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Automated Data Labeling in 2024: Guide, Benefits & Challenges

Since the past decade, Artificial intelligence (AI) has brought advancements in almost every aspect of human life. The rising1 market for AI applications (See Figure 1) has created an abundance of AI use cases in every industry. However, all that glitter is not gold; developing these AI/ML models requires a lot of effort, including collecting and labeling an ocean of data.

Figure 1. The global market for AI application 2018-2025. 

A bar graph showing the increase in the global market of AI applications from 2018 to 2025. by 2025 the market is projected to grow to $118 billion.
Source: Medium

Manual data labeling is infamous1 for creating delays in the development cycle of AI/ML systems, resulting in an increased number of research2 and usage3 on automated data annotation tools. However, before making premature investments in automated data labeling tools, we recommend learning about the technology and how it can benefit your data-hungry projects. In this article, we explore the following:

  • What automated data labeling is, and why is it important?
  • What are its challenges and solutions?

What is automated data labeling, and why is it important?

We have explained data labeling or annotation before. In a nutshell, it is the process of converting raw data into machine-readable data by adding labels or tags to it. Modern AI/Ml models require large and diverse datasets to be developed and improved. In such cases, if the labeling is done manually, it can lead to:

  • Humanoid errors (since its a repetitive and tedious task)
  • Reduced quality and efficiency of the labeling process
  • Delays in project timelines
  • Lack of uniformity
  • Adding additional labeling costs (Since excess labelers need to be hired)

That is where automated data labeling comes into play. Data labeling is automated by integrating an AI/Ml model in the process, which learns how to label the data to automate marking it . However, this is a human-in-the-loop process since it requires a human annotator to provide a sample dataset for the auto-labeling model to learn from, watch over the automated process, and step in when necessary.

This is how it’s done:

An image of a flowchart explaining the basic process of automated data labeling

Even though humans can create high-quality labels on small datasets, they can not deliver the same quality on large datasets. That is because working with large datasets makes the process highly repetitive and error-prone. Leveraging automation can help avoid the previously mentioned problems of manual data labeling.

Similarly, with new solutions such as the segment anything model (SAM)4, image annotation can be automated without the need for training annotation models. This makes the annotation process faster and the development of computer vision models cheaper.

What are the challenges in automated data labeling, and how to overcome them?

While automation brings notable benefits to the data labeling process, there are also some negatives to the story.

1. Excess training time challenge

Usually, data labeling automation proves to be more efficient in the long run than the manual method; however, one thing to consider is that the labeling model also requires training. 

This issue usually occurs using pre-made models because they are already developed to provide a certain type of output. Suppose that the output of that auto-labeling model does not match the use cases of the new model that will be trained. In that case, the developing team might have to spend extra time and effort retraining the auto-labeling model to fit the project specifications. For instance, if an auto-labeling model is only trained to label images in daylight, it will not be able to label darker images taken at night.


Asking the following questions might help overcome this challenge:

  • Does the existing model satisfy the project requirements?
  • Is it worth developing a new model specific to the labeling requirements or re-training an existing one?
  • Can the amount of training time for the auto-labeling model be added to the development timeline of the project?

2. Accuracy challenge

The level of accuracy of the auto-labeling model depends on the accuracy of the sample-labeled dataset that is provided to it. Human annotators label these sample datasets. If the quality of the sample dataset is poor, then the labels created by the automated model will also be flawed. In simpler words, it’s a garbage-in, garbage-out situation. 


To avoid such an issue, the labeling team must put extra effort into preparing the sample dataset. This dataset should be strictly created for training auto-data labeling models. The team can create a standard dataset created through an efficient manual process.

3. Error continuity challenge

The issue with auto-labeling models is that they will keep going even if an error occurs in the output. That is because of the level of proclivity in their mechanism. A human, on the other hand, would stop and fix the issue before moving forward. The challenge is that future errors can go unnoticed because the incorrectly labeled data will create a chain of errors.


Evaluating the trustability of the auto-labeling model can effectively remedy this problem. This can be done by using an uncertainty estimation tool, which can measure the level of trust a team can put in the outputs of an AI/ML model.

You can also check our data-driven list of data annotation/labeling services to find the option that best suits your business needs.

Further reading

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  1. Heller, Matthias (Mar 9, 2020). “Data Labeling: AI’s Human Bottleneck”. Accessed: 01/Nov/2022.
  2. Kim, J., Le, D. X., & Thoma, G. R. (2000, December). Automated labeling in document images. In Document Recognition and Retrieval VIII (Vol. 4307, pp. 111-122). Accessed: 01/Nov/2022.
  3. Goodin, C., Sharma, S., Doude, M., Carruth, D., Dabbiru, L., & Hudson, C. (2019). Training of neural networks with automated labeling of simulated sensor data (No. 2019-01-0120). Accessed: 01/Nov/2022.
  4. Meta. Segment anything model. Accessed: 05/May/2023.
Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Shehmir Javaid is an industry analyst in AIMultiple. He has a background in logistics and supply chain technology research. He completed his MSc in logistics and operations management and Bachelor's in international business administration From Cardiff University UK.

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