What is the role of data annotation in TELUS Digital's AI strategy?

• A. It enables faster code compilation.
• B. It is used only for user interface design.
• C. It replaces the need for human labeling.
• D. It supports training and validation of AI models for effective deployment.

Answer: (D)

Data annotation is labeling data so models can learn from it. In TELUS Digital's AI strategy, this labeling provides the ground-truth examples that train supervised models and enable their evaluation. With well-labeled data, the model learns the correct input-output mappings during training, and you can measure how well it performs during validation by comparing predictions to those labels. High-quality annotations lead to more reliable models, better generalization, and the ability to detect biases or gaps before deployment. After deployment, annotated data supports monitoring and retraining as new data comes in, helping the model stay effective over time.

Faster code compilation isn’t achieved through labeling data, and data annotation isn’t restricted to user interface design. It also doesn’t replace human labeling; rather, it complements it, as human-labeled data often remains essential for quality and accuracy.