. You can specify:

Customers who made at least four purchases in the last twelve months and none in the past 90 days.

The routine then relies on a measurable criterion. It can then prepare a list or a message suited to the segment. The point isn't to automatically send more communications, but to highlight the inactive customers who truly match the chosen criteria.

6. Automate checks and verifications

Not all routines produce a report or a sales action. Some simply check that something stays consistent. For example:

Every evening, look for items with negative stock.

Flag products with no price.

Check whether any orders have stayed in the same status for more than three days.

Highlight unusual differences between two points of sale.

This type of monitoring is particularly useful because the absence of an alert can itself become information: nothing needs attention according to the criteria monitored. The manager therefore no longer needs to systematically go through all the data.

Scheduled routine or conditional monitoring?

The two mechanisms are different.

A scheduled routine

It runs at a set time. For example:

Every Monday at 8 a.m., produce the sales report.

Conditional monitoring

It regularly checks whether a specific situation has arisen and only reports something when it's useful. For example:

Flag when an item falls below its stock threshold.

In practice, conditional monitoring still has to be triggered technically: at each relevant change or at a check frequency. The difference lies mostly in what the user receives: a systematic result or an alert only when the condition is met.

How do you build a good routine?

A useful automation starts with a sufficiently precise instruction. You need to define at least four elements.

1. What to checkItems close to stockout.
2. The data to useStock, sales and supplier lead time.
3. When to run the checkEvery morning.
4. What to do with the resultShow only the at-risk items.

You can then add rules. For example:

Every morning, look for products whose available stock may not cover the next seven days at the recent sales pace. Show only the items concerned and their current stock.

This instruction is far more usable than:

Monitor my inventory.

Should the action itself be automated?

Not systematically. There is an important difference between observing, alerting, preparing and acting.

The further along this chain you go, the more the action can have a financial or operational consequence. A mistake in a report is annoying. An incorrect supplier order can cost money. The level of validation should therefore depend on the risk, the reliability of the data and whether the action can be reversed.

Which tasks should you not automate?

A task isn't a good candidate simply because it's repetitive. Automation is less relevant when:

• the rules change constantly;
• the necessary data is unreliable;
• each case requires significant human judgment;
• an error would have a high cost;
• the task occurs too rarely to justify setting it up.

You should also avoid automating a bad procedure. If a task exists only because two systems communicate poorly, fixing the integration may be more relevant than creating an additional automation. This is a point that is often overlooked.

Automating doesn't necessarily mean using AI

This is probably the most important distinction.

To send an alert when stock < 10, a classic rule is enough. To produce an export every Monday, a scheduled task is enough. To copy data from one system to another, a classic integration may be enough.

AI becomes particularly useful when you need to:

• understand an instruction written in everyday language;
• use less structured information;
• bring several pieces of data together;
• adapt an analysis to the context;
• produce a summary;
• prepare an action whose parameters depend on the situation.

Using AI where a simple rule is enough doesn't necessarily make the automation better.

How Gillia turns a request into a routine

In Gillia, a request that's useful day to day can be saved so it can be replayed automatically. For example:

“Which products are at risk of running out in the next seven days?”

Once turned into a routine, the check can be run regularly with up-to-date data. Another example:

“Every Monday, summarize last week's sales and highlight the main variations.”

Depending on the task, the result can take the form of information, an alert or a prepared action. For operations that create, modify or send something, the level of validation depends on the rules and permissions defined for the routine.

The benefit is no longer having to remember to ask the same question at the same time, while keeping the ability to control the actions that need it.

A good automation should also be monitorable

A routine shouldn't become invisible simply because it runs in the background. For important automations, it's useful to be able to know:

• when they ran;
• what data they used;
• what result they produced;
• whether an error occurred;
• what action, if any, was carried out.

This traceability becomes especially important when a routine triggers an action in another system. It lets you understand what happened instead of only discovering the end result.

How do you measure the benefit of an automation?

Time saved is the most obvious benefit, but not the only one. You can also look at:

• the number of manual checks eliminated;
• the delay between a problem appearing and its detection;
• the number of oversights avoided;
• the number of actions prepared automatically;
• the false-positive rate of alerts;
• the number of human interventions still needed.

That last point is important. A routine that generates twenty useless alerts a day doesn't save time. A good automation should reduce noise as much as manual work.

Start with a single useful routine

There's no need to automate the whole business at once. A better approach is often to start with a task that is:

• frequent;
• simple to describe;
• fed by reliable data;
• time-consuming enough or easy enough to forget.

You then measure its real usefulness before going further. A successful automation isn't the one that does the most things. It's the one the merchant no longer has to think about, except when it flags something worth their attention.

To go further, see concrete examples of Gillia use cases.