AI for Asset Integrity: Prolonging Lifespan, Preventing Failures, Maximizing ROI

Designed for integrity and maintenance professionals, this course introduces how AI predicts failures, optimizes inspection intervals, and models remaining useful life (RUL). It gives participants frameworks and tools to begin AI pilots focused on reducing risk and maximizing asset value.

Course Description:

This course gives asset management, maintenance, and integrity professionals a powerful, practical introduction to how AI can extend asset life, reduce failure risk, and drive smarter long-term investment decisions. From inspection scheduling to corrosion prediction, participants will explore how AI enables proactive, data-driven integrity strategies that outperform reactive or calendar-based methods.

Real-world case studies, visual tools, and guided frameworks help attendees bridge the gap between AI potential and asset reliability goals—without requiring data science skills. It’s the perfect foundation for launching digital integrity initiatives that pave the way to L2 consulting and L3 platform deployments.

Who Should Attend:

• Asset Integrity Engineers
• Maintenance & Inspection Managers
• Asset & Reliability Engineers
• Corrosion and Pipeline Integrity Specialists
• Turnaround & Shutdown Planners
• Digital Transformation Leaders focused on asset performance

Course Objectives:

Knowledge Acquisition:

• Understand the impact of AI on asset integrity, inspection, and reliability
• Learn about AI tools for corrosion detection, remaining life estimation, and anomaly prediction
• Identify the connection between data quality, inspection accuracy, and AI performance

Skills Development:

• Recognize degradation mechanisms that can be modeled using AI
• Prioritize assets for AI-based risk scoring and inspection optimization
• Evaluate AI tools for asset condition monitoring and failure prediction

Practical Application:

• Design a pilot plan for AI-enhanced inspection scheduling
• Apply an AI readiness assessment to your asset portfolio
• Create an ROI map for AI in long-term asset management

What will I Learn From it:

• How AI predicts asset failure before it’s visible
• How to optimize inspection intervals using machine learning
• How AI supports RBI (Risk-Based Inspection) and lifecycle cost modeling
• What data is needed to apply AI to static and rotating assets
• How to justify AI adoption using cost avoidance, downtime reduction, and regulatory compliance

Training Methodology

• Case Study Walkthroughs: Including refinery tanks, offshore platforms, and pipeline networks
• Interactive Templates: Visual planning and ROI analysis frameworks
• Workshop Simulation: Simulated condition-monitoring decisions
• Expert Facilitation: Practical insights from real integrity use cases
• AI Tool Demos: Focused on no-code and low-code platforms for inspection and risk

Requirement/
pre-requisites:

• Familiarity with inspection, maintenance, or reliability workflows
• No coding, AI, or statistical background needed
• Bring: Laptop, asset register (if allowed), or list of critical assets
• Open to rethinking inspection and integrity practices with data