Training objectives
- Understanding the fundamentals of Artificial Intelligence (AI) – introducing participants to key concepts, algorithms and AI tools (machine learning, natural language processing, deep learning).
- Exploring areas of AI application within the company – identifying specific processes and departments (sales, marketing, logistics, HR, customer service) where AI can deliver measurable business outcomes.
- Acquiring practical project skills – learning how to plan, prepare and implement an AI project in an organization (from needs and data analysis, through prototyping to measuring results).
- Raising awareness of challenges and risks – familiarizing with core issues such as AI ethics, risk management, legal and regulatory aspects and implementation barriers.
- Inspiring further development – presenting current trends and innovations in AI to help participants continue their growth and effectively apply the acquired knowledge in practice.
After the training, participants will be able to:
- Introduce concrete AI-based solutions.
- Use cloud platforms and AI tools consciously.
- Collaborate more effectively between business and IT/data science teams.
- Avoid common implementation mistakes and barriers.
Training symbol
AI-BIZ-ONL-ENGDates and location
Downloads and regulations
Estimated contribution of the practical part: 55%
Duration: 2 days for 8 h
Programme and exercises:
Day 1: Foundations of AI and Business Applications
- Introduction to AI in Business
- What AI really means – practical definitions.
- Key AI technologies: machine learning, NLP, deep learning.
- How to separate real AI capabilities from marketing hype?
- Common Types of AI in Business Context
- Predictive and classification models simplified.
- Business process automation with AI.
- Data analysis and management decision support.
- Main Areas of AI Application in Companies
- AI in Sales: offer personalization, segmentation, customer evaluation, forecasting.
- AI in Marketing: customer behaviour analysis, campaign automation, content generation.
- AI in Logistics/Supply Chain: demand forecasting, process optimization.
- AI in HR: recruitment, talent development, attrition risk analysis.
- AI in Customer Service: chatbots, sentiment analysis, omnichannel support.
- Case Studies: AI Use Across Industries
- Review of successful implementations.
- Key success factors and common pitfalls.
- How to avoid repeating others’ mistakes?
- Workshop: AI Opportunity Mapping in Your Organization
- Identifying high-potential areas and processes.
- Prioritizing implementations based on business value.
Day 2: Managing AI Projects, Challenges, and Competence Development
- From Business Needs to AI Projects
- How to assess whether AI brings real business value?
- Planning AI implementation: needs, goals, available data.
- Creating AI MVPs – when to start small?
- AI Project Lifecycle in Business
- Project phases: from problem definition to deployment.
- Business involvement in AI – what should not be left to IT alone.
- Sample AI project structure (business template).
- Managing Risks and Implementation Challenges
- Organizational barriers and how to overcome them (competence gaps, change resistance).
- Minimizing the risk of AI project failure.
- Importance of data quality and accessibility.
- Ethics, Regulations and Responsibility in AI Projects
- Practical ethics principles for AI in business.
- Overview of legal frameworks (EU AI Act, GDPR and data processing by AI).
- The role of transparency and explainability in responsible AI use.
- Tools and Platforms Supporting AI Deployment
- Overview of no-code/low-code solutions for business.
- Intro to cloud platforms facilitating AI deployment.
- When to build custom solutions vs to use off-the-shelf services?
- Trends and the Future of AI in Business
- Growth of Generative AI and its impact on business processes.
- Multimodal models and AI using multiple data types (text, image, video).
- Rising importance of Edge and Operational AI.
- How AI is transforming business models and workforce competencies.
- Final Workshop: Creating Your Company’s AI Development Map
- Drafting an initial AI strategy for your organization.
- Identifying required competencies and first steps.
- Planning personal and team development paths in AI.
Exercises Include:
- AI project simulation – designing an AI-based project concept.
- Case study: chatbot implementation analysis.
- Risk and barrier analysis for AI deployment.
Methodology:
Interactive lecture, hands-on workshops, case studies, Q&A sessions, moderated discussions, group work.
Oferees:
- Managers and executives – responsible for strategic decisions and technology investments.
- Business development and innovation specialists – defining and implementing innovative projects, including AI.
- IT project leaders and data analysts – managing technical aspects and deploying AI models.
- Operational, marketing and sales staff – seeking ways to improve efficiency and optimize processes using AI.
- Anyone interested in AI – including those without programming experience, looking to expand their knowledge of emerging technologies.
Application:
Application:
AI-based solutions can bring many benefits to organizations, such as:
- Process automation and cost reduction.
- Personalized sales offers and increased revenue.
- Better data analysis and decision-making support.
- Optimization of logistics and production processes.
- Improved customer service (chatbots, recommendation systems).
Through this training, organizations gain the knowledge to effectively plan, implement, and evaluate AI-related projects – creating real competitive advantage.
Identyfikacja szkolenia
ID szkolenia (TQM ID):
30627
Training symbol:
AI-BIZ-ONL-ENG
Training name:
Artificial Intelligence in Business
Status produktu szkoleniowego:
Active
Training type:
open and closed training
Ostatnia synchronizacja:
30 July 2025 10:21
Slug szkolenia:
artificial-intelligence-in-business
Dane szkolenia
Language of training:
English
Duration:
2 days for 8 h
Training days:
2
Estimated contribution of the practical part:
55
Kategorie
Main topics:
IT
URL tematyki:
/en/information-technology/
Dane terminów
Online training:
Guaranteed date:
Guaranteed training date:
Nearest training date price nett:
0
Nearest training date price:
0
Treści opisowe
Programme and exercises:
Day 1: Foundations of AI and Business Applications
- Introduction to AI in Business
- What AI really means – practical definitions.
- Key AI technologies: machine learning, NLP, deep learning.
- How to separate real AI capabilities from marketing hype?
- Common Types of AI in Business Context
- Predictive and classification models simplified.
- Business process automation with AI.
- Data analysis and management decision support.
- Main Areas of AI Application in Companies
- AI in Sales: offer personalization, segmentation, customer evaluation, forecasting.
- AI in Marketing: customer behaviour analysis, campaign automation, content generation.
- AI in Logistics/Supply Chain: demand forecasting, process optimization.
- AI in HR: recruitment, talent development, attrition risk analysis.
- AI in Customer Service: chatbots, sentiment analysis, omnichannel support.
- Case Studies: AI Use Across Industries
- Review of successful implementations.
- Key success factors and common pitfalls.
- How to avoid repeating others’ mistakes?
- Workshop: AI Opportunity Mapping in Your Organization
- Identifying high-potential areas and processes.
- Prioritizing implementations based on business value.
Day 2: Managing AI Projects, Challenges, and Competence Development
- From Business Needs to AI Projects
- How to assess whether AI brings real business value?
- Planning AI implementation: needs, goals, available data.
- Creating AI MVPs – when to start small?
- AI Project Lifecycle in Business
- Project phases: from problem definition to deployment.
- Business involvement in AI – what should not be left to IT alone.
- Sample AI project structure (business template).
- Managing Risks and Implementation Challenges
- Organizational barriers and how to overcome them (competence gaps, change resistance).
- Minimizing the risk of AI project failure.
- Importance of data quality and accessibility.
- Ethics, Regulations and Responsibility in AI Projects
- Practical ethics principles for AI in business.
- Overview of legal frameworks (EU AI Act, GDPR and data processing by AI).
- The role of transparency and explainability in responsible AI use.
- Tools and Platforms Supporting AI Deployment
- Overview of no-code/low-code solutions for business.
- Intro to cloud platforms facilitating AI deployment.
- When to build custom solutions vs to use off-the-shelf services?
- Trends and the Future of AI in Business
- Growth of Generative AI and its impact on business processes.
- Multimodal models and AI using multiple data types (text, image, video).
- Rising importance of Edge and Operational AI.
- How AI is transforming business models and workforce competencies.
- Final Workshop: Creating Your Company’s AI Development Map
- Drafting an initial AI strategy for your organization.
- Identifying required competencies and first steps.
- Planning personal and team development paths in AI.
Exercises Include:
- AI project simulation – designing an AI-based project concept.
- Case study: chatbot implementation analysis.
- Risk and barrier analysis for AI deployment.
Methodology:
Interactive lecture, hands-on workshops, case studies, Q&A sessions, moderated discussions, group work.
Oferees:
- Managers and executives – responsible for strategic decisions and technology investments.
- Business development and innovation specialists – defining and implementing innovative projects, including AI.
- IT project leaders and data analysts – managing technical aspects and deploying AI models.
- Operational, marketing and sales staff – seeking ways to improve efficiency and optimize processes using AI.
- Anyone interested in AI – including those without programming experience, looking to expand their knowledge of emerging technologies.
Application:
Application:
AI-based solutions can bring many benefits to organizations, such as:
- Process automation and cost reduction.
- Personalized sales offers and increased revenue.
- Better data analysis and decision-making support.
- Optimization of logistics and production processes.
- Improved customer service (chatbots, recommendation systems).
Through this training, organizations gain the knowledge to effectively plan, implement, and evaluate AI-related projects – creating real competitive advantage.
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