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Industrial Ai For Maintenance & Reliability Engineers
Published 8/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 16m | Size: 1.07 GB
Practical, no-code AI tools for predictive maintenance, RCA, FMEA, CMMS reporting & reliability engineering workflows
What you'll learn
Use AI tools like ᑕᕼᗩTGᑭT and Claude to spot failure patterns in your maintenance data and draft root cause analysis (RCA) reports in minutes.
Draft FMEA failure modes, effects, and criticality rankings faster with AI - while keeping full engineering judgment in your hands
Automate work order cleanup, SOP creation, and technical documentation using AI - no coding or technical background required
Turn CMMS data exports and KPI numbers into executive-ready monthly reports and clear MTBF/MTTR narratives with AI
Evaluate vendor claims about "AI-powered" CMMS and predictive maintenance features using a 5-question due-diligence framework
Build a practical AI usage policy and rollout plan to bring AI tools safely into your maintenance department
Apply proven prompting techniques (context, task, format, constraints) to get reliable results from ᑕᕼᗩTGᑭT, Claude, and Copilot
Know exactly where AI can help - and where engineering judgment and human review must stay in control
Requirements
No coding or data science experience required - this course is 100% prompt-based, no technical background needed
A free account with ᑕᕼᗩTGᑭT, Claude, or Microsoft Copilot.
Basic familiarity with maintenance, CMMS, or plant operations is helpful but not required
Access to a computer and internet connection - that's it
Description
Note: This course contains the use of artificial intelligence.
Maintenance and reliability teams are under constant pressure to do more with less, andAI is finally practical enough to help, without requiring a data science background or a single line of code.
This course teaches you how to use tools like ᑕᕼᗩTGᑭT, Claude, and Microsoft Copilot to solve real problems across the maintenance function: spotting failure patterns in your CMMS data, drafting root cause analysis (RCA) reports in minutes instead of hours, getting AI-assisted support for FMEA and criticality ranking, cleaning up messy work orders, generating SOPs from technician notes, and turning KPI numbers into executive-ready monthly reports.
You'll also learn how to responsibly roll AI out across your team, includinghow to evaluate vendor claims about "AI-powered" CMMS features, build a simple AI usage policy, and know exactly where AI can help versus where your engineering judgment must stay firmly in control.
Every technique is taught through hands-on projects using realistic maintenance scenarios, with downloadable templates and prompt libraries you can start using in your own plant the same day.
No coding. No data science. Just practical AI skills built specifically for maintenance managers, reliability engineers, plant managers, and operations leaders exploring AI adoption.
Note"Already taken my AI in Operations Management course? This course goes deeper and wider, covering the full maintenance function specifically, including predictive maintenance, RCM/FMEA support, CMMS reporting, and reliability engineering workflows that fall outside that course's scope."
Who this course is for
Maintenance managers and supervisors who want practical, ready-to-use AI tools for their day-to-day work
Reliability engineers looking for AI-assisted support with RCA, FMEA, and criticality analysis
Plant and facility managers using a CMMS (SAP PM, Maximo, Fiix, UpKeep) who want to work faster with AI
Operations leaders and executives exploring how AI applies specifically to maintenance and industrial functions
Anyone in industrial operations who wants to use AI without learning to code or becoming a data scientist