In our latest Environmental, Social, and Governance (ESG) roundtable with clients hailing from North America, we began to explore how AI can be harnessed to support ESG efforts while also discussing the challenges and risks of this technology. We also discussed initial steps businesses can take for responsible AI development. It all begins with the role that AI plays in advancing sustainability initiatives.
The Role of AI in Driving ESG
AI is emerging as a key driver in advancing ESG initiatives. Knowing this, we began by looking closer at the ways in which AI tools could accelerate ESG projects set by businesses to help them achieve their goals on time or even ahead of schedule.
As businesses grow more adept with AI, it becomes clear that the technology’s application in data analytics is proving to be indispensable in sustainability efforts. AI can help:
- Manage the vast amount of data required for carbon reporting.
- Identify key focus areas for sustainability action.
- Assist in spotting gaps in compiled data or during the reporting process.
Variations in emissions data among supplier bases, which often display differing levels of rigor and assurance, can also be analyzed using AI. Market systems are beginning to incorporate the technology as part of their Quality Analysis (QA) and Quality Control (QC) processes, allowing anomalies to be identified within large data sets.
AI has shown the capability to aggregate and analyze data that can translate into strong predictive insights, and these insights provide businesses with carbon trajectories that serve as the foundation for developing effective emission reduction strategies.
Understanding the Challenges of AI in ESG Projects
Despite the widespread promotion and use of AI, industries like banking and retail still face challenges or adoption overall due to the sensitive nature of their data. We then discussed the potential risks that AI could pose, from adoption to execution, and that’s why it’s especially important for businesses to put measures in place to ensure accuracy of its narratives and rationale. These are some of the risks and challenges that businesses must address to ensure that the collaboration between AI and ESG remains successful:
- Technological limitations: While AI can yield impressive results, there are limits to its capabilities. Because of this, its outputs must be carefully verified by humans to avoid the risk of inaccurate or incomplete data.
- Social risks: The absence of chronological context in AI outputs requires human intervention to mitigate biases that could affect the data’s quality and negatively impact the business’s reputation.
- Workforce readiness: Businesses must also make efforts to create a workforce that is adequately and consistently trained on AI platforms and tools as it relates to ESG initiatives.
- Energy consumption: As businesses harness AI, they will need to find ways to balance increased power consumption from Generative AI with service resiliency and performance continuity. This is especially relevant in regions with a high concentration of data centers.
Data centers that process AI, particularly GenAI, must transition to renewable energy sources to mitigate their carbon footprint, while continued innovation in processing and storage technologies will be key to improving energy efficiency and reducing overall impact.
There is, of course, the question that businesses must ask themselves: which AI tool or platform provides relevant assistance while keeping emissions as low as possible? For example, Large Language Models (LLMs) will provide more functionality and higher emissions, while smaller-scale tools may have limited functionality but produce fewer emissions. This raises important questions about governance in ESG initiatives.
Responsible AI Development
In the final part of our roundtable, we addressed the implications of designing, building, and scaling AI tools on ESG initiatives, as well as the governance surrounding them. Governance plays a major role in carrying out responsible AI development and usage. The challenge lies in the way AI regulations vary significantly across different geographical regions.
Companies abide by the laws of their respective regions while also prioritizing the alignment of meeting internal data management standards, although AI-specific governance is still in its early stages. The integration of ESG considerations into supply chains is advancing, with companies focusing on empowering third-party providers. By engaging with suppliers post-transaction, our clients can identify high-impact partners and monitor their progress toward ESG goals, with foundational principles now embedded in contracts.
AI and ESG: A Powerful Collaboration
As an overall result of our roundtable, our participants agreed that while the application of AI in ESG initiatives is currently limited in its scope, the potential of its impact is promising, especially as it relates to the collaboration between AI and ESG teams. Increased communication and collaboration between these two domains can enhance mutual appreciation and understanding, ultimately fostering more responsible and effective AI development in ESG contexts.
Learn more about how Concentrix can help you create an effective ESG strategy for your business.
