AI agents are everywhere. But are they the right choice for sustainability data?
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.
| Data Method | Key Business Criteria | Also Consider |
|---|---|---|
| Manual or Semi-Automated Methods | Manual and semi-automated methods require in-house data quality checks for accuracy and completeness. And these methods are costly and slow, so only minimal data is captured and it's not ready for business use or AI. | Automation delivers accurate, pre-tested data. What can you do with more time and better data? |
| AI Agents | Easy to set up, but harder to maintain at required data quality levels. Current benchmarks show 65% accuracy rates. Expect to add another layer of agents that monitor other agents. | Dedicated resources required. Who is doing this work? How long will it take? How will the company be protected against rising token costs? |
| Data Systems — All-in-Software | Some sustainability software providers have added data capture and preparation to their systems. This lifts the burden from the sustainability team, saving many hours. | This may be a good solution if the software is already in place, but increases lock-in to a SaaS system. And the only path to business wins or AI adoption is through the software, constraining the sustainability team. |
| Data Systems — Pre-Built Solutions | Pre-built systems deliver verified accuracy and completeness from the start. They boost data confidence with data quality and management reports, eliminating hours of work. Customized data flows enable sustainability data to fit with other business systems. Granular, highly accurate data enables business analytics and future AI. | A pre-built data system saves time and money. Shift hours from data wrangling to managerial-level data management. Be audit-ready and compliant from the start. Manage cost and ensure privacy with an AI system that does not rely on LLMs. |
