How to Extract Data from PDFs into Excel Using AI: A Step-by-Step Guide

If your day involves opening a PDF, squinting at a table, and retyping numbers into Excel — you already know the pain. Whether it's supplier invoices, bank statements, purchase orders, or scanned delivery notes, manually moving data from documents into spreadsheets is slow, error-prone, and frankly, a poor use of anyone's time. The good news: AI has made this process dramatically faster, and you don't need to be a developer to take advantage of it.

The Short Answer

You can extract tables and structured data from PDFs, images, and scanned documents directly into Excel using an AI-powered tool like Tablola. Upload your file, select or apply a preset that matches your document type, and your data lands in a clean, editable spreadsheet — no manual copying required. It works on native PDFs, scanned files, and even photos of receipts.

Why Traditional Methods Fall Short

Most people try one of three approaches before discovering AI extraction:

• Copy-paste from PDF: Works only on text-layer PDFs, breaks on scanned files, and destroys table formatting.
• Adobe Acrobat export: Decent for simple tables, but often merges cells, scrambles columns, and requires a paid subscription.
• Manual re-entry: Accurate, but absurdly time-consuming at scale — and one typo in a financial figure can cause real problems.

None of these scale. If you receive 50 invoices a month, even a "fast" manual process becomes a significant overhead. AI extraction removes that bottleneck entirely.

How AI Extraction Actually Works

Modern AI document extraction uses a combination of OCR (optical character recognition) and large language models to understand the structure of a document — not just its text. That means it can:

• Identify table headers and map them to the correct columns
• Handle multi-page documents and merge data consistently
• Recognise common document types (invoices, bank statements, purchase orders) and apply the right extraction logic
• Process scanned images and low-resolution photos, not just digital PDFs

The result isn't just raw text dumped into a cell — it's properly structured, column-aligned data ready for analysis.

Step-by-Step: Extracting PDF Data to Excel with Tablola

1. Choose your preset. Tablola offers ready-made extraction workflows for the most common document types. For example, the invoice to Excel preset is pre-configured to pull line items, totals, dates, and vendor details automatically. For bank statements, the bank statement to Excel preset handles transaction rows cleanly.
2. Upload your file. Drag and drop your PDF, image, or scanned document. Tablola accepts multi-page PDFs and common image formats.
3. Review the extracted table. The AI populates a structured spreadsheet. You can review, edit, and adjust any cells directly — the built-in AI table editor lets you reformat columns, rename headers, or apply calculations without leaving the tool.
4. Export to Excel or CSV. Download your finished file in the format you need. For converting directly, the PDF to Excel converter preset is a fast general-purpose option.

Which Document Types Can You Extract?

The range of supported document types is broader than most people expect:

• Invoices and bills — vendor name, line items, VAT, totals
• Bank and account statements — transaction dates, descriptions, amounts, balances
• Delivery notes and shipping documents — product codes, quantities, destinations
• Purchase orders — item lists, unit prices, order references
• Receipt photos — even crumpled or slightly blurred photos of paper receipts work with the receipt photos to Excel preset
• Scanned PDFs — documents with no text layer, only a scanned image, via the scanned PDF to Excel converter

Tips for Getting the Best Results

AI extraction is powerful, but a few practices will consistently improve output quality:

• Use the right preset. Generic extraction works, but document-specific presets are trained on thousands of examples of that exact format — results are noticeably more accurate.
• Scan at 200 DPI or higher. For physical documents you're photographing or scanning, resolution matters. Blurry images reduce OCR accuracy.
• Batch similar documents together. If you're processing 30 invoices from the same supplier, doing them as a batch ensures consistent column mapping across all rows.
• Review totals first. When extracting financial data, spot-check the totals row against the source document. This catches the rare edge case quickly.

Beyond Extraction: Editing Tables with AI

Once your data is in Tablola's spreadsheet editor, the work doesn't have to stop at extraction. The AI table editor lets you:

• Ask questions in plain English, like "sum all amounts for March" or "highlight rows where quantity exceeds 100"
• Reformat and clean data — standardise date formats, fix inconsistent currency symbols, remove blank rows
• Merge data from multiple documents into a single unified table

This turns Tablola into more than a converter — it's a complete document-to-spreadsheet workflow, from raw PDF to analysis-ready data.

Frequently Asked Questions

Can Tablola extract data from scanned PDFs with no text layer?

Yes. Tablola uses OCR combined with AI understanding, so it can process scanned documents, photographed pages, and image-only PDFs — not just digital files with embedded text. The scanned PDF to Excel preset is specifically optimised for this use case.

Do I need to configure anything, or do the presets handle it automatically?

Presets handle the configuration for you. Each one is pre-trained on a specific document type, so you upload your file and get structured output without setting up column mappings manually. You can still edit the output after extraction if you need to adjust anything.

What's the difference between using a preset and a general PDF-to-Excel conversion?

A general conversion extracts whatever text and table structure it finds in the PDF, which works well for straightforward documents. A preset applies document-type-specific logic — for example, an invoice preset knows to look for line items, tax fields, and vendor details and maps them to the correct columns automatically. For routine business documents, presets produce cleaner results with less manual cleanup.