AI-Enhanced Maintenance Manager
AI-enhanced management training from DU&T Consulting.
Course overview
This course equips maintenance managers to use artificial intelligence to strengthen maintenance planning, asset reliability, troubleshooting, work management and performance reporting. It provides a practical foundation for evaluating predictive maintenance and other data-driven approaches.
The course keeps safety, technical competence and human authorisation at the centre of all maintenance decisions. No programming experience is required.
Learning objectives
- Identify appropriate AI applications in maintenance and reliability.
- Improve maintenance planning, prioritisation and work-order quality.
- Support fault finding and root-cause analysis with structured AI assistance.
- Use asset information to identify patterns and maintenance opportunities.
- Evaluate predictive maintenance proposals and data requirements.
- Improve spares, shutdown and workforce planning.
- Establish safety, security, validation and governance controls.
- Develop a measurable maintenance AI adoption roadmap.
Who should attend
Maintenance Managers; Reliability Managers; Engineering Managers; Asset Managers; Maintenance Planners; Supervisors; Facilities Engineers; and operations leaders responsible for equipment availability and lifecycle performance.
Course modules
Module 1: AI Fundamentals for Maintenance and Reliability
Capabilities, limits and applications across planning, diagnosis, knowledge access and condition monitoring.
Module 2: Asset Criticality and Maintenance Strategy
Supporting asset registers, criticality reviews and comparisons of preventive, predictive and corrective strategies.
Module 3: Work Planning Scheduling and Backlog
Improving job scopes, task steps, resource estimates, backlog classification and weekly planning with competent review.
Module 4: Fault Diagnosis and Root Cause Analysis
Structuring symptoms, histories and evidence; generating hypotheses without bypassing safe isolation, inspection or technical procedures.
Module 5: Condition Monitoring and Predictive Maintenance
Understanding data, alerts, model limitations and business cases for vibration, temperature, oil and other condition signals.
Module 6: Spares Shutdowns and Resource Coordination
Supporting inventory decisions, shutdown planning, contractor coordination and communication of dependencies.
Module 7: Safety Cybersecurity and Maintenance Governance
Managing hazards, permits, technical authority, sensitive asset data, connected-system security, supplier claims and audit records.
Module 8: Reliability Improvement and Implementation Plan
Selecting a pilot asset or workflow, establishing baselines, assigning owners and reviewing reliability and cost outcomes.
Training methodology
Training combines interactive teaching, demonstrations, asset-management case studies, fault-analysis exercises and planning workshops.
Participants use fictional or appropriately authorised maintenance records to improve work packages, investigate recurring failures and assess predictive-maintenance proposals. Safety procedures and technical approval remain mandatory.
The course concludes with a maintenance AI implementation plan identifying the target asset or process, data requirements, controls, responsibilities and reliability measures.