Helpful — here’s a practical, end‑to‑end plan to automate AASB S2 climate reporting with AI. I’ll cover: scope mapping to the standard, required data sources, an architecture and component design, AI/ML use cases and models, validation & auditability, governance and controls, a phased implementation plan with deliverables, and key risks. I cite the AASB S2 standard text for the core disclosure requirements and appendix on general requirements. (standards.AASB.gov.au)

1. What you must meet (quick summary of the Standard)

• AASB S2 requires disclosures on governance, strategy (including transition plans), risk management, and metrics & targets (incl. scenario analysis and Scope 1/2/3 emissions). It also defines general presentation, materiality, and requirements for judgments/uncertainties. Use this mapping as the checklist for automation. (standards.AASB.gov.au)

2. Data sources you need (minimum)

• Financial systems (ERP, general ledger, fixed assets, CAPEX / OPEX).
• Operational systems (SCADA, building management, asset management).
• Energy & fuel data (utility bills, fuel purchase ledgers, fuel cards).
• Procurement / supplier data (spend, supplier locations, product categories).
• HR & travel systems (business travel, company cars, staff numbers).
• Fleet telematics (if applicable).
• Environmental & emissions monitoring (on-site sensors, continuous emissions monitoring).
• Project & investment databases (transition projects, CAPEX plans).
• External reference datasets: emissions factors (national/international), GWP values (IPCC latest available at reporting date), supplier emissions data, climate scenarios (IPCC/NGFS/IEA as appropriate).
• Audit trail & metadata (who supplied/approved each data item; timestamps; versioning).

3. Map data to AASB S2 disclosure buckets

• Governance → org charts, committee minutes, role descriptions, training logs.
• Strategy & scenario analysis → capex plans, strategy docs, scenario model outputs, resilience assessments.
• Risk management → risk register, risk appetite, mitigations, insurance data.
• Metrics & targets → Scope 1/2/3 emissions, intensity metrics, capital deployment to climate-related activities, internal carbon price, remuneration links, progress against targets. (standards.AASB.gov.au)

4. System architecture (high level)

• Data ingestion layer: connectors to ERPs, files (CSV/XLSX), APIs, databases, telemetry streams. Use event-driven ingestion (e.g., Airflow, Prefect, or cloud-native).
• Data lake / warehouse: raw zone, curated zone, reporting zone. Store provenance metadata.
• Data transformation & calculation engine: implement GHG calculations (Scope 1/2/3) using deterministic code (Python/SQL) with versioned emissions factors.
• AI/LLM layer (optional but valuable): natural language drafting for disclosures, Q&A interface, anomaly detection, supplier emissions estimation (imputation), scenario sensitivity analysis assistance.
• Scenario & financial modelling module: scenario inputs (temperature pathways), Monte Carlo / stress testing, cash‑flow impacts mapped to P&L / balance sheet.
• Controls & validation: automated reconciliation rules, data quality scoring, human-in-the-loop review workflows, electronic approvals.
• Reporting & output: templates for AASB S2 sections (governance, strategy, risk, metrics/targets), export to PDF/Word and XBRL or tagged reporting if required.
• Audit & lineage: immutable logs, dataset versioning (DVC), and signed attestations.

5. AI / ML use cases and implementation notes

• Drafting disclosures (governance, strategy, risk narrative): Use a large language model (LLM) to generate first-draft narrative from structured inputs (board minutes, risk register, KPIs). Keep LLM outputs as draft only — require human review/approval. Maintain prompts and templates for consistency.
• Emissions gap filling & supplier estimation: ML regression models or rule-based mapping to estimate Scope 3 where supplier data missing (use industry emission intensities and spend‑based conversion). Flag estimates and confidence levels.
• Anomaly detection / data quality: time-series ML to detect outliers in energy/consumption and flag for investigation.
• Scenario analysis assistant: AI to translate scenario outputs into plain‑English impacts (e.g., “Under 2°C scenario, this asset’s revenue impact is X% by 2030”).
• Automated reconciliations: deterministic scripts augmented by ML to prioritize exceptions. Important: keep all deterministic financial/emissions calculations auditable (code + inputs) — do not rely on “black‑box” LLM outputs for numeric calculations or material metrics.

6. Calculation & methodological controls

• Implement GHG calculations per GHG Protocol style guidance: clearly document boundaries, covered gases, GWP source (e.g., IPCC AR6 or latest at reporting date), and conversion factors. AASB S2 requires CO2e aggregation using latest IPCC GWP at reporting date. (standards.AASB.gov.au)
• Scope 3: follow supplier data first; where unavailable, transparently disclose methods (spend-based, supplier average, hybrid). Log assumptions and sensitivity ranges.
• Internal carbon price: store scenarios, price per tonne used, and show how it affects investment decisions as required by AASB S2. (standards.AASB.gov.au)

7. Validation, assurance & auditability

• Version controlled code & factors (Git).
• Data lineage: for each disclosed number, link to source file/transaction and person who approved.
• Produce a “disclosure pack” for external assurance: methodology doc, reconciliations to financial statements, sensitivity analyses, and exception log.
• Enable export of machine-readable evidence packages for auditors (CSV + metadata + checksums).

8. Governance, roles & policies

• Assign a disclosure owner (CFO or equivalent) and named board committee oversight; capture role responsibilities in the system.
• Policies: materiality policy, data quality policy, emissions boundary policy, model governance policy (for scenario models and ML).
• Human-in-the-loop: all narrative & material numeric outputs require named approvers before publication.

9. Implementation phases, minimum viable product (MVP) and timeline (example)

• Phase 0 (2–4 weeks): project kickoff, inventory of data sources, gap analysis versus S2 checklist. Deliverable: gap report + prioritized backlog.
• Phase 1 – Core automation (8–12 weeks): ingest financials + energy data, implement Scope 1 & 2 deterministic calculations, produce metrics & basic narrative templates. Deliverable: S2‑ready draft sections for metrics & targets (Scope 1/2).
• Phase 2 – Scope 3 & scenario analysis (12–16 weeks): integrate procurement/spend data, supplier estimation models, basic scenario models (NGFS/IEA inputs). Deliverable: scenario outputs and Scope 3 estimates with confidence scores.
• Phase 3 – AI drafting & workflow (6–10 weeks): deploy LLM drafting with review workflow, generate full draft disclosures mapped to AASB S2 sections. Deliverable: full draft S2 report with approvals audit trail.
• Phase 4 – Assurance readiness & productionization (6–8 weeks): finalize controls, produce evidence packs for external assurance, train staff. Deliverable: audited/assurance-ready reporting process. Total typical time: 5–8 months for medium-sized company (varies by data readiness). Adjust for larger companies or complex supply chains.

10. Example tech stack (flexible)

• Cloud: AWS / Azure / GCP.
• Orchestration: Airflow / Prefect.
• Storage: S3 / ADLS + Snowflake / BigQuery.
• Compute: Python (pandas), PySpark for scale.
• Emissions calculations: in code with a versioned factors table.
• LLMs: enterprise LLM (OpenAI/Anthropic/Mistral or on‑prem alternatives) — run prompts via a controlled prompt library and store outputs with provenance.
• Front-end: BI tools (Power BI / Tableau) + a reporting engine (Word templates / LaTeX / PDF generator).
• Security: IAM, encryption at rest & in transit, role-based access.

11. Controls to avoid compliance & reputational risk

• Never publish AI‑only draft without named human approver.
• Keep all assumptions and methodologies explicit and embedded into report text.
• Require sign‑off for material changes to emissions factors, scenario inputs, or boundary definitions.
• Keep immutable logs for audit (timestamps, approver signatures).
• If using third‑party LLM, ensure data privacy (don’t send confidential raw data without contract and controls).

12. Example minimal disclosure automation deliverables (MVP)

• Single place where board/committee evidence (minutes, role definitions) is stored.
• Automated Scope 1 & 2 emissions calculations with factor provenance.
• Template-generated draft narratives for governance, metrics & targets sections (human review).
• Scope 3 top‑down spend‑based estimate + flags for high‑impact suppliers requiring deeper data collection.
• Scenario analysis summary (outputs + plain‑English interpretation).
• Evidence package for assurance.

13. Typical costs & resourcing (very approximate)

• Small implementation (single country, good data): USD 100k–300k professional services + cloud costs.
• Medium (global, complex supply chain): USD 300k–1M.
• Ongoing run & maintenance: 10–25% of initial implementation annually. (Estimate depends on licensing, data cleanup effort, assurance needs.)

14. Key risks and mitigations

• Poor data quality → mitigation: data quality scoring, exception workflows, prioritized manual remediation for material items.
• Over-reliance on LLM-generated narrative → mitigation: enforced human approval, explainability logs.
• Scope 3 uncertainty → mitigation: clearly disclose method & sensitivity, prioritize supplier engagement for material categories.
• Regulatory change → mitigation: modular architecture so you can update templates and calculations quickly; keep external data sources (e.g., emission factors, IPCC GWP) configurable and dated.

15. First 30‑day checklist (practical starting steps)

• Assemble cross‑functional project team (Finance, Sustainability, IT, Procurement, Legal).
• Run an S2 compliance gap analysis vs. current disclosures (use AASB S2 checklist). (standards.AASB.gov.au)
• Identify and map primary data sources and owners.
• Pick an MVP scope (e.g., Scope 1/2 + governance + metrics) and schedule Phase 1 delivery.
• Decide hosting (cloud/on‑prem) and LLM procurement policy.

Resources & references

• Use the official AASB S2 standard and Appendix D for authoritative requirement language and presentation rules; these are the ground truth for what must be disclosed. (standards.AASB.gov.au)

If you want, I can:

• Draft a prioritized technical backlog tailored to your org (based on its size/data maturity).
• Produce example prompt templates and LLM guardrails for disclosure drafting.
• Provide code snippets for a reproducible Scope 1/2 calculation workbook and a sample data lineage JSON schema.

Which of those would you like me to produce next?