But the iterative nature of predictive analytics clashes with customers' desire for rapid go-to-market timelines. Without a solid business case outlining ROI for various use cases - like predicting lifespans, optimizing schedules, or enabling AI-driven inspections - projects risk failure before they start.
But what does it take to build predictive maintenance applications?
Success hinges on four key elements - labeled data, mature teams with specialized knowledge and skills, the right estimation of the complexity and dependencies, and technology fitment to build a business case.
Predictive maintenance is no longer optional - it’s essential to staying ahead. However, building it requires expertise, strategy, and execution tailored to real-world challenges. That’s where we come in.