The double materiality assessment, a cornerstone of the CSRD (Corporate Sustainability Reporting Directive), is essential for companies aiming to understand both the financial and sustainability impacts of their operations. This assessment evaluates not only how sustainability issues affect a company financially but also how the company’s operations impact the environment and society. The process can be broken into three critical steps: understanding context, identifying stakeholders, and identifying and assessing impact relevance (IROs). With the growing complexity of ESG (Environmental, Social, and Governance) requirements, Artificial Intelligence (AI) offers a transformative way to optimize each stage of this analysis.
AI in the Double Materiality Assessment Process
1. Understanding context
The first step in a double materiality analysis involves grasping the context in which a company operates. This includes mapping the value chain to identify potential ESG-related impacts and opportunities. AI can significantly streamline this process through tailored prompts. For example:
- Prompt for AI: “Create a typical value chain for a company in the [industry] that primarily manufactures [Product A] and [Product B]. Highlight the key activities, responsible parties, business relationships, and potential IROs related to ESG concerns.”
By leveraging AI, companies can quickly generate detailed and customized insights about their value chains, saving time and ensuring no critical aspects are overlooked. These insights provide a solid foundation for identifying key ESG risks and opportunities relevant to the organization’s operations.
2. Stakeholder identification and analysis
Identifying relevant stakeholders and understanding their perspectives is a pivotal part of double materiality assessment. AI can assist in creating comprehensive stakeholder lists and prioritizing them based on their relevance to ESG topics. For instance:
Prompt for AI: “I am conducting a stakeholder analysis as part of the CSRD. My company operates in the [industry]. Please generate a list of potential stakeholders relevant to ESG concerns, assign each a relevance score (1 = low, 2 = medium, 3 = high) based on their influence and impact, and suggest key perspectives they might hold.”
AI also aids in stakeholder-specific analysis, helping companies anticipate how different groups might perceive and evaluate ESG impacts:
Prompt for AI: “How would stakeholder [name] evaluate the IRO [description of IRO] based on the following criteria: scale, scope, likelihood, and reversibility? Use a scale from 1 (very low) to 5 (very high).”
This approach ensures a systematic evaluation of stakeholder expectations, which is critical for aligning sustainability initiatives with stakeholder priorities.
Additionally, AI can analyze stakeholder sentiment through natural language processing (NLP). By reviewing public statements, media coverage, and social media activity, AI provides real-time insights into how stakeholder opinions might evolve over time, offering a dynamic perspective rather than a static snapshot.
3. Identifying and assessing impact relevance (IROs)
The identification and assessment of IROs represent the most resource-intensive part of the assessment. Each IRO must be evaluated based on specific criteria such as scale, scope, reversibility, and likelihood. AI can significantly reduce the time and effort required for these assessments:
Prompt for AI: “As part of the double materiality assessment (CSRD/ESRS), evaluate the IRO [IRO type – description] in the [industry]. Assign values for:
– Scale (impact magnitude): [value]
– Scope (affected areas): [value]
– Reversibility (impact reversibility): [value]
– Likelihood (probability of occurrence): [value]
Explain each rating, detailing the connection between the IRO and its implications for ESG issues.”
AI’s ability to generate consistent, well-reasoned justifications for these criteria can save organizations significant time. For example, if 200 IROs require at least two justifications each and each justification takes five minutes to craft manually, this equates to over 33 hours of work. AI can reduce this to approximately one minute per justification, saving over 26 hours while maintaining quality and consistency. And since impact materiality IROs need more than 2 justifications, it’s very likely that AI will save companies even more hours.
Moreover, AI can assist in describing IROs with precise, ESG-aligned language, ensuring compliance with CSRD and ESRS standards.
Beyond evaluation, AI-powered platforms can also prioritize IROs by comparing them against industry benchmarks or sustainability goals. This ensures that the most critical impacts are addressed first, aligning with both regulatory requirements and corporate strategy.
Enhancing Collaboration and Efficiency
One of the often-overlooked benefits of AI in double materiality assessment is its ability to enhance collaboration across departments. By centralizing data and insights, AI tools enable sustainability teams, finance departments, and executive leadership to work more cohesively. Visual dashboards, automated reports, and real-time updates make it easier to align stakeholders on key priorities and decisions.
Furthermore, AI simplifies the process of scenario analysis. Companies can model different ESG scenarios to understand how varying factors—such as regulatory changes or shifts in consumer behavior—might impact their materiality assessments. This forward-looking capability adds strategic value to the analysis, helping organizations stay ahead of emerging risks and opportunities.
Materiality Master: AI as an assistant, not a decision-maker
Materiality Master integrates AI tools to assist customers at every stage of their double materiality assessment. From generating IRO descriptions to crafting justifications for ESG impacts, AI serves as an invaluable assistant. However, it’s important to note that AI is not a decision-maker. Customers have complete control over the process, with options to:
Improve AI-generated suggestions.
Extend or shorten content.
Ignore AI outputs and use their own inputs.
This collaborative approach ensures that companies retain ownership of their materiality assessment while benefiting from the efficiency and scalability AI provides.
The future of double materiality with AI
As regulatory requirements like the CSRD become increasingly demanding, leveraging AI in double materiality assessment is no longer a luxury but a necessity. By automating complex tasks and providing actionable insights, AI empowers companies to conduct thorough and compliant assessments without overburdening their teams. With tools like Materiality Master, organizations can focus on strategic decision-making while meeting the highest standards of ESG reporting.
