Course Content

Learn how to

• Build foundational expertise in Asset Management principles using an AI-enhanced lens

• Master core asset management components from the ground up, with real-world deployment use cases showing how ML-driven models

• Integrate legacy asset and maintenance data into a modern, ML-powered EAM/CMMS framework

• Analyse EAM/CMMS data with ML to interpret fault conditions

• Use configuration and data‑presentation tools within EAM/CMMS enhanced by ML workflows

Understand how

• To deploy the practical principles of utilising AI & ML for Asset Management

• To utilise real time data and AI & ML to enhance whole-life asset decisions and investments

• To utilise an industry recognised EAM/CMMS and AI & ML tools to deploy asset management

Know know to

• Explain the fundamentals of Asset Management and its core components

• Develop an AI-enhanced Asset Management Plan

• Design ML-driven solutions to minimise faults and optimise asset longevity

Asset Management is coordinated activity of an organisation to manage its assets whilst taking account of costs, risks, and system performance and how they align to company objectives.

It is an entire asset lifecycle approach to the management of the entire asset base — from planning & acquisition through operation, maintenance, and disposal — to optimise whole-life value and align asset performance with organisational goals.

Digital Asset Management (DAM), enhanced by Artificial Intelligence (AI) and Machine Learning (ML), uses real-time data, predictive models, and digital systems (e.g., sensors, cloud-based Enterprise Asset Management (EAM)/ Computerised Maintenance Management System (CMMS), advanced analytics platforms) to monitor asset health continuously, enable predictive maintenance, and optimise whole-life value.

These technologies transform traditional lifecycle approaches into intelligent, proactive systems that improve performance, reduce risk, and maximise organisational alignment.

It is recommended that learners complete ‘Asset Management Digitalisation’ module before attending this module.

The course is 50% theory and 50% interactive using practical based exercises and is delivered over 2 days, delivered over two evenings online and one day onsite.

Areas covered include:

• Asset Management & the Role of AI/ML

• Asset Management Principles & Frameworks with Intelligent Extensions

• Asset Management Policy, Strategy & AI Governance

• Lifecycle Asset Management with ML‑Driven Optimisation

• Tools, Implementation & Practical AI/ML Application

• Risk Management & Ethical Considerations in AI Enhanced DAM

• Predictive maintenance using ML algorithms

• Fault pattern recognition and anomaly detection

• AI-driven asset lifecycle cost modelling

• Implementing an Asset Management System with Integrated AI/ML

The course provides a hands-on approach to utilising AI & ML for Asset Management via tailored practical exercises based on real-life industry scenarios. These real-life scenarios can be tailored to mimic the learner’s area of employment.

Suitability