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 Duration 14 hours

Course Outline

Day 1: Foundational AI Principles and Document Comprehension Capabilities

  • 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.
  • 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.
  • 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

  • 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.
  • 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.
  • 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.
  • 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.
  • 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

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