What teams build in Prowork for document digitization

Digitization sits upstream of nearly everything else in this list, because until a document becomes a row in a table, no system downstream knows that the shipment arrived, the invoice was issued or the material passed inspection.

Of the 139 companies whose usage went into this study, 104 run some version of this work in Prowork, spread across 1,641 flows that were active in the last 90 days. Grouping those flows by what each one actually does gives the breakdown below.

Customs & Trade Compliance is the largest of those patterns at 462 flows across 30 companies, with Document Digitization accounting for another 371 across 80 companies. The grouping follows what a flow does rather than which team happens to own it, which is why one company often turns up in several of these rows at once, and the businesses building the most of them work in apparel, health and beauty, and food and beverage.

Three of those flows in more detail, described by what each one does rather than by who built it. Company names are withheld, so each is identified only by the size and category of the business running it.

A $25M+ logistics provider. Processes vendor repair invoices by extracting parts, labor, tax, and work-order details, then matching them to maintenance records for AP review. Allocates costs across line items and flags missing or ambiguous matches so exceptions can be resolved before posting. Built on API, email attachments, SharePoint.

A $5B+ logistics provider. Extracts key shipment details from emailed arrival notice PDFs and turns them into validated records for operations teams. Matches notices to existing shipment profiles, flags missing or conflicting information for review, and updates downstream tracking/reporting systems with new or changed arrival details. Built on email attachments, MS SQL, FTP, SharePoint.

A $50M+ baby products brand. Turns BOL and packing slip PDFs into clean, accumulated shipment datasets with PO, carrier, carton, weight, date, and item details. Adds QA checks for missing document pairs and load-total mismatches so operations teams can trust the exports and quickly resolve shipment-document exceptions. Built on email attachments.

Read enough of these flows and the same shape keeps appearing. A flow pulls the same records from each system that holds a version of them, standardizes whatever identifiers are needed before those records can be joined at all, applies the comparison or the calculation as explicit logic, and then labels every row with an outcome so that the handful needing attention can be routed to whoever is able to act on them.

What differs from one flow to the next is which single step genuinely calls for interpretation. Reading a supplier PDF, resolving a merchant name that never quite matches the ledger, or deciding which category a vague line description belongs in are all handled by AI steps inside the flow, while the matching rules, the tolerances and the thresholds around them stay explicit, since those are the parts a controller or an auditor will eventually want to read for themselves.

Most of these run on a schedule or fire from a trigger rather than waiting for someone to remember to open them, and that is largely what separates a report describing what happened last month from a process that surfaces the problem while there is still time to do something about it.

The sources they read from are worth noting, because they are rarely the tidy ones. Across the documented flows in this category the most common inputs look like this:

Email attachments and spreadsheets sit at the top of that list on most pages, which is a fair description of where operational data actually lives once it leaves a system of record.