Artificial intelligence (AI) is one of the most talked-about tools in climate and sustainability right now. From carbon accounting platforms to ESG reporting software, AI promises faster workflows, sharper insights, and reduced manual effort. Yet for many sustainability professionals, the specifics of how it works and where it truly adds value, are still unclear.
AI is already helping sustainability teams work more efficiently and make better use of their data. For teams balancing multiple priorities, it can free up time for strategy and stakeholder engagement, while improving the consistency and credibility of results. The key is knowing where AI delivers the most impact, and where human expertise remains essential.
Whether you are managing your first emissions inventory or scaling reporting across regions, learning how to apply AI in targeted ways can make your work more effective. In this article, we break down five practical, real-world use cases for AI in sustainability.
1. Matching business activity data to emissions factors
Carbon accounting begins with matching your business activity data to the correct emissions factors. This is essential for accuracy, but it can be one of the most time-consuming stages. Whether you're working with spend-based records, SKU-level inventory, or logistics data, manually searching through factor libraries and spreadsheets can take hours, and small mistakes in matching can distort your results.
AI can simplify this with its ability to process large datasets quickly, automatically linking each transaction or activity to the right factor based on parameters such as geography, activity type, and unit of measurement. This creates a consistent process that improves reliability, saves time, and reduces risk of human error.
Example: A food manufacturer exports products to multiple countries. Instead of manually looking up the correct transport emissions factor for each route, AI analyses shipment data and matches it to the right factors based on distance, transport mode, and country-specific datasets. This not only accelerates reporting but ensures that the right factors are used every time.
2. Spotting data gaps and outliers
Reporting greenhouse gas (GHG) emissions depends on complete and accurate data. Gaps in records, unusual spikes, or sudden drops in activity can all affect your carbon footprint and raise questions from auditors, investors, or regulators. The trouble is, these issues can be difficult to spot when you are working with hundreds or thousands of data points.
AI can help improve data quality by automatically scanning datasets to detect anomalies, missing entries, and outliers in real time. By flagging issues early, teams can investigate and correct them before they cause delays or force rework during reporting season.
Example: A retail chain uploads monthly electricity usage from hundreds of stores. AI detects that two locations have no data for March and that one store shows a 45% drop in usage compared to its average. The sustainability team investigates and discovers a reporting error for one site and a temporary closure for the other. This proactive approach avoids inaccurate reporting that could lead to a failed audit or non-compliance fines down the road.
3. Automating ESG reporting
Preparing sustainability disclosures can be a resource-heavy task. Teams must gather information from multiple systems, align it with relevant frameworks, and draft a narrative that explains performance and progress. This process is often repeated each year from scratch, even when much of the content is similar.
AI can help streamline report preparation by automatically tagging raw datapoints to the relevant ESG indicators, suggesting content for narrative sections based on past reports, and identifying where additional context or data may be needed. While this does not replace the need for expert review, it can significantly reduce the time spent compiling, structuring, and checking data, especially in response to evolving standards.
Example: A consumer goods company is preparing its annual CDP reporting submission. AI tags carbon intensity data to the correct disclosure categories, drafts the first version of the Scope 1 and 2 summaries, and flags missing evidence for renewable energy claims. This frees up the sustainability team to focus on refining the content and ensuring it reflects the company’s strategy.
4. Processing documents in multiple formats and languages
Global operations generate data in many formats and languages, from invoices to utility bills and waste records. Processing this manually is slow, error-prone, and can delay data consolidation efforts.
AI-powered optical character recognition (OCR) can complete the task in minutes by reading scanned images, PDFs, or even handwritten documents, then extracting key details such as units and quantities, and translating them into your preferred language. This capability is especially valuable for Scope 3 measurement, where supplier formats and languages can vary significantly.
Example: A European clothing retailer receives waste disposal receipts from suppliers in Italy, Spain, and Portugal. AI-powered OCR technology scans the documents, identifies the waste type and weight, translates the details into English, and exports the results into the company’s central carbon tracking system. The process takes minutes instead of days and ensures nothing is missed in translation.
5. Identifying and prioritising emissions reduction opportunities
Measuring emissions is the first step, but deciding where to take action presents the real challenge. With limited resources, sustainability teams must prioritise interventions that deliver the greatest impact for the lowest cost or effort.
AI can analyse historical and current data to identify patterns and recommend which reduction opportunities to pursue first. AI can also model different intervention scenarios and simulate the impact of each one to compare the trade-offs between options. This allows teams to focus on projects that provide measurable emissions reductions, build a business case for taking action, and align with business priorities.
Example: A brewery wants to reduce its Scope 1 and Scope 2 emissions. AI models three scenarios: switching to renewable electricity, upgrading refrigeration equipment, and optimising delivery routes. The analysis shows that switching to renewable electricity offers the largest emissions cut at the lowest cost, helping the team make a data-backed recommendation to leadership.
Conclusion
These AI workflows are designed to support sustainability teams navigating increasingly complex emissions data and ESG reporting demands. The goal is not to replace your expertise and judgement, but instead to help make your work more efficient and effective. By embedding AI into the right parts of your workflows, teams can save time, reduce errors, and strengthen the credibility of their work.
If you are interested in how AI could support your sustainability goals, start by looking at the tasks that take the most time and involve repetitive data handling. These are often the areas where AI can deliver the biggest impact.
How Zevero supports this approach
At Zevero, we’ve spent years working alongside sustainability teams, from lean in-house roles to multi-site global operations. Our platform is designed to embed AI directly into your carbon accounting processes, without removing the human insight that makes sustainability work impactful. What sets us apart is our hybrid approach: AI-powered smart automation backed by sustainability experts who understand your reporting obligations, sector-specific challenges, and internal pressures.
Whether you’re stuck in spreadsheets or scaling a mature programme, we’re here to help you use AI responsibly, transparently, and in ways that unlock real climate progress.
Book a 15-minute strategy call to learn how these workflows can apply to your team.
