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.
*dates are subject to additional confirmation
excluding VAT
* VAT of 16% will be added to the invoice
This programme is designed for manufacturing companies that want to understand how to use AI agents not in theory but in the real work of the enterprise. Participants will understand how AI agents differ from ordinary chatbots, where they give the greatest effect in production, and how they can be used to automate process control, reduce the number of errors, speed up information processing, analyse large datasets and improve execution discipline.
Over three days, participants will move step by step from understanding the basics to designing practical implementation scenarios: monitoring indicators, quality control, working with regulations and instructions, analysing production deviations, generating reporting, supporting repair and logistics processes, as well as supporting managers and specialists in their daily work. Special attention will be paid to limitations, risks and the safe implementation of AI solutions in a production environment.
By the end of the training participants will:
— understand where AI agents bring real benefit specifically in production
— be able to identify processes that can be automated without losing control
— learn to formulate tasks for AI agents for their own production scenarios
— receive practical examples of applying AI in control, analytics, document flow and management
— be able to prepare a basic plan for implementing AI agents in their company
Day 1
Fundamentals of AI agents and their application in production
What AI agents are and how they are applied in manufacturing companies.
The difference between chatbots, AI assistants and autonomous agents.
Which tasks an AI agent can perform independently, and where human involvement is needed.
The role of AI in the digitalisation of production processes and operational management.
Where AI genuinely brings benefit in production:
controlling production processes and compliance with regulations;
reducing errors in processing and transferring information;
analysing production indicators and identifying deviations;
optimising routine operations and speeding up decision-making;
supporting staff in working with instructions, standards and regulatory documentation;
generating reporting and analytical summaries for managers.
Typical cases of applying AI agents at an enterprise:
monitoring the fulfilment of production tasks;
analysing the causes of downtime and deviations;
supporting maintenance and repair processes;
supporting warehouse, supply and internal logistics operations;
quality control and analysing comments on products or a process;
rapid retrieval of needed information from regulations, instructions and technical documentation.
Practical part:
identifying the processes in which AI can have the greatest effect specifically in a manufacturing company;
reviewing specific AI agent functions for a production unit.
Tools of AI agents and automating production tasks
What an AI agent consists of: role, tasks, rules, data sources, tools.
What data an AI agent needs to work correctly.
Integrating AI agents with the company's internal information sources:
production reports;
tables, databases and dashboards;
regulations, instructions, checklists and standards;
mail, messengers and working communication channels.
Automating production tasks with the help of AI agents:
gathering and structuring information from different sources;
preparing daily and weekly summaries;
monitoring deadlines, statuses and critical deviations;
identifying recurring problems and errors;
supporting the manager in analysing the situation and preparing decisions.
Using AI for analytics in production:
comparing planned and actual indicators;
finding the causes of deviations from data;
identifying risk zones and problem areas;
preparing clear analytical conclusions for management.
Limitations of AI in a production environment:
the risk of errors when data are lacking;
the importance of verifying critically important conclusions;
data confidentiality and security issues;
the limits of automation and the need for control by specialists.
Practical part:
developing a scenario for an AI agent to control production indicators;
reviewing an example: an AI agent as an assistant to the head of a production unit.
Day 2
Implementing AI agents in a manufacturing company
An approach to implementation without unnecessary costs or chaotic digitalisation.
Where to start implementation: choosing processes, setting goals, defining the expected effect.
How to determine priority zones for launching AI solutions:
processes with a large amount of routine work;
areas with a high risk of errors;
processes where fast analytics are important;
areas where employees spend a lot of time searching, checking and preparing information.
Stages of implementing AI agents:
diagnosing current processes;
describing tasks and requirements;
preparing data and the knowledge base;
launching a pilot scenario;
evaluating results and scaling up.
Which KPIs can be used to evaluate implementation effectiveness:
reducing time spent on routine operations;
reducing the number of errors;
speeding up report preparation;
increased transparency of processes;
improved quality of management decisions.
Organisational conditions for successful implementation:
the manager's involvement;
units' readiness to work under new rules;
the availability of clear regulations and good-quality data;
training staff to interact with AI tools.
Practical part:
forming a map of processes into which AI agents can be implemented;
developing a basic plan for implementing AI solutions in the participant's company;
discussing the pilot project and expected results.
Note: The programme can be adapted to the company's industry, the participants' level and the client's specific production tasks.
