AI for document processing and data entry

The business question

Which of our document-heavy workflows could AI handle, and what would it take to get there?

The current manual process

Most small and midsize businesses run at least one workflow where a person reads a document and retypes what it says into software. It usually looks like this.

• A vendor invoice, purchase order, packing slip, or intake form arrives by email, upload, mail, or scanner.
• Someone saves it to a shared drive or email folder, often with an inconsistent file name.
• That person opens the accounting system or ERP and keys in vendor, date, line items, totals, and account codes.
• They compare the document against a purchase order or contract to confirm the numbers agree.
• Exceptions go to someone more senior: a price mismatch, a missing field, an unreadable scan.
• The document is filed for retention, and the steps repeat for the next one.

Where AI can help

This work suits AI because it is repetitive, the inputs are semi-structured, and the output is checkable against records you already keep. Document AI tools generally do a few specific things.

• Classify an incoming file by type, so invoices, receipts, and forms route to the right queue.
• Extract named fields from scans, PDFs, and photos: vendor, invoice number, date, line items, totals, remit-to address.
• Return a confidence score for each field, so uncertain values are flagged instead of posted quietly.
• Validate extracted values against existing records: does this vendor exist, does the purchase order number match, do line items add up to the stated total?
• Prepare a draft entry and hold it for approval rather than writing to the ledger directly.

Data and connections you will need

Success depends less on which model you pick than on plumbing and data quality. Plan for the following.

• A clean, current vendor or customer master list. Extraction only helps if the extracted name matches a record you already have.
• A sample of real documents to test against, including the messy ones: crooked scans, handwriting, unfamiliar layouts.
• An intake point the tool can watch: a monitored mailbox, a scanner output folder, or a shared drive location.
• A connection into the destination system. Many accounting and ERP platforms offer an API; some only support file-based import, which changes the design considerably.
• Storage that keeps the original document linked to the record it created, so an approver can see the source.
• Written agreement on what a finished record looks like (required fields, coding rules, naming) before automation is switched on.

Security, privacy, and approvals

Business documents carry more sensitive information than people expect. Invoices contain banking details, and intake forms can contain personal or health information.

• Inventory what personal or regulated information appears in the documents you plan to process, before choosing a tool.
• Read the vendor's data-processing terms: where documents are stored, how long they are kept, whether your content trains models, and which vendor staff can access it.
• Prefer tools that let you turn off training on your data and set a short retention window.
• Limit who can view processed documents and extracted values. Permissions should mirror your existing access rules rather than widen them.
• Keep a trail tying each document to the record it produced, what the model returned, who approved it, and when.
• Bring in whoever handles your contracts or compliance obligations before the first live document is processed.

What should keep human review

Automating extraction is not the same as automating judgment. Some steps should stay with people regardless of how well the tooling performs.

• Any payment, payment change, or bank-detail update. Vendor-impersonation fraud targets exactly this step.
• Anything flagged as low confidence, plus a rotating sample of high-confidence records.
• Exceptions: price and quantity mismatches, missing purchase orders, duplicate invoice numbers, unfamiliar vendors.
• New document formats and new vendors, until they have run cleanly through a few cycles.
• Ledger coding decisions that affect reporting or tax treatment.
• A recurring accuracy review, so quiet drift gets caught instead of compounding.

A phased way to adopt this

1. 1
   Phase 1: Pilot one document typePick the highest-volume, most consistent document you handle, often the vendor invoice. Run it alongside the existing manual process so nothing depends on the tool yet. A two- to four-week parallel run usually exposes the failure patterns. Keep a written list of every failure and its cause.
2. 2
   Phase 2: Production with approvalOnce results are steady on that one document type, let the tool create draft entries that a person approves before posting. Track how often approvals need a correction, and write the approval rule down so it survives staff changes.
3. 3
   Phase 3: Expand to more document typesAdd the next document type only after the first is stable. Each type needs its own sample set, validation rules, and review threshold. Adding several at once makes it impossible to tell what broke.
4. 4
   Phase 4: Integrate and tightenConnect approved output into the accounting or ERP system, put storage and retention under a defined policy, and put an accuracy review on the calendar. Keep exception handling manual by design.

What value to expect

The clearest gain is time returned to people currently doing transcription. That time tends to move toward exception handling, vendor follow-up, and closing the books. That work rewards attention and does not scale by hiring alone.

A second gain is consistency. Manual entry produces small, hard-to-trace errors: a transposed invoice number, a date in the wrong format, a line coded to the wrong account. Extraction with validation rules makes errors more uniform and easier to catch.

Queue time matters too. Documents that sit in an inbox until someone has a quiet afternoon can delay a customer or push work into month-end.

We will not offer a savings figure. The answer depends on your document volume, master data quality, exception rate, and integration work, and all of that is only knowable after looking at your actual documents.

Illustrative scenario

Consider a distribution company with a small accounting team. Vendor invoices arrive in a shared mailbox in several formats. One person spends much of each morning opening PDFs, keying them into the accounting system, and matching them to purchase orders. Month-end runs late, and a few invoices are always found after the fact.

The team starts with invoices only. For several weeks the tool reads each invoice in parallel while the existing process continues untouched. The comparison surfaces two things: extraction is dependable on typed PDFs from regular vendors, and it struggles with photographed invoices from one supplier. That supplier stays on the manual path.

In the next phase, extraction creates draft entries. The clerk reviews each one, corrects what is wrong, and approves it. Anything involving a bank-detail change routes to the controller regardless of confidence score. Purchase-order mismatches still go to a person, because that is a conversation with a vendor rather than a data problem.

This scenario is hypothetical and illustrates a typical pattern. It is not a Days Dynamics client case study. It is written to show how a document-processing pilot is normally sequenced, and it does not describe work we have delivered.