This unit moves from foundational concepts to practical design and valuation of AI initiatives, focusing on data management, roles and competencies, project milestones, and the AI Value Factor Approach. Students learn to plan AI project lifecycles, source and value data, augment decision‑making, and address trust, limitations, and cultural factors that affect adoption and actionability.
Learning Objectives
- Analyze data management practices and data sourcing options to classify datasets by quality, provenance, privacy risk, and potential value for AI initiatives
- Apply the AI Value Factor Approach and algorithmic considerations to estimate and justify the business value of a dataset or AI use case, including output adaptation strategies to improve actionability
- Demonstrate how to construct an AI project lifecycle plan that maps tasks to competencies, defines roles and milestones, and aligns software capabilities along the AI pipeline
- Evaluate trust, ethical concerns, and limitations in AI decision‑making and recommend mitigation strategies and cultural interventions to increase stakeholder confidence and adoption
Curriculum
- 1 Section
- 18 Lessons
- 30 hours
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- Unit 2 — Designing AI Value and Data Pipelines18
- 1.1Data Management20 mins
- 1.2From Tasks to Competencies and Skills to Roles20 mins
- 1.3Michael Proksch20 mins
- 1.4Nisha Paliwal20 mins
- 1.5Wilhelm ‘Wil’ Bielert20 mins
- 1.6The Milestones20 mins
- 1.7The AI Value Factor Approach20 mins
- 1.8Decision‐Making Augmentation20 mins
- 1.9Algorithmic20 mins
- 1.10Putting a Value on Data20 mins
- 1.11Actionability Through Output Adaptation20 mins
- 1.12Establishing Trust in an AI Initiative20 mins
- 1.13The Limitations of AI’s Decision‐Making20 mins
- 1.14Aspiration20 mins
- 1.15AI Project Life Cycle and Project Development20 mins
- 1.16Culture as a Framework for Corporate Identity20 mins
- 1.17AI Software Capabilities Along the AI Pipeline20 mins
- 1.18Data Sourcing20 mins
Instructor
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