In principle, processes that are recurring, data-driven, and rule-based can be automated with Artificial Intelligence. Activities with high manual effort, clear decision patterns, or large volumes of data are particularly suitable.
In practice, suitable AI use cases can be found in almost all business areas. In the sustainability sector, for example, large volumes of data, complex requirements, and recurring tasks are common. AI can provide targeted support to companies by reducing manual effort, making complex requirements understandable, analyzing data, and automating repetitive processes:
- Reduce manual research and documentation effort: Sustainability-relevant information is often distributed across numerous documents, guidelines, and reports. AI can locate, consolidate, and make content usable for specific questions. For example, relevant content can be identified for customer, supplier, or EcoVadis questionnaires, or internal documents can be reviewed for potentially critical statements in the context of new regulations (e.g., EmpCo Directive).
- Make complex regulatory requirements understandable: Sustainability regulation is based on extensive, interconnected frameworks. AI can derive concrete action requirements from these and present them in an understandable way, for example from CSRD, ESRS, or supply chain regulations, and answer specific questions about implementation at the company level.
- Analyze and compare large volumes of data: AI supports the structured evaluation of internal and external data and enables well-founded analyses and benchmarks. Examples include competitive analyses on circular economy or climate targets, as well as comparing own measures and objectives with market or industry benchmarks.
- Automate repetitive tasks and streamline processes: Many sustainability processes are recurring and data-driven. AI can automate these tasks, for example in data preparation for sustainability reports or pre-structuring report content and key performance indicator overviews, thereby relieving specialized departments.
The various examples show that many sustainability processes can be made more efficient through the targeted use of AI. This gives employees time to focus on strategic and value-adding tasks again.
Suitable use cases for your company can be systematically identified through an AI potential analysis. In this process, suitable processes are systematically identified and potential AI solutions are evaluated in terms of benefits, feasibility, and scalability. This creates a prioritized list of company-specific use cases that provides a solid decision-making basis for further steps such as customized AI agents or the development of a holistic AI strategy.
