On October 28, 2025, members of the InsurTech Hub Munich gathered at QAware in Munich to take an in-depth look at the future of data literacy and AI readiness - with a clear focus on data literacy as the key to artificial intelligence in companies, practical challenges and solution modules as well as strategic experience reports on successful AI integration.
The path to AI readiness starts with the data
"Many companies manage to build quick proof-of-concepts based on training data - but when it comes to bringing AI systems into production and working with real live data, things get complex," explains Josef Adersberger, Managing Director at QAware. This is exactly where the Deep Dive Day came in: with the question of how companies prepare their data so that it can be used for AI applications.
The requirements are clear: AI-ready means that data must be easily accessible, of acceptable quality and provided with very well maintained metadata. Organizationally, clear responsibilities are needed, and technically, a data platform that can integrate, describe and make data available - while adhering to compliance requirements and closely interlinked with the existing IT landscape.
Strategy before technology - people before tools
A common thread ran through the event: without the right people in the right positions, no AI strategy will work. The human factor plays a central role in all decisive decisions. The frameworks presented clearly showed that a successful AI strategy starts with vision and ambition, continues with value creation and an AI-capable organization and ends with the right technological basis - data and technologies.
Data competence means more than just technical know-how: it is about the ability to recognize and unlock the value of data - a combination of methodological competence, transparency of objectives and technical dexterity. "As many people as possible in a company should be competent in recognizing the value of data," says Adersberger. Only through continuous training can employees combine their existing interest in AI tools with the necessary know-how.
Practical and interactive
Participants were given insights into different types of AI - from machine learning and deep learning to large language models and agentic LLM - and learned how they can be used in a corporate context. Modernized legacy systems were identified as the key to valuable data that is crucial for the ability to act and bring AI applications from the niche into real business.
The Deep Dive Day made it clear that the path to AI readiness is iterative and requires a strategic approach. Regulatory requirements remain a key challenge, but with the right data strategy, AI solutions can be developed that are compliant and protect sensitive information. In the end, it's not about technology for its own sake, but about how data can make the difference in day-to-day business.
