Today, we are going to discuss a topic where manual processes and AI operate differently, each with its own set of Pros. and Cons; however, by working together, they can overcome each other’s limitations and deliver far superior results.
Whether it’s financial records, supply chain logistics, or healthcare records, data is an invisible engine that keeps the business alive. Every customer detail saved, shipping receipt tracked, and invoice generated plays a crucial role. However, the raw data obtained from various sources cannot be used as it arrives in different unstructured formats, including images, physical invoices, messy emails, scanned documents, receipts, and PDF files. To obtain actual insights from such data, a proper data extraction method needs to be followed.
For decades, companies have been hiring data entry professionals who manually extract the data and feed it into software. However, with the advancement in technology, AI (Artificial Intelligence) and ML (Machine Learning) have introduced an automated data extraction process where the data is read and extracted in milliseconds.
But is AI more accurate than manual data extraction? Getting into the right extraction method isn’t merely a technical decision, as it directly affects the processing speed, data reliability, and operational costs. Through this blog, let’s evaluate both the methods, compare the accuracy levels, and learn about the hybrid approach that shapes modern data management.
What is Manual Data Extraction?
As the name suggests, manual data extraction relies completely on human intelligence. Humans review the document, extract reliable information like names, line items, invoice numbers, contact details, etc. from it, and feed the data into a structured system like an Excel sheet, CRM, or ERP.
Pros:
- Context interpreting: Humans are excellent at interpreting context. They can easily detect any information that doesn’t belong to any particular section and make changes accordingly.
- Easily understand complex layouts: Be it irregular forms, poorly scanned documents, or handcrafted letters, humans can easily deal with such structural anomalies compared to software.
- Zero Technical Setup: Model training, API integrations, or software deployment are not required when manual data extraction is carried out. Workers merely need a document to initiate the work.
Cons:
- High Operational Cost: Companies need to hire more staff, resulting in increased workstations, labor expenses, etc., adding to the operational costs, making the model highly expensive.
- Low Speed: When the volume of documents increases, it affects the reading and typing speed of workers. This gradually reduces the typing speed, slowing the business workflows.
- Fatigue leading to errors: Data entry is a tedious, repetitive task. This results into lack of focus after a particular time, resulting in skipped entries, misplaced decimals, or typing errors.
What is AI Data Extraction?
When systems like Optical Character Recognition (OCR), Intelligent Document Processing (IDP), Large Language Models (LLM), and Natural Language Processing (NLP) are involved in the process of data extraction to scan, recognize the data structure, and export fields automatically, it is termed as AI Data Extraction.
Pros:
- Improved Speed and Scalability: The documents that took hours with humans are now transcribed, structured, and saved in a few seconds with the AI data extraction process.
- Reduced operational costs: No doubt, businesses have to pay subscription fees or installation fees for the set-up, but it still eliminates other operational charges, improving the ROI.
- Continuous self-improvement: Through ML feedback, AI learns from past mistakes. Hence, verified documents refine their accuracy for future extractions.
Cons:
- Incorrect outputs: When documents contain details with overlapping texts, low-quality scans, unfamiliar layouts, AI would scan and store incorrect details or information confidently.
- Configurability: Training or setting models for customized output needs platform configurations, testing phases, and engineering resources to have the desired accuracy, which is tedious at times.
Which is Better: AI or Human Data Processing?
To clearly understand how these two processes work for different business metrics, let’s have a look at the core differences through the table below:
| Feature | Manual Data Extraction | AI Data Extraction |
| Processing speed | Workers might take minutes to an hour processing documents | AI processes several documents in seconds |
| Scalability | Needs more staff to complete a large volume of documents | A single software handles high-volume work instantly |
| Context and Nuance | Humans are capable of understanding the context and messy layouts even if its blur or poorly written. | AI models struggle with scanning such documents, leading to incorrect information. |
| Operational cost | Companies only need to hire a team for manual data extraction. | Companies need to invest in purchasing and installing software, along with configuration settings. |
| Long-term ROI | The companies pay on an hourly basis for each document processed through human labor, which is expensive. | No additional cost once the setup is ready to use for massive documents processed. |
| Tiredness and Errors | As data entry is a repetitive task, it can be tedious for humans, leading to typing mistakes and missed information. | AI systems never get distracted, bored, or tired of extraction, which gives equal consistency in data extraction. |
The Hybrid Approach – The Best from Both Worlds
No doubt, the debate on which is the best, Manual or AI data extraction, often ends up with them opposing each other. But the truth lies in the idea of combining them for mesmerizing results.
Businesses usually select either of the options depending on the data volume and type of documents to be scanned. Hence, the accuracy levels and the outcome suffer. The hybrid approach combines the contextual understanding and nuanced judgment from the manual data extraction method with the scalability and speed of the AI data extraction method. In this way, the businesses don’t have to select between precision and swiftness.
In the hybrid approach, the heavy-lifting task, like working on massive datasets, is carried out by AI in seconds. While human experts step in to verify the validation of complex fields and anomalies, ensuring complete reliability. This combined method eliminates the loop-falls of both methods, effectively delivering a new level of accuracy that neither method could achieve on its own.
Final Words
Regardless of your industry, do you struggle with processing large volumes of documents or managing thousands of files every month? DataPlusValue offers a smart hybrid business solution that combines the strengths of various data extraction methods to guarantee complete accuracy. It leverages the speed of AI systems to handle unstructured documents while incorporating human oversight much like manual data extraction. This hybrid approach simplifies document organization and creates an integrated workflow that transforms raw data into a reliable, accurate database.
Schedule a free trial run with DataPlusValue today, and our team will help you with AI and manual data extraction solutions as per your business needs.
