Most companies started their journey from analytics to AI some years ago by jumping on the data science train. In those days data scientist was assumed to be the most sexy job of the 21st century. With the first use cases the appetite for more analytics increased. But there is also a long list of failed projects. Now, we have GenAI and everybody is jumping on the AI train. That is a good point in time, to take a moment to ask what are the lessons learned from the journey so far? And where will we go next?

Join this session, to learn more about:

• Why some data science and AI initiatives succeed while others fail, exploring common pitfalls
• How the role of data science has evolved with the advent of generative AI, including shifts in required skills, tools, and approaches that are reshaping AI-driven strategies
• Actionable steps to advance the AI journey, focusing on aligning AI initiatives with business goals, scaling AI responsibly, and preparing for future advancements in the AI landscape

• How has your understanding of AI capabilities and limitations evolved throughout your journey?
• How did you handle setbacks or unexpected results, and what did you learn from these experiences?
• How did the outcomes of your AI projects align with your initial expectations, and what adjustments did you make along the way?
• What technical tools, frameworks, or platforms proved most effective in your AI development process, and why?
• How did you address data challenges such as quality, volume, and preprocessing to improve model performance?

In the Harvesting Session, presenters will summarize the results of the day’s discussions and highlight key findings from the World Cafés