The CPMAI Glossary defines Augmented Intelligence as "enhancing human abilities with AI," where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .
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When training data under-represents or skews certain groups, the resulting model can systematically discriminate against those groups-a phenomenon termed algorithmic discrimination in the CPMAI Glossary.
Such bias in outcomes arises directly from biased training data.
Moreover, the CPMAI Exam Content Outline emphasizes that Trustworthy AI must apply laws pertaining to AI ethics, bias, and fairness to detect and remediate these issues early in the lifecycle.
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The process of reducing a dataset's feature set while retaining its most informative components is formally known as dimensionality reduction. CPMAI describes techniques such as Principal Component Analysis (PCA) and t#distributed Stochastic Neighbor Embedding (t-SNE) under this category, enabling teams to simplify high-dimensional data for more efficient modeling.

In CPMAI's Executing the Business Understanding Phase, teams first "formulate AI-specific business questions" and "estimate time-to-ROI for various AI project types" before matching business needs to cognitive patterns . This ensures ROI-driven objectives guide the selection of one or more of the Seven Patterns of AI, rather than the reverse.
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CPMAI emphasizes that data is only as valuable as the team's ability to manage, prepare, and harness it effectively. In Phase I: Business Understanding, one of the first tasks under Assess Situation is an "AI Skills Assessment," which ensures that the project team has the right mix of experience and tooling expertise to handle data- intensive AI work. Without skilled data engineers and AI practitioners, even the largest datasets cannot be transformed into business value.
The Workbook's Task Group: Assess Situation in Phase I explicitly calls out "AI Skills Assessment" alongside resource and tooling considerations, highlighting that team capability is a foundational requirement for any data-centric initiative.
Furthermore, in Domain IV: Data for AI of the CPMAI Exam Content Outline, managing data fundamentals and Big Data concepts hinges on having personnel who can "apply Big Data approaches to enhance AI capabilities", which presupposes the presence of experienced data professionals.
Thus, the single most critical factor is ensuring you have team members with the right experience and tool expertise to handle and derive value from massive volumes of data.