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Course Outline
Day 1 — Overview of Artificial Intelligence and Enterprise Applications
Module 1 — Fundamentals of Artificial Intelligence
- Defining the scope and limitations of AI
- Classification of AI system types
- Generative AI and Large Language Models (LLMs)
- Distinguishing misconceptions from factual capabilities
- Current trends in organizational AI integration
- Potential benefits and constraints of AI technologies
Module 2 — AI Integration in Modern Business Functions
- Contemporary applications of AI within enterprises
- AI utilization in manufacturing and operational contexts
- AI deployment in sales and customer engagement
- AI support for human resources and talent acquisition
- AI enhancement of procurement and supply chain logistics
- AI applications in financial management and reporting
- AI-driven quality assurance and regulatory compliance
Practical Exercise
Participants evaluate AI tools for the following tasks:
- text summarization,
- automated report generation,
- correspondence drafting,
- process workflow assistance,
- document review and analysis,
- meeting documentation,
- and strategic planning support.
Day 2 — Enhancing Productivity and Automating Workflows via AI
Module 3 — AI-Driven Productivity Enhancement
- Leveraging AI assistants for managerial tasks
- Prompt engineering techniques for business professionals
- Designing effective prompts for enterprise outcomes
- Applying AI to:
- business reporting,
- strategic planning,
- presentation development,
- technical documentation,
- meeting preparation,
- and decision-support analysis
Module 4 — Data Analytics and Business Intelligence
- Conducting business analysis using AI methodologies
- Data extraction from unstructured documents and spreadsheets
- AI-supported forecasting and trend identification
- Key Performance Indicator (KPI) tracking and operational insights
- Processing structured and unstructured enterprise data
Practical Workshop
Teams address realistic business scenarios:
- production metrics reporting,
- sales projection modeling,
- vendor performance analysis,
- human resources documentation,
- operational dashboard development,
- and quality defect analysis.
Participants construct practical AI-supported workflows aligned with their respective departments, for government and private sector applicability.
Day 3 — AI in Operations, Strategic Planning, and Decision-Making
Module 5 — AI in Operational Management
- Optimizing operational efficiency through AI
- Process workflow optimization
- Inventory management and warehouse support systems
- Concepts of predictive maintenance
- Process standardization strategies
- AI-assisted decision-making frameworks
Module 6 — Department-Specific AI Applications
Production and Operations
- Real-time production monitoring
- Root-cause analysis techniques
- Standard Operating Procedure (SOP) development
- Operational performance reporting
Sales and Business Development
- Lead qualification processes
- Proposal creation and management
- Customer engagement optimization
- Competitive landscape analysis
Human Resources
- Job description drafting
- Interview protocol preparation
- Training curriculum development
- Internal communication strategies
Finance and Accounting
- Financial summary generation
- Invoice and document analysis
- Regulatory compliance support
- Automated financial reporting
Quality Management
- Nonconformity investigation
- Documentation assistance
- Audit readiness preparation
- Risk monitoring and tracking
Practical Workshop
Participants develop:
- a department-specific AI use case,
- a workflow automation opportunity,
- and a quantifiable productivity improvement initiative.
Day 4 — AI Governance, Risk Management, and Implementation
Module 7 — AI Governance and Regulatory Compliance
- Principles of responsible AI usage
- Data privacy protections and confidentiality standards
- Risk assessment of generative AI technologies
- Establishment of AI governance policies
- Requirements for human oversight and validation
- Overview of the EU AI Act and international standards
- Ethical implications and operational considerations
Module 8 — Strategic AI Implementation
- Strategies for organizational AI integration
- Identification of high-impact, low-effort initiatives (quick wins)
- Criteria for selecting appropriate tools and processes
- Change management strategies for technology adoption
- Evaluating Return on Investment (ROI) for AI projects
- Developing an organizational AI adoption roadmap
Group Exercise
Teams assess:
- criteria for determining suitable vs. unsuitable AI applications,
- operational risk factors,
- implementation prioritization frameworks,
- and internal barriers to adoption.
Day 5 — Business Simulation and AI Strategy Development
Module 9 — AI Strategy Workshop
Teams collaborate to develop:
- departmental AI action plans,
- implementation priorities,
- risk mitigation assessments,
- and measurable operational objectives.
Final Practical Project
Teams present:
- a comprehensive AI implementation proposal,
- anticipated business outcomes,
- expected operational impact,
- identified risks,
- and adoption strategies.
Concluding Discussion and Recommendations
- Actionable next steps for AI adoption
- Identification of internal AI champions
- Recommendations for optimal tools and workflows
- Strategies for long-term AI capability development
Requirements
Intended Recipients
- Production Management Personnel
- Strategic Planning Officers
- Commercial and Business Development Executives
- Human Resources Administrators
- Procurement and Warehouse Supervisors
- Innovation Directors
- Finance and Accounting Specialists
- Quality Assurance Managers
- Operational and Administrative Supervisors
35 Hours
