Generative AI is an artificial intelligence technology capable of learning from vast amounts of data and automatically generating text, charts, images, and even code. It possesses the ability to understand context, summarize information, and produce content. Digital tools encompass cloud databases, cross-departmental collaboration platforms, process automation systems, and machine-readable report templates, emphasizing the replacement of traditional manual operations with digitalized processes. For enterprises that must periodically prepare sustainability reports, the combination of these two technologies is key to breaking through long-standing bottlenecks.
This article will explain step by step the pain points, operating mechanisms, efficiency, challenges, and implementation process, accompanied by actual cases to help you understand how enterprises utilize generative AI and digital tools to simplify and accelerate the sustainability report process.
Three Major Pain Points of Traditional Sustainability Reports
- Scattered data collection: Carbon emissions, energy, water resources, and supply chain information from various departments have inconsistent formats, making manual compilation time-consuming and error-prone.
- Complex framework alignment: Enterprises often need to comply with international standards such as GRI, SASB, IFRS S1/S2 while also meeting local regulations and various sustainability rating requirements.
- Heavy writing and review burden: Reports often exceed hundreds of pages, requiring repeated proofreading and multiple layers of internal and external review, resulting in lengthy processes and consuming significant manpower.
Operational Mechanisms of AI and Digitalization
Automated Data Integration/Cleaning:
With the development of AI technology, data collection can now largely rely on automated systems. Take the Berlin-based climate tech startup Climatiq as an example. They developed an AI-driven data engine that can process messy raw business data such as invoices and purchase orders, automatically collecting carbon emission raw data and comparing it with emission factors to ultimately produce carbon emission data. Unlike traditional data collection models, AI and digital tools can significantly reduce the manpower and time consumed in the process, lowering the difficulty of data acquisition.
Source: Climatiq
Automated Report Generation:
Using AI to generate reports is no longer news. Many sustainability practitioners use AI tools like ChatGPT and Gemini to generate drafts. Sustaihub's Syber reporting system also has a built-in AI writing assistant to help enterprises quickly complete reports. However, as AI technology continues to evolve, perhaps we can do more than just complete drafts with AI?
Google's 2024 sustainability report differs from previous ones. Its report lead, Luke Elder, stated that this year's report was first produced and published using AI, including both a traditional PDF version and a new AI chatbot version. In the chatbot version, users can quickly obtain needed sustainability information through conversation with the bot.
Source: Google
Survey and Data Auto-Summarization
For complex data and surveys, AI and digital tools can automatically summarize and sort, reducing work time.
For example, handing raw information filled in by various departments to AI to produce summarized explanations; after collecting materiality surveys, using digital tools to automatically generate matrices and rankings, combined with AI to summarize key points for each topic.
Data Verification
Sustainability reports often require processing large amounts of data and producing extensive text. Consistency and accuracy throughout the process are crucial. AI and digital tools can help enterprises quickly compare and identify differences through formatted data verification processing and AI text interpretation when facing large amounts of information.
Framework and Standard Alignment
Facing international standards like GRI, SASB, IFRS S1/S2, local regulations, and various sustainability rating requirements such as CDP and DJSI, enterprises often encounter issues with misalignment and inconsistent connectivity in information collection and disclosure, increasing data collection time and potentially leading to confused overall report structure. AI and digital tools can help enterprises improve through index connection between data and reports. AI can help enterprises identify corresponding indexes through large amounts of data, while digital tools like Sustaihub's Syber management system can connect data collection forms according to different frameworks and help enterprises produce reports with consistent structure and complete information through report index linking.
Source: Sustaihub
Results and Case Studies
In a survey conducted by Mitie Group among sustainability decision-makers in the UK, approximately 55% of respondents indicated there were too many administrative tasks when preparing reports, 70% said related requirements reduced their ability to execute strategic planning, and 80% had already invested in digital solutions to simplify related operations.
It's evident that most sustainability practitioners indeed face complex work issues and hope to solve them through digital tools. The next question is: how much burden can AI and digital tools reduce?
Data analytics company Gardenia Technologies, in collaboration with the AWS team, developed Report GenAI, powered by the latest generative AI models on Amazon Bedrock. Using tools with Retrieval-Augmented Generation (RAG) and text-to-SQL capabilities, it helps customers automate undifferentiated tedious work, reducing ESG reporting time by up to 75%.
Source: Gardenia
In Sustaihub's case statistics from consulting multiple domestic enterprises, through the implementation of reporting systems, carbon inventory systems, and data databases, overall digital transformation can effectively reduce approximately 30% of preparation costs and save 50% of operation time, effectively reducing the burden on enterprises.
Realistic Challenges of Generative AI in Sustainability Reports
Although generative AI and digital tools can effectively improve sustainability report efficiency, there are still multiple limitations and risks that prevent all processes from being significantly shortened. Enterprises must understand and face the following challenges before implementation:
- Data quality issues: If data sources are scattered, incomplete, or have inconsistent formats, AI cannot be directly applied. Time-consuming cleaning, verification, and correction are still required, otherwise biased or erroneous conclusions may result.
- Regulatory and standard changes: Sustainability reports must comply with multiple frameworks such as GRI, SASB, IFRS S1/S2, and regulations in various countries continue to update. AI models must be constantly adjusted and retrained, increasing maintenance and regulatory costs.
- Traceability and error risks: Although text generated by generative AI is fluent, it may contain inaccurate or misleading statements. Without manual review, report credibility will be affected, bringing reputation and compliance risks.
- Security and privacy concerns: Sustainability reports involve supply chain data, employee information, and internal operational details. Without rigorous access control and encryption measures, data leakage or violation of personal data regulations may occur.
In summary, AI and digital tools are powerful accelerators for sustainability reports, but they must be combined with complete verification and risk control processes to truly realize benefits and reduce potential risks.
How Can Enterprises Implement Generative AI and Digital Tools?
When implementing generative AI and digital tools to shorten sustainability report timelines, enterprises should proceed steadily with systematic steps. The following steps can be referenced:
- Inventory existing data and standardize: Comprehensively inventory and organize existing data, identify scattered or unstructured information, and perform standardization to lay the foundation for subsequent automation.
- Choose appropriate tools and platforms: Based on company size and needs, select appropriate tools and platforms, such as Climatiq which integrates carbon emissions and data management, or Report GenAI which can pre-fill report drafts, ensuring seamless integration with existing systems.
- Establish human-machine collaboration mechanisms: During the implementation process, establish human-machine collaboration mechanisms, letting AI serve as an assistant rather than a replacement, especially with data verification and narrative accuracy requiring oversight by members with professional backgrounds.
- Set up internal version control and review processes: As internal control and regulatory stringency gradually increases, companies need to establish internal version control and multi-layer review processes, ensuring AI-generated content is reviewed by legal and sustainability executives before external disclosure to ensure report compliance.
- Pilot and gradual implementation: First apply on a small scale in specific departments or single report chapters, accumulate experience and optimize processes, then gradually expand the scope. Through this series of strategies, enterprises can effectively improve report efficiency and accuracy while maintaining the professionalism and credibility of sustainability disclosure.