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PARTLY

As of 13 August 2026, AI can only partly extract data from email attachments.

This still needs a person who signs their name to it.

Can you do it?

15 minutesto a draft.

30 minutesto something you’d act on.

Cost, all in£0

Skill neededchat-fluent

Who has to check ityou

What the alternative costsA purpose-built alternative is Rows, which provides built-in AI analysis and live data connections for spreadsheet work.

If this goes wrong, an incorrect value can enter a business record or trigger a wrong payment, report or customer message.

What to actually do

  1. Hand it to a person

    The route this page recommends

    A person who owns the outcome does this end to end, worth it when the failure is dear.

  2. Use a tool built for this

    Second choice
  3. Do it yourself

    The distant third

    A chat interface, chat-fluent skill, and roughly 30 minutes until you can act on the result.

    How to actually do it

    1. Open the mailbox folder containing the relevant messages and download the original emails and every attachment, keeping the original filenames.
    2. Create a field list for the extraction, such as invoice number, supplier, date, amount and VAT, and define the exact format required for each field.
    3. Remove unrelated messages and attachments, then upload the selected files to a chatbot that supports document and image uploads.
    4. Paste the prompt with your field list and ask for one separate row per document, including the source filename, location, confidence and notes.
    5. Open each source attachment and compare every extracted value marked medium or low confidence with the original page, cell or image.
    6. Check a sample of high-confidence rows against the originals, then correct the table without accepting values that the source does not contain.
    7. Export the checked table to the approved spreadsheet or business system, retaining the source filenames and the original files for audit purposes.

    Prompt

    Extract the requested data from the attached email files and their attachments.
    
    Requested fields:
    [LIST EACH FIELD, FOR EXAMPLE: SUPPLIER NAME, INVOICE NUMBER, INVOICE DATE, DUE DATE, NET TOTAL, VAT TOTAL, GROSS TOTAL]
    
    For each attachment, return one row in a table with these columns:
    - source email or attachment filename
    - page or cell reference where available
    - each requested field
    - confidence: high, medium or low
    - notes
    
    Rules:
    1. Copy values exactly as shown. Do not calculate, correct, infer or invent anything.
    2. Preserve dates, reference numbers, currencies and decimal places as displayed.
    3. If a field is missing, write "not found". If it is unreadable or ambiguous, write "unclear" and quote the relevant text.
    4. Keep separate rows for separate documents. Do not merge values from different attachments.
    5. Identify duplicate-looking documents but do not delete any row.
    6. For scanned or handwritten content, mark the affected field as low confidence unless it is clearly legible.
    7. After the table, list every field that needs a human check and explain exactly why.
    8. Do not include personal or confidential information that is not needed for the requested fields.
    
    Attachments and email files:
    [PASTE OR UPLOAD THE FILES HERE]

    Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.

What it gets wrong

  • It cannot enter a mailbox or bypass your organisation's access controls without a configured connector and permission.
  • It cannot reliably read every blurred scan, handwritten note, unusual layout or damaged attachment.
  • It cannot decide whether two similar documents are duplicates or whether a conflicting value should be accepted.
  • It cannot take responsibility for incorrect records, payments, reports or customer messages created from the extraction.
  • It does not replace a tested workflow with logging, access control, exception handling and an audit trail.

What caps this at PARTLY: private data access, verification cost and context depth.

How we scored this

Five axes, each scored nought to two by hand: ten means AI carries the task cleanly, and the thresholds that turn a total into YES, PARTLY or NO are published in the methodology. Each axis name links to its definition.

AxisScore (0–2)
Output2
Inputs1
Verification1
Liability1
Effort delta2
Total7 / 10

FAQ

Can ChatGPT extract data from PDF attachments?
Yes, it can extract text and fields from many PDF attachments when you upload them and specify the required columns. Check the returned values against the original pages, especially for scanned PDFs, tables and totals.
Can AI read attachments from my email automatically?
Not from a normal chat window without access to the mailbox and a configured integration. You need to provide the files or build a permitted workflow that selects, sends and records attachments.
Is it safe to upload business email attachments to AI?
Only after checking your organisation's data policy, supplier terms and access controls. Remove unnecessary personal or confidential data, use an approved service and do not treat the extracted table as correct until it has been checked.
Can AI extract invoice data from email attachments?
Yes, it can usually extract fields such as supplier, invoice number, dates and amounts into a table. It can misread scans, combine fields from different documents or miss tax details, so compare the result with the original invoice before using it.

Nearby answers

Assessed by gpt-5.6-luna (gpt-5.6-luna) on 2026-08-13, second-checked by an independent model. Wrong somewhere? Email [email protected] and it gets re-checked.

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