GenAI and ESG Course

Lesson 1 • What ESG Means for Business

Defines the three ESG pillars and their origin in responsible investing. Establishes the vocabulary used throughout the course.

Lesson 2 • Global ESG Reporting Frameworks

Surveys major voluntary and mandatory disclosure frameworks used worldwide. Connects framework choice to organizational reporting strategy.

Lesson 3 • Materiality and Stakeholder Mapping

Explains how organizations identify which ESG issues are most significant. Grounds later data collection in business-relevant priorities.

Lesson 4 • ESG Ratings and Market Signals

Examines how rating agencies score ESG performance and how markets respond. Prepares learners to interpret external ESG assessments.

Lesson 1 • AI Limitations and Risk Awareness

Identifies key failure modes including bias, data cutoffs, and factual errors. Builds the critical mindset required for responsible AI use in ESG.

Lesson 2 • Types of Generative AI Outputs

Surveys text, image, structured data, and code generation capabilities. Connects output types to practical ESG tasks introduced later.

Lesson 3 • Prompt Engineering Fundamentals

Teaches the craft of writing effective prompts to control AI output quality. Directly enables the applied ESG tasks in subsequent chapters.

Lesson 1 • AI Governance Frameworks for ESG

Designs internal policies and controls to govern AI use across ESG functions. Builds organizational accountability structures for AI decisions.

Lesson 2 • Greenwashing Risk in AI-Generated Content

Identifies how AI can inadvertently produce misleading ESG claims. Establishes review controls to protect organizational credibility.

Lesson 3 • Data Privacy and Security in ESG AI

Addresses the handling of sensitive employee, supplier, and community data in AI systems. Ensures compliance with data protection obligations.

Lesson 4 • Bias and Fairness in ESG AI Models

Identifies how algorithmic bias can distort ESG assessments and ratings. Teaches mitigation techniques to ensure equitable AI outputs.

Lesson 1 • Building an ESG Data Architecture

Designs the systems and workflows that store and route ESG data. Connects data infrastructure to reporting and analytics needs.

Lesson 2 • Data Quality and Governance

Establishes standards for ESG data accuracy, completeness, and auditability. Ensures data meets the rigor required by external assurance providers.

Lesson 3 • Using AI to Extract ESG Data

Applies generative AI to extract structured ESG metrics from unstructured documents. Reduces manual effort in data ingestion workflows.

Lesson 1 • Carbon Footprint Calculation with AI

Uses AI tools to automate greenhouse gas accounting across emission scopes. Builds the quantitative foundation for climate-related disclosures.

Lesson 2 • Benchmarking and Peer Comparison

Positions an organization's ESG performance against industry peers. Enables strategic gap identification and target setting.

Lesson 3 • ESG Risk Identification with AI

Applies AI to scan news, supply chains, and regulatory signals for ESG risks. Integrates risk intelligence into enterprise risk management.

Lesson 4 • Scenario Analysis and Forecasting

Projects future ESG performance under different business and climate scenarios. Supports long-term target setting and risk planning.

Lesson 1 • Structuring an ESG Report with AI

Maps framework requirements to report sections and uses AI to generate outlines. Reduces time-to-draft for complex disclosure documents.

Lesson 2 • Drafting ESG Narratives with AI

Applies prompt engineering to generate accurate, on-brand ESG narrative text. Teaches human review protocols to catch errors and greenwashing.

Lesson 3 • Quality Assurance and Compliance Checks

Uses AI to audit reports for completeness, consistency, and framework alignment. Reduces assurance risk before external review.

Lesson 1 • Scope 3 Emissions in Supply Chains

Quantifies upstream and downstream emissions using AI-assisted spend and activity data. Addresses the largest and most complex emission category.

Lesson 2 • Supplier Engagement and Improvement

Designs AI-supported programs to help suppliers improve ESG performance. Converts assessment findings into measurable supplier development.

Lesson 3 • Continuous Supplier Monitoring

Implements AI-driven monitoring of news, certifications, and incident data. Shifts due diligence from periodic to real-time.

Lesson 4 • Supplier ESG Assessment Tools

Designs and deploys AI-enhanced questionnaires and scoring models for suppliers. Standardizes assessment across diverse supplier bases.

Lesson 1 • Integrating ESG into Business Strategy

Embeds ESG considerations into core business planning and capital allocation. Ensures ESG is a driver of value, not a compliance exercise.

Lesson 2 • Monitoring Progress and Adaptive Management

Establishes AI-powered dashboards and review cycles to track ESG target progress. Enables course correction when performance deviates from plan.

Lesson 3 • Science-Based Target Frameworks

Explains the methodologies behind science-aligned emissions and nature targets. Grounds target setting in globally recognized standards.

Lesson 4 • AI-Assisted Gap and Opportunity Analysis

Uses AI to compare current performance against targets and identify priority actions. Translates data into strategic investment decisions.