Artificial Intelligence in Industry: Tools and Practices

Course ID : AI-003

Duration In-class (в days) : 16 academic hours

Duration Online : 16 academic hours

Сurriculum : in-class, Virtual Instructor-Led Training - ONLINE

Delivery : 05.10.2026 - 08.10.2026

Overview

This course focuses on the practical aspects of implementing AI in industry, from basic concepts to industry-specific case studies. Participants will explore how AI technologies are used in industry to optimize production processes, including predictive equipment maintenance, automated instrumentation, non-invasive quality control of products and equipment, and improved efficiency of related and supporting processes.

The program covers machine learning, computer vision, and predictive analytics, as well as tools for big data processing and decision-making.

The practical component is based on case studies of AI applications in industry, using examples from companies in various industries, including oil and gas, mechanical engineering, chemicals, manufacturing, and other sectors.

This will help students understand where AI application in industry can yield measurable results, and where its implementation is associated with risks and is impractical.

Audience for this course

Managers involved in managing production processes, implementing digital technologies, operating and repairing equipment, and improving production processes.

Outcomes

Upon completion of the “AI in Industry” course, participants will gain the following skills:

  • Understand the basic concepts of artificial intelligence (AI), its capabilities, and limitations, including in the context of industrial manufacturing.
  • Understand the potential of AI for optimizing production processes, increasing energy efficiency, ensuring industrial safety, and managing supply chains.
  • Understand the fundamentals of industrial engineering as a tool for effectively interacting with AI language models to solve management problems (report preparation, data analysis, strategic planning, communications).
  • Understand the differences between concepts such as machine learning and neural networks, including the hierarchy of these technologies.
  • Identify promising areas for implementing AI in manufacturing environments and assess the risks and benefits of such initiatives.
  • Enhance and/or acquire new competencies in digital transformation, including an understanding of key AI tools (including generative AI and decision support systems) and their applicability to the chemical industry.

Outline

The practical part of the program will be based on case studies of AI application at industrial facilities (including in the oil and gas, chemical, and related industries), including predictive maintenance systems, instrumentation automation, emissions monitoring, product quality management, energy efficiency improvement, etc.

1. Introduction to Artificial Intelligence (AI)

  • The concept of artificial intelligence: definition, key development areas
  • Differences between AI and traditional automated systems
  • General classification of AI technologies: weak and strong AI, highly specialized systems

2. Machine learning and neural networks: concepts, differences, applications

  • What is machine learning: operating principles, types of learning
  • Neural networks: operating principles, architecture, areas of application
  • Relationship between concepts: AI, machine learning, neural networks
  • Artificial intelligence tools
  • Overview of modern AI platforms and solutions (including generative AI, LLM, data analysis systems)
    Predictive tools
  • Computer vision

3. Tools for processing large datasets Data, Visualization, and Decision Making

  • Practice – Case Study

4. Prompt Engineering Fundamentals

  • What is a prompt and why is it needed
  • Principles for creating effective prompts for solving management problems
  • Practical examples: preparing reports, analyzing regulations, formulating decisions, working with technical documentation
  • Limitations and risks of using generative AI
  • Practice – Case Study

5. Practical Application of AI in Industrial Sites

  • Applicability of Various Tools in Chemical Production
  • AI Implementation Cases in the Chemical, Oil and Gas, and Related Industries
  • Experience of Leading Global Companies

6. Interactive Practice and Group Assignments

  • Working with real-life scenarios similar to your company’s operations – discussion and hypotheses generation.
  • Case study and hypothesis development in groups (2 groups) with instructors
  • Analysis of generated cases (presentation of group results): identifying opportunities for implementing AI in production areas, discussion
  • Discussion of strategic initiatives and barriers to digital transformation

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