You get through an order peak without hiring by taking away from your sales admin team the part of their work that grows with volume: reading purchase orders, entering them in the ERP, routine checks, and answering stock and lead-time questions. The team keeps what does not grow with volume: judgement calls, exceptions and sensitive customers. The catch is that this automation has to be running before the peak, not from the day the inbox overflows.
Peaks are not only seasonal
When people think of peaks, they think of the run-up to Christmas. In B2B the calendar is more varied, and every sector has its own:
- a heating and plumbing merchant sees orders for boilers and spare parts climb with the first cold snap;
- a hygiene products distributor receives back-to-school orders from schools, local authorities and cleaning contractors in August and September;
- a food wholesaler starts preparing for the festive season in October;
- an electrical wholesaler sees customers order in bulk just before a manufacturer’s announced price increase;
- almost everyone knows the rush before the August shutdown, and the one from public buyers committing their budget before the financial year ends.
Other peaks appear in no calendar at all: a promotion, a large new account, a competitor running out of stock. And a peak is always relative to available capacity. A normal volume in July, with half the team on holiday, has the same effect as a doubled volume in November.
What happens in an overloaded sales admin team
The problem with a peak is not only that there are more orders to enter. It is that the backlog feeds itself.
An order entered a day late does not get its acknowledgement. The customer writes to ask whether it arrived. Another asks whether the 300 rolls of hand towels will be delivered before term starts. A third resends the order, worried it got lost, and creates a duplicate. Each of these emails takes time from a team that has none left, and delays entry even further.
In the rush, errors follow: a carton entered instead of a single unit, an old price carried over, a site delivery address forgotten. They come back three weeks later as disputes, just when the team thought it could breathe. And urgent orders, buried in the pile, are not handled first because nobody has time to sort them.
Why temporary staff only half solve the problem
Adding a person for six weeks seems logical. But what makes sales admin work hard is not typing, it is knowledge: knowing that one customer writes “ref 4020” for an item that has a different code in your system, that another always orders by the carton, that this one has a negotiated price that only exists in an old email, that that one is over their credit limit.
A newcomer can type, but cannot judge. They ask questions, and the most experienced people answer them, at the very moment they are overloaded themselves. By the time the newcomer is trained, the peak is often over. And next year, you start again with someone else.
Overtime holds for a few days, then fatigue pushes errors up. And letting lead times slip is a bet on the patience of customers who can order elsewhere.
Separating what grows with volume from the rest
To size the team differently, look at its work in two columns.
What grows with every order: opening the email and attachment, finding the customer and each item, checking price, stock and credit, entering the lines, sending the acknowledgement, answering “where is my order?” or “do you have stock?”.
What grows little or not at all: deciding how to split a shortage between two customers, negotiating an exceptional discount, calling a key account whose order looks odd, handling a delicate dispute, deciding on a partial delivery.
The first column is exactly what an AI agent can take on. Eledone reads purchase orders as they arrive (PDF, Excel, scan, email body), finds the items in your catalogue, checks prices, stock and credit in the ERP, then enters the order and sends the acknowledgement. It also answers stock, lead-time and order-tracking questions from ERP data. The second column stays roughly stable during the peak: that is the team’s work.
A quick calculation
Take a hypothetical example to illustrate the reasoning. A hygiene distributor normally receives 60 orders a day, and around 140 during the six weeks of the back-to-school period. Assume an order takes 6 minutes of manual handling on average.
- Normal period: 60 × 6 = 360 minutes, or 6 hours of entry a day.
- Peak period: 140 × 6 = 840 minutes, or 14 hours a day.
That leaves 8 hours a day uncovered, the equivalent of one full-time person, before counting the chaser emails the delays will generate.
Now assume standard orders go through without intervention and the team only picks up the flagged ones, at a few minutes each. The peak workload no longer depends on the total number of orders, but on the number of exceptions. Run this calculation with your own figures from your ERP history: it gives a far more useful order of magnitude than a gut feeling.
Preparing for the next peak: a realistic timeline
The classic mistake is to look for a solution once the peak has started. Here is a sequence that leaves time to do things properly.
- Three months before: find your peaks in your history (weekly volumes, customers involved, order formats received). Identify the orders that cause trouble every year.
- Two months before: go live on your current flow. Rules settle best on normal volumes, when there is time to check the results. At MP Hygiène, integration took two weeks.
- In parallel, define the control rules with the team: what goes straight through and what must be flagged. An unusual price, an ambiguous item, an exceeded credit limit, a quantity far from the customer’s habits. During a peak, an order ten times the usual size may be perfectly legitimate: better to decide in advance how to handle it.
- One month before: extend to email replies (stock, lead time, tracking), which are the ones that explode when orders fall behind.
- During the peak: the team monitors the queue of flagged orders and handles priorities. Afterwards, review: which exceptions came up often, which rules to adjust.
You can also start by testing one of your real orders, the one that looks most complicated to you, with the online trial.
What the team does during the peak
Freed from data entry, the team focuses on what matters most in high season: splitting tight stock between several customers, warning a customer that their delivery will be partial before they find out, calling the buyer whose order looks unusual, settling a dispute before it escalates. No six-week temp can do this work, and the team rarely has time to do it well.
Frequently asked questions
Does automation keep up if volume doubles or triples?
Yes. An AI agent processes orders as they arrive, whether there are 50 or 200 in a day. What increases during a peak is the number of exceptions to review, which is why the control rules are worth defining in advance.
How long before the peak should we start?
Allow a few weeks for integration and a few weeks of running on your current flow. Two to three months before the peak gives a comfortable margin. Starting in the middle of a peak is possible, but it is when the team has the least time to check the first results.
What happens to unusual orders?
They are flagged to the team instead of being entered automatically: price outside the price list, item not found, credit limit exceeded, abnormal quantity. The team decides, and recurring cases are used to refine the rules.
Do we need to ask customers to change the way they order?
No. They keep sending orders by email, in whatever format they use today. That matters during a peak, when nobody has time to learn a new portal.
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