Iterative Project Management with AI: AI Use Cases, Prioritization, and Review Loops

Course overview

The training "Iterative Project Management with AI: AI Use Cases, Prioritization, and Review Loops" imparts fundamental and advanced knowledge in the areas of "AI Use Cases", "Prioritization", and "Review Loops". Selection criteria, work steps, and outcome characteristics for AI in project management are documented. The learning sequence connects role clarification, transparent task management, team coordination, and reflective adjustment of the approach. At the end, work results from Iterative Project Management are reflected upon and compared with the learning objectives related to AI in project management. The guiding theme is the task "Iterative Project Management with AI"; it is addressed from a professional, methodological, and quality-related perspective. The focus is on the competence field of Iterative Project Management and the application focus of AI in project management. Participants work on comprehensible practical cases from agile and digital projects, compare solution paths, and document assumptions, decisions, and results. The goal is a structured understanding of the task areas described in the title and their application in different work situations. Program The professional classification of Iterative Project Management is supplemented by concrete tasks related to AI in project management. The educational content is divided into seven interrelated learning sections: - Fundamentals of Task Management: The focus "Iterative Project Management with AI" is classified in terms of task framework, typical areas of application, and the necessary work steps. The orientation is specified through task management, role coordination, visible progress, and the gradual adjustment of the approach. Professional situations are examined to determine how Iterative Project Management is classified and what role AI plays in project management. - AI Use Cases: Application cases are classified in terms of benefits, data reference, outcome quality, and responsible use. The transfer part deepens Iterative Project Management and translates the insights into a coherent task related to AI in project management. - Prioritization: Tasks, dependencies, and sequences are structured, prioritized, and linked to verifiable interim results. - Review Loops: Quality criteria and test cases are established; deviations are assessed, and suitable improvements are derived. The learning tasks connect Iterative Project Management with AI in project management and make the interfaces between both topics visible. - Roles, Collaboration, and Decisions: Responsibilities, coordination paths, and decision criteria are clarified and applied in a common working rhythm. - Result Verification and Collaboration: Interim results are jointly reviewed, feedback is categorized, and concrete improvements are derived. In AI in project management, the focus is on a fact-based and comprehensible approach. - Application Task and Reflection: A realistic example is planned, implemented, and e…

Upcoming dates

• 2026-08-09T22:00:00.000Z · 2026-10-04T22:00:00.000Z · München · Combined Learning
• 2026-08-09T22:00:00.000Z · 2026-11-29T23:00:00.000Z · München · Combined Learning
• 2026-09-06T22:00:00.000Z · 2026-11-01T23:00:00.000Z · München · Combined Learning
• 2026-09-06T22:00:00.000Z · 2026-12-27T23:00:00.000Z · München · Combined Learning