Welcoming AI to the ESG Stakeholder Table
Our sustainability expert elaborates on an innovative AI use case for ESG data and how this technology complements the CSRD framework and our decision making.
In recent years, the landscape of sustainability reporting has evolved dramatically, especially within Europe. The introduction of the Corporate Sustainability Reporting Directive (CSRD) and the European Sustainability Reporting Standards (ESRS) has underscored the importance of thorough and transparent disclosure.
One concept which has gained prominence is the double materiality assessment, which focuses on the inside-out as well as on the outside-in perspective. The assessment requires the involvement of various internal and external stakeholders and is based on a wide variety of data queries in qualitative and quantitative form.
How to stay up to the task
At RBI Group ESG & Sustainability Management, we are committed to staying ahead of the curve. To enhance our decision-making processes and better align with these emerging standards, we have embarked on an innovative journey: inviting artificial intelligence (AI) to join us at the ESG stakeholder table and to deliver data about RBI itself.
We have chosen to incorporate AI specifically to evaluate how the outside world perceives us based on publicly available sources about RBI. This data provides insights into the current data landscape and the external image of a company. Data is becoming increasingly important, and over time, we anticipate the availability of even more robust ESG data, further improving our assessments and strategies.
Partnering with Tetranomics
To achieve this, we have partnered with Tetranomics, a leader in AI technology and fundamental research. Tetranomics SE is dedicated to harnessing new technological capabilities and scientific methodologies to enable economic actors to design, manage, and evaluate businesses with multi-dimensional responsibility. The company specializes in providing businesses with AI-enhanced methods and tools for sustainability transformation.
One of its partners is the Parmenides Foundation, a highly interdisciplinary research organization established in 2001 to understand how the human brain copes with complexity. This focus on reducing complexity is central for aiming to simplify the management of sustainability challenges through advanced AI solutions.
Double materiality and AI
The scope of this AI case study included large language model-enhanced research aligned with the specifically requested questions of the Corporate Sustainability Reporting Directive (CSRD) framework.
The project covered RBI Group and separately also its 5 European Union based subsidiaries which must also deliver a standalone sustainability statement. We incorporated
- publicly accessible documents and sources (utilizing general web sources) and
- RBI-specific documents,
implementing a hybrid approach that combines AI technologies with human sustainability expert know-how.
The results, a detailed understanding of impacts, risks and opportunities in the context of
- global sustainability challenges,
- the CSRD framework and
- the potential influence a company has on ecological and social issues,
were additionally used to validate RBI’s internal double materiality assessment. It serves us as a further source of arguments and thoughts and complements our expert assessment.
What the use case teaches us
Unfortunately, it’s not a matter of simply pressing a button to achieve perfect results. AI requires human oversight for analysis and validation, with its effectiveness hinging on the quality of questions asked and the relevance of sources used.
But it completed the picture of our expert analysis, allowing us to critically scrutinize some of the partial results. AI offers unparalleled capabilities in data analysis, pattern recognition, and predictive modeling.
By leveraging these strengths, we gain deeper insights and identify trends, marking a leap toward efficient sustainability management. The use case prepared us for the future – a future in which ESG data will be as commonplace and robust as financial data and in which AI will evaluate the value of a company quickly and efficiently.