AI/ML does not fail because of bad algorithms. 

It fails because the foundations are weak.

In this video, Zoltan Gelencser, Group CFO at MotorK, shares the five pillars every AI/ML project needs to succeed:

• Robust data governance – quality, lineage, compliance
• Cross-functional collaboration – break the silos
• Scalable infrastructure – from lab to production
• Continuous evaluation – monitor drift and fairness
• Ethics and explainability – transparency from day one