Microsoft Document AI: Extract and Review Business Data

Microsoft document AI is a broad search term for using Microsoft services to turn documents into usable text, tables or business fields. A reliable workflow defines the fields first, tests extraction against the original documents and sends uncertain or consequential results to a person before they affect another system. fenbs can retain those extraction rules and unresolved review tasks across the assistants helping with the project.

How do you start a checked extraction workflow?

1. Choose one document family and one business outcome, such as preparing supplier invoice fields for review.
2. Define each output field, its source location, permitted format and treatment when the source is missing or ambiguous.
3. Select the Microsoft extraction approach that fits your documents and existing applications. Check access and availability with its current documentation.
4. Run a pilot on permitted examples, compare each required field with the original and record both errors and corrections.
5. Agree the review rules, test the output destination and keep a way to stop a run before incorrect data is written.

Start without a live write to your accounting or customer system. A proposed record in a review file gives you room to discover ambiguous dates, repeated page headers and fields that share similar labels. An apparently successful extraction is only one part of getting a correct business record.

The broader document automation workflow covers routing and downstream actions; this guide concentrates on extraction and checking what the document actually says.

Which Microsoft approach fits the documents?

Microsoft’s Document Intelligence overview↗ (opens in a new tab) describes a cloud service with text and layout extraction, prebuilt models for common documents and custom models. It is a useful starting point when a developer needs structured extraction inside an application. Choose the model by the information required, rather than assuming every document needs custom training.

AI Builder↗ (opens in a new tab) is a Power Platform feature for using prebuilt or custom AI models with Power Apps and Power Automate. Consider that route when the team already maintains its business processes there. Follow Microsoft’s linked documentation for model availability, licensing and regional requirements; do not assume an existing Microsoft account enables every extraction feature.

A readable summary and a set of extracted fields are different outputs. If your system needs an invoice identifier and payment date, evaluate those fields directly rather than accepting an accurate-sounding paragraph. Our Microsoft AI tools guide provides the wider product context without replacing a document-specific pilot.

What should the field rules say?

Write a small field specification before processing the examples. Include the original value, the normalized value you intend to store and any reason it needs review. Normalizing means putting a value into an agreed format, such as using a consistent date representation. It should never silently change an uncertain source into a definite answer.

Decide how to represent absent values. A missing due date is not the same as a date the model could not read, and neither should become today’s date. Keep those cases distinct so a reviewer can resolve the right problem. Record the person’s agreed field rules in fenbs Decisions and rules for subsequent sessions.

How do you test the extraction before trusting it?

Build a permitted test set that includes clean examples and realistic difficult cases: rotated scans, multiple pages, different layouts, blank optional fields and repeated labels. Have a person establish the expected values from the originals. Keep some examples apart from the material used to configure or train the model so the test measures new documents.

Check results by field and document type. A good overall result can hide a repeatedly wrong payment date. Count missing fields, incorrect values and values attached to the wrong row. For tables, compare complete rows with the page image; correct individual numbers can still be assigned to the wrong supplier or line item.

Keep a record of the document version, model or configuration used, expected result and observed error. After changing a rule, rerun the previous failing examples and a few that worked before. This tells you whether a correction fixed the problem without creating a different one. Do not put sensitive source documents into an unrestricted test folder.

What does a confidence score tell the reviewer?

A confidence score is the service’s estimate about its prediction, rather than proof that the value is safe to use. Microsoft’s accuracy and confidence guidance↗ (opens in a new tab) discusses field and text confidence, human review and the effect of document variation. It also notes that not every field returns a confidence score.

Set review thresholds using your pilot results and the consequence of a mistake. Do not borrow an arbitrary threshold from a tutorial and assume it is appropriate for every field. A high-confidence amount on the wrong invoice remains wrong. Check business rules and source matches separately, including a sample of results that would otherwise pass.

Give reviewers the source page, proposed value, reason for the flag and a clear way to accept, correct or leave it unresolved. Keep the corrected value distinct from the machine output. Use an AI approval workflow to separate reviewed data from authorization to change a live record.

How to do this with fenbs

1. Create a free account and add a project for the document extraction pilot, including its scope and the person responsible for review.
2. Record the agreed field definitions, missing-value treatment and approval requirements in Decisions and rules. Keep proposed changes open until a person decides.
3. Add links to the approved document location and test results in Links and listings. Use references instead of copying sensitive document contents into tasks.
4. Create tasks for failing layouts and unresolved fields. Record the checks run, their results and what is still unverified.
5. Ask a connected assistant to read the project context at the start and save Where we left off before stopping, including the next test and any decision waiting on a person.

fenbs does not extract document fields or connect directly to Azure Document Intelligence. Use Microsoft’s tools for processing; use fenbs when the rules and follow-up work need to survive a change of assistant. If your only requirement is a single extraction, the processing tool and a reviewed output file may be enough.

How do you keep the workflow useful after the pilot?

Treat a new layout or model configuration as a reason to repeat the relevant checks. Watch for recurring corrections and turn them into specific lessons: which source pattern caused the error, what the reviewer learned and what to test next time. A vague instruction to be more accurate will not help the next run.

Keep a compact work record rather than every processing conversation. The AI brain guide explains that distinction, and the AI at work hub collects other workflows built around reviewable results.