The seminar can be held online on the official International Business Academy platform. On completion of the training you will be given a link to the recording, which will be available for one month.
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excluding VAT
* VAT of 16% will be added to the invoice
Artificial intelligence has already become not just a technology trend but a real tool for improving business efficiency. Today AI helps companies to analyse data faster, reduce the number of errors, cut employees' routine workload, improve the quality of management decisions and find new points of growth.
The topic is especially relevant for manufacturing companies: artificial intelligence can be applied in analysing production indicators, controlling processes, managing quality, preparing reports, working with documents, planning workload, predictive equipment maintenance, and also in automating internal communications and supporting management decisions.
The programme gives a comprehensive overview of the modern capabilities of artificial intelligence for business: from a basic understanding of how AI works and where it brings practical benefit, to using AI tools in everyday work, understanding the logic of AI agents and building an approach to implementing AI in the company.
Training participants will not only get to know the key directions of AI development, but will also see how to use these solutions in relation to their own organisation, which processes can be improved right now, which risks and limitations need to be taken into account, and how to build a consistent path of implementation — from the first quick solutions to more complex AI systems.
Target audience
Managers, specialists of production units, employees of development, automation, quality, analytics and management services
— A comprehensive understanding of the modern capabilities of artificial intelligence for business and production
— An understanding of which AI tools and approaches can be useful specifically for a manufacturing company
— An idea of how to use AI in analytics, documents, reporting, management support and process automation
— An understanding of the logic of AI agents and the possibilities of applying them in the company
— A ready-made list of ideas and first steps for implementing AI in the organisation
Artificial intelligence in business and production: basic understanding, capabilities and real application scenarios
1. What artificial intelligence is and why this topic has become key for business
The concept of artificial intelligence: what stands behind this term, and how AI differs from ordinary automation.
The difference between AI, algorithms, machine learning, neural networks and generative artificial intelligence.
Which AI solutions are already actively used at companies around the world, and why business is implementing them right now.
What has changed in companies' work with the emergence of accessible AI tools.
Which tasks used to be solved only by a human but today can be sped up or partially handed over to AI.
2. Key directions of using AI in business
Using AI to analyse data and prepare conclusions.
Applying AI to work with texts, documents, instructions, reports and regulations.
Using AI to search for information, structure knowledge and support decision-making.
AI as a tool for increasing employee productivity and reducing operational load.
Supporting management functions: analysis, planning, preparing materials for management.
3. Where artificial intelligence brings practical benefit in manufacturing companies
Controlling and monitoring production processes.
Reducing errors and the influence of the human factor.
Analysing production data and finding deviations.
Managing quality and supporting standards.
Supporting the planning of production, workload, resources and deadlines.
Working with technical documentation, regulations, instructions and internal knowledge bases.
Using AI in occupational health and safety, industrial safety and monitoring compliance with procedures.
Applying AI in predictive equipment maintenance and preventing downtime.
4. Overview of modern AI tools already available to business today
Generative AI tools: creating texts, analytics, reports, letters, instructions and presentation materials.
AI tools for working with the company's documents and knowledge.
AI for analysing tables, indicators and data arrays.
AI as an assistant for a manager, analyst, methodologist, quality specialist, engineer or head of unit.
The limits of applicability: in which tasks AI helps best, and where mandatory human control is needed.
5. First-day practice
Reviewing typical tasks of a manufacturing company where using AI gives a quick effect.
Participants identifying their own processes where AI can be useful.
Forming an initial list of tasks for automation, analytics and decision support.
Discussing the barriers, expectations and potential benefits of implementing AI.
Day 2AI tools in the company's work: process automation, working with data and improving efficiency
1. Practical use of AI in employees' daily work
How employees can use AI in their current activities without complex technical preparation.
Applying AI to prepare reports, memos, instructions, explanatory materials and summaries.
Using AI to analyse documents, compare information and quickly extract the essence.
Preparing meeting summaries, minutes, task lists and work materials.
Using AI to speed up the search for solutions, systematise information and prepare options for action.
2. Automating business processes with AI
Which processes in the company can be automated first.
Document flow, processing typical requests, internal reporting, summaries, analytical notes.
Processing repetitive tasks and reducing the workload on staff.
The logic of automation: incoming data, processing rules, monitoring the result, human involvement.
How to determine that a process is truly suitable for AI automation.
3. Using AI for data analysis and management analytics
AI working with tables, indicators, production reports and data arrays.
Identifying trends, deviations, bottlenecks and factors affecting efficiency.
Preparing management conclusions based on data.
Using AI to support KPI analytics and operational monitoring of indicators.
The role of AI in shortening the time between receiving data and making a decision.
4. Integrating AI with the company's digital environment
How AI can interact with ERP, CRM, MES, BI and internal corporate systems.
AI working with internal knowledge bases, regulations, document archives and operational information.
Practical integration scenarios without a complex overhaul of all processes.
Why it is important to start not with technology but with priority business tasks.
5. Risks, limitations and rules for the safe use of AI
AI errors and why the result must always be checked in critically important tasks.
Limitations of AI when working with internal and confidential data.
Risks of unreliable conclusions, incomplete analysis and incorrect interpretation of information.
Where AI cannot be used without human involvement.
Basic rules for the safe, ethical and controlled use of artificial intelligence in the company.
6. Second-day practice
Reviewing participants' specific processes that can be improved through AI.
Building an automation scheme for the selected process.
Identifying quick implementation points: where a first noticeable result can be obtained.
Preparing a list of AI tools suited to the specific company's tasks.
Day 3. AI agents and implementing artificial intelligence in the company: from idea to systematic application
1. What AI agents are and how they differ from ordinary AI tools
The concept of an AI agent: not just a response to a query, but performing a sequence of actions to achieve a result.
The difference between a chatbot, an AI assistant and an autonomous AI agent.
How AI agents work: receiving a task, analysis, choosing actions, using tools, forming a result.
Why AI agents are considered the next stage in the development of practical AI application in business.
2. Where AI agents can be applied in a manufacturing company
Agents for preparing and consolidating reporting.
Agents for analysing data and identifying deviations.
Agents for supporting managers and units in decision-making.
Agents for working with internal documents, regulations, standards and the corporate knowledge base.
Agents for monitoring indicators, controlling compliance with procedures and supporting business processes.
Agents as digital assistants for specific functions: production, quality, analytics, supply, administrative support.
3. Principles of creating AI agents for business tasks
How to define an AI agent's role and the limits of its responsibility.
Choosing functions, input data, work logic and the expected result.
Selecting tools and data sources for the agent's work.
Levels of autonomy: where an agent can act independently, and where human approval is required.
Why it is important to build clear control scenarios rather than simply «turning on AI».
4. From one tool to a system: how an AI approach is built in a company
The difference between one-off use of AI and systematic implementation of artificial intelligence.
Creating a set of AI solutions for different company functions.
An approach to forming an AI environment: tools, agents, processes, data, control, responsibility.
How to link AI with the current organisational structure without creating chaos in processes.
5. Implementing AI in the company: a practical approach
Where to start implementation: auditing processes and identifying zones with maximum effect.
How to choose a pilot project for launch.
Stages of implementation: analysis, design, testing, adjustment, scaling.
How to involve employees and reduce resistance to change.
How to measure the result: time savings, fewer errors, higher productivity, better decision quality.
Typical mistakes in implementing AI and why projects fail to deliver the expected effect.
6. Final practical work
Forming a map of AI opportunities for your company.
Identifying processes where it makes sense to use AI tools, and where — AI agents.
Preparing a roadmap of first implementation steps.
Determining priorities, responsible persons and expected results from pilot initiatives.
