Generative AI in Sustainability Communications: Improving Content, Engagement, and Reporting

Sustainability communications professionals are increasingly turning to generative AI to streamline their work and amplify impact. These specialists – responsible for crafting sustainability reports, engaging stakeholders, and communicating environmental and social outcomes – face growing data and content demands. Generative AI tools, such as advanced language models and content generators, offer a way to enhance productivity in content creation, stakeholder engagement, and impact reporting. A recent survey found that 67% of sustainability professionals expect generative AI to significantly affect their reporting efforts, highlighting the high hopes for AI-driven efficiency.

Written by the Fambirai team and OpenAI deep research.

This report explores how generative AI is being applied in sustainability communications, covering case studies across industries, available tools and platforms, broader trends, and key benefits, challenges, and future opportunities in the field.

AI-Powered Content Creation and Storytelling

Faster Writing and Reporting

Generative AI can drastically reduce the time needed to draft sustainability content. AI writing assistants produce first drafts of lengthy reports and articles, which humans then refine. For example, AI can analyse complex environmental data and automatically generate a structured first draft of a sustainability report, organising data into a clear narrative. Communications teams then review and edit the AI-generated text for accuracy and tone. This approach shifts professionals from spending hours writing initial copy to focusing on polishing and verifying content, making the process far more efficient.

Battery manufacturer EnerSys plans to use ChatGPT (OpenAI’s generative AI) to help write portions of its next sustainability report and even customise storytelling for different audiences. This indicates that even traditionally human-intensive tasks like annual sustainability reports can be partially automated, saving time while maintaining quality.

Marketing and Creative Content

Generative AI is also enhancing creative sustainability storytelling. AI tools can draft blog posts, press releases, and educational articles about a company’s sustainability initiatives, providing communication teams with well-structured starting content. They can also suggest catchy headlines and distil technical jargon into reader-friendly language.

In the marketing realm, AI image and video generators create visuals that support sustainability campaigns. For instance, global agribusiness Syngenta used AI to generate 75 unique images for a soil and water conservation campaign, delivering high-quality visuals faster and at a lower cost. AI-generated graphics and videos help organisations convey complex environmental topics in more engaging ways. Additionally, generative AI can adjust the style and format of content to suit different communication channels – from concise social media posts about climate action to long-form articles on ESG (Environmental, Social, Governance) performance – ensuring consistency with the company’s sustainability goals.

Consistency and Clarity

Another advantage is consistency in messaging. AI language models excel at maintaining a consistent tone and terminology, which is essential for sustainability communications that require alignment with frameworks or standards. A generative AI can be instructed to follow GRI or SASB reporting standards, ensuring compliance and accuracy in content.

In practice, sustainability teams have used enterprise AI platforms to extract data and draft responses for routine inquiries. EnerSys, for example, uploads its sustainability reports and policies into ChatGPT Enterprise and uses it to answer customer questionnaires about the company’s practices – reducing time spent on these Q&A tasks by roughly 50%. The AI generates draft answers based on approved data, and a human quickly verifies them. This results in clear, consistent responses derived from official sources, improving both speed and accuracy in content delivery.

Stakeholder Engagement and Personalisation

Personalised Communications

Generative AI is also transforming how organisations engage with stakeholders – including customers, investors, employees, and regulators – on sustainability topics. AI can tailor content to different stakeholder needs almost instantaneously. For example, one stakeholder (an investor) might care most about climate risk and governance, while another (a customer) is interested in product sustainability and community impact. Generative AI can craft narratives adapted to each audience’s focus, emphasising the relevant information.

Sia Partners, a consultancy, notes that GenAI can produce report narratives that adjust in tone and detail depending on whether the reader is an investor looking at risks or a regulator seeking data points. This means sustainability reports or updates can be auto-generated in multiple versions – e.g. a succinct highlights memo for executives, a detailed compliance report for regulators, and an engaging story for the general public – all derived from the same base data.

Chatbots and Virtual Assistants

Another emerging application is AI-powered chatbots that can interact with stakeholders in real time. Sustainability teams can deploy chatbots on their websites or internal platforms to answer frequently asked questions about their ESG initiatives. These bots, powered by generative AI, understand natural language queries and respond conversationally, drawing on the company’s sustainability data and reports.

For example, an employee could ask, “How are we doing on our renewable energy targets?” and the chatbot could reply with the latest statistics and generate a brief explanation of progress. This 24/7 availability and responsiveness enhance stakeholder engagement by providing instant information and feedback loops. In practice, EnerSys has been using ChatGPT Enterprise in this way: team members rely on it to generate responses to customer inquiries about sustainability, based on internal documentation, making responses faster and more efficient (with human review to ensure accuracy).

Listening and Analysis

Beyond outward communication, generative AI helps sustainability professionals process stakeholder input at scale. Gathering stakeholder feedback – through surveys, forums, or reports – is crucial for materiality assessments (identifying what issues matter most to stakeholders). Generative AI can rapidly analyse thousands of comments or documents from stakeholders and summarise the key concerns or themes.

A powerful case comes from Baker Hughes, an energy technology company, which leveraged a C3.ai generative AI application to analyse 3,500+ stakeholder documents (over 400,000 paragraphs). The AI sifted through this massive unstructured input to identify the most relevant 10% of ESG topics being raised by stakeholders, effectively pinpointing priority issues. This process saved an estimated 30,000 hours in a materiality assessment cycle that would have otherwise been spent by humans reading and cataloguing feedback.

By using AI to listen at scale, communications teams can be much more responsive to stakeholder concerns – focusing engagement efforts on the topics that truly resonate with or worry their stakeholders. It also means when they do engage (through reports or meetings), they can address those key issues directly, with confidence that no critical theme was overlooked in the sea of data.

Multi-Stakeholder Reporting

Generative AI's ability to parse and translate information helps meet different stakeholder expectations simultaneously. Sia Partners’ SiaGPT platform, for example, acts as an AI assistant that can extract information from ESG data and assist in creating responses via prompts.

With such tools, a sustainability communications lead could ask, “Summarise our carbon reduction achievements this year in layman’s terms,” and get a ready-to-use summary for a community audience. They could then ask, “Now draft a version highlighting financial implications for investors,” and the model would adjust the focus and language accordingly. This level of adaptability was previously time-consuming to achieve manually for each audience segment.

Generative AI enables more interactive and tailored stakeholder engagement, ensuring each group receives information in a form that is most meaningful to them.

AI-Driven Impact Reporting and Data Analysis

Streamlining Sustainability Reporting

Impact reporting – such as annual sustainability reports, ESG disclosures, and impact assessments – is a core responsibility for sustainability professionals. These reports are data-heavy, must follow evolving standards, and often require consolidating information from across an organisation. Generative AI is proving invaluable in automating parts of this process.

A 2024 Reuters survey found that 67% of sustainability practitioners expect GenAI to significantly impact their reporting tasks. The reason: AI can summarise complex data, handle unstructured information, and draft report text in human-like language much faster than manual effort. Unlike traditional analytics tools that only produce charts or figures, generative AI can write narratives explaining what the data means in plain English, which is central to effective reporting.

Automated Data Collection and Drafting

Companies are using AI to gather and aggregate sustainability data (from spreadsheets, utility bills, sensors, etc.), then employing generative models to turn that data into readable content.

EnerSys provides a clear example of this integration. They use an AI platform called ESG Flo to collect emissions and resource usage data from 180 sites (the AI extracts key info from utility bills automatically). After improving the data accuracy and completeness through AI, they feed the data into ChatGPT Enterprise to analyse trends (e.g., year-over-year emission changes) and even draft report sections. The team envisions that AI will help write significant portions of their sustainability report, which they will then refine.

Consulting firm Sia Partners similarly notes that generative AI can automatically generate structured, coherent ESG reports tailored to specific audiences, once the data is in place. What once took teams of writers weeks to assemble can now be produced in a fraction of the time by AI, then quickly edited for final review.

Ensuring Compliance and Insight

With regulations like the EU’s CSRD (Corporate Sustainability Reporting Directive) and proposed SEC climate disclosure rules, the volume of required disclosures is growing. Generative AI can help companies stay on top of these requirements by cross-referencing data against required metrics and highlighting gaps.

Nasdaq, for instance, has an ESG AI solution that uses a knowledge base of regulatory and company documents to assist in compliance benchmarking. Its AI assistant and intelligent search capabilities help teams quickly identify where their reports might have gaps compared to new regulations.

After data collection, AI can transform raw numbers into narrative explanations that ensure transparency. Workiva – a major integrated reporting platform – recently integrated generative AI throughout its system, allowing users to “author, edit, and rewrite content” within sustainability and financial reports with AI assistance. This means users can ask the AI to draft a section on, say, diversity metrics or carbon footprint, and the AI will produce a compliant draft based on the data available, which the user can then refine.

By embedding a “digital thought partner” that can answer questions and generate content, Workiva is turning report writers into editors, boosting productivity and freeing up time for higher-value analysis. These capabilities greatly streamline the tedious parts of impact reporting.

Case Study – Large-Scale Analysis

Generative AI can also extract insights from industry-wide sustainability data, informing better communications and strategies.

McKinsey’s QuantumBlack team recently applied generative AI (using OpenAI’s models) to analyse approximately 2,500 companies’ sustainability reports (some 300,000 pages of text) across 17 sectors. This was done to assess how businesses are addressing ocean sustainability (in partnership with the One Ocean Foundation).

The AI reviewed this enormous corpus of reports – something practically impossible for humans to do comprehensively – and helped identify patterns and gaps in how companies discuss ocean impacts. The project team noted that “with generative AI, we could develop a chain of thought and link one fact to the next” to uncover insights that weren’t evident before. Essentially, the AI could read all the text and then answer complex questions, with a human in the loop to refine prompts and verify answers.

This kind of analysis can feed into impact reporting by highlighting best practices, industry benchmarks, or areas for improvement, which communications professionals can then incorporate into their narratives. It exemplifies how AI can handle the heavy lifting of data analysis, leaving humans to focus on interpretation and strategic messaging.

Conclusion

Generative AI serves as a force multiplier for sustainability communications teams, enabling them to work faster and smarter.

As long as AI outputs are properly reviewed, AI assistance can lead to richer content and more effective stakeholder communication without proportional increases in effort. By automating data collection, report drafting, and compliance monitoring, AI is transforming sustainability reporting into a more efficient and insightful process.

For organisations looking to enhance their sustainability strategies, integrating AI-powered solutions can streamline operations, reduce workload, and provide new levels of accuracy and engagement in ESG communications.

Sources:

• Reuters (2024). Survey on sustainability professionals and GenAI impact. (cited in Aligned Incentives)
• Aligned Incentives (2024). Leveraging Generative AI for Sustainability Reporting: 5 Use Cases.
• Thomson Reuters (2024). ESG Case Study: How EnerSys uses GenAI for sustainability data and reporting.
• Sia Partners (2024). How Generative AI Is Transforming ESG Reporting.
• Global Witness (2025). Investigation into AI chatbots and climate change (greenwashing).
• McKinsey (2023). Gen AI analysis of 2,500 companies’ sustainability reports (QuantumBlack case).
• Accenture & UN Global Compact (2024). Generative AI for the Global Goals report – press release.
• Workiva (2023). Press Release: Workiva integrates Generative AI in its platform.
• Kyndryl (2024). Article: Generative AI and energy use (sustainable AI).
• Think with Google (2023). AI-powered video campaigns for sustainability messaging (brands case).
• Revvence (2024). Blog: Benefits of GenAI in ESG reporting (data analysis, RAG).
• All About AI (2024). Guide: Writing environmental reports with AI (workflow steps).
• Nasdaq (2023). ESG AI Assistant for compliance (as cited by Aligned Incentives).
• Syngenta case (2023). AI-driven sustainability campaign visuals (Superside)

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