Day01
AI Foundations for Predictive Maintenance
- AI capabilities, limitations and business value
- Machine learning, generative AI and large language model concepts
- Selecting high-value AI use cases
- Data requirements and model inputs
MAINT-016
Maintenance Engineering and Asset Management
PROGRAMME BROCHURE
AI Driven Predictive Maintenance
Five days of practical AI application, governance and implementation for Predictive Maintenance.
KAIZEN EUROPEAN ACADEMY
Executive & Professional Development
www.kaizeneurope.academy
01
Programme Overview
This five-day Kaizen European Academy programme is specifically about AI Driven Predictive Maintenance; AI is the core subject throughout all five days. Participants learn how artificial intelligence and generative AI can be applied to Predictive Maintenance, design practical workflows, evaluate tools and outputs, manage governance and risk, and build an implementation roadmap supported by hands-on exercises.
02
Learning Objectives
Explain the AI, generative AI and automation concepts most relevant to Predictive Maintenance.
Identify and prioritize high-value AI use cases in Predictive Maintenance.
Design practical human-AI workflows and validate AI outputs for Predictive Maintenance.
Manage privacy, bias, security, governance and human-oversight risks when using AI in Predictive Maintenance.
Build a practical AI pilot and implementation roadmap for Predictive Maintenance.
Who Should Attend
Professionals, specialists, supervisors and managers whose responsibilities involve ai driven predictive maintenance or who need to build practical capability in this area.
Programme Methodology
Instructor-led explanation, facilitated discussion, subject-specific case studies, practical templates, individual and group exercises, simulations or technical scenarios where appropriate, peer review and a final implementation workshop.
Learning Outcomes
Explain the AI, generative AI and automation concepts most relevant to Predictive Maintenance. Identify and prioritize high-value AI use cases in Predictive Maintenance. Design practical human-AI workflows and validate AI outputs for Predictive Maintenance. Manage privacy, bias, security, governance and human-oversight risks when using AI in Predictive Maintenance. Build a practical AI pilot and implementation roadmap for Predictive Maintenance.
Prerequisites
No formal prerequisite is required. Relevant professional experience is helpful; technical courses may assume basic familiarity with the operating environment.
03
Course Outline
Day02
AI Use Cases and Workflow Design in Predictive Maintenance
- Prompt design and interaction patterns
- Human-AI workflow design
- AI-assisted analysis and decision support
- Automation and intelligent agents
Day03
AI Tools, Data and Automation for Predictive Maintenance
- Evaluating accuracy, hallucination and output quality
- AI governance and accountability
- Bias, fairness and responsible AI
- Privacy, confidentiality and information security
Day04
Responsible AI, Risk and Governance in Predictive Maintenance
- Model risk and human oversight
- AI adoption and change management
- AI value measurement and ROI
- Building an AI implementation roadmap
Day05
Applied AI Lab: Predictive Maintenance Implementation
- Vendor and platform evaluation
- AI policy and acceptable-use controls
- Practical AI workflow prototyping
- Scaling AI safely across teams
04
What You Will Receive
Programme learning materials
Practical resources and templates where applicable
Kaizen completion certificate
Participant support throughout the learning week
Kaizen European Academy
Executive & Professional Development across selected European destinations.
CONTACT & INFORMATION
Website: www.kaizeneurope.academy
Contact: www.kaizeneurope.academy/#contact