Burt Mayer, director of product, sustainability applications, and Jessica Matthys, product manager, sustainability applications, at C3 AI, discuss how AI can assist companies in achieving sustainability goals and enhancing their competitiveness.
Companies face immense pressure to enhance transparency and performance regarding their environment, social, and governance (ESG) goals, amid heightened scrutiny and new regulations like the EU’s Corporate Sustainability Reporting Directive (CSRD). This attention presents opportunities for companies to differentiate themselves in the market while also benefiting the planet and society. Leveraging AI, businesses can transcend ESG reporting compliance challenges to shape and extract business value from their initiatives. AI addresses three main challenges for corporate sustainability teams: translating ESG goals into actionable plans, integrating fragmented ESG data, and addressing stakeholders’ shifting concerns. Despite the imperative of ESG reporting for large public companies, many still view it primarily as a risk management function, overlooking its potential for driving competitive differentiation:
- Companies with strong ESG scores benefit from a lower cost of capital.
- Customers pay a premium for low-carbon or otherwise ESG-friendly products.
- Companies with clear ESG commitments have superior employee retention.
- Data-sharing with suppliers for ESG disclosure may result in more efficient and resilient supply chain networks.
Translating ESG goals into action plans
While over 60% of the Fortune Global 500 have established formal targets for reducing greenhouse gas emissions, many face challenges in translating these commitments into actionable plans, leading to significant deviations from their targets. Stakeholders, including investors, now demand transparency and rigor akin to financial targets regarding corporate ESG goals. The looming threat of shareholder litigation over climate risk management further emphasizes the need for defensible, actionable plans from corporate directors. However, most companies lack the necessary capabilities to develop and monitor such plans, including automated emissions calculations, business-as-usual forecasts, and a prioritized set of mitigation strategies considering financial, timeline, and risk constraints. Long-term targets, like achieving net zero emissions by 2050, pose additional difficulties as companies struggle to assess risks and plan for various scenarios. Corporate boards recognize the sensitivity of decarbonization pathways to internal and external factors but often lack the data infrastructure and analytical tools to model and test different scenarios effectively.
Managing disparate ESG data
The absence of a robust, unified ESG data infrastructure hampers sustainability teams in meeting reporting requirements and making informed decisions. ESG encompasses a wide range of environmental, social, and governance topics, with disclosure obligations spanning disparate software systems like environmental health and safety, human resources, and enterprise resource planning (ERP). Moreover, multiple overlapping ESG reporting standards and frameworks add complexity to data collection and presentation. Adopting technologies to streamline data management and reporting tasks can reduce costs and allocate resources to higher-value activities. Internally, ESG data is vital for informing decisions across operations, supply chain, human resources, and other business functions. A unified, standardized ESG data platform is indispensable for effectively managing an ESG program.
Setting priorities in alignment with stakeholders
Corporate sustainability teams face the daunting task of managing a wide range of ESG issues amid limited internal resources. Keeping abreast of evolving stakeholder concerns is challenging, necessitating the prioritization of key areas aligning with stakeholders’ interests. The standard materiality assessment process, traditionally used for prioritization, is labor-intensive and inherently limited in scope and impact. Given the constantly evolving nature of ESG expectations, a real-time system is needed to capture timely insights for managing risks and seizing opportunities. Performing assessments every two years, for instance, is insufficient in meeting the dynamic demands of ESG management.
AI as a solution and enabler
Companies are leveraging enterprise AI to tackle critical ESG challenges, including translating commitments into actionable plans, establishing comprehensive data visibility for reporting and decision-making, and engaging stakeholders to manage risks and capitalize on opportunities.
With predictive analytics, sustainability professionals can accurately estimate greenhouse gas emissions across operations (Scopes 1 & 2) and the value chain (Scope 3) while forecasting emissions trajectories. Machine learning algorithms utilize vast amounts of internal and external data to generate numerous scenarios, aiding executives and directors in devising decarbonization strategies. Additionally, AI facilitates the optimization of plans by scheduling emissions mitigation projects to meet interim targets within budgetary, resource, and risk constraints.
AI for data fusion and reporting automation
Companies are transitioning from labor-intensive Excel sheets to AI-powered solutions for managing ESG data, which offer numerous benefits. AI can automatically unify, validate, and organize data in near real-time, simplifying data-wrangling processes and future-proofing against evolving standards and frameworks. Moreover, AI automates data validation and risk detection, alerting teams to emerging issues and assisting in report writing. Generative AI technologies can even draft initial reports, speeding up the insight generation process. Additionally, AI streamlines data disclosure requests by automating interpretation, mapping to key performance indicators, and generating appropriate output.
AI for responsiveness to stakeholder priorities
Advancements in natural language processing (NLP) and large language models (LLMs) enable the replacement of manual materiality assessments with continuous insights. AI transforms occasional, limited-scope, and backward-looking studies into near real-time solutions, expanding the scope to analyze up-to-date publications from various sources such as nonprofits, customers, investors, and competitors. This generative AI system offers continuous and actionable feedback to mitigate ESG risks, enhance stakeholder engagement, and seize new opportunities.
What’s Next?
As corporate demands intensify for detailed, defensible plans, data transparency, and responsiveness to shifting stakeholder priorities, executives are grappling with the purpose and value of ESG for their organizations.
For companies viewing ESG performance as a competitive advantage, there’s immense potential for value creation and enhanced resilience. AI digital solutions for forecasting, planning, data fusion, reporting, and real-time stakeholder analysis will remain crucial enablers of their success.
Tran Dung/ATES GLOBAL
Source: Fortune
