Companies are facing growing pressure to achieve environmental, social, and governance (ESG) goals. In fact, a 2023 study found that 94% of CFOs and their executive teams feel external pressure to prioritize ESG initiatives. But ESG data can be complex and difficult to track, leading to inconsistent reporting and, ultimately, unfulfilled promises that can put an organization’s reputation and profits at risk.
As the need to ensure data accuracy and integrity increases due to government regulations and stakeholder expectations, business leaders are looking for better ways to report their ESG data. Enterprise AI has proven to be a powerful tool that can gather, manage, and analyze disparate ESG data to help companies identify risks and capture opportunities to meet their ESG goals.
With 15 years of carbon analytics experience and an industry-leading enterprise AI platform, C3 AI developed an ESG application in 2022 that uses machine learning (ML), natural language processing (NLP), and generative AI techniques to help companies efficiently manage and improve their ESG performance.
C3 AI’s director of product, sustainability applications, Burt Mayer, discusses the challenges that come with ESG data reporting, why AI is a game-changer for managing these efforts, and how C3 AI ESG helps organizations reach their sustainability goals and make informed, strategic decisions.
What are the biggest challenges that business leaders face today when it comes to ESG management?
There are three central challenges facing corporate sustainability teams today. The first is that investors, regulators, and customers request various types of ESG data in different formats. It is incredibly complex and burdensome to navigate all these different standards and frameworks, as well as emerging regulatory requirements, while understanding which elements of ESG need to be tracked and disclosed to address stakeholders’ interests.
A second key challenge is once an organization has determined what it wants to track and report, it’s tactically really difficult to unify that data from what can be dozens of different systems across the enterprise to get it into one place, validate it, and model it in such a way that it can be easily analyzed and reported. And third, between those two challenges, most sustainability teams have little capacity left to grapple with the most important challenge of all: setting the right ESG targets and goals and figuring out a realistic plan to achieve them.
How is AI changing the ways organizations can address these challenges?
AI is changing the equation for sustainability teams in pretty much everything they do. AI is now being used to automate a lot of the burdensome work of ensuring data validity and mapping it to the proper frameworks and standards for reporting. And it is helping teams proactively address the shifting concerns and expectations across the stakeholder universe.
Lastly, for teams that don’t have enough time to think about their roadmaps to achieve their ESG goals, AI can be a tool to create specific, detailed plans and generate hundreds or thousands of different scenarios—the direction of energy prices, for example, or whether an organization is likely to hit the targets it’s been publicly announcing.
How does C3 AI’s ESG solution use AI to help companies optimize their ESG strategies?
AI can serve as a hugely important value driver within the company by understanding the needs of customers and investors, suppliers, local communities, and our planet. Our C3 AI ESG solution helps give strategic direction and risk mitigation insights to large and complex organizations that really want to create value out of ESG.
What are ESGBits™ and how does unifying disparate data into ESGBits™ enable more accurate and reliable ESG reporting?
ESGBit™ is a term that C3 AI coined to describe a new concept that we introduced in our C3 AI ESG application. An ESGBit is an elemental unit of ESG information. Think a kilowatt-hour of electricity consumed, at a specific facility, in a specific region, and at a specific point in time. Or a single health and safety incident that occurred at a certain plant and in a certain region. We’re using C3 AI’s application platform to allow our customers to model that highly specific data, which solves for two challenges. First, if we have unified data in the form of ESGBits, we can automatically populate it in reports to satisfy whatever evolving reporting frameworks and requirements emerge. Secondly, with that level of data, we can apply machine learning in a powerful way to help organizations create and optimize plans for how they’re going to achieve their ESG targets in the short, medium, and long term. One of our customers, a large global professional services firm, is using ESGBits today to track, analyze, and report their ESG data for separate organizational units defined by geographic and management areas and service lines.
How does C3 AI ESG help organizations proactively identify and stay abreast of material ESG issues across stakeholders?
It’s critical for organizations’ corporate sustainability teams to start by conducting what’s called a “materiality assessment” before investing in data collection, data management, and reporting infrastructure. They need to understand what is material and important to all stakeholders in their orbits, which can look very different depending on the stakeholder group and can shift quickly.
Over the past two years, C3 AI has developed and refined an ensemble of machine-learning models that look at a broad range of publicly available text data near-continuously to understand which ESG issues are being discussed—and to detect their materiality. For example: workforce health and safety or human rights in the supply chain; diversity, equity and inclusion; hazardous waste management; water consumption; biodiversity; greenhouse gas emissions—as these different issues become more or less material to different stakeholders at different times, our machine-learning models alert our customers so that they are proactively informed of both the risks facing their organizations as well as the value-creation opportunities.
We worked with a large energy services company to set up an ESG materiality assessment process that automatically reads investment policies, proxy voting guidelines, news, press releases, annual reports, and other documents. Within nine weeks, we configured AI models that analyzed over 400,000 paragraphs of text and generated a map of the changing importance of various ESG issues to investors, customers, peers, and NGOs. This analysis, which updates weekly, can now inform where the company focuses resources and how they tell their sustainability story. Without AI, these insights would’ve required effort roughly equivalent to 29,000 hours of analysts’ time, or $1.5 million per year. This is a new area we’re excited about. Not only are we alerting customers that a certain issue is becoming more material to a certain stakeholder group at a certain point in time, but we can synthesize in text form and natural language what that shift looks like. That’s an enormously powerful tool for sustainability teams that are looking to become a value engine within their organizations.
How does C3 AI ESG help organizations assess the impact of various scenarios on their sustainability goals so they can make more informed strategic decisions?
A lot of the sustainability goals today are long-term commitments. Think net-zero emission by 2040, or even 2050. Mature organizations are creating plans to achieve that goal over the next 20 or 30 years, but even the most mature organizations are still facing all of the uncertainty that comes with 25 years of shifting macroeconomic conditions, stakeholder expectations, and issue materiality. Not to mention uncertainties like how healthy your business is going to be over the next 25 years. What will your cost structure look like? What will your revenue growth look like? What will your competitive positioning look like?
We believe it’s imperative for these organizations to develop an internal perspective on what levers can be pulled, under what circumstances, so organizations can make sure they’re not caught off guard by change and suddenly no longer have a viable pathway to achieve a target that has been committed to their stakeholders. We think that is a slam dunk use case for machine learning.
How will C3 AI adapt C3 AI ESG to keep up with the rapidly changing sustainability landscape?
There are two ways C3 AI thinks about future-proofing our ESG solution. One is that we spend a lot of time tracking the evolution in ESG standards and frameworks as well as regulatory regimes—both at the regional and global levels—so that we can ensure our product will support our customers in whatever reporting standards and frameworks they want to use. We take a lot of pride in ensuring that we can flexibly support the range of reporting objectives our customers have at any given point in time.
Secondly, we support future-proofing through the underlying natural language processing and machine-learning technologies that we have developed within C3 AI ESG. An example of that is using natural language processing to read a set of requirements, automatically interpret it, and then map the data to respond. This machine-learning infrastructure can automate the work of deciphering ever-changing requests and requirements.
What do you see as the most transformative impact AI will have on organizations’ ESG efforts moving forward?
We’re already seeing AI change the mentality of the corporate sustainability function within organizations, as well as how CEOs and C-suite executives overall view the value and role of that function. No longer is it going to be boxed into a reporting and compliance-oriented set of tasks. AI unlocks the potential to transform the role of ESG teams—helping them become an engine of growth for an organization. With the power of AI, companies can be far more thoughtful, more comprehensive, and more rigorous in managing their ESG strategies and proactively responding to the needs of their stakeholders.
Learn more about C3 AI ESG and the entire C3 AI Sustainability Suite.
