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How AI solutions are evolving to manage supply chain requirements around ESG

Serena Dibra  Associate Product Marketing Manager / Risk & Fraud / 黑料大湿Posts

· 6 minute read

Serena Dibra  Associate Product Marketing Manager / Risk & Fraud / 黑料大湿Posts

· 6 minute read

Supply chain challenges persist and may intensify globally, yet they offer companies the chance to enhance enterprise value through strategic investments in advanced AI technologies for ESG management

The European Sustainability Reporting Standards (ESRS) recently established guidance for corporations on with the European Union鈥檚 Corporate Sustainability Reporting Directive (CSRD). While this is a major milestone in support of companies that are already dealing with a trifecta of regulations, there is still an absence of industry frameworks related to consistent data, assurance, and comparability.

This gap allows a degree of flexibility on how companies present their performance around environmental, social & governance (ESG) issue, with the likelihood that they might omit certain information and present a more favorable image. This has sparked significant public debate and concerns with so-called greenwashing.

British-based consumer goods conglomerate Unilever that has been a vocal advocate for sustainable business practices particularly with their Sustainable Living Brands, which has been reported to in terms of growth. However the company is now into greenwashing, a situation that has raised questions about the accuracy of environmental claims made to consumers. Further, 53% of investors about the scarcity of essential ESG data that is crucial for assessing an organization鈥檚 sustainability level, according to a survey from BlackRock.

reported that 81% of Scope 3 emissions reside in the two most material Scope 3 categories 鈥 purchased goods and services; and use of sold products and capital goods 鈥 across a 4,000-company universe of downstream leased assets impacting greenhouse gas emissions the most.

Key challenges in the supply chain

The challenges within the corporate supply chain remain and are likely to worsen as jurisdictions around the world tighten their scrutiny. Some of these challenges include:

      • Supply chain transparency 鈥 Collecting ESG data has posed significant challenges for companies operating globally, especially given their complex network of multiple tiers of suppliers and decentralized production processes. The absence of adequate oversight and visibility within the supply chain represents reputational risk for the reporting companies.
      • Supplier compliance and monitoring 鈥 Ensuring supplier compliance with ESG standards is contingent upon comprehensive knowledge of third-party entities involved in supply chain operations. However, the lack of visibility into supplier practices and the absence of monitoring mechanisms in global supply chains in which suppliers operate in diverse regulatory environments, has made ensuring adherence to ESG standards even more complex. More than ever, companies struggle to implement consistent oversight practices across different regions.
      • Quantifying environmental impact 鈥 Accurately measuring and reporting the environmental impact of supply chain activities is crucial for informed decision-making and sustainability initiatives. However, quantifying environmental metrics such as carbon emissions and resource usage across geographically dispersed operations presents significant challenges. The intricate nature of global supply chain activities, coupled with variations in production processes and regional regulations, complicates the standardization of environmental impact assessment methodologies.

AI likely to ease data challenges

While admittedly ESG management presents a complex challenge, it also creates an opportunity for companies to make strategic investments in cutting-edge technologies with artificial intelligence (AI) and generative AI to advance their enterprise value. AI-driven technology solutions can address many data challenges, including:

      • Enabling global multilingual data analysis 鈥 AI algorithms can process information in multiple languages and provide translation, enabling organizations to analyze data from various sources around the world. These algorithms can be trained to detect adverse media news related to a company or its global supply chain partners, which could indicate unethical practices, including forced labor issues, sanctions violations, and other illicit practices.
      • Detecting data issues 鈥 AI and machine learning can analyze vast datasets that anticipate possible disruptions or can pinpoint areas of inefficiency. Companies employing diverse ESG standards can benefit from AI’s ability to detect anomalies, data gaps, and predict relevant metrics based on a custom set of inputs, such as various policies across regions. This method leads to a more accurate ESG reporting based on reliable information.
      • Automating alerts 鈥 Performing due diligence involves a labor-intensive process that relies on manual efforts and extensive human reviews. AI interaction enables easy customization of to monitor various changes related to ESG factors. For instance, instead of manually re-running reports yearly, a dashboard could flag changes in third-party data since the last check, making it easier for companies to stay updated on new potential risks and opportunities.

Recommended actions

While AI may address many data challenges, the fundamentals of selecting a tool through the evaluation processes of potential tools do not change. Indeed, a cross-functional team from IT, operations managers, sustainability leaders, and others may be necessary to partner in the selection of the tool with specific priority on several key factors, such as:

      • Understanding ESG goals and data pain points 鈥 To effectively analyze how AI could address an organization鈥檚 data challenges, the first step is to address reporting needs to more thoroughly understand the company’s specific ESG goals and information gaps. This involves identifying relevant ESG areas that are most pertinent to the company鈥檚 operations and industry.
      • Defining requirements to address problem areas 鈥 To make sense of the vast range of technology options and to understand where to focus resources, companies must outline functional requirements and understand in detail how the technology address those requirements. And once the selection is complete, companies should perform a cost-benefit analysis of the chosen solutions to quantify the long-term return of investment.
      • Performing a trial run 鈥 Before full implementation, companies should conduct pilot tests to understand effectiveness and challenges, including how new solutions seamlessly integrate into existing systems and processes, to ensure minimal disruption. Companies should also provide necessary training to key stakeholders in the organization not only on the technical aspect but also on the broader context of ESG reporting.

Additionally, it is necessary for the company to iterate and improve based on the feedback it receives and the experiences it has. Indeed, companies should be prepared to adapt as ESG standards, investor expectations, and technological advancements evolve. It is also important to make sure that the tool has the ability to provide an audit trail of the data journey from raw data to that which is reported to government regulators.

By taking these actions, company leaders in this area will alleviate these headaches around supply chain compliance and ESG reporting, helping to improve efficiency and promote cross-functional collaboration while getting crucial data from unprocessed into a reportable format.

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