Programme purpose
Learning designed for immediate organisational application.
A practical programme for using artificial intelligence to improve ESG research, data quality, evidence review, reporting workflows and management insight while controlling hallucination, confidentiality, bias, greenwashing and accountability risks.
Available datesChoose the session that works for you.
Detailed two-day curriculumSix modules, practical exercises and workplace-ready outputs.
The programme moves from core concepts to guided application. Every major module includes focused submodules and a practical exercise.
Day 1Responsible AI foundations and high-value ESG workflows
Module 1 — AI fundamentals for sustainability professionals
- Generative AI, machine learning and document intelligence
- Capabilities, limitations, hallucination and model risk
- Use-case identification across ESG strategy, data, risk and reporting
Practical application: Prioritise AI use cases by value, feasibility and risk.
Module 2 — Prompting, research and document analysis
- Structured prompts, context, constraints and output schemas
- Analysing policies, standards, reports and evidence files
- Source verification, citation discipline and human review
Practical application: Build and test a reusable ESG analysis prompt workflow.
Module 3 — ESG data extraction and quality management
- Extracting metrics and metadata from documents
- Classification, mapping, validation and exception detection
- Sensitive data, access controls and traceability
Practical application: Extract, validate and reconcile ESG data from a sample evidence pack.
Daily participant outputA prioritised AI use-case portfolio, reusable prompt and validated data-extraction workflow.
Day 2AI-assisted analysis, reporting governance and implementation
Module 4 — AI-assisted risk, materiality and performance analysis
- Theme detection, issue clustering and stakeholder analysis
- Variance, anomaly and emerging-risk identification
- Scenario support, limitations and expert judgement
Practical application: Analyse a multi-source ESG dataset and document review decisions.
Module 5 — AI-assisted reporting and disclosure quality
- Narrative drafting grounded in approved facts
- Framework mapping, consistency checks and claim verification
- Tone, balance, greenwashing risk and approval workflow
Practical application: Produce and quality-review a controlled disclosure draft.
Module 6 — Responsible AI governance and pilot design
- Privacy, confidentiality, bias, copyright and regulatory risk
- Human accountability, model controls, logs and vendor due diligence
- Pilot scope, success measures, change management and scaling
Practical application: Design an AI-for-ESG pilot with governance controls.
Daily participant outputA governed disclosure workflow and implementation-ready AI pilot charter.
Who should attendDesigned for the people responsible for delivery.
- Sustainability and reporting teams
- ESG data owners and analysts
- Risk, compliance, audit and technology professionals
- Consultants and business transformation leaders
Learning outcomesWhat participants will be able to do.
- Identify safe, valuable AI use cases
- Design human-controlled AI workflows
- Improve ESG data and reporting productivity
- Create responsible-AI controls for sustainability work
Participant resourcesPractical take-away tools.
- ESG AI use-case canvas
- Responsible-AI risk-control checklist
- Prompt and review library
- 90-day pilot roadmap
Learning methodologyTechnical instruction translated into practical action.
Live demonstrations, guided prompting, document and data labs, risk-control exercises and pilot design.
Secure your place or train your organisation.
Register an individual participant, book a corporate cohort or request a tailored in-house version aligned with your sector and implementation priorities.