Agile AI Manager

Course overview

Agile AI Managers are responsible for the implementation of AI in the company and manage it using agile working methods: in short learning loops, with early user involvement, and regular reassessment. The training combines knowledge of AI implementation with agile practical application - in contrast to traditional AI management with long planning cycles and a pure team coaching role without substantive AI responsibility. Tailoring AI projects agilely Especially at the beginning of AI projects, much is uncertain - an agile approach makes this productive. - Break down use cases into small, verifiable increments - Test assumptions about benefits and feasibility early and cheaply - Agree on criteria for stopping and continuing before starting Managing implementation in iterations - Backlog work for AI projects: prioritize by value and insights gained - Short implementation cycles with verification on real work tasks - Reviews with users: present results, incorporate feedback - Retrospectives to improve one's own implementation work People and change AI implementation succeeds through participation, not through announcements. - Early involvement of future users in selection and testing - Dealing with skepticism, overwhelm, and inflated expectations - Organize competence building parallel to the implementation Tool and vendor decisions - Compare and test AI tools like ChatGPT, Claude, and Microsoft Copilot - Derive evaluation criteria from the iterations instead of defining them in advance - Keep an eye on switching costs and dependencies Guidelines and responsibility - Incorporate data protection, internal rules, and the EU AI Act into the iteration planning - Design approval processes to be lightweight but binding - Document decisions regarding the implementation process Key figures for agile AI implementation Progress is measured by usage and impact, not by plans. - Collect and interpret usage metrics for each use case - Demonstrate time and quality gains on specific tasks - Value insights from aborted experiments as results - Reporting formats for management and stakeholders - Plan dependencies on IT, data protection, and works council into the timing - Organize scaling of successful use cases to other areas - Manage expectations with management: promise a learning curve instead of a line graph Participants learn to set up AI implementations as agile-driven projects, validate them in short cycles, and successfully implement them with the involved people.

Upcoming dates

• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-11-03T23:00:00.000Z · 2027-07-03T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-11-03T23:00:00.000Z · 2027-07-03T22:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-11-17T23:00:00.000Z · 2027-01-19T23:00:00.000Z · Virtuelles Klassenzimmer