| Date | Venue | Fees | |
|---|---|---|---|
| 28 Sep - 02 Oct 2026 | Amsterdam - The Netherlands | $ 5,950 | |
| 28 Dec - 01 Jan 2027 | London - UK | $ 5,950 | |
| 15 - 19 Mar 2027 | London - UK | $ 5,950 | |
| 27 Sep - 01 Oct 2027 | Amsterdam - The Netherlands | $ 5,950 | |
| 27 - 31 Dec 2027 | London - UK | $ 5,950 |
Introduction
Asset‑intensive industries are under increasing pressure to improve reliability, reduce lifecycle costs, and extend asset value while operating in complex, data‑rich environments. Artificial intelligence is rapidly transforming how organizations plan, operate, maintain, and retire physical assets. This training course provides a comprehensive, practical exploration of how AI enhances asset lifecycle management—from design and commissioning to operation, maintenance, and end‑of‑life decision‑making.
Delegates will learn how AI enables predictive and prescriptive maintenance, improves asset health monitoring, enhances risk‑based decision‑making, and supports long‑term asset strategy. The training course covers machine learning, digital twins, intelligent inspections, automated data interpretation, and AI‑driven reliability engineering. Participants will also explore how AI integrates with existing systems such as CMMS, EAM, SCADA, and IIoT platforms.
By the end of the training course, delegates will be equipped to identify AI opportunities across the asset lifecycle, implement intelligent asset‑management solutions, and lead digital transformation initiatives that improve uptime, safety, and cost performance. This training course is ideal for organizations seeking to modernize their asset‑management practices and unlock the full value of AI‑enabled decision support.
This GLOMACS Artificial Intelligence (AI) in Asset Lifecycle Management training course will highlight:
- AI fundamentals for asset lifecycle management
- Predictive and prescriptive maintenance strategies
- Asset health monitoring and anomaly detection
- Digital twins for lifecycle optimization
- AI‑enabled inspections and condition assessment
- Intelligent risk‑based decision‑making
- Integration with CMMS, EAM, IIoT, and SCADA
- Data governance, ethics, and model validation
Objectives
By the end of this Artificial Intelligence (AI) in Asset Lifecycle Management training course, participants will be able to:
- Identify AI opportunities across the asset lifecycle
- Use machine learning to analyze asset performance and failure patterns
- Build and interpret predictive maintenance models
- Apply AI to optimize maintenance strategies and asset planning
- Use digital twins for simulation and lifecycle decision‑making
- Lead AI‑enabled asset‑management initiatives
Training Methodology
The Artificial Intelligence (AI) in Asset Lifecycle Management training course is delivered through a blend of instructor-led presentations, live demonstrations, guided exercises, and real-world case studies. Participants will work with actual AI tools, explore practical engineering scenarios, and complete hands-on activities that reinforce learning. The training course is structured to be interactive, engaging, and immediately applicable to workplace challenges.
Organisational Impact
Organisation will benefit as a result of:
- Improved asset reliability and reduced downtime
- Lower lifecycle costs through predictive insights
- Enhanced maintenance planning and resource allocation
- Stronger data‑driven decision‑making culture
- Better integration of asset, operations, and digital teams
- Accelerated digital transformation and Industry 4.0 readiness
Personal Impact
Participants will benefit as a result of:
- Increased confidence in applying AI to asset‑management challenges
- Stronger analytical and reliability‑engineering skills
- Enhanced ability to interpret asset‑health data
- Improved capability to design AI‑enabled maintenance strategies
- Greater career mobility in digital asset‑management roles
Who should Attend?
This GLOMACS Artificial Intelligence (AI) in Asset Lifecycle Management training course is suitable to a wide range of professionals but will greatly benefit:
- Asset managers and asset‑strategy professionals
- Reliability and maintenance engineers
- Operations and production managers
- Engineering managers and technical specialists
- Digital transformation and Industry 4.0 teams
- CMMS/EAM administrators and analysts
AI Foundations for Asset Lifecycle Management
- Introduction to AI, ML, and Industry 4.0
- Overview of the asset lifecycle: design to decommissioning
- Traditional vs. AI‑enhanced asset‑management methodologies
- Asset data types: operational, maintenance, condition, environmental
- Data quality, preparation, and governance
- AI in global asset‑intensive industries
Predictive Maintenance & Asset Health Analytics
- Machine learning for failure prediction
- Condition‑based monitoring and anomaly detection
- Time‑series analytics for rotating and static equipment
- Feature engineering for asset datasets
- Predictive maintenance vs. prescriptive maintenance
- Building a predictive maintenance model
Digital Twins, Simulation & Lifecycle Optimization
- Digital twins for asset performance and lifecycle modeling
- Simulation of degradation, failure modes, and maintenance scenarios
- AI‑enabled reliability engineering and RCM enhancement
- Asset‑strategy optimization using AI
- Using a digital‑twin scenario
AI Enabled Inspections, Risk & Decision Support
- Computer vision for inspections and defect detection
- AI for corrosion, fatigue, and structural assessment
- Intelligent risk‑based decision‑making
- Integrating AI with CMMS, EAM, SCADA, and IIoT
- Model validation, ethics, and governance
- Designing an AI‑enabled inspection workflow
Implementation, Scaling & Asset Management Transformation
- AI adoption roadmap for asset‑management systems
- Change management and workforce readiness
- Building cross‑functional AI asset teams
- Scaling AI across multiple sites and asset classes
- Designing an AI‑enabled asset‑lifecycle strategy
- Upon successful completion of this training course, GLOMACS Certificate will be awarded to the delegates. Continuing Professional Education credits (CPE): In accordance with the standards of the National Registry of CPE Sponsors, one CPE credit is granted per 50 minutes of attendance
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