The AI and Automation in Engineering Operations course in Budapest is a cutting-edge training course designed to help professionals integrate AI and automation to optimize engineering workflows and improve operational efficiency.
From:
24-08-2026
Course Overview
Engineering operations are evolving rapidly with the integration of artificial intelligence and automation. From predictive maintenance and process optimization to robotics and digital twins, these technologies enhance efficiency, reduce downtime, and create sustainable value.
This course covers AI applications, automation technologies, data-driven engineering, and smart operations management. Participants will learn how to harness AI and automation to streamline processes, improve safety, and strengthen competitive advantage in engineering-intensive industries.
At EuroQuest International Training, the program emphasizes practical implementation and strategic alignment, combining case studies, simulations, and global best practices for engineering excellence.
Key Benefits of Attending
Master AI and automation concepts applied to engineering
Optimize workflows with intelligent process automation
Strengthen predictive maintenance and operational reliability
Apply robotics and digital twins to engineering challenges
Lead digital transformation in engineering operations
Why Attend
This course prepares professionals to integrate AI and automation into engineering operations, ensuring higher efficiency, cost-effectiveness, and innovation in complex environments.
Course Methodology
Expert-led sessions on AI and automation frameworks
Case studies from engineering and industrial sectors
Hands-on labs with automation tools and software
Group projects on digital transformation strategies
Simulations of AI-enabled engineering scenarios
Course Objectives
By the end of this ten-day training course, participants will be able to:
Define AI and automation applications in engineering operations
Use predictive analytics for asset and process optimization
Integrate robotics and IoT into engineering workflows
Apply digital twins for monitoring and simulation
Strengthen safety and compliance through automation
Improve efficiency with intelligent process automation
Analyze data to support engineering decision-making
Manage risks associated with automation adoption
Leverage cloud and edge technologies for operations
Ensure sustainability in AI-driven engineering systems
Communicate AI benefits to executives and stakeholders
Design long-term roadmaps for smart engineering operations
Target Audience
Engineering managers and operations leaders
Industrial automation and systems engineers
Maintenance and reliability professionals
Digital transformation and innovation managers
Executives overseeing engineering operations
Target Competencies
AI-driven decision-making in engineering
Automation technologies and system integration
Predictive analytics and digital twin modeling
Robotics and IoT applications in operations
Risk and compliance in automated systems
Strategic digital transformation leadership
Sustainable engineering operations management
Course Outline
Unit 1: Introduction to AI and Automation in Engineering
Evolution of automation and smart engineering
AI’s role in industrial transformation
Benefits and challenges of adoption
Global case studies
Unit 2: Data and Analytics for Engineering Operations
Role of big data in engineering
Predictive and prescriptive analytics
Data collection, integration, and quality
Tools for engineering data management
Unit 3: Intelligent Process Automation
Workflow automation in engineering projects
Robotic Process Automation (RPA) applications
Integrating automation with ERP and SCM systems
Case studies of efficiency gains
Unit 4: Robotics and IoT in Engineering
Industrial robots and collaborative robots (cobots)
IoT-enabled sensors and monitoring
Smart manufacturing and predictive operations
Applications across industries
Unit 5: Predictive Maintenance with AI
AI-driven condition monitoring
Failure prediction models
Reducing downtime and costs
Practical predictive maintenance frameworks
Unit 6: Digital Twins and Simulation
Principles of digital twin technology
Applications in design, operations, and maintenance
Real-time monitoring and simulation
Case studies of digital twin deployment
Unit 7: Safety, Compliance, and Risk in Automation
Ensuring safety in automated systems
Regulatory frameworks for AI and automation
Managing cybersecurity in smart operations
Risk management strategies
Unit 8: Cloud, Edge, and Smart Infrastructure
Role of cloud computing in AI-driven operations
Edge computing for real-time control
Infrastructure requirements for automation
Integrating systems for efficiency
Unit 9: Sustainable and Green Engineering Operations
Energy efficiency through automation
AI for emissions monitoring and reduction
Smart resource and waste management
Aligning operations with ESG goals
Unit 10: Change Management in Digital Transformation
Overcoming resistance to automation adoption
Upskilling and workforce transformation
Building digital culture in engineering
Communication and stakeholder engagement
Unit 11: Strategic Leadership in AI and Automation
Leading innovation in engineering functions
Aligning digital transformation with business goals
Balancing technology investment with ROI
Global best practices in engineering leadership
Unit 12: Capstone AI and Automation Project
Designing an AI-enabled engineering operations plan
Group project on predictive maintenance or smart workflow
Presenting digital transformation strategies
Action roadmap for real-world application
Closing Call to Action
Join this ten-day training course to master AI and automation in engineering operations, equipping yourself to lead digital transformation and innovation across technical functions.
The AI and Automation in Engineering Operations Training Courses in Budapest provide professionals with the strategic knowledge and technical insight required to leverage artificial intelligence, machine learning, and automation technologies within engineering environments. Designed for engineers, operations managers, digital transformation leaders, and technical specialists, these programs explore how intelligent systems enhance efficiency, accuracy, safety, and decision-making across industrial and infrastructure operations. Participants gain a clear understanding of how digital technologies are reshaping design processes, maintenance strategies, production workflows, and asset lifecycle management.
The courses examine key applications of AI and automation, including predictive maintenance, real-time monitoring, robotics deployment, process optimization, and data-driven performance evaluation. Participants learn how to integrate sensor networks, digital twins, automated control systems, and advanced analytics tools to reduce downtime, improve operational reliability, and support continuous improvement. Through practical demonstrations, case studies, and scenario-based exercises, attendees gain hands-on experience in interpreting system data, identifying optimization opportunities, and applying automation solutions tailored to organizational needs.
These AI and automation training programs in Budapest also highlight the strategic and cultural dimensions of digital transformation. Participants explore change management approaches, workforce upskilling strategies, cybersecurity considerations, and governance frameworks that ensure responsible and secure implementation of intelligent technologies. The curriculum emphasizes cross-functional collaboration between engineering, IT, operations, and leadership teams to support seamless integration and long-term technology adoption.
Attending these training courses in Budapest provides an internationally oriented learning environment enriched by industry expertise and collaborative peer exchange. The city’s expanding innovation and technology ecosystem offers a stimulating backdrop for discussing future-oriented engineering practices. Upon completion, participants will be equipped to lead and support AI-driven initiatives—enhancing operational performance, enabling smarter resource utilization, and positioning their organizations for success in an increasingly automated and digitally connected engineering landscape.