Here’s 4 Ways that Google’s Sustainability Teams are Using AI for Reporting
Introduction
Sustainability reporting has evolved into a core governance function, with executives expected to deliver disclosures that are compliant, comparable, and decision-useful, even as data volumes rise and regulatory scrutiny tightens. Many organizations are experiencing pressure on reporting capacity, analytical rigor, and consistency of narrative disclosures, creating the need for innovative approaches to maintain quality and reliability.
A few months ago, I shared 7 ways Gen AI was being used, more broadly, for ESG purposes, including for ESG reporting. This week, we’re taking diving deep, covering strategic and tactical uses of AI for ESG and sustainability, from Google.
Google’s AI Playbook for Sustainability Reporting (December 2025) offers a practical framework for leveraging advanced AI to address these challenges. By enhancing data analytics, standardizing content generation, and enabling interactive stakeholder engagement, AI strengthens assurance, accelerates workflows, and supports richer engagement. The examples that follow demonstrate how Google moved from conceptual use cases to operational deployment, providing a reference point for executives exploring responsible AI adoption in sustainability reporting.
3 Key Areas of AI for Sustainability Reporting
The following three capability areas illustrate how AI can enhance the effectiveness and control of sustainability reporting. For senior managers, pulling their own reports together, they represent practical levers to improve data quality, narrative consistency, and stakeholder engagement. This supports stronger governance, faster cycles, and more reliable insights across complex reporting environments and long-term performance outcomes enterprise.
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Examples include:
Gap analysis: Identify missing metrics against specific standards, frameworks, or current or emerging regulations.
Supplier analysis: Analyze supplier data to suggest targeted mitigation strategies.
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Content standardization: Align draft content with corporate style guides, brand voice, or reporting standards.
Mock scoring: Evaluate drafts against transparent criteria to identify potential outcomes for scored or rated disclosures.
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Examples include:
Interactive querying: Enable internal and external stakeholders to query i.e. stress test report content or data via natural language interfaces.
Content localization: Translate and contextualize content for specific regions and languages.
4 Use Case To Actual Deployment Sustainability Reporting at Google
The following examples demonstrate how Google translated AI concepts into operational reporting tools. Each use case highlights how Google moved AI from the abstract into deployed solutions for Sustainability Reporting. For each use case, they share the challenge, the tools used, and practical tips to help you replicate the results in your own organization.
Example 1: Claims Validation
Opportunity Validation of green claims is time-consuming and carries meaningful reputational and regulatory risk, given the potential for misstatements or lack of sufficient clarity.
AI was used to systematically cross-reference draft claims against internal policies, ensuring the objective application of guidelines and establishing a consistent “first line” of review.
Action and Tool Used The sustainability teams programmed a Gem in Gemini to cross-reference draft claims against the company’s internal guidelines and best practices, and proposed necessary endnotes.
Output The model produced a structured assessment that acted as a first line of review before a human reviewed it. This strengthened control and accelerated first-pass assurance on draft claims before they reached senior reviewers.
AI Intention / Tip This tool intended to help streamline workflows, helping human reviewers focus on validating the model’s assessment rather than starting from scratch.
Example 2: Reactive Comms
Opportunity Internal teams can have blind spots regarding how their narrative will be perceived by critical external audiences. The Google teams used AI simulation to stress-test your report through role-playing. This proactively identifies potential risks, like perceptions of greenwashing or data gaps, and ensures your disclosure is robust before publication.
Action and Tool Used Google’s sustainability teams uploaded a late stage draft report into Notebook LM and prompted the model to adopt the persona of a skeptical investigative journalist, explicitly asking it to hunt for perceived greenwashing, vague claims, or data gaps.
Output The tool generated a list of challenging questions and drafted evidence-based responses using only the source text.
AI Tip / Intention Expand your analysis by rotating the persona to represent other key stakeholders. For instance, you can prompt the AI to read as an ESG investor focused on financial risk, a customer focused on partnership opportunities, or an NGO focused on community impact.
This use case has to be my favourite point from Google’s report! A real demonstration of depth of thought.
Example 3: Customer Request
Opportunity For many organizations, responding to customer sustainability inquiries is slow and fragmented, with teams searching across multiple disclosure documents to find reliable answers. This creates operational inefficiency, and more importantly, increases the risk of inconsistent messaging, misinterpretation, or erosion of client trust. You can use AI to unify these disparate sources of truth into a single retrieval engine. This empowers teams to deliver accurate, consistent answers derived strictly from verified data.
Action and Tool In this vain, Google’s sustainability teams consolidated their public reports across environmental and social topics into a Notebook using Notebook LM to assist client-facing teams, who submitted inquiries they receive from customers.
Output The model responds with comprehensive answers with citations derived strictly from these verified documents.
AI Tip / Intention Grounding the model in specific sources prevents hallucinations by restricting the AI to the provided text. You can apply this same technique to improve accuracy in benchmarking exercises, policy research, and more.
Example 4: Content Interaction
Opportunity Traditional sustainability reports are lengthy, technical, and difficult for many stakeholders to navigate, from investors and policymakers to customers and employees. This creates a risk that critical insights are overlooked or misunderstood, limiting the strategic value of reporting. By using AI to convert static disclosures into interactive, multimodal experiences, Google broadened accessibility, deepened engagement, and enabled stakeholders to explore complex topics in formats that better match how they consume information
Tool We used NotebookLM and Google’s experimental Learn About model.
Action and Tool Google’s sustainability team uploaded the full 2025 Environmental Report into these tools to create public-facing, interactive companions alongside the standard report PDF.
Output The models generated, podcast-style Audio Overviews for passive listening and deliver cited, conversational answers that helped users decode complex technical disclosures.
AI Tip / Intention NotebookLM parses plain text better than PDFs. Convert your report to a text file before uploading to improve accuracy.
Conclusion
In today’s complex ESG landscape, AI has become an indispensable tool for sustainability reporting. By enhancing data analytics, standardizing content generation, and enabling interactive stakeholder engagement, AI strengthens governance, accelerates reporting cycles, and improves disclosure reliability.
Google’s deployment examples illustrate how AI can move from concept to operational impact, supporting compliance, decision-useful insights, and risk mitigation.
For senior sustainability professionals, leveraging AI responsibly offers a strategic advantage: faster, more accurate sustainability reporting that meets investor, regulatory, and customer expectations. Embracing AI-driven reporting positions organizations to drive transparency, operational efficiency, and long-term performance in an increasingly accountable business environment.
