Software engineering for AI: architecting reliable AI-enabled high-tech systems

Artificial intelligence is increasingly being embedded into high-tech systems, ranging from autonomous machines and robotics to manufacturing equipment, medical devices and other software-intensive products. This opens up many new opportunities, but it also brings new engineering challenges. Unlike traditional software components, AI components learn from data, deal with uncertainty and their behavior may change as models, data and operating conditions evolve.

This talk discusses what this means for the way we design and build reliable AI-enabled systems. The focus isn’t on AI as a standalone model, but on AI as part of a larger system that needs to be safe, robust, maintainable and useful in practice. The talk addresses topics such as requirements, architecture, validation, monitoring, human oversight and the collaboration needed between software engineers, data scientists, system architects and domain experts.

The main message is that successful AI-enabled systems aren’t created by better models alone. They also require careful engineering decisions, clear system boundaries, continuous evaluation and a realistic understanding of where AI creates value and where it introduces risk. The talk offers practical insights for engineers, architects and technology leaders working on the next generation of intelligent high-tech systems.