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Duration 14 hours
Course Outline
Day 1: Foundational AI Principles and Document Comprehension Capabilities
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Module 1: Introduction to AI for Professional Stakeholders
- Clarifying AI, Machine Learning, and NLP: Simplified explanations without excessive technical terminology.
- AI as a Supportive Instrument: Reconceptualizing the technology as an asset to assist operations rather than a potential disruption.
- Exemplary Case Studies: Practical instances of AI adoption in legal and financial sectors for government reference.
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Module 2: Fundamental NLP Functions for Document Review
- Document Categorization: Configuring AI to automatically organize document types (e.g., Establishment Deeds, Financial Statements, Environmental Permits).
- Information Extraction: Instructing AI to isolate specific data points, such as officer names, capitalization values, effective dates, or Tax ID numbers (NPWP).
- Sentiment Evaluation and Risk Detection: Identifying potentially hazardous clauses or negative sentiments within regulatory documents.
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Module 3: Applied Machine Learning Methodologies
- Mechanisms of Machine Learning: The application of supervised learning using existing document samples.
- Data Integrity Imperatives: Addressing the principle that low-quality input results in low-quality output within AI systems.
- The Machine Learning Project Cycle: From data acquisition through to model assessment.
Day 2: Operational Deployment, Tool Selection, and Strategic Development
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Module 4: Workshop - Aligning Operational Workflows with AI Solutions
- Interactive session to pinpoint the most labor-intensive manual tasks within the licensing process.
- Collaborative Analysis: Applying NLP and ML concepts to resolve identified operational challenges.
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Module 5: Overview of AI Technological Ecosystem and Tools
- Tool Hierarchy Analysis: From off-the-shelf Software-as-a-Service (SaaS) options to highly customizable platforms.
- Live Demonstration: Practical review of multiple AI tools designed for document analysis.
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Module 6: Structuring and Executing an AI Initiative
- Protocol for initiating a pilot project.
- Establishing Performance Metrics: Measuring time efficiency and error reduction rates.
- Human Oversight Protocols: The critical role of expert verification in AI-driven workflows.
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Module 7: Ethical Frameworks and Risk Mitigation
- Data Privacy and Confidentiality Standards in AI systems.
- Addressing Algorithmic Bias: Strategies for identifying and mitigating potential model bias.
- Establishing Credibility: Building confidence in the accuracy of AI analytical results.
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Module 8: Consolidation and Strategic Action Planning
- Formulating an implementation roadmap for AI adoption in the licensing division.
- Closing Debrief and Q&A Session.
Requirements
Target Audience
- Licensing Department Personnel
- Documentation Specialists
Testimonials (1)
I got to learn more about Microsoft Copilot, something that I thought was the same as chatGPT but I got to discover more exciting options that I will forever use to make my life easy.