In the heart of modern manufacturing, many production steps hinge on previous results, which makes their automation difficult, causing inefficiencies and resource wastage. Typical examples include inspection and rework stages, where rework depends on the deviations that were found. With this in mind, the EU-funded SeConRob project will address the challenge of non-automatable manufacturing steps that rely on previous outcomes. Pioneering self-configuring robotic processes and using AI-driven data analysis, the project will extract insights from inspection data, generating robot programmes and parameters for downstream tasks. A feedback loop, powered by reinforcement learning, will refine the process in the long term. Test cases will encompass multi-stage processes (inspection, gouging, welding, grinding, and polishing), and demonstrations will target sectors like automotive and aerospace.