Highlights
By the end of the day, participants will be able to:
- Identify high-value operational use cases for AI across incidents, maintenance, and process optimisation
- Apply AI techniques to incident detection, root-cause analysis, and fault diagnosis
- Use AI tools to automate repetitive operational tasks and decision support
- Understand how AI supports predictive maintenance and asset health monitoring
- Evaluate AI solutions realistically in terms of data quality, reliability, and operational risk
- Build a simple, defensible AI business case for operational improvement
Course Details
Session 1: AI in Operational Engineering
What Actually Works
Purpose - Cut through AI hype and ground participants in engineering-relevant applications.
Key Topics
- What AI is and is not in an operational context
- Where AI adds value vs traditional rules-based systems
- Types of AI used in operations:
- Pattern recognition & anomaly detection
- Predictive models
- Natural language analysis (logs, tickets, reports)
- Common operational myths (“AI replaces engineers”, “needs perfect data”)
Wrap-Up
Personal action plan: One operational use case to trial in the next 90 days Key takeaways & next steps
Session 6: Implementation, Risks & Operational Readiness
Purpose - Ensure participants leave knowing how to deploy responsibly.
Key Topics
Data readiness checklist for ops teams Operational risks:- Model drift
- Over-automation
- Loss of situational awareness
- Downtime reduction
- MTBF / MTTR improvements
- Safety and compliance benefits
Case Discussion
Where predictive maintenance fails and why Data quality, sensor placement, and organisational readiness When simpler statistical models outperform “clever” AI(Design-level, not coding)
Session 5: Asset Monitoring, Predictive Maintenance & Optimisation
Purpose - Connect AI directly to asset life, uptime, and cost reduction.
Key Topics
Predictive vs preventive maintenance Asset health scoring and degradation modelling AI inputs:- Vibration
- Temperature
- Usage cycles
- Maintenance history
- Maintenance intervals
- Spares planning
- Resource allocation
Hands-On Exercise
Operational Assistant Design:Participants design a simple AI assistant for:
- Incident summaries
- Maintenance recommendations
- Operational reporting
Session 4: Process Automation & Decision Support
Purpose - Use AI to remove friction from day-to-day operational work.
Key Topics
AI for operational workflow automation:- Incident triage
- Ticket classification
- Maintenance scheduling support
- Shift handovers and reporting
Practical Exercise
AI-Augmented RCA:Participants walk through a realistic incident scenario and:
- Perform a standard RCA
- Compare it to an AI-supported RCA
- Identify time saved and insight gained
Session 3: Root Cause Analysis & Fault Diagnosis with AI
Purpose - Move from “what happened” to “why it happened” faster and more reliably.
Key Topics
Traditional RCA vs AI-assisted RCA Using AI to:- Analyse logs and incident reports
- Identify recurring fault patterns
- Surface hidden dependencies
- FMEA
- 5 Whys
- Fault Tree Analysis
Tools Discussed (Vendor-neutral)
AI-enabled monitoring platforms Open-source anomaly detection concepts Where spreadsheets and BI still fitPractical Exercise
Incident Signal Exercise:Given a simplified dataset (sensor + logs), participants:
- Identify patterns humans miss
- See how AI flags “weak signals” earlier than rule-based systems
Session 2: Incident Detection & Early Warning Systems
Purpose - Show how AI improves early detection before failures escalate.
Key Topics
AI-based anomaly detection vs threshold alerts Using AI to correlate:- Sensor data
- Logs
- Alarms
- Environmental conditions
- Utilities
- Manufacturing lines
- Transport infrastructure
- Data centres / facilities
Activity
Operational Pain Mapping:Participants map their top 5 downtime / reliability issues and identify which are:
- Detection problems
- Diagnosis problems
- Decision-making bottlenecks
Who should attend
Operational Engineers, Reliability Engineers, Maintenance Engineers, Process Engineers, Site Engineers, Ops Managers, and Technical Leads.
Feedback
4.8 out of 5 average
"Our tailored course provided a well rounded introduction and also covered some intermediate level topics that we needed to know. Clive gave us some best practice ideas and tips to take away. Fast paced but the instructor never lost any of the delegates"
Brian Leek, Data Analyst, May 2022
