February 27th, 2025
Why AI Projects Fail: The Hidden Costs of Bad Data Annotation
By Nick Pegg · 7 min read
Did you know that the popular food & restaurant chain McDonald's called off its unsuccessful 3-year partnership with IBM for AI-assisted system used in the drive-thru orders in June 2024? Reason - Poor AI model performance. Thousands of customers reported that the AI systems used by McDonald’s were struggling to take orders and repeatedly kept adding more chicken nuggets or other food items to their carts.
This is just one recent example of a poorly performing AI models. The web is filled with thousands of examples of big brands like Tesla, Microsoft, and Air Canada facing backlash, financial losses, and reputational damage due to AI project failures. But what is the reason behind AI model accuracy issues? It’s poor data annotation.
Any AI system (from simple to advanced) is only as good as the quality of the data it is trained on. If the training dataset is of poor quality (inaccurate, mislabeled, or incorrect) or data labeling workflows are inefficient, AI systems fail, leading to incorrect predictions and outcomes. High-quality data annotation is the foundation of AI success. So, let’s understand through this blog the hidden costs of data annotation errors and what businesses can do to avoid losses.
Possible Ways How Poorly Labeled Data Derail AI Models
Poorly labeled data can negatively affect the performance of AI systems in more than one way. Let’s understand each of these ways to understand the impact of poor data annotation on AI models:
1. Conceptual Inconsistencies
It occurs when the same training dataset is labeled differently by different annotators who understand one concept distinctly from each other. If two annotators classify similar data points differently, the AI model fails to develop a clear understanding of what each label represents.
Let’s understand its real-world impact:
In healthcare, AI-powered diagnostics systems are used to analyze medical images and accurately identify diseases. If the training data contains mislabeled images, such as benign tumors being mislabeled as malignant, AI systems will perform incorrect diagnoses, and thus, patients will get ineffective care. This will not only risk patients’ lives but also the hospital’s reputation and trust.
2. Edge Case Oversights
AI models depend on diverse training data to handle real-world conditions. If rare but important data points are missing or mislabeled, the model won’t know how to react when such situations (or edge cases) arise. Hence, the AI systems perform poorly in edge case handling. These edge case failures can lead to critical mistakes, especially in self-driving cars, security systems, and fraud detection AI.
Let’s understand its real-world impact:
In autonomous vehicles, ADAS (Advanced driver assistance systems) applications are used for safe navigation. The AI models used in these systems are trained on millions of road images to correctly identify cars, pedestrians, traffic signs, and obstacles. However, if edge cases, such as a person pushing a bicycle or a fallen tree, are underrepresented or mislabeled in the training datasets, these AI systems fail to react the way they should. This can lead to frequent road accidents or safety issues.
3. Inaccurate Recommendations or Increase False Positives and Negatives
To accurately identify patterns, understand complex situations, and provide relevant responses, AI models need high-quality labeled datasets. If the training data is inaccurate, subjective, or outdated, AI model accuracy issues occur. Also, since the training data is not correctly labeled, the chances of false negatives (AI systems failing to detect something important) and false positives (incorrectly identifying something important) increase.
For example:
If an AI system used for finance fraud detection is trained on poorly annotated data - where many fraudulent transactions are labeled incorrectly as "legitimate," the model learns the wrong signals. As a result, the model will identify fraud transactions as false negatives and allow them while wrongly flagging genuine transactions as fraud (due to false positives).
4. Longer Training and Higher Computing Costs
Every time errors are discovered in the AI training data, the in-house teams start working on manually refining annotations. This process is not just resource and time-intensive but also delays the model deployment. Also, a vast amount of computational resources get consumed in the model’s retraining, not only putting a financial strain on an organization but also affecting its overall productivity/efficiency.
Why In-House Teams Struggles with Data Labeling - Key Reasons Behind Poor AI Training Data
We have understood the importance of accurate data annotation in AI projects. However, its implementation is not as simple as it seems. A large amount of training data is required to be labeled to train any AI model, and labeling such vast data is not an easy task. Here are some key data labeling challenges in-house teams face:
1. Lack of Domain Expertise
Subject matter expertise is needed to label industry-specific terminologies and data for AI model training. For example, to create a training dataset for the AI system used in medical imaging AI, expert radiologists are needed who can label CT scans correctly with relevant context. Not all organizations have access to such domain experts to label training data, which leads to more contextual inaccuracies in annotations.
2. Resource Scalability Issues
To make AI systems able to perform well in complex real-world scenarios, you have to train them on a huge amount of training data, and this is not a one-time process. As scenarios evolve, the training data becomes obsolete, and you need to either update it or add more labeled data to fed to AI systems to remain effective. Now, labeling tons of data for continuous training of AI models arises scalability issues.
Companies need a large workforce that can dedicatedly collect and label data as per business needs. This means more investment not only in terms of time but also in resources (workforce and annotation tools needed to efficiently handle growing requirements), which is challenging to handle for budget-constraint organizations.
3. Increased Operational Costs and Long Hiring Cycles
When building an in-house team for data labeling, you need experienced annotators who can label data according to your business needs and criteria. Also, you will need to appoint one dedicated manager who can oversee this team and ensure quality, accuracy, and consistency across annotations. In hiring and training of these resources, you have to invest thousands of dollars and hundreds of hours (detailed breakup here). And don’t forget the cost of data labeling tools that your team will need to label a vast range of data.
4. Inconsistent Annotation Quality
Many companies don’t have standardized workflows for data annotation and quality assurance. Since no specific criteria or guidelines are followed by in-house teams of annotators in such scenarios, inconsistencies in annotated data are common. This data variability directly affects the AI model's performance.
5. Compliance and Data Security Issues
In industries like finance, healthcare, and legal, a lot of sensitive or personal information is used to create training datasets. If the data labeling tools or processes used are not secured properly, this sensitive information can be hacked, and companies will have to pay legal penalties for violating data privacy regulations like GDPR and HIPAA.
Thus, it becomes important for businesses to keep the data secure during the labeling process by using security protocols and data anonymization techniques. But, since many businesses don't know how to implement these techniques or practices, securing data during in-house data annotation becomes difficult for them.
6. No Prior Knowledge of Using Data Labeling Tools
In-house data labeling also becomes challenging if the team doesn’t have the required knowledge or expertise in using advanced annotation tools such as CVAT, LabelBox, and SuperAnnotate. In such scenarios, additional training is required, which consumes time and slows down productivity.
How Data Labeling Services Solve In-House Team Challenges?
One practical solution to the above-stated challenges is outsourcing data annotation services to an experienced and reliable service provider. These providers can streamline large-scale data labeling workflows and benefit businesses in several ways as they have:
1. Access to Advanced Annotation Tools and Subject Matter Experts
Data annotation outsourcing service providers have dedicated teams of domain experts who understand cultural nuances and label data as per your specific needs. These experts are proficient in using a wide range of data labeling tools for text, video, and image annotation. Using their subject matter expertise, they check the annotations (provided by the data labeling tools) for contextual relevance and accuracy and add required details where needed.
2. Cost-Effective Solutions
Outsourcing data annotation is more cost-efficient as compared to building in-house teams, as you don’t need to invest additionally in infrastructure and resource hiring & training. These service providers usually work on pay-as-you-go models, which means you have to pay for the resources/services you need without long-term contracts or commitments. Additionally, if your data volume grows with time, you always have the flexibility to scale up your team for seamless scalability.
3. Standardized Quality Control
Data labeling service providers have standardized workflows to ensure consistency and accuracy across all annotations. They use multi-level quality checks to make sure the labeling data is accurate, up-to-date, and meeting your quality standards.
4. Faster Project Turnaround
The service providers can work in your preferred timezone, and their scalable workflows ensure that you get labeled data within the required turnaround time without compromising the data accuracy & quality.
5. Strict Adherence to Data Security and Regulatory Compliances
The data annotation teams follow the best practices to comply with data privacy regulatory compliances such as HIPAA, GDPR, and CCPA. For data safety, they sign NDAs and implement end-to-end data encryption, data anonymization, role-based access control, and secure file sharing methods. This way, they make sure your data remains safe and secure during the entire project.
Conclusion
Poor data annotation is responsible for AI model accuracy issues, unexplained behavior, and costly retraining efforts. If you want to make your AI systems remain advanced and performant, you must invest in the best data labeling techniques and practices. If it is getting difficult for you to manage data labeling in-house, consider outsourcing. However, when partnering with a company for data annotation services, check the service providers’ experience in your domain, contract terms, pricing models, and past work. By focusing on training data quality, you can avoid AI project failures, financial losses, and reputational damage.
