AI in Maintenance: Why Many Projects Fail
Many companies invest in AI, expect quick results, and fail. Not because of the technology, but because of data, structure, and a lack of clear use cases.
In the latest episode of the AI and TECH Podcast KI und TECH Podcast | #KIundTECH #ITundTECH our colleague Domenico Carapezza discusses what maintenance really needs today for AI to deliver measurable value.
Key takeaways:
- AI is only as good as the underlying data. Data quality isn’t a “nice-to-have”—it’s a prerequisite
- Successful companies don’t start with visions, but with concrete, actionable use cases
- Smart maintenance is built step by step, not through a single, grand AI initiative
- Digital solutions help secure knowledge and actively address the shortage of skilled workers.
- Technicians benefit directly: faster access to information, more efficient processes, and fewer downtimes.
This shows that AI in maintenance is not a topic for the future, but already a clear competitive advantage today—provided the foundation is right.
Listen now and learn how to strategically improve your maintenance operations: Warum 80 % der KI-Projekte in der Instandhaltung scheitern | SPIE RODIAS bei #KIundTECH | KIundTECH Podcast | #KIundTECH #ITundTECH | Official Website
Youtube: Warum 80 % der KI-Projekte in der Instandhaltung scheitern | SPIE RODIAS bei #KIundTECH
