In our fast-moving era of AI, accurate and complete sustainability data is more important than ever before. It’s a must-have inside the company, as it enables informed business decisions and future AI. Externally, customer requests for sustainability reports are rapidly increasing, and investors are using their AI to scrub corporate data. If there’s an error it is easily found.

Meanwhile, sustainability teams know they have to change, spending less time on data and more time on business impact. Those spreadsheets have got to go! It’s time for a more reliable and scalable solution to the sustainability data challenge.

Typically there are three options:

• Manual, Semi-Automated Methods: Continue with manual efforts or outsource it to contractors
• AI Agents: Keep the data work in-house and use AI Agents to prepare sustainability data
• A Systems Approach: Build a complete, compliant system in-house or work with a partner who has already built the system

The choice between solutions is not made in isolation, it is typically part of a corporate tech stack, and a corporate data and AI strategy. Further, sustainability teams need the confidence that they are delivering accurate and complete data with every update. Otherwise, why change?

And don’t forget cost and risk. Surveys show that companies hit a “scaling wall” when deploying AI Agents: Only 25% of pilots deliver results that can be used in production. Isolated agentic AI deployment is risky. And with today’s fast-rising cost of AI tokens, it faces an uncertain cost future.

Putting it all together, here are the key decision criteria for choosing your sustainability data solution.

Choosing the right sustainability data solution is a strategic decision — and the stakes are high. GLYNT.AI’s data system delivers verified accuracy and completeness from the start, so your team spends less time on data work and more time on business impact.

Talk to GLYNT.AI to see how it works