Which step is crucial in the deployment phase of an AI system?

In the deployment phase of an AI system, conducting post-hoc testing is crucial because it involves assessing the performance and impact of the AI model after it has been implemented in a real-world environment. This step ensures that the AI system is functioning as intended and meeting the expected objectives. Post-hoc testing helps identify any issues that may arise during the system's operation, allowing for adjustments and improvements to be made based on real user interactions and data.

This process typically involves gathering performance metrics, evaluating user feedback, and continuously monitoring the AI's outputs to ensure reliability and fairness. By incorporating this testing into the deployment phase, organizations can mitigate risks associated with the unexpected behavior of AI systems and ensure they are operating safely and ethically.

In contrast, while developing a customer service strategy, hiring additional personnel, and launching a promotional campaign may be valuable components of a broader deployment plan, they do not directly address the immediate need to evaluate and validate the AI system's performance following its launch. These activities are often more aligned with the organizational infrastructure and communication around the AI system rather than evaluating its technical functionality and effectiveness.