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Duration 35 hours
Course Outline
Day 1 — Introduction to AI and Business Applications
Module 1 — Introduction to Artificial Intelligence
- Defining AI: capabilities and limitations
- Categorization of AI systems
- Overview of Generative AI and Large Language Models
- Distinguishing AI myths from reality
- Current trends in corporate AI adoption
- Opportunities and constraints of AI technologies
Module 2 — AI in Modern Business Operations
- Contemporary corporate utilization of AI
- AI applications in manufacturing and operations
- AI in sales and client engagement
- AI in human resources and talent acquisition
- AI in procurement and supply chain logistics
- AI in financial analysis and reporting
- AI for quality control and regulatory compliance
Practical Exercise
Participants will test AI tools for:
- content summarization,
- automatic report generation,
- email composition,
- workflow assistance,
- document analysis,
- meeting note capture,
- and operational planning tasks.
Day 2 — Practical AI Productivity and Workflow Automation
Module 3 — AI-Powered Productivity
- AI assistants tailored for managerial roles
- Prompt engineering techniques for business users
- Crafting effective business prompts
- Utilizing AI for:
- reporting,
- planning,
- presentation creation,
- documentation,
- meeting preparation,
- and decision support
Module 4 — Data Analysis and Business Insights
- Conducting business analysis with AI
- Extracting key information from documents and spreadsheets
- AI-assisted forecasting and trend identification
- KPI monitoring and generating operational insights
- Handling structured and unstructured business data
Practical Workshop
Teams will tackle realistic business scenarios, including:
- production reporting,
- sales forecasting,
- supplier analysis,
- HR documentation,
- operational dashboards,
- and quality issue analysis.
Participants will develop practical, AI-supported workflows relevant to their respective departments.
Day 3 — AI for Operations, Planning, and Decision-Making
Module 5 — AI in Operations Management
- Leveraging AI for operational efficiency
- Optimizing workflows
- Supporting inventory and warehouse management
- Concepts of predictive maintenance
- Standardizing processes
- 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
- Job description creation
- Interview preparation
- Training plan development
- Internal communications
Finance and Accounting
- Financial summaries
- Invoice and 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 specific department,
- one automation opportunity,
- and one measurable productivity improvement initiative.
Day 4 — AI Governance, Risk, and Implementation
Module 7 — AI Governance and Compliance
- Principles of responsible AI usage
- Data privacy and confidentiality standards
- Risks associated with generative AI
- Establishing AI governance policies
- Human oversight and validation protocols
- 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
- Measuring ROI from AI initiatives
- Building an AI adoption roadmap
Group Exercise
Teams will evaluate:
- which processes are suitable for AI application,
- potential operational risks,
- implementation priorities,
- and internal adoption challenges.
Day 5 — Business Simulation and AI Strategy Workshop
Module 9 — AI Strategy Workshop
Participants will work in teams to 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,
- identified risks,
- and an adoption strategy.
Final Discussion and Recommendations
- Next steps for AI adoption
- Identifying internal AI champions
- Recommended tools and workflows
- Long-term AI capability development
Requirements
Target Audience
- Production Managers
- Strategic Planning Managers
- Sales and Business Development Leaders
- Human Resources Managers
- Procurement and Warehouse Managers
- Innovation Leaders
- Finance and Accounting Professionals
- Quality Managers
- Operational and Administrative Managers
