Quadrant Knowledge Solutions’ market insight research on Intelligent Document Processing (IDP) platforms provides a detailed insight to users about the significant role of Human-in-the-Loop to verify the predictions of the AI model and send that feedback to either replace the AI-generated prediction or to be used for future re-training and fine-tuning of the model.
Today’s Artificial Intelligence tools have progressed so much that the margin of error has decreased considerably. This is important because AI tools are now used in critical applications including invoice processing, medical report processing, loan forms processing, etc where mistakes are catastrophic. However, when the data collected is less, it requires a feedback from the human that enables periodic course corrections of the AI system to improve performance and enhanced autonomy. Thus emerges the need for the use of Human in the Loop.
Human-in-the-Loop incorporated in IDP has enabled smooth document processing where bots can ask humans to review and verify actions when the machine’s confidence is below a certain threshold. It also enables users to improve decision-making accuracy, build more streamlined processes, and perform process orchestration.
According to Madhu Kittur “Human-in-the-loop (HITL), is the process of leveraging the power of the machine and human intelligence where users can create and continuously build machine learning-based AI models. With the involvement of Human-in-the-Loop technology in the document processing industry, document extraction has become more streamlined. Human-in-the-Loop concept has enabled applications that use artificial intelligence to improve faster and more effectively than training by themselves.”
Table of Content:
- Introduction
- Data Extraction: its needs and types
- Pros and Cons of Proper data extraction
- Benefits and Limitations of Human-in-the-Loop
- Areas of Improvement: Human-in-the-Loop
- The Final Thought
This Market Insight is a part of Quadrant’s Intelligent Document Processing Report from the BPM Domain.
Author: Madhu Kittur, Analyst, Quadrant Knowledge Solutions.
Apoorva Dawalbhakta, Associate Research Director, Quadrant Knowledge Solutions.
