Two years ago, applying artificial intelligence to corporate sustainability was a pilot project reserved for the most technologically advanced companies. Today, 94% of ESG professionals use generative AI tools at least once a week. Close to 60% do so daily. Not a single participant in the SUST4IN study published this May claims not to use them.
The leap is real. But the most recent data from the UN Global Compact Spain shows that behind this figure lies a significant divide: while the most mature sustainability departments have turned AI into an operational tool, 1 in 3 Spanish companies still does not know where to begin. Only 8% have managed to integrate it transversally across their ESG processes.
The question is no longer whether AI has a place in sustainability. The question is what separates the organisations that have crossed that threshold from those that have not yet.
The applications that are proving real value
The SUST4IN report draws a clear map of where effective use is concentrated. Regulatory analysis (CSRD, EU taxonomy, ESRS standards) is the most widespread application, with 77% of respondents. ESG report and disclosure drafting follows at 65%. In both cases, AI is acting as an accelerator for knowledge-intensive regulatory processes: it reduces interpretation time, facilitates document synthesis, and helps structure complex information.
Time savings is the only benefit mentioned by 100% of participants. There is not a single organisation that has incorporated AI into its ESG workflows without perceiving an efficiency gain. The fact that this value is so universal and so immediate partly explains why adoption has been so rapid. But it also reveals its limits.
The next step: from efficiency to precision
The UN Global Compact report points out that companies already working with AI in sustainability have two priority applications: operational efficiency (37%) and ESG reporting automation (28%). But it also points to where value is going to be concentrated in the coming years. Reducing the carbon footprint and supplier traceability are beginning to emerge as the areas where AI will have a transformative impact.
These two areas share a requirement that distinguishes them from generic reporting: they need precise data, verifiable methodologies, and full calculation traceability. Automating a sustainability report can be done with a general-purpose model. Calculating the carbon footprint with the rigour required by the GHG Protocol or ISO 14064, and doing so in a way that makes the number auditable, requires more than speed. It requires a system built specifically for that purpose. That direction, integrating AI with reliable environmental data, is exactly what we describe in the future of ESG reporting.
The AI that understands your environmental data
94% of the ESG professionals surveyed by SUST4IN say they want to go deeper into AI applied to reporting and the CSRD. Motivation is not lacking. What is lacking, for many organisations, is a platform that connects the capabilities of AI with the company's actual environmental data.
Manglai has built that platform. Our AI Copilot allows sustainability teams to ask directly about their carbon footprint, consult ESG regulations applied to their context, and get answers based on their own data, not generic information. The difference is not just in speed, it is in reliability. Every answer has a traceable origin, a recognised methodology, and evidence that can be subjected to an audit, something critical when double materiality analysis comes into play.
AI has arrived in ESG departments. What defines the organisations that are getting real value from it is not that they use it, but how they use it. A good starting point is to centralise the calculation of the carbon footprint on reliable data.
