PMI CPMAI_v7 - Cognitive Project Management in AI CPMAI v7 - Training & Certification

During Phase I’s Cognitive Project Requirements tasks, CPMAI instructs teams to “Determine when to implement automation versus AI.” Explicitly distinguishing between simple rule-based automation (RPA) and true cognitive solutions prevents misapplication of AI methodology to non-AI use cases. Ensuring everyone understands this distinction up front would have avoided misalignment on methodology.

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This reaction is a classic example of the Uncanny Valley phenomenon, where a nearly human-like robot triggers discomfort or eeriness in users because it sits in the valley between clearly robotic and convincingly human appearances. Although not explicitly named in the CPMAI glossary, addressing this user experience concern falls under Continuous Improvement and Respect for People, ensuring cognitive solutions are designed for positive user acceptance.

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CPMAI emphasizes that the largest barriers to AI adoption are organizational and cultural, not technical. In Phase I’s Assess Situation task group, teams inventory not only tools and data but also resources, constraints, and the cultural readiness for AI—ensuring leadership and staff embrace data-driven decision-making and experimentation. Without the right AI culture in place, even the best technology investments fail to deliver value .

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CPMAI’s Domain IV: Data for AI – Task 1: Managing Data Fundamentals and Big Data Concepts emphasizes that leaders—not just technical practitioners—must grasp the core characteristics of Big Data (the V’s: volume, velocity, variety, veracity) and its strategic role in delivering business advantage. Ensuring senior leadership is data literate and understands how to leverage Big Data concepts across teams is critical for sustaining a competitive edge; merely upskilling the technical team or distributing data literacy unevenly will leave strategic gaps.

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In Phase III: Data Preparation, CPMAI specifies that data pipelines must be designed to address the specific modeling pattern and model requirements of the current project. Even if two projects use similar ingestion or cleaning steps, the pipeline must be tailored for the exact feature transformations, label mappings, and data schemas of the new model. Therefore, pipelines are pattern- and model-specific, and blindly reusing one from a prior project without adaptation will likely break downstream model training or inference requirements.

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The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project’s domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.

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Generative AI is defined in the CPMAI Glossary as “AI systems that create new data (e.g., text, images, music) based on patterns learned from existing data.” Using Generative AI for content generation directly addresses the need to produce marketing images automatically.

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CPMAI’s Model Development phase includes a specialized task—Fine-Tuning / Re-training of Pre-Trained Models—which requires teams to “determine and document what approach will be used to…re-train pre-trained models.” Implementing retraining pipelines ensures the model can be iteratively updated with new data and configurations in a reproducible, automated fashion .

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Phase III: Data Preparation focuses on constructing and documenting reusable data pipelines—including training and inference pipelines—alongside cleansing, augmentation, and labeling tasks to prepare data for modeling . This is where teams build the end-to-end data preparation workflows for AI solutions such as chatbots.