Industrial manufacturing has gained largely from artificial intelligence (AI). Particularly, generative design has leveraged cloud computing and AI to allow fine-grained control over manufacturing processes, loads, and constraints. On one hand, AI-powered solutions simulate complex geometries with customizable material properties. On the other, AI has enabled control over processes without compromising design safety and with significant material savings. Additionally, the foundation models in computer vision and natural language have opened the doors to limitless possibilities.

Delivering a product that needs to meet global high-quality standards requires controlled design and manufacture, navigating the often confusing logistical supply chain, and customer-driven time-to-market deadlines. According to Forbes, unplanned downtime costs industrial manufacturers $50 billion a year. As manufacturing companies scale AI initiatives, the struggle remains to align their operational needs with AI capabilities.

The heavy reliance on limited or siloed data can lead to severe compromises on accuracy and the value of insights driven by AI systems. Moreover, with the increasing complexity of the product and strict quality-control standards, fast-moving production companies lag behind in being able to explain AI models. This inhibits the visibility into patterns that can lead to potential problems or failures that result in unplanned outages and low production efficiency.

Join a session with Ayush Patel, Co-founder at Twelvefold as he takes you through the AI-enabled generative design landscape and its challenges. Explore key strategies to maintain scalable yet reliable AI solutions while achieving significant ROI from manufacturing operations.