Structure

✅ Day 1: Introduction to AI in Corporate Governance

Theme: Understanding Governance Gaps and the Role of AI

Analyze a company's governance data (Board composition, ESG metrics, compliance reports) using Python & Pandas
Build a simple dashboard to track key governance KPIs using Streamlit or Excel + Python
Use NLP to extract board-related insights from annual reports or policies

🎯 Outcome: Learn to interpret governance data and visualize it for internal decision-makers.

Theme: Automating Governance Risk and Compliance (GRC)

Create a rule-based red-flag system for compliance breaches (e.g., late disclosures, missing filings)
Use ML to score risk levels of vendors or board decisions
Train a simple fraud detection model on governance-related data
Run AI audit simulations with explainability outputs (using SHAP or LIME)

🎯 Outcome: Automate governance surveillance workflows with explainable AI models.

Theme: Intelligent Decision Support in Corporate Leadership

Use OpenAI or Hugging Face models to summarize board agendas and resolutions
Design a board assistant chatbot using prompt engineering (e.g., for board queries, ESG updates)
Automate ESG reporting pipelines (from data collection to summary generation)

🎯 Outcome: Prototype intelligent decision-support tools that enhance accountability and governance performance.