This task actually consists of two very different activities that are often mentioned in the same breath. On the one hand there is the physical work: packing products, labeling them and placing them in the right spot in the warehouse. On the other hand there is the administrative work: drawing up the shipping documents, such as packing slips, transport documents and customs papers where applicable. These two parts call for a very different answer, and that is exactly why this task does not fit into a single category.
The physical axis scores low here, and that is the crux of the matter. Filling a box, sticking a label in the right place, stacking a pallet: this is manual work that requires a gripper or robotic arm, not a language or reasoning model. Software can determine which product needs to go where, but the action itself — picking up the product, packing it, putting it away — remains human work until physical automation is in place, such as a picking robot or an automated sorting line. That is a very different investment than an AI subscription, and that choice is separate from what is being assessed in this analysis.
This places the task in the second category: partially, with people doing the physical work and systems preparing or handling the administrative part.
Opposed to that physical limitation is a high volume. Companies that make hundreds or thousands of orders ready for shipment per day have recurring patterns: the same type of labels, the same document structure, the same check steps. That is exactly the type of repetition that automation based on old, reliable technology such as RPA (robotic process automation) has been handling well for years. RPA reads order data from the WMS or ERP and automatically generates a packing slip, transport document or invoice reference to match, without an employee having to retype it manually.
The structure scores high because the data needed for shipping documents is already in the system: order number, address, weight, number of parcels, carrier. Little interpretation is needed — it is mainly a matter of putting the right fields in the right place in the right document. That is the reason automated document generation is already usable today, while the packing itself is not.
Suppose a webshop ships 800 parcels per day. The WMS indicates the correct location in the warehouse per order, an employee picks the product, packs it, sticks on the label and places it ready for the carrier. Meanwhile the system automatically generates the packing slip and the shipping label data, and forwards the tracking data to the carrier and the customer. The physical part — picking, packing, placing ready — stays with the warehouse employee. The administrative part — drawing up documents, forwarding data — can largely be handled by software, with an employee assessing and approving or rejecting exceptions (wrong address, missing weight).
This outcome is not universal. At a company with an automated sorting line and picking robots, the physical axis shifts, and a larger part of the process can proceed without human hands — although oversight of exceptions remains necessary. At a company with a lot of custom packaging, fragile products or varying packaging requirements per customer, the physical share remains larger, and the judgment scope is also higher than assessed here. Also with cross-border shipments involving customs formalities, the compliance axis rises, and drawing up certain documents may require specialized knowledge that does not simply fit into a template. These kinds of differences are exactly why a task labeled "shipping administration" yields a different answer at one company than at another — which is why a scan looks at company and task level, rather than generalizing by job title.
The warehouse employee and logistics employee remain needed for the physical work and for checking exceptions: a damaged label, a deviating weight, an unclear destination. WMS and ERP supply the source data and, coupled with RPA, can let the document process run largely automatically. That is a collaboration model, not a replacement of one by the other.
This task is not separate from the wider chain. Similar trade-offs between physical work and administrative automation also play a role in what can AI take over in the transport sector and in what can AI take over in the wholesale sector, where order processing and document flows show a similar split between physical and administrative work.
This is not personnel advice and not a basis for a decision on staffing or headcount. It concerns an estimate of which part of the task can be supported by software today and which part remains physical work. Decisions about personnel are subject to their own legal requirements and should not be made on the basis of this page.
If you want to know what this division looks like for your own shipping process, that depends on your packaging complexity, order volume and the extent to which your WMS and ERP are already linked. The free quickscan from ftetoai gives, in twelve questions, without an account, a first indication of which part of the hours in your profile can be taken over by AI today. The full work scan, which goes deeper into individual tasks, is still under construction — so we do not yet offer that here, but the quickscan already provides an honest starting point.
Vraag maar. Ik ken de kennisbank van deze site; wat ik niet weet, zeg ik erbij.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.