What is AI operationalization in the context of TELUS Digital?

• A. Training models, validating data, and deploying AI into production workflows.
• B. Buying AI licenses and archiving datasets.
• C. Building static dashboards for KPIs.
• D. Replacing human agents with AI only.

Answer: (A)

Operationalizing AI means taking models from development into production and weaving them into real-world workflows. That involves more than just building the model—it covers validating the data that feeds the model to ensure quality and fairness, training and updating the model as needed, and deploying it so it can operate within production systems and business processes. It also implies ongoing governance, monitoring, and retraining to keep AI outcomes reliable and aligned with changing needs.

This is why the best choice is the one that describes training models, validating data, and deploying AI into production workflows. It reflects the end-to-end lifecycle required to turn AI research into practical, scalable solutions within TELUS Digital.

The other options miss this production-focused, integrated lifecycle. Buying licenses and archiving datasets is more about procurement and data management than operationalizing models. Building static dashboards for KPIs centers on reporting rather than deploying intelligent systems. Replacing human agents with AI only promotes a fully automated vision without the necessary balance of human-in-the-loop oversight and continuous governance.