Sustainability has become a key consideration for organizations across various industries. One of the challenges they face is implementing a robust reporting system for environmental, social, and governance (ESG) metrics. This necessitates not only a solid foundation but also the consideration of how artificial intelligence (AI) can enhance sustainability reporting. AI can help manage the influx of data, track sustainability activities, and provide valuable insights, making the process more manageable for employees.
Read more about navigating the ESG seas and how sustainability success is underpinned by data governance and management in our previous post.
Focus of AI in ESG reporting
AI can be leveraged to support various aspects of an organization's sustainability journey. In this context, buzzwords like “green AI”, “sustainable AI”, and “AI for ESG” are often mentioned. The latter refers to initiatives where AI is used to enhance ESG sustainability activities within an organization, such as climate risk analysis and optimizing waste management. The goal of “AI in ESG reporting” specifically focusses on empowering and accelerating ESG reporting for organizations. Today, it’s challenging for organizations to set up sustainability/CSRD reporting due to the high volumes of required data to be collected and the set-up of (semi) automated reporting flows, in combination with significant pressure on timing and resources. That’s why we’re taking a closer look at how AI can power your sustainability reporting, aiming to enable more and more organizations to take their sustainability reporting to the next level.
More power needed to tackle ESG challenges
We notice the following key challenges that organizations encounter in managing and reporting on ESG-related data:
- Data Volume and Complexity: Collecting and processing large volumes of ESG-related data can be time-consuming and resource-intensive. Consider a factory tracking its carbon footprint; it needs to gather data from many sources such as energy bills, transport logs, fuel receipts, and production records. This data originates from different departments and parts of the business, which makes it challenging to aggregate and confirm the accuracy of the data. Data required for ESG reporting needs to be extracted from a wide range of formats, including text files, complex multi-page excel spreadsheets or through database connections. This complexity in collecting and consolidating data can lead to errors in calculating the company's carbon emissions, potentially affecting its ability to report accurately on its sustainability performance. The variety in ways of working and capturing large volumes of complex data without clear governance results in inconsistent data quality, making it challenging for organizations to ensure qualitative reports and other data products. Moreover, current sustainability management tools depend on clean and structured data to generate insights, highlighting the importance of good data quality to enable ESG reporting.
- Evolving Regulatory Requirements: ESG reporting standards are dynamic and can change over time. The introduction of new ESG reporting regulations such as the AI Act, which will have a section on sustainable use, means organizations must stay up to date to avoid non-compliance. Companies must be able to adjust and respond quickly to these changes in ESG reporting standards to ensure they remain compliant. We observe that for many organizations, their current data infrastructure is not adequately prepared to facilitate ESG reporting.
- ESG inspired decision-making: Even with access to large volumes of ESG-related data, organizations may struggle to understand and derive actionable insights from the data. The complexity and diversity of ESG data sources require sophisticated analytics and interpretation to extract meaningful information that can drive decision-making and strategic actions. Furthermore, the ability to translate ESG data into measurable targets and concrete actions and outcomes poses a significant challenge for organizations aiming to align their business practices with sustainability goals. This requires advanced analytics capabilities and expertise in ESG domain knowledge to effectively interpret the data and derive insights that guide impactful and meaningful actions.
ESG challenges come from both internal and external factors, and organizations are accountable to both regulators and stakeholders for their ESG performance. Meeting these challenges requires effective data management systems and tools that can efficiently collect, analyze, and report on complex and evolving ESG data. Key questions you should be asking your organization: Where do you see the need for more power in your current ESG reporting setup? Are you struggling with data collection and analysis, or struggling to keep up with evolving regulatory requirements?
The power of AI
The transformative potential of AI in facilitating ESG reporting and analysis can be clearly demonstrated through a range of compelling examples showcasing its capabilities and potential. When leveraging Generative AI (Gen AI) and advanced analytics, organizations can analyze vast amounts of data, identify patterns, and provide valuable insights into environmental impact, social responsibility, and governance practices.
With its ability to analyze and understand different data sources, along with quickly analyzing and understanding loads of information, Gen AI proves to be a powerful tool in ESG reporting. Gen AI can understand and draw out useful information from unstructured data like scanned documents faster and more accurately than traditional Optical Character Recognition (OCR) and has even shown its strengths for processing satellite images. This helps to make more data available and easily transforms this data to be suitable for reporting purposes. Even the process of the report preparation itself can be accelerated when implementing Gen AI by allowing it to propose first drafts based on the data. Moreover, business users can converse with their data dashboards as if it's a team member. Questions such as "What were our total carbon emissions last year?" can be asked in everyday language, simplifying the data comprehension process. This enables quicker insights and accelerates decisions related to ESG reporting.
On the other hand, traditional advanced analytics methods in ESG reporting focus on leveraging algorithms and analytics to automate and optimize sustainability activities within organizations. These applications can include climate risk analysis, waste management optimization, and other sustainability-focused initiatives. Anomaly/outlier detection is utilized to spot unusual patterns or discrepancies within environmental data, providing early warning for potential issues. Energy Consumption Optimization can leverage large datasets from IoT devices and smart meters to optimize energy usage in real-time, reducing costs and environmental impact, while A-B testing analytics allows for the comparison of different strategies to select the most efficient one. These diverse applications of traditional AI not only handle large volumes of data and ensure quality and consistency, but also accelerate the reporting process, enhancing the ability of organizations to meet evolving ESG reporting demands.
Harness the power
As the importance, volume, and complexity of ESG reporting continues to grow, organizations need to find ways to meet the new reporting requirements. AI can be leveraged for powerful solutions for accelerating and enhancing ESG reporting, but it requires solid foundations and an active effort to integrate it into your sustainability journey. Don't fall behind – start exploring how AI can power your sustainability reporting today!
Author: Robin Vanden Ecker and Hanne Gielen
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