Why AI projects fail - and how to make yours succeed

AI initiatives often begin with a strong focus on models, technology and hardware. While important, they are only a small part of what is needed to build a functional and sustainable AI solution.

In practice, many ideas struggle to move beyond the proof-of-concept phase not because the core technology fails, but because critical factors such as precise problem definition, human-in-the-loop design, data readiness and solution adoption are underestimated. In this talk we will explore the most common pitfalls in AI projects and the practical steps needed to move from concept to a working real-world solution.