AI for documents, invoices and spreadsheets

Which document task is worth doing first?

We recommend starting with a repeated preparation task: entering invoice fields, comparing a document with a checklist or assembling a report from agreed records. Decide who uses the result and what they need to verify. If that person cannot point to the source behind an important value, the workflow is not ready.

Separate reading, calculation and judgment. Reading finds the invoice date. Calculation checks the totals. Judgment decides whether the charge should be accepted. Giving each step a clear owner makes the design easier to test and prevents a neat spreadsheet from quietly becoming payment authority.

Keep the original file unchanged, create a working copy and save proposed outputs separately. Agree which system becomes the final record after review. A folder full of generated summaries is not a reliable replacement for a properly maintained accounting or document system.

Which type of tool should we compare?

Document extraction services: Azure Document Intelligence's invoice model extracts fields and line items from invoice documents. Google Document AI lists an Invoice Parser for fields such as invoice number, supplier, dates and amounts. These are candidates when structured extraction is the recurring job. Microsoft invoice model; Google processor list.

A spreadsheet assistant: Copilot in Excel can propose formulas and provide summaries, charts, PivotTables and other insights. Microsoft explicitly tells users to review and verify generated work. This is a candidate when the team already works in Excel and needs assistance inside the workbook. Microsoft Copilot in Excel guidance.

A custom workflow: connect intake, extraction, deterministic checks, a review queue and the destination system. The benefit is a complete handoff; the drawback is ownership of failures, updates and permissions. We would compare an existing accounting tool's import or capture feature before commissioning a custom build.

Check the exact document type, language and region supported by the processor you select. Google's invoice processor lists specific languages; do not infer Thai invoice support merely because a general OCR product can read Thai. Require a test using your actual permissioned formats.

What should an extraction result contain?

Ask for a proposed value, its source location, any normalization applied and a review state. An invoice reference should remain text if leading zeroes matter. Keep the printed date beside the interpreted date when the format is ambiguous. Do not let an empty field become a guessed value.

A useful review screen puts the original page next to the extracted fields. The reviewer should be able to answer: is that the document total or a line subtotal? Is that the supplier's tax identifier or the customer's? Is the due date printed, inferred from terms or missing?

This is our review design, not a claim that any named extraction vendor performs every check automatically. Confidence scores can help prioritize a queue, but they do not prove that a supplier exists or a payment is legitimate.

Which invoice checks should stay deterministic?

Use ordinary arithmetic to reconcile line amounts, discounts, tax and totals under the rules agreed with your accountant. Compare the proposed invoice against the purchase order or accepted scope where one exists. Keep credits, refunds and negative amounts distinct from ordinary charges.

A changed bank account deserves a separate verification route using trusted supplier contact information already on file. Do not let a convincing document, a confident model or a reply to the same suspicious email become the sole basis for changing payment details.

Keep preparation, approval and payment as separate steps. AI can prepare a record and highlight differences. The authorized finance process decides what gets posted and paid. Record who accepted the result and preserve the relevant evidence according to your retention policy.

If the accounting system is temporarily unavailable, hold a reviewed item in a visible state. A retry must not create a second invoice. The final check is the correct entry in the destination system, with the correct attachment and status.

How do we check a workbook result?

Define the question before asking for a chart. Revenue booked, invoices issued and cash received answer different questions. Specify the period, currency, tax treatment, exclusions and source table. A polished graph based on the wrong definition can be more misleading than a messy source sheet.

Before using generated formulas, inspect their ranges and assumptions. Check blank cells, zero values, negative values, duplicate rows, missing periods and mixed currencies. Confirm whether hidden or filtered rows should be included. Compare important totals with an independent known result.

Prefer formulas you can inspect over unexplained values pasted into cells. Ask the assistant to explain the calculation, then test that explanation against the actual formula. An explanation is another generated output, not independent verification.

Illustrative example: a monthly sales workbook contains a cancelled order, a credit note and a late payment for an earlier month. Define whether the report concerns orders, invoiced sales or cash before asking the assistant to summarize performance. Otherwise the same records can support different totals without an arithmetic error.

What belongs in the exception queue?

Build a test set containing poor scans, rotated pages, stamps over numbers, multi-page tables, missing pages, conflicting totals and unusual supplier layouts. Include the languages and formats your team actually receives. Treat these as cases to test, not a claim about any vendor's measured error rate.

For contracts or policy documents, ask for source passages and unresolved questions. A summary can omit an exception that changes the meaning. Where a decision has legal consequences, have the appropriate professional review the source wording.

Documents can also contain instructions addressed to an AI system. Our recommended rule is that document content is evidence to inspect, not authority to change the workflow. A sentence in an attachment should not grant permission to contact someone, reveal other records or alter a payment destination.

Provide a visible unknown or needs-review outcome. If the system must always return a completed answer, you have removed its safest response to missing evidence.

How do we run a useful pilot?

1. Choose one document class and an owner who understands it.
2. Collect permissioned examples with known accepted answers.
3. Hold some examples back from prompt and workflow tuning.
4. Check each important field and the complete destination record.
5. Measure review, correction and exception time as well as extraction time.
6. Record serious errors separately from ordinary corrections.
7. Repeat the relevant checks when a model, layout or business rule changes.

The advantage is less repetitive preparation and a clearer review queue. The disadvantages are setup work, maintenance and the temptation to approve a tidy output too quickly. Continue only if the complete process is easier to operate at an acceptable level of risk.

Do not present a small pilot as a universal accuracy rate. Report what was tested, what failed and which document types remain out of scope. A workflow that works for a clean digital invoice has not thereby passed a photographed handwritten receipt.

What do we use, and what should you do next?

At DLVX, our documented operating habit is to use current source records and verify the final destination. We do not claim here that Azure Document Intelligence or Google Document AI powers our own invoice processing. They are verified product options to evaluate for this job. DLVX editorial review.

Common mistakes are uploading a whole finance archive for a small question, accepting invented missing values, measuring speed before review, and allowing extraction to cross into payment approval. Keep access limited to the data and action needed for the current stage.

Next step: select one representative document and write the fields, evidence and stop conditions your reviewer needs. Pair it with a difficult example. Ask the supplier to demonstrate both, including the exception queue and the saved result.

Sources and review date

Reviewed 12 October 2026. Prices and plans are as of October 2026. DLVX recommendations are editorial judgements, not a vendor ranking or a promise of results. Recheck the linked terms before buying or connecting business data.

• Azure Document Intelligence invoice modelChecked 2026-10-12. Extracts invoice fields and line items.
• Google Document AI processor listChecked 2026-10-12. Invoice Parser field extraction and processor-specific supported languages.
• Copilot in Excel data insightsChecked 2026-10-12. Formula, chart, summary and PivotTable assistance with verification guidance.
• DLVX editorial reviewChecked 2026-10-12. Source and destination verification habits; no claim of using the named extraction vendors.