The opportunities and challenges AI brings into double materiality assessments

In my last piece, I focused on what the role of AI looks like when it comes to the concept of double materiality. Now, I want to highlight the opportunities and challenges that AI can bring to an organization regarding double materiality.

Opportunities with AI

Bringing AI into an organization can bring a breath of fresh air to leaders who are trying to streamline their reporting and remain competitive in the ESG landscape. Outside of the main benefits that AI creates with double materiality, there are several other rewards that this technology can bring to the table: ?

• Optimizing supply chains: Leveraging AI-enabled double materiality optimizes logistics, resilience planning, ethical sourcing insights, demand forecasting, resource efficiency, and supplier risk management to move toward sustainability goals.
• Energy efficiency: AI can help bring energy efficiency through smart buildings and facilities management, industrial production systems optimizations, and renewable energy integration.
• Reducing waste: AI can predict maintenance, analyze data and needs, and optimize production planning for resource allocation to minimize overproduction and reduce excess.

Challenges with AI

While AI offers many opportunities to companies, it can also be a risk for ESG strategies. Below are some challenges that organizations can encounter when integrating this technology into double materiality:

• Data unification: Collaborative and holistic approaches are needed to handle the challenges of data unification that include data silos. Leaders need to ensure data quality and data reliability by establishing data governance policies and procedures, producing complex and transparent results, securing integration with legacy systems, and bringing scalability and flexibility to their business.
• Traceability: Investments in data management capabilities allow tackling traceability challenges, such as regulatory compliance, technology limitations, cross-organizational collaboration for end-to-end traceability, authenticity and reliability assurance, and data interoperability.
• Carbon footprint: AI has often been energy intensive, and now that AI is more heavily relied on for double materiality assessments, the organization’s carbon footprint may increase. As your company’s carbon footprint increases, the energy consumption of AI infrastructure and data center emissions through electricity consumption and cooling systems expands. Companies can take steps to decrease the carbon impact of running AI models, including optimizing training data selection and maintaining algorithmic efficiency to achieve high performance while minimizing energy consumption.

We believe that double materiality serves as a paradigm shift in the way that businesses are incorporating sustainability at every step of the value chain. By taking this leap, companies can make better choices, earn stakeholder trust, and build a better future for the planet.