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Course Outline
Day 1 — Introduction to AI and Business Applications
Module 1 — Introduction to Artificial Intelligence
- Defining what AI is and is not
- Different types of AI systems
- Generative AI and Large Language Models
- Separating AI myths from reality
- Current trends in business AI adoption
- Opportunities and limitations associated with AI
Module 2 — AI in Modern Business Operations
- Contemporary corporate use of AI
- AI applications within manufacturing and operations
- AI integration in sales and customer engagement
- AI usage in HR and recruitment processes
- AI in procurement and logistics management
- AI applications in finance and reporting
- AI support for quality management and compliance
Practical Exercise
Participants will experiment with AI tools for:
- content summarization,
- report generation,
- email drafting,
- workflow assistance,
- document analysis,
- meeting note-taking,
- and operational planning.
Day 2 — Practical AI Productivity and Workflow Automation
Module 3 — AI-Powered Productivity
- AI assistants designed for managers
- Prompt engineering techniques for business users
- Developing effective business prompts
- Utilizing AI for:
- reporting,
- planning,
- presentation creation,
- documentation,
- meeting preparation,
- decision support
Module 4 — Data Analysis and Business Insights
- Conducting business analysis using AI
- Extracting information from documents and spreadsheets
- AI-assisted forecasting and trend analysis
- KPI monitoring and operational insights
- Working with structured and unstructured business data
Practical Workshop
Teams tackle realistic business scenarios involving:
- production reporting,
- sales forecasting,
- supplier analysis,
- HR documentation,
- operational dashboards,
- and quality issue analysis.
Participants will develop practical AI-supported workflows tailored to their respective departments.
Day 3 — AI for Operations, Planning, and Decision-Making
Module 5 — AI in Operations Management
- Leveraging AI for operational efficiency
- Workflow optimization techniques
- Support for inventory and warehouse management
- Concepts of predictive maintenance
- Process standardization
- AI-assisted decision-making frameworks
Module 6 — Department-Specific AI Applications
Production and Operations
- Production monitoring
- Root-cause analysis
- SOP generation
- Operational reporting
Sales and Business Development
- Lead qualification
- Proposal generation
- Customer communication
- Competitive analysis
Human Resources (HR)
- Job descriptions
- Interview preparation
- Training plans
- Internal communications
Finance and Accounting
- Financial summaries
- Invoice/document analysis
- Compliance support
- Reporting automation
Quality Management
- Nonconformity analysis
- Documentation support
- Audit preparation
- Risk tracking
Practical Workshop
Participants will design:
- one AI use case for their department,
- one automation opportunity,
- and one measurable productivity improvement initiative.
Day 4 — AI Governance, Risk, and Implementation
Module 7 — AI Governance and Compliance
- Responsible AI usage guidelines
- Data privacy and confidentiality measures
- Risks associated with generative AI
- Establishing AI governance policies
- The importance of human oversight and validation
- Understanding the EU AI Act
- Ethical and operational considerations
Module 8 — Practical AI Implementation
- Strategies for introducing AI within an organization
- Identifying quick wins
- Selecting appropriate tools and processes
- Change management considerations
- Evaluating ROI from AI initiatives
- Building an AI adoption roadmap
Group Exercise
Teams will evaluate:
- which processes should or should not utilize AI,
- operational risks,
- implementation priorities,
- and internal adoption challenges.
Day 5 — Business Simulation and AI Strategy Workshop
Module 9 — AI Strategy Workshop
In teams, participants will create:
- departmental AI action plans,
- implementation priorities,
- risk assessments,
- and measurable operational goals.
Final Practical Project
Teams will present:
- a real-world AI implementation proposal,
- expected business benefits,
- operational impact,
- risk factors,
- and an adoption strategy.
Final Discussion and Recommendations
- Next steps for AI adoption
- Identifying internal AI champions
- Recommended tools and workflows
- Long-term development of AI capabilities
Requirements
Target Audience
- Production Managers
- Strategic Planning Managers
- Leaders in Sales and Business Development
- Human Resources Managers
- Procurement and Warehouse Managers
- Innovation Leaders
- Finance and Accounting Professionals
- Quality Managers
- Operational and Administrative Managers
35 Hours
