With the Corporate Sustainability Reporting Directive (CSRD) and the EU taxonomy coming into force, around 15,000 companies in Germany alone will have to carry out a double materiality analysis by 2028. But how does this actually work? How do I determine an IRO (Impact, Risk, Opportunity)? What are the typical ESG risks for my industry? How can silent stakeholders such as nature be taken into account in the materiality analysis?
AI technologies, especially AI prompts, are revolutionizing the way we collect, analyze and interpret data and gain a better understanding of complex or emerging issues. The use of AI through so-called prompting, i.e. the input of queries to AI models such as ChatGPT or Google’s Gemini, offers an innovative solution to the challenges of materiality analysis.
What is a materiality analysis?
The double materiality analysis in the context of the European Sustainability Reporting Standards (ESRS) is the basis for sustainability reporting in accordance with CSRD. It is a method for determining the material or significant impacts, opportunities and risks. Impacts refer to the influence of the company’s activities on the environment and society (impact materiality), while opportunities and risks for companies arise from (expected) changes to the environment (e.g. due to climate change) and society (financial materiality).
This two-pronged approach ensures that reporting takes into account not only the sustainability risks and opportunities for the company itself, but also its impact on the sustainability goals of the EU Green Deal. The CSRD materiality analysis is already being used as a strategic tool in progressive companies.
How do you carry out a materiality analysis?
EFRAG recommends preparing the materiality analysis in 4 steps:
- Understanding the context: A company should develop an overview of its activities and business relationships and the context in which these take place. It should also understand who the most important stakeholders are. This overview is crucial in order to identify the company’s key internal and external issues.
- Identify IROs: Organizations should identify material environmental, social and governance (ESG) issues that relate to its own activities as well as to the upstream and downstream value chain. The result is a comprehensive list of impacts, risks and opportunities (IROs), which are further assessed and analyzed in the following steps.
- Assess IROs: Companies apply impact and financial materiality assessment criteria to determine the material actual and potential impacts as well as the material risks and opportunities. This then forms the basis for determining the topic-specific disclosures of the ESRS.
- Create a report: The CSRD sustainability report must include information on the materiality analysis, such as the process for identifying and assessing its material IROs or details on stakeholder engagement.
Basics of AI prompting
AI prompts are instructions or inputs that are entered into artificial intelligence systems in order to receive a specific response or action from the AI. They serve as an interface between humans and machines, enable targeted interaction and control how the AI processes and presents information.
How materiality analysis AI prompts work
AI prompts work by providing the AI with a context or a specific question to which it can respond. The AI analyzes the prompt using machine learning algorithms and draws on an extensive database of information to generate a suitable response. The quality and relevance of the response depend heavily on the clarity and precision of the prompt. AI systems can use various data sources, including text, images and more, depending on the type of AI model and the specific application.
Different types of AI prompts and their applications
There are different types of materiality analysis AI prompts that are used depending on the area of application:
- Text-based prompts: These are often used in chatbots, virtual assistants and text generation. They can contain questions, commands or instructions that the AI should answer or execute in natural language.
- Image-based prompts: Used in image recognition and generation, these prompts ask the AI to analyze and classify images or generate images based on textual descriptions.
- Code-based prompts: These prompts ask the AI to generate, understand or optimize code. They are used in software development and automated testing.
- Audio and voice prompts: Used in speech recognition and processing, these prompts include voice-based input that the AI should convert into text or interpret as a command.
Each of these prompt types is used in different areas, from automation and creative processes to decision-making and, for example, materiality analysis. Materiality analysis AI prompts enable users to control complex AI systems in an intuitive and effective way, making them an indispensable tool in human-machine interaction.
Tips for effective prompting
- Information richness: The more detailed the information you provide to the AI, the more targeted and useful the answers will be. Include specific information about your company, the industry and relevant stakeholders.
- Framing: Assign the AI a clear role. Do you want it to act as an analyst, consultant or research assistant? The defined framing controls the perspective and depth of the answers generated.
- Contextualization: Give context to your request. For example, are you in the initial phase of data collection or in the final phase of report creation? Context helps the AI to understand the stage of development of your project and respond in a more relevant way.
- Concretization: Formulate specific questions. General questions lead to general answers. Specific questions such as “How can I measure the carbon footprint of my supply chain in the textile industry?” lead to more precise and action-oriented answers.
- Iteration: Prompting is an iterative process. Refine your prompts based on the answers to get even more accurate information.
- Objectivity: Make sure to formulate neutrally and impartially to ensure an objective perspective of the AI.
- Traceability: Ask the AI for explanations or reasons for its answers to increase the traceability and verifiability of the information.
By following these tips, you can harness the full potential of AI to optimize your dual materiality analysis and gain valuable insights for your business.”
The 12 best materiality analysis AI prompts
Materiality analysis AI prompts 1-4
1. prompt: How can [company name] present the concept of dual materiality in its annual report?
Companies subject to CSRD must clearly explain how they apply the concept of dual materiality in their sustainability reports as part of the management report. This means that they must show how their business activities affect the environment and society and, conversely, how environmental and social factors affect the business.
2. prompt: What approaches should [company name] use to integrate qualitative ESG data into its materiality analysis?
Qualitative ESG data plays an important role in materiality analysis as it often provides nuanced insights into topics such as corporate culture or stakeholder relationships that quantitative data alone cannot provide.
3. prompt: What is the most common financial impact of [specific ESG-Topic] on companies in the [Industry] industry?
This prompt helps companies identify industry-specific financial impacts of an ESG issue and understand what is material for financial planning and risk assessment.
4. prompt: Can you help me design a process to quantify the IRO’s [name or description of IRO] financial risk for [company name]?
The ability to quantify financial risks is complex, but crucial to a company’s risk management strategy and supports sound decision-making.
Materiality analysis AI prompts 5-8
5. prompt: How does [company name] assess the long-term impact of its sustainability initiatives on its financial performance?
This prompt focuses on the long-term perspective and helps to assess the impact of sustainability initiatives beyond the immediate future.how can [Name des Unternehmens] present the concept of dual materiality in its annual report?
6. prompt: Create a checklist of ESG risks that [company name] might typically encounter in the [industry] industry.
A checklist of ESG risks serves as an important tool for companies to prepare for potential challenges and develop response strategies.
7. prompt: What methodology do you recommend for assessing the extent of the impact that [company name] has on [specific environmental topic]?
This prompt helps companies to find a suitable methodology to measure and assess the impact of their activities on specific environmental issues.
8. prompt: What strategies do you recommend [company name] to effectively monitor and report on its ESG progress?
Monitoring and reporting on ESG progress is essential for transparency and continuity in corporate governance.
Materiality analysis AI prompts 9-12
9. prompt: How can [company name] best understand the interests and priorities of the stakeholders [Name of stakeholders] in relation to the topic [ESG-topic]?
Understanding stakeholder perspectives is crucial to ensure that corporate strategies are aligned with their expectations and needs.
10. prompt: How can [company name] ensure that all relevant stakeholders are involved in the materiality analysis process?
The involvement of all relevant stakeholders is necessary to ensure a complete and balanced materiality analysis.
11. prompt: What are effective communication strategies for [company name] to report its sustainability efforts to stakeholders?
Developing effective communication strategies is essential to inform stakeholders about sustainability efforts and gain support for these initiatives.
12. prompt: Please generate a script for [company name] to conduct a stakeholder interview on the topic [specific ESG-topic].
Creating a structured interview script is an important basis for effective communication with stakeholders. It enables us to gain deeper insights into their views and concerns on specific ESG issues.
Our Excel template contains a bonus tab with many more useful materiality analysis AI prompts. See our frequently asked questions (FAQs) about the template for more information.
Artificial intelligence and the CSRD
The future of materiality analysis and CSRD in general is inextricably linked to the integration of artificial intelligence. AI prompts have proven to be influential tools that not only increase the efficiency and accuracy of data collection and analysis, but also open up new dimensions of interpretation and strategy development. By refining AI prompts, companies can gain deeper and more nuanced insights into complex sustainability issues and thus make more accurate, data-driven decisions.
The introduction of AI Prompts makes it possible to process ESG-related data, quantify financial and non-financial risks, assess the impact of business activities on society and the environment and strengthen the dialog with stakeholders. This leads to a more comprehensive and dynamic materiality analysis, which is essential for the future-proof orientation of a company.
