The project team at an IT services company is working on an AI-based customer support...

For an AI-based customer support chatbot, PMI-CPMAI–aligned lifecycle guidance stresses that defining required data starts fromreal, historical interactions that reflect actual customer needs and behaviors. Gatheringhistorical customer interaction logs for training data(option B) is the method that best meets this requirement. These logs typically include customer questions, intents, issues, resolutions, and escalation paths, providing a rich, labeled or label-ready corpus that is highly representative of real-world use.

By analyzing these logs, the team can identify the most frequent intents, common phrasing, edge cases, and areas where customers are confused or dissatisfied. This directly informsdata schema design, labeling strategies, and coverage requirementsfor the chatbot. It also helps define performance metrics (such as resolution rate for top intents) and guardrails. Synthetic data (option A) may supplement coverage but should not be the primary basis for defining required data, as it risks encoding designer assumptions instead of reality. Feedback from beta customers (option C) is valuable later in the evaluation and improvement phases. Developing scripts based on anticipated queries (option D) aids dialogue design but does not truly define the underlying data required for robust training. Therefore,gathering and leveraging historical customer interaction logsis the most appropriate method to define required data for an effective support chatbot.