Designing Intelligence for the Shop Floor: A Software Architect’s Guide to Industrial AI

Closed-loop AI has the potential to transform manufacturing by making production processes more adaptive, efficient, and resilient. However, many industrial AI initiatives struggle to scale beyond prototypes due to fragmented architecture and a lack of integration between data collection, model training, deployment, and retraining.

This talk offers a software architect’s perspective on designing intelligent, closed-loop AI architectures that operate reliably on the shop floor. Drawing on real industrial projects, we will highlight critical lessons learned: what worked, what failed, and why.

These insights cover technical, organizational, and architectural problems encountered in the plant.

We will share pragmatic design principles and proven architectural patterns for building end-to-end AI solutions, from sensors and programmable logic controllers (PLCs) to edge inference and local retraining. The talk includes reference architectures for both time-series and visual data, covering a wide range of industrial use cases.