Scrum and AI: Backlog, Sprints, and Team Management

Course overview

In the training course "Scrum and AI: Backlog, Sprints, and Team Management," the topics "Backlog," "Sprints," and "Team Management" are systematically explored and deepened through professional tasks. At the end, work results from Scrum are reflected upon and compared with the learning objectives related to Artificial Intelligence. The learning sequence connects role clarification, transparent task management, team coordination, and reflective adjustment of the approach. Participants independently apply Artificial Intelligence in a defined task segment. The guiding theme is the task "Scrum and AI"; it is addressed from a technical, methodological, and quality-related perspective. The learning tasks connect Scrum with Artificial Intelligence and make the interfaces between both topics visible. 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 fields of activity described in the title and their application in different work situations. Program For Artificial Intelligence, boundaries, dependencies, and suitable control steps are considered. The educational content is divided into seven interrelated learning sections: - Project context and work framework: The focus on "Scrum and AI" is categorized regarding 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. Work results related to Artificial Intelligence are discussed and improved based on clear criteria. - Backlog: Tasks, dependencies, and sequences are structured, prioritized, and linked to verifiable interim results. - Sprints: Goals are translated into manageable steps; effort, sequence, and potential risks are incorporated into the preparation. Commonalities, differences, and sensible transitions between Scrum and Artificial Intelligence are worked out. - Team Management: Collaboration is designed based on clear responsibilities, suitable conversation formats, and comprehensible agreements. - Planning and control process: Goals are translated into tasks, priorities are aligned, and progress as well as open points are transparently tracked. Based on professional situations, it is examined how Scrum is categorized and what role Artificial Intelligence plays in it. - Result verification and collaboration: Interim results are jointly reviewed, feedback is categorized, and concrete improvements are derived. - Documented final example: The acquired knowledge is consolidated in a complete task and documented with work steps, decisions, and results. The exercises address Scrum in a clear workflow and incorporate Artificial Intelligence at appropriate decision points. The technical classification of Scrum is supplemented by concrete tasks related to Artificial Intelligence. The exercises are d…

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