Context: AI technologies are increasingly embedded in products and software engineering processes of industrial IoT, autonomous systems, and cyber-physical systems. It is therefore essential to ensure alignment with safety, reliability, and ethical standards. However, practical software engineering methods for managing misalignment risks remain underdeveloped. Objective: This study aims to explore industry awareness of misalignment risks and current practices for monitoring them within real-world software engineering contexts. Method: We conducted seven interviews with industry professionals to examine perceptions of misalignment risks, gather insights into existing practices, and understand approaches to alignment across various industrial settings. Three recently proposed taxonomies guided our discussions: one on ethical guidelines for trustworthy AI published by the EU, another summarizing identified AI risks, and a third addressing “double-edged components” (aspects of AI systems that can simultaneously yield positive and negative effects.) Results: Our analysis identified common misalignment risks across these settings and revealed limited use of dedicated testing or monitoring for AI alignment. Most organizations rely on general oversight rather than specialized tools. Conclusion: These findings highlight the need to develop tailored governance practices for alignment in industrial software engineering settings.