AI for ESG: How to Meet Your Sustainability Goals Faster

With a crush of new and emerging environmental, social, and governance (ESG) disclosure requirements and standards, ESG and sustainability teams are increasingly being burdened with administrative and reporting mandates. The result is additional pulls on time, money, and energy on the various tasks needed to document ESG progress. According to the International Data Corporation, spending on ESG business services is projected to grow from $37.7 billion in 2023 to nearly $65 billion in 2027.

However, ESG practitioners can tap into generative artificial intelligence (AI) to streamline this operation using ESG software. You can use generative AI for ESG reporting processes like finding relevant data, building components of various reports, and adding required or voluntary disclosures. All of which help you achieve your sustainability goals faster, freeing up more time for strategic thinking and execution in your ESG program.

Generative AI unlocks boundless possibilities for ESG teams to tap into, offering many routes to experiment with both strategy and reporting. One clear opportunity is the ability to analyze large datasets using machine learning algorithms, which can pinpoint performance risks. You can tailor AI to specific scenarios and outcomes and even provide recommendations on next best actions to reach your goals.

Developing an ESG strategy, setting goals, creating plans to achieve those goals, executing your plan, and, finally, reporting on your progress, is highly complex and extremely data-driven. The most successful ESG programs have deep subject-matter expertise, research, and comprehensive data collection. All of which are necessary to meet organizational impact goals and align with regulatory standards and frameworks. AI will also impact company strategy across sustainability, social challenges, ethics, and governance. For example, in environmental analysis and reduction, AI can help create detailed reports for consumption, waste management, and carbon reduction.

Employees can also have a better picture of company ethics and wellbeing, with AI capable of projecting diversity, equity, and inclusion metrics and supply chain sourcing. We found that 95% of knowledge workers surveyed said easier-to-understand ESG reporting would build trust in companies’ commitments.

Generative AI lets you analyze your company’s ESG data and metrics quickly, so you can identify precise methods for reducing both financial and environmental impact, such as by reducing power consumption or saving water. And it can show you how much impact to expect with each method.

You can also identify new business opportunities as a result of your ESG practices, helping to drive company-wide innovation. With the rise of climate-related opportunities, ESG management platforms such as Net Zero Cloud have incorporated AI technologies, thanks to the Einstein 1 Platform, to help customers better calculate, report, and reduce their environmental footprint. This technology helps generate answers to questions in framework-specific report builders and save report preparers time and resources. Using generative AI for sustainability opens up new frontiers for how your company can build new initiatives for the greater good.

Lastly, ESG is good for the brand. Customers are more willing to support and remain loyal to companies that embody ethical and sustainable values. Now with the help of generative AI for ESG, you have the opportunity to accelerate your progress, measure it accurately, and communicate it effectively through ESG reports. This can ultimately differentiate yourself in the market.

Although generative AI for ESG can be a powerful tool, it needs to be used with care and intention. Here are several approaches to help you get started.

ESG teams tend to be small, and increasing productivity is crucial to meeting reporting objectives. Using generative AI for sustainability allows you to accelerate your initiatives with ease. For instance, by allowing AI to automate ESG tasks such as benchmarking, analysis, and reporting, you can offload tasks and prioritize more thoughtful activities that can create deeper impact.

ESG professionals spend much of their time finding and parsing through specific data from various parts of the company as well as key stakeholders, a very time-consuming process. Generative AI can help. Paired with the right technology, generative AI can comb through your company’s past reports and current metrics to find the relevant information needed for a given task.

This is especially helpful when creating framework-specific reports, which essentially ask a series of complex questions that require laborious answers. Generative AI can analyze past reports and current metrics and then use natural language processing (NLP) to generate those answers. Generative AI can also create data visualizations out of a company’s data.

Furthermore, tapping into AI for ESG efforts can also help increase your team’s practical expertise. Often, teams can struggle to collect and gather the most up-to-date information in tackling sustainability issues. With generative AI, you can quickly research new topics and rapidly analyze data to gather critical information that can inform your business decisions.

With the growing use of AI, it’s important to minimize the environmental impact of this technology, as it has the potential to require significant resources. We’ve developed, implemented, and published Salesforce’s approach to developing sustainable AI, which has resulted in significantly lower-carbon models. Be sure to evaluate how using AI will affect your company’s emissions.

AI for ESG has clear benefits, but it also needs a clear business strategy. When developing a roadmap for your ESG goals, you should focus on specific AI use cases that you can implement into your goals and timelines.

One of the easiest ways to identify how to use AI within your own ESG team is to observe how other teams across the company are implementing AI into their own operations. Teams that work in silos often don’t have the knowledge of what your sustainability team does. It’s important to build relationships with other departments, such as IT, to see how you can best use AI for sustainability efforts.

When crafting use cases, it’s critical to understand what you want AI to do for you, which can inform how to use tools such as Einstein. This can be accomplished by applying the jobs to be done (JTBD) framework. In addition to documenting your team’s JTBD, ESG and sustainability professionals must spend time working with and testing generative AI tools. Various closed- and open-source models are built with differing large language models (LLMs) and training data, and getting comfortable with prompting approaches for your selected model(s) is a prerequisite to extracting maximum value out of your AI.

Further, your deeper knowledge of generative AI model behavior will better help you to align and clarify the specific JTBD that can be supported with your generative AI model of choice. Of course, all of these activities should be done under your organization’s guiding principles for responsible AI.

Remember that ultimately, AI isn’t something that can solve all of our problems, so be sure to take a measured approach.

ESG teams face many challenges: numerous requests, resource constraints, information overload, and impending disclosure regulations. AI is a pathway toward a more efficient future in ESG, delivering new ways of managing and using data that will make our work more efficient.

To truly take advantage of all AI has to offer, teams will need to develop a comprehensive AI and ESG strategy that takes into account data privacy and transparency, while complying with government regulations.

Bottom line: The returns are worth the investment. By using AI for ESG, we can all have more freedom, fulfillment, and creativity to focus on tackling the world’s biggest challenges.