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Course Overview

Engineering operations are increasingly shaped by digital transformation. AI and automation now drive predictive maintenance, design optimization, resource allocation, and safety systems. Leaders must not only understand the potential of these technologies but also ensure compliance, governance, and sustainability in their adoption.

Delivered by EuroQuest International Training, this ten-day course explores AI applications, robotics, digital twins, industrial IoT, machine learning models, and automation governance. Participants will analyze global case studies, assess risks of over-reliance on AI, and develop foresight-driven strategies for engineering innovation.

The program balances technical depth, operational insights, and governance frameworks, ensuring participants can lead AI-driven transformations in engineering organizations.

Course Benefits

• Understand AI and automation applications in engineering operations

• Strengthen governance and risk oversight of intelligent systems

• Apply predictive analytics for maintenance, safety, and efficiency

• Integrate digital twins, IoT, and robotics into engineering processes

• Align AI adoption with ESG, compliance, and sustainability frameworks

Why Attend

This course enables professionals to move beyond traditional engineering management to AI-enabled operational excellence. By mastering AI and automation strategies, participants can increase reliability, optimize resources, and future-proof engineering operations.

Training Methodology

• Structured knowledge sessions

• Strategic discussions on digital and AI governance

• Thematic case studies of AI-enabled engineering

• Scenario-based exploration of operational risks

• Conceptual foresight frameworks for sustainable adoption

Course Objectives

By the end of this training course, participants will be able to:

• Define AI and automation concepts in the context of engineering operations

• Apply machine learning for predictive maintenance and optimization

• Strengthen safety, risk, and compliance frameworks with intelligent systems

• Integrate robotics, IoT, and digital twins into engineering workflows

• Evaluate ROI and efficiency improvements from automation projects

• Anticipate risks of bias, cybersecurity, and over-reliance on AI

• Communicate AI-driven insights effectively to stakeholders

• Build foresight-driven strategies for engineering transformation

• Benchmark best practices from global case studies

• Institutionalize sustainable AI and automation adoption frameworks

Course Outline

Unit 1: Introduction to AI and Automation in Engineering

• Evolution of AI and automation in industrial contexts

• Strategic role of intelligent systems in operations

• Risks of poor governance in AI adoption

• Global perspectives

Unit 2: Machine Learning Applications in Engineering

• Predictive maintenance and fault detection

• Process optimization through ML models

• Risk forecasting and anomaly detection

• Case examples

Unit 3: Robotics and Industrial Automation

• Robotics in assembly, inspection, and maintenance

• Collaborative robots (cobots) in engineering workflows

• Automation of repetitive engineering tasks

• Governance and safety in robotics

Unit 4: Digital Twins and Simulation Models

• Virtual replicas for system performance monitoring

• AI-driven simulation of engineering processes

• Risk reduction through virtual testing

• Case studies of digital twin applications

Unit 5: IoT and Smart Engineering Systems

• Sensors and IoT networks in engineering operations

• Data integration for real-time decision-making

• Cybersecurity risks in connected engineering systems

• Lessons from global adoption

Unit 6: AI for Safety and Risk Management

• AI-based monitoring of workplace safety

• Predictive modeling for accident prevention

• Risk governance in AI-driven safety frameworks

• Case perspectives

Unit 7: Automation in Project and Resource Management

• AI in scheduling and project planning

• Resource allocation and cost optimization

• Governance of automated decision-making

• Case studies

Unit 8: ESG, Ethics, and Compliance in AI Adoption

• ESG implications of engineering automation

• Responsible AI and ethical decision-making

• Data privacy and compliance frameworks

• Governance structures for accountability

Unit 9: Financing and ROI of AI in Engineering

• Cost-benefit analysis of AI-driven systems

• Financing models for automation projects

• Risk-adjusted ROI assessment

• Case illustrations

Unit 10: Emerging Technologies in Engineering Operations

• Generative AI in design and engineering solutions

• Quantum computing potential in engineering

• Autonomous systems and future disruptions

• Strategic foresight in technology adoption

Unit 11: Global Case Studies and Best Practices

• Successful AI and automation adoption in engineering

• Failures and lessons learned from poor governance

• Comparative analysis across sectors

• Strategic insights

Unit 12: Designing Sustainable AI and Automation Frameworks

• Institutionalizing governance and oversight mechanisms

• KPIs for monitoring AI-enabled operations

• Continuous improvement and foresight integration

• Final consolidation of course insights

Target Audience

• Engineers and project managers

• Operations and production managers

• Risk and compliance professionals in engineering

• Technology strategists and digital transformation leaders

• Executives overseeing industrial innovation

Target Competencies

• AI and automation applications in engineering

• Predictive maintenance and risk forecasting

• Robotics, IoT, and digital twin integration

• ESG and compliance in AI governance

• Strategic foresight in engineering operations

• Sustainable digital transformation in engineering

Join the AI and Automation in Engineering Operations Training Course from EuroQuest International Training to master the strategies, governance models, and foresight tools that embed AI and automation into engineering operations for long-term resilience and competitiveness.