Labor shortages, rising cost pressures, and increasing demands for flexibility are driving industrial enterprises to seek new levels of operational efficiency. This is exactly where artificial intelligence offers unprecedented opportunities opportunities to automate and optimize tasks across industrial automation systems and production environments, from shop-floor operations and production lines to robotics, quality inspection, predictive maintenance, and process control, that were previously difficult or impossible to automate, thereby elevating efficiency to new levels.

Yet many organizations remain hesitant to adopt AI in these high-stakes environments. Limited experience with AI technologies, uncertainty about real-world performance, and the severe consequences of failure such as production downtime, quality issues, financial losses, or even safety risks can make AI investments appear risky.

Industrial AI promises to combine the transformative potential of AI with the reliability, robustness, and quality standards required in industrial settings. This must be achieved under real-world constraints such as limited computing resources, scarce or noisy data, legacy systems, and limited in-house AI expertise.

This talk explores what it takes to successfully deploy AI in industry at scale. Drawing on real-world projects and lessons learned, it highlights key challenges, common pitfalls, and practical strategies for building AI systems that deliver measurable operational value while meeting strict industrial-grade requirements.