Part 10/11:
Throughout the presentation, Jan reiterated a fundamental principle: the importance of selecting appropriate AI models for specific problems—whether diagnostic, predictive, or prescriptive. Simple models suffice for straightforward issues, whereas complex nonlinear models are reserved for intricate tasks, ensuring efficiency without unnecessary complexity.
He emphasized that adoption is the ultimate success metric—fascinating technology is meaningless if it isn't integrated into decision-making processes across the organization. Moreover, data quality and infrastructure form the backbone of all AI initiatives; without reliable data and scalable systems, outcomes falter.