How to Extract Data from Excel Documents with AI

Did you know data teams spend over 28% of their time just preparing spreadsheets? Extracting data from Excel files is tedious and time-consuming, especially when dealing with large datasets spread across multiple spreadsheets. Yet, businesses remain tied to manually extracting data from Excel documents, making operations slower and error-prone. Spreadsheets often become sources of inaccurate data due to human errors.

This is where AI-powered document processing can help. Tools like Unstract overcome these barriers by automating extraction with high accuracy. AI parses financial models, shipment trackers, and payroll sheets, transforming Excel’s static grids into real-time, queryable data. You get structured JSON in seconds, without complex formulas or cell collisions.

This article explores the need for automating Excel data extraction, industry use cases, and how Unstract and LLMWhisperer use AI to solve common challenges.

Why Automating Excel Document Extraction Is Crucial

Excel remains the backbone of everyday operations that support budgeting, financial planning, reporting, and data analysis across departments and industries. But as businesses grow, the manual handling of Excel files becomes increasingly inefficient. Tasks like data extraction, formatting, validation, and reporting are time-consuming, repetitive, and highly prone to human error.

Manual Excel operations can delay reporting, introduce inconsistencies, and compromise decision-making in data-intensive workflows. Automating Excel tasks emerges as a vital solution. It helps eliminate bottlenecks and improves the speed, accuracy, and reliability of data operations.

Excel automation helps businesses:

• Save time by cutting down on repetitive tasks, so teams can focus on more important work.
• Avoid mistakes by reducing manual data entry and improving accuracy in calculations and formatting.
• Connect easily with other software and systems to automate the entire data process.
• Get up-to-date insights by linking Excel with external tools for faster and clearer decisions.
• Handle more data and complexity without needing extra manual effort.

However, not every part of Excel document processing can be automated. Automation can include structured tasks like extracting tables, cleaning layouts, or sending data to other systems. Human input is still needed to check unclear fields, review comments or notes, and work with messy spreadsheets that have mixed formats, charts, or macros.

Automation simplifies routine Excel tasks but doesn’t fully replace human review where decisions or judgment are involved.

Excel Document Extraction Use Cases Across Industries

Spreadsheets remain deeply ingrained in everyday operations. Despite the rise of specialized software, nearly 90% of modern companies continue using spreadsheets like Excel for essential tasks. 

Below are industry-specific examples showing both Excel’s flexibility and the growing need for automating data extraction:

Finance

Businesses use Excel for financial planning, profit and loss reports, and budgeting. Banks and companies rely on spreadsheets to prepare quarterly forecasts, but manually combining files can create version mismatches and slow decision-making.

For example, Natixis uses Excel for enterprise content management, supporting financial modeling and reporting processes.

Insurance

Actuaries and claims teams track policies and claims through Excel. Yet reconciling data across multiple spreadsheets or extracting tables from semi-structured reports takes time and invites human error.

For instance, even major markets like Lloyd’s of London rely on Excel for underwriting and rating workbooks, underscoring both its widespread use and the need for automation.

Logistics

Logistics teams manage shipment tracking, freight cost breakdowns, and dispatch schedules using Excel. For example, Intel’s supply chain analysts and database administrators use Excel to manage supply chain data and logistics metrics. Automating these workflows improves efficiency and reduces manual effort.

Marketing and Sales

Lead lists, campaign trackers, and customer data often reside in spreadsheets. Sales managers track pipelines and forecast revenue in Excel by analyzing historical win/loss data and current deals. But manually aggregating data across sources slows down workflows and delays insights into campaign performance.

Human Resources (HR) and Admin

HR and administrative teams rely on Excel to manage employee databases, leave tracking, payroll, and shift schedules. For example, teams at JP Morgan Chase use Excel to handle HR data and reporting as part of their daily operations.

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Challenges in extracting data from excel

Given Excel’s widespread use, it’s critical to understand the structural and data quality challenges that make accurate extraction difficult.

Structural and Data Quality Issues

Extracting structured data from Excel is rarely straightforward. Here are a few common hurdles:

• Inconsistent headers, merged cells, and mixed data types make manual extraction difficult and error-prone.
• Hidden rows and unresolved formulas often break the structure, leading to corrupted output.
• Sheets with mixed text, numbers, and formulas are harder to extract than clean, structured tables.
• Multi-line text, comments, and notes often get flattened, losing important context.
• Without version control, it’s difficult to tell which spreadsheet contains the correct or most recent data.

Challenges with Processing via PDF Conversion and Optical Character Recognition (OCR)

Extracting data from Excel files becomes especially unreliable when using PDF conversion and OCR-based methods. These approaches often introduce the following issues:

• Excel files are not page-based, so converting them to PDFs often disrupts the original layout.
• Wide spreadsheets are split across pages, which can reorder or misalign related columns.
• Scaling down large tables to fit a page often makes text unreadable, causing OCR tools to misread or skip data.
• Visual indicators like bold borders or highlighted cells often signal totals or exceptions, but standard parsers typically ignore them.
• Misinterpreting even a single value during OCR can lead to significant data errors, especially in high-stakes documents.
• Spreadsheets containing mixed languages confuse OCR tools, especially those using simpler character recognition algorithms, causing skipped or misread text.

How AI and LLMs Enable Intelligent Excel Document Extraction

Large Language Models (LLMs) help extract data from Excel more accurately by understanding semi-structured tables without fixed templates. They can:

• Detect Schemas and Headers: Automatically recognize column types, header rows, and table structures, even when formatting is inconsistent.
• Extract Relationships: Understand how data points across rows and columns relate, even if connections aren’t explicitly defined.
• Interpret Context: Grasp surrounding text, formulas, and formatting to accurately extract information from complex or irregular layouts.

However, LLMs are not designed to parse raw Excel files directly. They work best when the input is already cleaned, structured, and contextually clear.

Raw Excel files often contain layout complexities such as hidden rows, merged cells, inconsistent formatting, and visual cues like borders that carry meaning. These complexities can confuse LLMs and reduce extraction accuracy.

That’s where preprocessing tools like LLMWhisperer come in.

Understanding LLMWhisperer: Pre-processor for efficient excel data extraction

LLMWhisperer is a non-LLM text parsing tool built directly into Unstract’s processing stack. It plays a foundational role by preparing documents for further processing by LLMs or other systems within Unstract. 

LLMWhisperer focuses on accurately extracting and structuring text from various document types, including PDFs, images, scanned documents, and Excel files, before they are fed into an LLM.

Unlike conventional approaches that rely on PDF conversion or OCR, LLMWhisperer reads Excel files natively. This means:

• It extracts all values directly from the document without any form of conversion or rendering.
• It preserves Excel’s original horizontal layout, no matter how many columns wide the sheet is.
• It retains user-defined formatting like borders and emphasized cells, which often carry important contextual meaning.

This native parsing approach addresses common Excel extraction issues by avoiding column shifts during conversion, preserving data clarity without zoom-related OCR errors, and recognizing visual cues like borders and highlights.

LLMWhisperer provides LLMs with clean, layout-consistent, and structurally accurate input. This improves field extraction accuracy, enhances context understanding, and ensures higher-quality structured output. The result is more reliable performance in LLM-powered document processing workflows.

How Unstract Uses AI to Extract Data from Excel

Unstract is an open-source no-code platform purpose-built for automating document processing with AI. 

Unlike conventional Intelligent Document Processing (IDP) tools that rely on manual annotation and separate integration teams, Unstract allows engineers to handle everything from prompt engineering to ETL pipelines themselves.

It connects multiple components:

• LLMs for context understanding.
• Preprocessing tools like LLMWhisperer for layout-preserving text extraction.

Unstract uses AI to extract structured data from Excel without relying on fixed templates or fragile scripts.

When processing Excel files, Unstract combines:

• LLMWhisperer: LLMWhisperer prepares raw Excel files by natively parsing their layout. It preserves tables, formatting, merged cells, and borders that traditional converters or OCR tools often miss.
• LLMs: Once the Excel content is cleaned, LLMs handle schema detection, context interpretation, and relationship extraction across rows and columns.

This combination allows Unstract to enable businesses to:

• Extract Actionable Insights From Excel Sheets: Capture data like financial summaries, transaction details, shipment breakdowns, or payroll reports, all structured as clean JSON.
• Achieve High Data Fidelity: Preserve the original layout, formatting, and complex cell values to ensure the extracted output reflects the full context of the source data.
• Automate Large-Scale Excel Processing Workflows: Use the workflow builder to connect sources like Dropbox and destinations like Snowflake, automating the entire Excel extraction process without manual effort.

Unstract converts Excel data into structured, machine-readable JSON, ready for use in downstream systems. This reduces manual effort, cuts operational costs, and speeds up decision-making through automation.

Extracting Excel Data Using LLMWhisperer 

LLMWhisperer Playground provides a fast way to visually validate Excel text extraction before integrating it into larger workflows.

Parsing an Economics Spreadsheet Using the LLMWhisperer Playground

The spreadsheet’s merged cells, multi-section tables, and overall layout are preserved as LLMWhisperer extracts the text in the next step.

Upload an Excel sheet containing economic data into the LLMWhisperer Playground.

Once uploaded, LLMWhisperer instantly processes the file and outputs structured text while preserving the original layout, including column alignment and merged cells.

Extracting Data from a Financial Spreadsheet Using the API

The following steps show how to process Excel files using LLMWhisperer’s API through Postman.

Step 1: Downloading the API Collection

Access the API Keys section in LLMWhisperer and download the pre-configured Postman collection.

Step 2: Uploading the Financial Spreadsheet via POST Request

After importing the collection into Postman, use the POST request to upload a financial analysis Excel file.

The API acknowledges the upload with a “processing” status.

Step 3: Checking Processing Status

Monitor progress using the GET status request with the whisper hash ID.

The system confirms when processing is complete.

Step 4: Retrieving the Extracted Text

Perform a GET request to retrieve the extracted text.

The response delivers structured, layout-consistent text ready for downstream processing.

Building an End-to-End AI-powered Excel Data Extraction Workflow in Unstract

LLMWhisperer manages document preprocessing, while Unstract powers end-to-end extraction workflows by integrating prompt logic, LLMs, vector databases, and embedding models into a deployable API. 

This walkthrough outlines how to build an insurance performance extraction workflow using Unstract’s Prompt Studio and Workflow Builder.

Getting Started With Unstract: Best AI-Powered Excel Data Extractor

Step 1: Creating a New Prompt Studio Project

Start by creating a new project in Prompt Studio specifically for insurance performance sheets.

Step 2: Writing Extraction Prompts

Extract data from the Excel sheet in the Prompt Studio project. 

Write prompts to identify specific insights, such as single premiums, other premiums, and investment revenue.

Step 3: Configuring Project Settings

• Choosing an LLM Model

• 2. Adding Vector Database

• Choosing the Embedding Model

• Connecting Text Extraction Tool

Step 4: Exporting the Prompt Project as a Tool

Once prompts and settings are finalized, export the project as a reusable tool that integrates into any Unstract workflow.

Step 5: Building and Deploying the Workflow

Create a new workflow in Unstract. 

• Creating the Workflow

• Configuring the Workflow

Set the API file upload as the input, connect the insurance performance tool for data extraction, and define the API as the output.

Step 6: Deploying and Testing the Workflow API

After the workflow is configured, deploy it as an API service.

• Downloading the Postman Collection

• Testing the API with Postman

Upload the insurance performance Excel file in Postman and submit a POST request.

The API returns structured insurance data as clean JSON output.

Benefits of Building End-to-End Workflows in Unstract

Once deployed, the insurance performance extraction workflow isn’t limited to a single use case. Unstract’s modular design connects Excel data extraction pipelines to various business systems, including dashboards, APIs, and data warehouses.

Here are the benefits of building end-to-end workflows in Unstract:

• Automate Excel document processing without writing custom code.
• Centralize data extraction, transformation, and loading (ETL) using no-code workflows.
• Connect to your preferred LLMs, embedding models, and vector databases.
• Seamlessly integrate outputs into business apps, analytics platforms, and storage systems.

This modular, integration-ready setup ensures your document extraction workflows stay flexible, secure, and scalable across industries.

How to extract data from Excel: What’s next?

Manually extracting data from Excel sheets slows down business work, whether it’s finance, insurance, or logistics. Errors creep in, reporting gets delayed, and teams end up spending more time cleaning spreadsheets than making decisions.

Automating Excel data extraction solves that, but most tools rely on clunky templates or break down with complex files.

We are building Unstract. Unstract is a no-code platform to eliminate manual processes involving unstructured data using the power of LLMs. The entire process discussed above can be set up without writing a single line of code. And that’s only the beginning. The extraction you set up can be deployed in one click as an API or ETL pipeline.

With API deployments, you can expose an API to which you send a PDF or an image and get back structured data in JSON format. Or with an ETL deployment, you can just put files into a Google Drive, Amazon S3 bucket or choose from a variety of sources and the platform will run extractions and store the extracted data into a database or a warehouse like Snowflake automatically. Unstract is an open-source software and is available at https://github.com/Zipstack/unstract.

Sign up for our free trial if you want to try it out quickly. More information here. 

LLMWhisperer is a document-to-text converter(best ocr). Prep data from complex documents for use in Large Language Models. LLMs are powerful, but their output is as good as the input you provide. Documents can be a mess: widely varying formats and encodings, scans of images, numbered sections, and complex tables.

Extracting data from these documents and blindly feeding it to LLMs is not a good recipe for reliable results. LLMWhisperer is a technology that presents data from complex documents to LLMs in a way they can best understand.

If you want to take it for a test drive quickly, you can check out our free playground.

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Get started with LLMWhisperer: Best OCR for Excel Document Data Extraction

🌍 Excel data extraction: Related topics to explore

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3. Best OCR for reading bookkeeping documents
4. Best OCR for accounts payable documents