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Four Ways AI is Transforming Sustainability Reporting

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Artificial intelligence (AI) has made its way into multiple facets of our life, shaping the new ways in which we work and collaborate with each other. The sustainability industry is experiencing its own digital transformation when it comes to AI. From creating operational efficiencies to streamlining reporting to simplifying risk management and due diligence for investors, AI’s potential to transform the industry is quickly being recognized across the world. In a 2023 IBM survey, nearly half of the executives (46%) view AI to be key in their organization’s sustainability reporting efforts.    

Over the past few years, sustainability reporting has become a must-have for companies. New reporting mandates launching each year including CSRD and the California Climate regulations. Thus, companies are quickly realizing the increasing demand for greater data transparency, accuracy, and the importance of monitoring against targets. To collect and manage this large volume of data, more companies are leveraging software and automation (like AI). In doing so, they streamline processes and capture data across their value chains and beyond.  

Here are four key AI use cases we’ve identified for sustainability reporting.  

1. Streamlining Data Collection and Increasing Accuracy

AI can simplify the ESG data collection process by being able to pull complex data and insights from numerous types of sources (texts, videos, emails, social media posts, PDF documents, and more) through Natural Language Processing (NLP) and consolidate it into one central place. Through machine learning (ML) mechanisms and tools, AI can detect and learn patterns in data collected. It can also create new data structures where required, and provide additional context. Additionally, it can uncover any incongruencies between sources, leading to better source data accuracy and ultimately better reporting. 

Prior to leveraging AI, it is necessary to ensure you have a financial-grade data set as a basis for all your sustainability initiatives. Collecting high-quality data is critical as it impacts the reliability of the AI outputs. At Cority, we have deep domain expertise in enabling our clients to build and maintain their sustainability datasets. Our market-leading Sustainability Performance Management software empowers companies to capture data from any organizational level through multiple capture options, with a visible audit trail of what has been captured and when. Our data quality dashboards allow you to define data thresholds, alerts, and approvals. Therefore, ensuring you can have assurance that your data is audit-ready.  

2. Monitoring the Changing Regulatory Landscape

With the fast-paced, complex regulatory sustainability landscape, it can be difficult to keep tabs on the latest mandates and ensure you are meeting key requirements and deadlines. Leveraging AI and NLP can help to extract information from various academic and regulatory sources, provide additional guidance on specific steps required (e.g., reporting formats like XBRL), and enable all departments (not just those focused on sustainability) to better understand key regulatory requirements and how they impact their operations and the business as a whole.  

3. Supporting Decarbonization Strategies

AI can play a key role in a company’s decarbonization strategy and execution. Emissions data entry is often a manual process. It requires individuals to add specific data points on electricity and water consumption from multiple regions or locations in which they operate. This can often result in human errors that can compromise carbon emissions calculations and ultimate carbon accounting. By using ML capabilities to understand historical trends in a company’s carbon usage and management, AI can discover any missing data points and variances in the data. Even supplementing missing data points based on historical averages and assumptions-ensuring accuracy in a large, complex set of data.  

AI can also be used to forecast results by taking historical and present data to map out how a company is tracking toward their net-zero targets and how their decarbonization efforts are supporting these goals. This enables companies to make data-driven decisions in their strategy development and pivot proactively and confidently.  

4. Risk Mitigation in the Financial Sector

With the rise in sustainable investing and ESG analysis for investors, there is also an increased need for risk mitigation and due diligence. AI can play a key role here by analyzing ESG performance data across companies (including supply chain and third-party data) and flagging potential risks to investors. It can ensure that the data is compliant with regulations, while also conducting regular assessments and automating due diligence processes. Companies can use modeling tools to conduct risk assessments based on potential market or external risks. These can include economic market fluctuations, supply chain disruptions, or natural disasters.  

Note, that it is important to continue to have humans verify data to confirm accuracy. However, AI tools make it easier to synthesize extremely large sets of data. This significantly reduces manual time and allows companies to focus on more high-value work.  

What’s next?

There is no doubt that AI will continue to grow in the sustainability industry. In the meantime, we will continue to understand new ways to leverage the powerful functionality in our workplace and beyond. More developments continue in the sustainability space each year, Therefore, it is helpful to have services and support from a trusted advisor.  

Cority’s Sustainability Cloud, along with our team of ESG and sustainability experts, supports organizations to advance their sustainability strategies across their value chain and beyond. Interested in learning more? Connect with our experts today.