Six ways purpose-built AI is giving sustainability teams their time back

Sustainability teams are managing more data, more frameworks, and more customer requests than ever. Atlassian saw an 83% year-over-year increase in RFPs alone. The pressure keeps growing. The headcount doesn't.

Generic AI tools often make things harder—mischaracterizing methodology changes, presenting uncertain results as fact, or filling gaps with invented content. Teams end up spending more time verifying AI outputs than doing the work that matters.

Purpose-built AI changes that equation. Because it's trained on real emissions data, reporting frameworks, and sustainability expertise, it produces outputs your team can trust and trace.

In this guide, six Watershed experts share how companies like Smiths Group, Burton, Royal Mail, and Atlassian use AI across their most time-intensive sustainability workflows—and the results they're getting.

You'll learn:

• How companies cut data prep time by 65–80% and complete ESG disclosures in days instead of months
• Why auditability and traceability matter more than speed—and how purpose-built AI delivers both
• Six use cases with expert tips you can apply today, from data ingestion to product carbon footprints