Manager:in for AI-supported transformation

Context and basics

• Necessity and different reasons for transformations (e.g. reorganization, digitalization, system / AI introduction, M&A, restructuring, etc.).
• Why traditional approaches often fail: Avoid conflicts; recognize overloads; ensure progress.
• CIP rethought: continuous improvement with AI - data-based, dynamic and scalable.
• Understanding transformations holistically: Clarity about interactions and levers.

Analyzing and planning transformation

• Analysis of workflows, processes, roles and resources with AI-supported data analysis.
• Scenario work: Thinking organizational structures in different development paths.
• Develop and communicate effective target images (AI story prompting).

Organizing transformation - creating clarity

• Design project organization and role clarification.
• Centralized and decentralized transformation control structures are forming.
• Stakeholder mapping: Identify key people & involve them in a targeted manner.
• Compare targets with actual structures - top-down meets bottom-up.

Carrying out transformation - operational, AI-supported implementation

• Efficiently capture & automatically visualize process data (BPMN/DMN).
• Right sizing of the organizational structure: tapping potential and distributing resources in a targeted manner.
• Business continuity management: securing important roles and minimizing dependencies.
• "Task-skill matching": allocate tasks appropriately and promote talent in a targeted manner.
• Optimize IT landscapes: Making shadow IT and duplicate structures visible.

Maintaining the transformation - ensuring sustainability

• Measuring success - with clearly defined KPIs.
• Promoting reflection - ensuring long-term learning in the company through up-to-date data.
• Fluctuation management: securing knowledge and strengthening retention.
• Risk management: preparation for disruptions and incident response.
• Understand transformation as an ongoing competence rather than a one-off project.

Model your own approach and set it up successfully

• Development of an individual transformation approach for your own area of responsibility.
• Combination of proven methods (e.g. CIP, Canvas) with AI-supported tools.
• Simulation and feedback with realistic scenarios.
• Determine concrete next steps for implementation in the company.