Choose one document type and a specific output
Start with one type of document and a list of information that is actually needed downstream. This could include the document number, date, business partner’s name and a specified amount. Define the output format, required fields and what the system should do when information is missing.
Separate three tasks: reading the content, understanding its meaning and performing an operation in another system. Correct text recognition does not prove that an amount has been assigned to the right field. Extracting data does not have to trigger an automatic entry in accounting software or an ERP.
Check whether you need AI
If the source provides a structured export or an API, using it may be simpler than analysing an image of the document. Scans may require text recognition. An AI model may be worth considering for variable descriptions and layouts, but you need to assess it using your own materials.
Do not assume a tool will correctly identify missing data. Require a way to mark a field as unreadable or ambiguous rather than filling it with a guess.
Build a sample that includes difficult files
Gather typical documents and harder cases: different layouts, poor quality, multiple pages, duplicates and missing information. Before sharing them, agree which data may be processed by the chosen tool, who can access it and how long it will be kept. Safely prepared test materials can be used for an initial trial.
Use some files to refine the solution and keep others for the final assessment. Otherwise, it is easy to achieve good results only on examples already used to improve the system.
Measure errors as well as successful extractions
For each important field, compare the result with the correct answer. Record omissions, incorrect values and cases requiring a human decision. A typo in a description has a different cost from an incorrect bank account number or amount.
Agree which results always need human approval and which may proceed automatically after rule checks. Allow for service outages and a file being processed again. The process should be possible to stop and continue manually.
Calculate the work that remains after deployment
Compare the current handling time with the time needed to check results, correct errors and resolve exceptions. Add tool, integration and maintenance costs. If extraction is fast but a person still needs to check every field from scratch, the saving may be small.
A pilot can lead to deployment, a different tool or a decision to improve source data first. At Webmode, we are developing automation and practical AI applications. The first step is choosing a process where the value of the solution can be tested fairly.
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