Data strategy first, then AI
Artificial intelligence is often seen as a gimmick by large tech companies, but it also offers concrete opportunities for SMEs. The key is to use AI in a targeted and strategic way in your own company.
Artificial intelligence is often dismissed as a toy of the big tech companies - exciting, but for SMEs seemingly far removed from their own day-to-day business. However, AI actually offers concrete opportunities and added value for companies. The challenge lies in moving from gimmickry to the targeted use of the right strategies.
But what does it take to establish a robust data strategy and thus create the basis for successful AI projects? To what extent do companies need to get to grips with the underlying technology in order to make the most of its potential? And how is it possible to identify relevant use cases for AI in your own company and measure investments in them against other strategic initiatives?
Peter Johannes Wöstheinrich and Alexander Rockstroh from W&R present the building blocks that make up a needs-based data strategy and how this becomes the foundation of successful AI projects.
Julius Weissmann from Bosch Digital will present a practical example of AI-supported maintenance and illustrate the specific benefits Bosch is achieving as a result. He also describes the necessary implementation steps and talks openly about the challenges encountered.
Ralf Walther, mindUp Web + Intelligence GmbH, will explain which technological advances in the AI sector are particularly important for small and medium-sized enterprises and how companies can learn to make sense of these developments and use them effectively.
Thematic focus:
- Data strategy and AIWhat makes a successful data strategy?
- Success factors, important cost-benefit considerations
- The link between corporate strategy, data strategy and IT strategy
- How well-organized data increases the performance of AI systems
- Economic benefits of AI Practical case of predictive maintenance
- Which steps lead to demonstrable results?
- What experience has been gained during implementation?
- Trends and developments in the field of AI
- Technological aspects
- Application of large language models and optimized maintenance in production
