AI strategy in controlling

Strategic planning and goal setting

• AI as a strategic investment: Why implementation is a management task and not purely an IT project.
• Key questions of the strategy: Resource analysis, feasibility, and identification of competitive advantages for your company.
• Holistic performance measurement: The Harvard model in controlling for management across the dimensions of finance, technology, and culture.
• Competitive analysis: How to use AI to sharpen your profile and strategy against competitors.

Technological basis and data management

• Infrastructure: Understanding the interplay between cloud systems and AI applications in the enterprise.
• Data readiness: Ensuring data quality and AI-compliant document structures as "fuel."
• Tool stack and recruiting: Selecting suitable software and building effective project teams.
• Project controlling: Which AI projects are successful?

Implementation and pilot projects

• The 10-step roadmap: From analyzing the current situation to defining goals to continuous optimization.
• Focus on use cases: Why annual financial statements or planning are ideal starting points for AI.
• Scaling: Analyze processes, evaluate needs, and scale results.
• Convincing presentations: Win over decision-makers with clear rhetoric.

Compliance and culture

• Regulatory Framework: The EU AI Regulation and AI literacy requirements (Art. 4) in the financial sector.
• Data security: Strategies for securing sensitive financial data in automated processes.
• Change management: Who's the boss here? Ensuring leadership and acceptance in the digital transformation.