Yubing Zhang Head of AI Products Watershed
Scott Hofmann Chief Revenue Officer
GFT
Difficult tasks made simple by sustainability AI include reporting, data cleaning and data ingestion. Impossible tasks made possible by sustainability AI include strategic analysis to identify emissions hotspots and reductions levers deep within corporate supply chains.
Q. WHAT MAKES SUSTAINABILITY AI PURPOSE-BUILT FOR SUSTAINABILITY?
» Sustainability has a few realities that make using generic AI risky:
• Sustainability measurement outputs end up in regulatory filings and audited reports; companies and auditors need transparency into calculations.
• Climate science and measurement methodology factor into the accuracy of carbon footprints, and small errors can lead to big consequences.
• Primary data is often incomplete, inconsistent, and spread across many systems.
“ Purpose-built” means we design the system around those realities. Sustainability AI requires three foundational elements:
• A strong data foundation – a single source of truth for all ESG data, standardised, approved and traceable to raw sources. Without this, AI outputs become liability risks. With it, every number in every report traces back to its origin, eliminating hallucination risk. This also needs to be a living database,
84 May 2026