Across every industry, an increasingly diverse array of stakeholders—including not only major shareholders or board members but also customers and even rank-and-file employees—are holding companies accountable for their commitment to improving ESG. Shorthand for “environmental social governance,” a company’s ESG reporting strategy serves to transparently convey that organization’s efforts to mitigate climate change, promote racial and gender equity (particularly in leadership-level decision-making), and engage in a range of social impact initiatives. What in yesteryear may have been an ancillary company arm devoted to “feel-good” philanthropic initiatives is now a major vertical that can no longer be siloed away from other teams. ESG is now seen in many industries as a necessary due diligence step, as well as a value-generating proposition, as companies’ strategies face renewed scrutiny and expectation as a result of the pandemic, globalization, and mounting concerns about ecological shifts.
Though many leaders earnestly set out to improve their ESG metrics, most don’t know how to go about it. They may struggle to “connect the dots” between different goals across the ESG spectrum, or to accurately distinguish areas in which they have made great strides from those in which they need improvement. They may find rigorous self-evaluation challenging or lack the foresight to plan their ESG initiatives in the future.
By looking at ESG assessment and reporting in a more nuanced way, across a matrix as opposed to as a linear proposition (or a rudimentary “yes” or “no” evaluation), companies can develop a more robust and multi-layered approach to ESG, leveraging an array of meaningful data to drive what must be an evolving strategy.
A Holistic, Engaged Viewpoint
Eather than viewing ESG as a triad, we recommend a comprehensive approach to sustainability that examines six aspects:
• Governance and strategy
• Supply chain management
• Environmental sustainability
• Social sustainability
• Economic sustainability
• Partnerships and collaboration
To manage your ESG reporting and strategy holistically, it’s important to view these issues as interconnected, in that partnerships and collaboration underlie each of them. Each of these domains must be considered from the perspective of how best to engage stakeholders.
These stakeholders are not restricted to the company’s board of directors, operational leadership or even employees—rather, to truly view ESG holistically, it’s important to involve suppliers, customers, governments, NGOs and local communities. A mature and well-developed ESG strategy will involve these entities in planning any initiative, from the beginning to the end.
Moreover, a mature strategy doesn’t just require that companies inform stakeholders of the environmental or social impacts of their products and services after the fact, or simply report broadly on results. Rather, a company must actively engage stakeholders in the process of surfacing and setting clear, measurable, time-bound ESG targets, and eliciting feedback both on the formation and attainment of these targets——again, not just from shareholders, but from employees, staff, suppliers and producers up and down the chain. Truly leveraging the feedback of these constituents to improve, modify and identify areas in need of change is essential to collaborative and adaptive ESG management.
A well-developed strategy cannot exclusively be quantitative, or solely concerned with monitoring, processing and analyzing traditional data streams (how much money, how many under-represented groups, how much carbon offset). It should also be about who gets to sit at the decision-making table, and how their voices and feedback are integrated alongside those quantitative measures.
Utilizing Multiple Data Streams
Usable data takes many forms. In the era of ChatGPT and other advanced AI platforms, companies have new, accessible tools for processing qualitative data: stories of change or success or novel approaches; interview data; testimonials from up and down the supply chain. These forms of data are valuable to collect and curate and can provide a clearer snapshot of a company’s current ESG standing—and what work it still has ahead.
It is the responsibility of multinational companies to lead the way in constantly monitoring and evaluating their ESG measures, separately and holistically, at all levels of the supply and distribution chain and against an array of regulatory and compliance standards. AI, ML, and other tools can enable companies to have a remarkably current and accurate picture of the status of their ESG initiatives, not just through numbers but also through other more personal, narrative types of indices that AI can help them collect, analyze and categorize. These data streams, taken together, can help companies go from emergent levels of design and engagement to optimized levels in every one of the key sustainability domains.
Driving Sustainable Performance: A Role for AI in ESG Reporting
Developing a comprehensive and interconnected approach to ESG is vital for multinational companies to enhance their sustainability performance and advance these objectives. By considering governance, supply chain management, environmental and social sustainability, economic sustainability, partnerships and collaboration, companies can engage stakeholders at all levels and involve them in the formulation and achievement of clear, measurable and time-bound ESG targets. The integration of qualitative data alongside traditional quantitative measures can provide a more holistic view of ESG standing and guide companies toward optimized sustainability practices.
The advent of generative AI models like GPT offers a transformative solution for companies aiming to meet and exceed ESG compliance and disclosure standards, such as those recently proposed by the International Sustainability Standards Board (ISSB). Generative AI holds the capability to efficiently manage, interpret, and report vast and diverse data sets, many of which are crucial to comprehensive ESG reporting. Generative AI tools can automate the process of disclosure, translating raw, complex data into clear, understandable narratives. This could lead to more efficient, consistent, and comprehensive reporting, ultimately improving transparency and trust among stakeholders.
Moreover, generative AI’s scalability means that companies of all sizes can leverage its benefits to improve their ESG reporting. As these models evolve, they could potentially assist companies in not just adhering to current regulations but also predicting future ESG trends and standards. This predictive power could allow companies to proactively align their strategies and meet ambitious sustainability goals.
A Clear-Eyed View Of Technological Capabilities
As with any technology, the use of generative AI in ESG presents some challenges. AI output is only as good as the data it receives. If the data input is incomplete, biased or erroneous, the output will reflect those same inaccuracies. Likewise, data privacy and security are a concern, particularly in the management of sensitive financial data, as well as the ethical considerations involved in data discovery and categorization of personal information and narratives.
Additionally, deep learning models can often act as a “black box,” meaning the decision-making process isn’t always clear. Algorithmic biases can inadvertently perpetuate existing biases if they are present in the training data. This could potentially undermine the transparency that ESG initiatives seek to promote. It is therefore critically important to maintain a balance between AI automation and human judgment. Companies must leverage AI tools with the same combination of optimism and circumspection that they do in other industries.
However, viewed through a broader lens, AI emerges as more than a mere tech trend—it represents a paradigm shift in the landscape of corporate sustainability. When employed effectively, these AI tools can underpin the development of a robust, transparent, and accountable ESG strategy. This capacity to leverage an unprecedented range of data surfaces new opportunities, enabling business leaders and decision-makers who harness its transformative potential to make significant strides toward a more sustainable future.