- Demand for annotated data continues to grow as AI adoption expands across industries, making high-quality labeled datasets critical for training reliable models.
- Automation is reshaping annotation workflows, enabling teams to process large datasets faster while reducing manual effort.
- GenAI improves speed and scalability but still requires human oversight, particularly when handling complex scenarios, ambiguity, or contextual interpretation.
- The effective annotation approach combines human expertise with automation, using hybrid workflows that maintain quality while supporting scalable AI development.
The Growing Demand for Data Annotation in the AI Era
The global data annotation services sector is entering a period of exceptional expansion, growing from a valuation of approximately USD 1.30 billion in 2024 to an estimated USD 14.40 billion by 2034, supported by a powerful ~27% annual growth rate between 2025 and 2034.1 This surge is largely driven by AI adoption in sectors such as healthcare and automotive, where large volumes of quality data are required to train models. With organizations expanding their use of GenAI, the demand for reliable data annotation continues to increase.
Quality data is fundamental to training and improving AI models. A model performs well only when it is trained on the right datasets. This process requires large volumes of varied data to enable ML systems to learn different patterns and operate effectively across scenarios. With effective training, models produce more reliable outputs and better user outcomes, making the quality of data annotation a critical factor in overall model performance. However, manually annotating data is complex and time-consuming. Automation, on the other hand, helps accelerate model development and allows teams to handle large-scale annotation projects more efficiently.
Data processing to strategic enablement, effective annotation is the foundation for every reliable AI model.
From Automation to Intelligence: How GenAI is Redefining Value Creation
Today, GenAI is increasingly used in annotation workflows as model and technical capabilities improve. Annotation teams now combine manual review with GenAI-assisted labeling to maintain accuracy while keeping projects on schedule .
Automation in data annotation: Manual annotation -> Assisted manual annotation -> Rule-based annotation -> Machine learning models and active learning -> GenAI based annotation -> GenAI automated annotation.
Tech Mahindra’s annotation programs have successfully used assisted manual annotation, rule-based automation, and machine learning models. Current efforts are expanding toward active learning, GenAI-assisted annotation, and autonomous annotation workflows. While GenAI improves speed and scale, it still faces limitations. Here’s a quick rundown of the key challenges and the areas where it delivers the most value.
| Data Type | GenAI Challenges | Where GenAI Excels |
| Text | Ambiguity, sarcasm, domain jargon, long context | Entity extraction, classification, PII redaction |
| Image | Hallucination or missing attributes, occlusions | Object detection, tagging, basic captioning |
| Video | Scene complexity, fast objects, long context | Segmentation, activity classification, keyframes |
| Audio | Noise, dialects, diarization drift | ASR transcripts, keyword spotting, basic intent |
Automation Meets Reality: Key Learnings from a Real Annotation Implementation
Annotation automation demands successful coordination between people, processes, and technology. The following use case illustrates key challenges in real-world projects and TechM’s structured approach in addressing them.
Objective: To identify participants and objects within a given video scene and describe participant activities and object properties like size, shape, color, and build for AI model training. The project scope included data collection and data annotation.
TechM's Solution:
Data Collection
Manual data collection required participants to act out scripted activities that were recorded on video. These activities ranged from relaxing in the living room and cooking in the kitchen to cleaning the house and performing tasks within a defined area.
Data Annotation
Using our data management platform, we built a GenAI model to support automation in the annotation workflow. The model first analyzed the data to identify participants and objects within the scenes, after which an expert human annotation team reviewed the output and corrected any inaccuracies before finalizing the model.
Limitations: The complexity of the scenes, limitations in prompting techniques, object misidentification, and inconsistent adherence to process guidelines led to GenAI hallucinations, resulting in false attributes and incorrect descriptions. In addition, human reviewers had to meet strict quality thresholds while identifying subtle model-generated inaccuracies, which increased the operational burden.
TechM’s Fix: After identifying gaps, we solved the hallucination and false attribute challenge by streamlining the process.
- Incorporated client feedback into the process:
- LLM did not follow the correct spatial order of objects. We introduced additional training and manual annotation to address this.
- LLM generated unnecessary object attributes; we implemented prompt refinement to resolve this.
- Missing object attributes were identified using automated scripts that validated JSON output for missing information.
- Introduced changes to the existing process
- Deployed hybrid working model: AI + Human evaluations
- Enhanced the prompts by providing sufficient data for learning
- Refined the scripts accordingly to validate missing information
These refinements improved model performance and reduced reviewer workload, supporting a sustainable workflow.
Prompt refinement, validation scripts, and human-AI collaboration turned early challenges into an annotation workflow built to scale.
Learnings
With this experience, we learned to always start with human-led manual annotations for complex projects and later introduce automation to check on the efficacy of the model. Also, we recognized the importance of expanding automation only while maintaining annotation quality and delivery timelines.
Designing for Impact: Principles Every GenAI Automation Strategy Must Follow
For GenAI automation to deliver real value, organizations must:
- Have a clear understanding of the use case.
- Have a sufficient amount of data trained for AI to learn and generate appropriate outputs.
- Continuously tune prompts to enhance the quality of the outputs.
- Apply a complexity-based approach, which is ideal for simple, medium, and complex use cases.
- Start with a pilot to gather the required information and identify potential challenges of the process.
The Bottom Line
Annotation workflows are moving toward hybrid models in which automation accelerates the process while human expertise ensures accuracy. This approach helps enterprises build reliable and scalable AI models. With GenAI now in the picture, organizations with strong process control and oversight can augment the annotation process and build better AI models efficiently.
Frequently Asked Questions
Our FAQ section is designed to guide you through the most common topics and concerns.
High quality data annotation is essential for training reliable AI models. As industries accelerate AI adoption, large and diverse labeled datasets are required to help models recognize patterns and operate accurately across scenarios. The growing complexity and scale of AI applications make consistent, high fidelity annotation a foundational requirement for model performance.
Automation accelerates data annotation by reducing manual effort and enabling large scale processing. Techniques such as rule based systems, machine learning models, and GenAI assistance streamline labeling tasks. While automation improves speed and consistency, human oversight remains critical for resolving ambiguity, interpreting context, and maintaining annotation quality.
GenAI enhances scalability by supporting tasks such as entity extraction, object tagging, and basic classification across text, images, audio, and video. Challenges arise when handling ambiguity, complex scenes, domain specific nuances, and long context interpretation. Human review is necessary to correct hallucinations, missing attributes, and misidentifications.
Real world implementations show that hybrid workflows—combining AI assistance with expert human review—deliver the best outcomes. Early challenges often stem from prompting limitations, complex scenes, and inconsistent process adherence. Refinements like prompt tuning, validation scripts, and structured human AI collaboration significantly improve accuracy and reduce reviewer workload.
Effective strategies start with clearly defined use cases, sufficient training data, and continuous prompt tuning. Organizations should apply a complexity based approach, beginning with pilot phases to identify risks and refine workflows. Strong process control, human oversight, and iterative improvements ensure that GenAI contributes responsibly and efficiently to annotation pipelines.
