Artificial intelligence is making headlines across every industry. Whether it’s transforming customer support, accelerating software development, or reshaping marketing content, AI is now a tool many teams are being asked to explore.
But what does that look like inside corporate sustainability teams?
To answer that question, we surveyed our clients to understand how they’re experimenting with AI, where they see potential, and what’s holding them back.
The results? Sustainability leaders are approaching AI with a mix of curiosity, creativity, and caution.
In a few weeks, we’ll publish a full report unpacking the results of our survey. But in the meantime, here’s a sneak peek at what we learned.
AI adoption is at an early stage, but momentum is building
Most sustainability teams aren’t deeply integrated with AI tools yet, but very few are ignoring them altogether. Nearly every respondent indicated that they’ve either started using AI in small ways or are actively discussing its potential. In some cases, teams are using it regularly across day-to-day workflows.
Current use cases are general, not climate-specific
When it comes to how sustainability teams are using AI today, the patterns closely mirror what’s happening across other business functions. Most sustainability teams are starting with the same general use cases seen across other departments.
The most common use cases include:
- Researching questions and using AI as a search engine
- Basic brainstorming activities
- Drafting or editing written content
Some respondents mentioned using AI to help interpret climate disclosure rules or pull insights from sustainability data, but these uses were less common. The majority of examples aligned with what you might expect from teams in operations, HR, or communications. That’s an important signal. It suggests that AI isn’t yet integrated into the core, climate-specific parts of the sustainability workflow.
However, when we asked which use cases were most interesting for the future, the story evolved. Respondents were most excited by ideas that could simplify and accelerate core parts of sustainability work, such as:
- Gathering and extracting data for greenhouse gas (GHG) calculations
- Estimating supplier emissions when little or no primary data is available
- Drafting disclosures and mapping data to regulatory frameworks
These are the kinds of time-consuming, data-heavy tasks that, if streamlined, could free up sustainability teams to focus more of their time on strategy, engagement, and impact.
Interestingly, use cases that touched the “last mile” of climate work, such as verifying emissions estimates or building decarbonization roadmaps, were among the least selected. This may suggest that while AI is appealing for tedious or repetitive work, it is not yet trusted for decision-making or goal-setting without significant human oversight.
The biggest hesitations come down to trusting the data
What’s keeping sustainability teams from going deeper with AI? The top three concerns in our survey all point back to the same theme: trust in data.
Respondents expressed concern over:
- Whether company data would be secure when used with AI tools
- Whether the outputs from AI models would be accurate or reliable
- Whether they could verify how AI models arrived at specific results
These concerns reflect a high-stakes reality. For sustainability teams, data must often stand up to audit requirements, investor scrutiny, and public reporting standards. If an AI model can’t clearly show its work, it likely won’t be used for climate disclosures or decarbonization planning anytime soon.
Another concern that stood out was the environmental impact of AI use.
For reference, studies have found that a single query to a large language model can consume around 500 milliliters of water (about one standard water bottle) and that training a single large AI model can require around 1,300 megawatt-hours of electricity (enough to power approximately 120 U.S. homes for an entire year).
In our survey, responses were nearly evenly split on whether they believed the benefits of using AI outweigh its climate and resource impacts. This suggests that sustainability professionals are carefully weighing both the costs and benefits of AI adoption.
Where we go from here
The data tells a story of cautious exploration. Sustainability professionals are curious about how AI can support their work, but they are not rushing to apply it without clear, credible use cases and safeguards in place.
We’ll share the full findings in our upcoming report, AI in Corporate Sustainability: Adoption trends, use cases, and sentiments. It will take a closer look at adoption trends, use case opportunities, and the trust gap that still needs to be addressed.
The full report will be available in a few weeks. Stay tuned.
