Module #1: Discover the ESG Reporting Framework
Towards an integrated report: The evolution of the Sustainability Report
Fundamentals of the Sustainability Report
Regulatory frameworks and international standards: CSRD, ESRS, GRI, IFRS - voluntary, mandatory, global, regional, sectoral, etc. - applicability, selection, comparison and interoperability
ESG Reporting Framework 2.0: Project Planning
Module #2: Run a Double Materiality analysis with AI
Understanding the business context - business model, value chain, stakeholders.
Define the purpose, scope of the sustainability report and the framework to be used by the organization.
Step-by-step guide to developing a double materiality analysis, identifying the organization's impacts, risks, and opportunities.
+AI Workhsop: The step-by-step process of Double Materiality Analysis with AI (Claude)
- Introduction to Artificial Intelligence and its responsible, sustainable use.
- Building an AI assistant to conduct Double Materiality Analysis: identification and assessment of Impacts, Risks, and Opportunities through a simulated case.
- AI success stories — and cautionary tales.
Module #3: Apply Data Analytics to ESG
From report to decision: how ESG requirements evolve and what stakeholders expect.
Contextualizing sustainability and digitalization (twin transitions) — understanding the role of companies, and your role, within both.
Data governance: typology, lifecycle, traceability, and regulatory compliance.
Cross-functional coordination across sustainability, IT, finance, and operations — under a unified logic.
ESG critical thinking: working with imperfect data, identifying biases, and validating sources.
Module #4: Define the KPI’s to report with AI
Definition of KPIs to be reported — both quantitative and qualitative — and their role in decision-making.
KPI typology, application, and level of obligation.
Identification of ESRS indicators linked to material topics, and gap analysis between current information and regulatory reporting requirements.
+ AI Workhsop: Gap Analysis of the Report with AI (Claude)
- Build prompts using the Impact Prompt Engineering methodology.
- Design a diagnostic tool to assess the current state of your ESG data and translate it into concrete actions through a clear, structured prompt sequence.
- Build an actionable roadmap to improve the quality, traceability, and strategic value of your data.
Module #5: Manage ESG Data
Identifying data by source and reporting relevance to structure ESG databases and build actionable indicator dashboards.
Simplifying indicators, interpreting ratios, and evolving the report into an active sustainability management tool.
Validating, communicating, and building databases as a single source of truth — meeting both regulatory requirements and stakeholder expectations.
Module #6: Communicating the ESG Report with AI
ESG Storytelling: How to build a coherent, strategic narrative with an AI assistant (Copywriter).
Data visualization and layout.
Internal review, external verification, and information assurance.
Publication and dissemination of the ESG Report.
Preventing greenwashing in communication: how to avoid it and spot red flags.
Risks, limitations, and the ethical and responsible use of AI in report writing.
Module #7: Align ESG metrics with business goals
The evolution of Reporting: From compliance to a strategic tool for corporate sustainability management.
Defining the monitoring process for the areas covered in the report and incorporating lessons learned to improve year after year.
Technology Solutions: an overview of ESG Software (Platforms, ERPs, APIs)
From impact to financial metrics: How to incorporate ESG into the P&L.
+ P&L Workshop: A practical introduction to Impact P&L with Dinamo.io
From ESG to P&L: translating sustainability metrics into real financial impact (OpEx, COGS, revenue, LTV).
Speaking the language of the C-suite: building solid arguments to defend budgets and scale initiatives with business logic.
From metrics to decisions: turning your ESG data into an internal influence tool.
