Use human-in-the-loop automation

Artificial intelligence is being successfully used to automate business processes, e.g. for data extraction in the application of a IDP Software used.

Despite advanced technology, it is almost impossible to achieve 100 % accuracy in data extraction. Even 1 % error can cost millions in certain industries.

There are limitations that AI and machine learning cannot overcome despite IDP software. For success, they must be coupled with human feedback in the form of human-in-the-loop automation.

human in the loop definition

Human-in-the-loop - definition

Human-in-the-loop (HITL) describes a mechanism that uses human interaction to train, refine, or test specific systems such as AI models or machines to achieve the most accurate results.

A simple example of this is the self-scanning machines in supermarkets. Although customers can scan their products themselves, there is always an employee on site to help out with any problems and prevent fraud attempts.

The approach is similar for HITL and KI.


Modern technology is not perfect. That's why people have to be involved in automation and always align it with current goals and needs.

Not only does the AI need to be trained by the human at the beginning to make correct decisions, the human also needs to intervene and correct any mistakes. This is called a feedback loop and is used to improve the accuracy of the AI.

You can use HITL in the following application areas:

  • OCR software
  • self-driving cars
  • Document & Email Processing
  • Processing of receipts for loyalty actions
  • Invoice processing for accounts payable
  • Anonymization of sensitive information for compliance 
  • Badge verification for KYC processes

With HITL, you can quickly identify problems and make improvements through a feedback loop (also called HITL annotation). 

This process is explained below.

human in the loop diagram

Human-in-the-loop & annotations

When AI models are developed, human data labeling is usually part of the process.

To produce reliable results, AI models require large amounts of data to be annotated, tagged, and organized by humans, which requires a lot of time, money, and effort. 

A data annotator and human-in-the-loop help AI models focus on specific data fields to make the best predictions. 

For example, companies may need to feed thousands of labeled receipts to achieve reliable results. 

Although there are a variety of solutions that can achieve 97 % accuracy, HITL automation is the better option to obtain a labeled dataset for AI model training.

human in the loop advantages

The advantages of HITL automation

There is no solution that can achieve a 0 % error rate for complex processes a fully automated solutions without human assistance.

To get as close as possible to this error rate while reducing manual labor, combining AI with automation through the human-in-the-loop process has proven effective.

Using HITL to train AI models or improve workflows offers several benefits, including:

  • Risk Mitigation: Mitigate financial risks arising from inaccurate data, e.g., invoice amounts, billing addresses, credit amounts, etc.
  • Simplification of exception handling: Easily implement a workflow for human review and exception handling.
  • Efficient deployment of personnel: Manage, monitor and improve productivity of personnel performing human review.
  • Cost Control: Control human review costs with configurable filters.
  • Data Completeness: Ensure that extracted data is complete for downstream business applications.
  • The application of HITL leads to a Improved precision in prediction, extraction, classification and validation, and to increased quality of results.
  • Human input can be used to Improve algorithms step by step and the AI so for more application areas usable to make.
  • The Efficiency of AI models is not limited by the quality of the data on which they are trained.
  • Improved and efficient handling of incomplete and difficult data sets

However, there are limitations to this approach that you need to be aware of.


If you are going to use HITL, be aware of the challenges and limitations that come with it:

  • Human-in-the-loop identification: Companies need to figure out who will operate which part of the automation process and which interface to identify the human-in-the-loop system.
  • Big data: HITL cannot always efficiently handle large amounts of data due to a higher need for human involvement in the automation loop. The successive expansion of the solution up to the final comprehensive AI solution is particularly crucial here in order to plan the company's strategic goals in a way that can be implemented operationally.
  • Limited scalability: When a human is involved in a process, scalability can become an issue. The challenge is to adjust the confidence that only uncertain cases require human verification.

However, compared to the challenges and drawbacks of the same workflow in manual form, these limitations are minor and should not prevent you from using AI in your business.

When should human-in-the-loop take place?

It makes the most sense to use human-in-the-loop either at the beginning of the cycle or at the end.

HITL at the beginning

If there is no standard solution, you should include HITL right at the beginning of the cycle.

If you don't currently have AI models or algorithms to automate specific processes, but you do have a significant amount of raw data, you can use human-in-the-loop to label and clean (remove or correct inaccurate data) that data. 

After the data is labeled, you can use it to train your own AI models to recognize invoices or extract data from them. 

For example, you can tag many different invoices to train AI models for invoice recognition.

This allows you to go from 0 % automation to +80 % automation. 

So, in the following situations, it makes sense to put the human at the beginning of the cycle:

  • Structure of data sets
  • Create your own AI models
  • No or low automation with the target of +80 % Automation
  • In-house data annotators and AI experts available

HITL at the end

The use of "human-in-the-loop" to complete the process is common in many cases. This approach combines automation to process recurring tasks and human intelligence to ensure that everything is done correctly.

Often 80 % of the workflow is already automated and 20 % is done by a human. So when is it worth choosing this approach over the previous one?

  • They strive for maximum precision in data retrieval, prediction, validation, anonymization, etc.
  • You want to reduce the need for human intervention by 20 % to reduce overhead costs
  • You want to reduce costly errors (e.g., inaccurate data, duplicates, etc.).
  • You want to optimize execution time while maintaining high precision.

External vs. self-managed HITLS.

There are two different ways to take the HITL approach:

  • Externally managed HITL: Human-in-the-loop provided by an external party (e.g., SaaS provider, data annotation service provider).
  • Self-managed HITL: companies that include a person in the circuit themselves.
Externally managed HITLCoping with high data volumes at peak timesData goes to external party if software provider does not allow license to install on own servers, so called on-prem
Fast, often 24/7 availabilitySecurity measures for SaaS solutions depend on external party (solution: On Premise)
Cost-effectiveLegal compliance of SaaS providers mostly unclear
No time investment for employee training
Self-managed HITLData remains in the companyIT capacities required for initial installation
Employees gain more knowledgeTraining and implementation may be cost-intensive
Good way to collect data
Development of an own service offer

Conclusion - Optimize AI with Human-in-the-Loop OCR

With human-in-the-loop automation, you can achieve the following:

  • Increase the accuracy of data extraction
  • Acceleration of the processing time
  • Reduction of overhead costs 
  • Improved employee engagement
  • Minimizes costly human errors through advance work by AI
  • 4-eyes principle through the combination of an AI and a human being

You can find the right provider by answering the following questions:

  • Does your organization need to achieve near 100 % accuracy in data extraction?
  • Do you need externally or internally managed HITL?
  • Do you have in-house AI experts? 
  • How important is the fact that data remains 100 % in your internal infrastructure?
  • What is important for your use case?
  • Want to build your own data sets?

The advantage of Artificial Intelligence is that it can perform functions like a human to quickly and accurately develop and understand key insights. 

Regardless of your business model, an OCR solution using AI can help you make data work for you.

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