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Course Outline
Day 1: Foundations of AI and Its Capabilities in Understanding Documents
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Module 1: Introduction to AI for Professionals
- Demystifying AI, ML, and NLP: A simple explanation without technical jargon.
- AI as an "Expert Assistant": Shifting the paradigm from a threat to a supportive tool.
- Successful case studies: Real-world examples of how other industries (legal, finance) use AI.
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Module 2: Core NLP Capabilities for Document Analysis
- Document Classification: Teaching AI to automatically sort document types (e.g., Deed of Establishment, Financial Reports, Environmental Permits).
- Entity Extraction: Training AI to find and extract specific information from text, such as directors' names, investment values, effective dates, or Tax ID numbers (NPWP).
- Sentiment Analysis & Risk Identification: Identifying potentially risky clauses or sentiment within documents.
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Module 3: Machine Learning Concepts in Practice
- How Do Machines "Learn"? The concept of supervised learning using existing document examples.
- The Importance of Quality Data: "Garbage in, garbage out" in the world of AI.
- The ML project lifecycle: From data collection to model evaluation.
Day 2: Practical Applications, Tools, and Strategic Planning
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Module 4: Workshop - Mapping Your Work to AI Solutions
- Interactive session to identify the most time-consuming manual tasks in the licensing process.
- Brainstorming: How NLP and ML can be applied to solve these problems.
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Module 5: The Landscape of AI Technology and Tools
- Understanding different tiers of tools: From ready-to-use software-as-a-service (SaaS) to customizable platforms.
- Live demo of several AI tools for document analysis.
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Module 6: Designing and Implementing an AI Project
- Steps to start a pilot project.
- Defining success metrics (time efficiency, error reduction).
- The "Human in the Loop" Role: The importance of verification by experts.
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Module 7: Ethical Considerations and Risk Management
- Data security and confidentiality in AI systems.
- Potential bias in AI models and how to mitigate it.
- Building trust in the results of AI analysis.
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Module 8: Summary and Action Plan Development
- Drafting an action plan for AI implementation in the licensing division.
- Final discussion and Q&A.
Requirements
Audience
- License Department
- Documentation personnel
14 Hours
