Data and AI: Leaders Help Their Teams Get Up to Speed

Why AI Fails Without Data and Culture

• Where AI projects fail in practice and what the most common causes are.
• How data literacy (the ability to read, understand, and use data) relates to success with AI.
• Analyze successful real-world examples and apply them to your own context.

Clarify Structures and Responsibilities

• Break down data silos and develop a data map for your own area.
• Clarifying Responsibilities: Who Decides What Regarding Data Within the Team?
• Assess internal competencies and develop them in a targeted manner.
• Getting Started the Right Way: How to Identify Your First AI Project and Implement It Step by Step.

Building a Data Culture and Team Skills

• Promote acceptance of data-driven work methods; identify and overcome resistance.
• Targeted Development of Data Literacy in a Team: What Does Each Person Need to Understand?
• Dealing with data quality, bias, and missing data in day-to-day work.
• Visualize and communicate data in a way that is easy to understand.

Using AI in Everyday Life

• Experience it live: How AI answers your data questions in seconds.
• Asking the Right Question: How to Use AI Effectively for Analysis and Decision-Making in Your Field.
• Come up with your own questions for typical situations in your day-to-day work.
• Classifying Tools and Platforms: When Is a Simple Tool (Low-/No-Code) Enough, and When Do You Need IT?

Data Protection and Ethical Guidelines

• What You Need to Know Before Your Team Starts Using AI Tools: The GDPR and the EU Data Act Explained in a Nutshell.
• Responsible use of data, even when using AI.
• What you decide on your own and when you involve the IT or legal department.

Transfer: Your AI Use Case

• Develop and prioritize an initial AI use case for your field.
• Determine the next steps: What data, structures, or skills are still missing?
• What should I start with tomorrow?