CPMAI's Phase III: Data Preparation is a distinct phase that encompasses data cleansing, augmentation, labeling, and pipeline construction. Because data engineering often accounts for the majority of AI project effort, omitting this phase from initial estimates leads to significant timeline underestimation. Project timelines must explicitly include Phase III activities to be realistic .
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In CPMAI's Trustworthy AI requirements, the Explainable AI layer specifically covers "legal, compliance, and risk considerations [that] might require that the AI system used for decision-making ... provide some level of explainability for audit, root cause analysis, or other purposes." Providing applicants with the ability to contest hiring decisions depends on furnishing clear, human-understandable explanations of how and why the model arrived at its result-exactly the focus of the Required AI Explainability Considerations task.
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The CPMAI Glossary's entry for Generative AI highlights its use in creating new content (text, images, or code), enhancing training datasets via data augmentation, powering virtual avatars/characters, and serving as an Augmented Intelligence tool to boost human productivity . It also underpins programmatic content generation across multiple media types. Generative AI is not designed primarily for explainable decision- support interfaces.
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During Phase I: Business Understanding, one of the foundational CPMAI tasks is to "determine when to implement automation versus AI," ensuring that rule-based or non-cognitive alternatives are considered first and AI is only selected when those approaches won't suffice.
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Phase V: Model Evaluation is dedicated to validating a trained model's performance against technical metrics and the business success criteria defined earlier. This phase encompasses tasks such as generating performance results, measuring KPIs, and deciding whether to retrain or proceed to operationalization.
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