Manufacturers stuck in AI pilots need predictive maintenance, automated quality inspection, and AI-driven demand forecasting to see real results. Success depends on having clean, high-quality data before you scale.
So many manufacturers are really excited about AI, of course, but they often struggle to move beyond pilots and experimentation. So where are you seeing AI create practical, measurable value today inside manufacturing and supply chain operations?
Predictive maintenance. Many plants are already working in a lot of implementations on predictive maintenance, and AI is helping a lot on this, getting the benefits on the machines on that. Quality inspection. Quality inspection is one of the great projects we are implementing. And again, AI can help you to really get better and better and better quality on that. Production planning and demanding forecast. We have amazing results now Based on your history, based on the market inputs and market analysis. Now we can have a much more reliable demand planning in place for our customers. So these are a lot of examples that if you do have good data implemented, AI can bring a lot of resources and analysis on that.
Yeah, that's— I think that's a great reflection on where you have to start, you know, and rather than jumping right into experimenting with AI, step back and think about your data and make sure that it's robust first. I think it's a, a good takeaway from that.
