AI Starts With the Data: Building a Trusted Sustainability Data Foundation

What has to be true about your sustainability data before AI makes it more useful, instead of producing faster, more sophisticated garbage? And what actually needs standardizing, centralizing, and owning, and what can stay where it lives?

Sustainability data grows up fragmented, in spreadsheets, site systems, and supplier files, assembled one reporting request at a time rather than designed as a foundation. AI raises the stakes on that inheritance, because a model is only as good as the data underneath it and produces wrong answers just as fast as right ones. Hear from practitioners at every stage of the build: standardizing data models across facilities, assigning ownership, controls, and quality checks, connecting sustainability data to enterprise systems, deciding what earns a single source of truth, through to running an internal AI tool on governed data. Leave knowing what to build first, and what your data has to be before AI earns a place on top of it.