Why should people focus more on data instead of “simply” using AI?
Understanding data is the key to approaching AI correctly. Everyone talks about AI, but hardly anyone talks about what drives it: data. AI has transformed data from a silent follower into a star – it provides the raw material from which smart applications are created. Those who only look at half the picture risk mediocre results. Because AI does not refine poor data. On the contrary, it amplifies its weaknesses.
What is the biggest challenge we will face now and in the coming years in terms of data?
Not only are we all collecting more and more data, but we also suddenly have significantly more access to data from others. Many are already taking advantage of these opportunities, but often only superficially. What is overlooked is that behind this data lie enormous opportunities, but also real risks. One of the biggest challenges is that we are increasingly using data automatically without knowing where it comes from, how reliable it is, or what it really means. The use of AI in particular pushes these questions into the background – because the systems appear to provide reliable answers, but these may be based on questionable or outdated foundations. Those who lose control over the origin and quality of their data also relinquish a degree of responsibility – and that can be dangerous.
And on a personal note: What data would you like to access if you had the opportunity?
Adam: I would like to see an analysis of how often I smiled in the presence of which person and in which situation. That would reveal so much about relationships, energy, and genuine connections, far more than any calendar tracking.
Riccardo: I would like a heat map of my thoughts, including automatic tagging, repetition patterns, and context. Which topics really concern me, which ones keep coming up—and which ones are only superficial?
Thank you very much for the interview, Adam and Riccardo!
