Use case
ESG packs built from owned data — not last-minute spreadsheets
Sustainability teams still chase facilities for numbers that disagree with finance. Arcloops designs AI-assisted ESG reporting that collects metrics, flags gaps, and drafts narrative against your framework — without inventing sustainability performance.
The problem: ESG as an annual scramble
Metric owners sit in operations, HR, facilities, and supply chain. Definitions differ by site. Spreadsheets arrive late with undocumented estimates. Assurance providers ask for evidence trails that never existed. Marketing wants positive stories while controllers refuse ungoverned claims.
Frameworks multiply — CSRD-aligned packs, GRI-style tables, buyer questionnaires, lender ESG covenants. Teams rewrite the same underlying data into new shapes. Scope 3 and supplier data are incomplete; gaps are papered over in prose. Leadership discovers material omissions weeks before publication.
Manual effort does not scale with entity count or questionnaire volume. Hiring more ESG analysts to copy-paste will not create a metric dictionary. AI that “writes the sustainability report” from the open web creates greenwashing risk.
Sustainability owns the programme; finance owns assurance alignment; legal owns disclosure risk; operations own activity data. Anti-patterns include generative claims without source metrics, mixing estimates with measured values without labels, and publishing before control owners certify.
Manual effort does not scale with entity count or questionnaire volume. Hiring more ESG analysts to copy-paste will not create a metric dictionary. AI that “writes the sustainability report” from the open web creates greenwashing risk and assurance nightmares. Buyer and lender questionnaires often ask the same underlying questions in different shapes — without a dictionary, every request restarts collection.
Labelling estimates versus measured values is non-negotiable. Facilities under deadline pressure invent numbers; generative tools then polish fiction into fluent disclosure. Governance must make gaps visible early and keep certification with named owners before publication or board packs reuse the figures.
AI ESG reporting should orchestrate data collection, validate completeness, draft from governed figures, and leave certification with humans — decision support for disclosure, not a creativity engine for impact claims. Cycle-time and completeness are honest pilot metrics; invented ESG score improvements are not.
AI approach
Build an ESG metric dictionary and owner map
Each KPI has definition, unit, frequency, source, and owner. Frameworks map to the same underlying metrics to avoid duplicate collection. Estimates are labelled as estimates.
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Collect, validate, and flag gaps
Facilities and entity owners submit or connect data into controlled intake. Models flag missing periods, outliers, and unit errors. What good looks like: a gap list weeks before the disclosure deadline, not the night before.
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Draft disclosures from governed numbers
Narrative drafts cite metric IDs and periods. Qualitative sections use approved talking points. Buyer questionnaires are answered from the same dictionary where possible.
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Certify, version, and feed board packs
Sign-off follows Approvals or sustainability governance. Versions are immutable for the publication of record. Board ESG sections reuse the same numbers. Failure modes: unlabelled estimates presented as fact, and scope creep into unauditable marketing claims.
How Arcloops delivers this
ESG reporting programmes typically span /solutions/ai-in-finance for metric control, /solutions/ai-for-executive-teams for board-facing packs, and /solutions/ai-in-legal-compliance for disclosure risk. Policy and governance framing maps to /ai-consulting/ai-governance-risk when boards want explicit AI-in-disclosure rules.
Delivery starts with a framework subset and a pilot entity set, not a global big bang. Integration notes cover data collection portals, ERP/HRIS extracts where relevant, and document generation. We track completeness and cycle time; we do not invent ESG score improvements or impact ROI.
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FAQ
We map to the frameworks you must report against. The metric dictionary is the core; framework-specific tables are views on the same data. Scope is agreed in discovery.
No. Drafting is constrained to governed metrics and approved qualitative libraries. Missing data stays a gap — it is not filled with plausible fiction.
No. It improves readiness and evidence organisation. Assurance scope and opinions remain with your providers.
Where supplier data exists and owners are clear, yes — often starting with a subset. Thin supplier data is treated as a data programme, not magically completed by a model.
Pilot ESG metrics on a framework subset
Share your disclosure calendar and metric owner map. Arcloops will outline collection, validation, and drafting under finance and executive AI solutions.