Companies face increasing pressure from investors, regulators, and consumers to produce comprehensive sustainability reports. Investors are a primary driver, as they view a company’s sustainability efforts as a measure of risk management and a way to expose new opportunities, such as innovation in climate adaptation. Consumers are making purchasing decisions based on a company’s ethical practices and values using sustainability reports to vet brands, build trust, and ensure that their spending aligns with their personal values. In addition, the European Union’s (EU) Corporate Sustainability Reporting Directive’s (CSRD) new disclosure requirements are designed to level the playing field and hold companies accountable for their environmental claims.
Overcoming Data Challenges and Ensuring Compliance
With companies under increasing pressure to produce comprehensive and transparent sustainability reports, they face key challenges such as data fragmentation, manual collection, human error, and the sheer volume and complexity of evolving frameworks. Artificial intelligence (AI) provides a game-changing solution by streamlining and enhancing every stage of the reporting process. A major hurdle in sustainability reporting is the challenge of gathering disparate data from a wide variety of sources that are often siloed and difficult to consolidate. Companies must pull information from numerous internal and external locations including:
- Supply chains: Data on raw material sourcing, labor practices, and transportation emissions.
- Energy meters: Real-time data on electricity, gas, and water consumption across all facilities.
- Internal systems: Data from enterprise resource planning (ERP) systems, financial records, and human resources databases.
- Public documents: Information on regulatory compliance, legal cases, and community engagement.
Collecting, normalizing, and verifying this fragmented data is time-consuming and prone to error, creating a significant barrier to producing accurate and comprehensive reports.
How AI Transforms Sustainability Reporting
As regulatory requirements and stakeholder expectations for sustainability disclosures increase, AI offers a way to streamline, automate, and enhance the entire reporting lifecycle. AI can be leveraged at every stage of the sustainability reporting process:
- Data collection and integration: Sustainability data exists in various sources such as Internet of Things (IoT) devices, spreadsheets, and unstructured documents. AI-powered tools automate the collection and integration of this data, ensuring consistency across disparate datasets, which drastically reduces the manual, time-consuming effort traditionally associated with gathering information.
- Data quality and accuracy: AI algorithms clean, standardize, and validate raw data, along with identifying errors, correcting them, and flagging anomalies. This is crucial to ensure the accuracy of reports, which is essential for regulatory compliance and building stakeholder trust.
- Advanced analytics and insights: AI performs sophisticated analysis on sustainability data, uncovering patterns and trends that might be missed by traditional methods. This allows companies to gain deeper insights into their performance, predict future trends, and identify areas for improvement. For example, AI can help in carbon accounting by integrating diverse data sources to provide accurate emissions estimates.
- Automated reporting and content generation: Generative AI streamlines the reporting process by automatically drafting reports based on predefined templates and standards. It can also rephrase or elaborate on content to ensure it is clear, concise, and tailored to specific audiences, such as investors or regulators. This not only saves time but also ensures consistency and compliance with evolving reporting frameworks.
- Regulatory compliance: AI continuously monitors a company’s disclosures against hundreds of data points aligned with various global frameworks such as CSRD. This approach helps to uncover missing information and uncover inconsistencies, which in turn ensures compliance and future-proofing reports against changing regulations.
Limitations of AI in Sustainability Reporting
While AI offers immense potential, its implementation in sustainability reporting is not without challenges. Since AI models are only as good as the data they are trained on, it can be difficult to achieve valid and reliable results as sustainability data can be inconsistent, incomplete, and unstructured. AI models, especially complex ones like deep learning, can be opaque, making it difficult to understand how they arrive at their conclusions. This lack of transparency can be a challenge for auditors and may erode trust, as stakeholders need to understand the basis of the reported information. Finally, the energy and water consumption of large-scale AI models and data centers is significant, presenting an important ethical consideration and potential counterpoint to the sustainability benefits AI aims to deliver.
EnerSys, A Case Study in Sustainability Reporting
EnerSys, a global industrial battery manufacturer, has faced significant challenges in sustainability reporting. With 180 sites worldwide, the company’s team had to manually collect Scope 1 and 2 emissions and resource consumption data from utility bills, which is a time-consuming and error-prone process. In addition, they had to answer numerous customer questionnaires and surveys about their sustainability practices, a process that was very labor intensive.
In order to address its challenges, EnerSys implemented a platform that uses heat map-based machine learning (ML) to automatically extract key information, such as date range, usage amount, and cost, from PDF utility bills uploaded by each site. In addition, they used a generative AI tool to analyze large datasets and to draft responses to customer questionnaires. EnerSys reports that the use of AI has made data collection more efficient and auditable, as the generative AI tool has cut the time spent on customer questionnaires by roughly 50% and has helped the team uncover insights from their data more quickly than with manual analysis.
The Future of AI-Powered Sustainability Reporting
AI is a powerful tool for sustainability reporting, tackling the challenges of data collection, analysis, and compliance and freeing up sustainability professionals to focus on higher-value tasks, like innovating new eco-friendly products and optimizing operations for a smaller carbon footprint. This technological shift represents a fundamental change in how companies can develop comprehensive sustainability reports more efficiently. As we look ahead, AI will become an indispensable tool for corporate sustainability, empowering organizations to not only meet their reporting obligations but to proactively drive meaningful, positive change for both their business and the environment. The Canopy Edge consulting team has many years of experience in helping clients utilize emerging technologies such as AI for practical business applications – and if your organization needs assistance developing and implementing technology-based sustainability program enhancements, please contact us for an initial consultation.
