In high-risk, asset-intensive environments, early detection of faults and anomalies is critical to maintaining uptime, reducing risk, and optimizing performance. This workshop explores how AI-powered visual inspection technologies are transforming traditional inspection methods. By combining deep learning, edge computing, and real-time image analysis, organizations can move from reactive maintenance to predictive, data-driven decision-making.
You will learn how to:
• Use real-time image and video analytics to detect early signs of wear, corrosion, or misalignment—reducing unplanned downtime and maintenance costs.
• Leverage edge AI to monitor infrastructure remotely and at scale, with instant alerts and automated fault classification—especially valuable in hard-to-access or hazardous environments.
• Replace manual inspections with automated, high-frequency analysis that supports faster, more accurate operational decisions.
• Explore how visual AI compares and complements other technologies like natural language processing, robotics, and drone-based inspections—each with unique strengths and limitations.
• Integrate visual insights into existing workflows and control systems—while addressing challenges like data overload, system integration, and workforce readiness.
• Prioritize opportunities, select the right tools, and scale adoption across your operations.
Whether you're exploring AI inspections for the first time or looking to expand existing capabilities, this session will equip you with the knowledge and strategy to drive measurable improvements in reliability, safety, and operational efficiency.