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AI emerges as an ESG solution

Business is facing immense pressure to improve transparency and performance on environment, social, and governance (ESG) goals. With more pushback on greenwashing than ever, there is an increased scrutiny of companies’ commitments and their results. Further, governments are enforcing new ESG reporting regulations, such as the EU’s Corporate Sustainability Reporting Directive (CSRD). With this heightened attention on data management and transparency, there are about $10 trillion worth of investments specifically tied to ESG criteria across the current investment landscape.

New opportunities arise for businesses not just to do good for the planet and society, but also to differentiate themselves in the marketplace. Yet, despite significant growth in recent years, funds allocated toward ESG are currently at the center of controversy. Where companies have historically wanted to tout their commitment to “green” business, now they don’t want to talk about or promote their ESG goals as prominently as they once did. With the current downward trend in ESG commitments, artificial intelligence (AI) may be the only thing that can save it. As a powerful digital technology, AI plays an increasingly critical role in the solutions needed to address our most pressing global challenges – including mitigating climate change, decarbonizing the energy sector, alleviating poverty, and conserving biodiversity.

Using AI to solve the ESG data gap

Companies’ claims to meeting ESG criteria often rely on fragmented and hard-to-verify data, leaving investors unsure what to believe and ESG investment managers vulnerable to financial volatility or political attack. This problem leaves a gap between where business wants the industry to be and where it actually is, with data challenges the biggest obstacle to achieving ESG goals. In light of the growing scrutiny of the viability of corporate sustainability, AI is promising better organized company data and more transparent insights.

Data management setbacks include concerns about data quality, such as greenwashing and inconsistent ratings. If there is a scarcity of reliable ESG data, then quantifying risk becomes risky in and of itself. Not only does this make it harder for progressive companies to follow ESG trends, but it also opens investors up to considerable losses. By embedding AI into the organizational structure, businesses can move beyond these volatile and complicated situations. In doing so, the technology enhances ESG reporting compliance to shape and extract specific and tailored business value from ESG initiatives.

Machine learning is a powerful tool that can help companies with ESG reporting by streamlining the data collection and analysis process. By automating those two, companies can reduce the time and resources required to complete ESG reports, while also improving the accuracy and reliability of the data. Current numbers suggest that firms leveraging AI to close the ESG data gap and beyond could increase profits by nearly 40%, and that AI will add $14 trillion in gross value to corporations by 2035. Goldman Sachshas estimated that the collaborative AI-ESG initiatives alone could raise global GDP by 7% by 2033.


Harnessing the power of AI for sustainable business

AI can be harnessed in a wide range of economic sectors and situations to contribute to managing environmental impacts and climate change. Through its predictive capabilities, pattern recognition, and optimization of processes, AI shows that sustainability has the potential to be used as a competitive advantage for business. From precision farming to energy-efficient manufacturing, AI offers an opportunity for investors simultaneously to act responsibly and align their ESG goals to a profitable agenda. This shift has not only challenged the traditional view of sustainability but has also redefined what success looks like in the business world, thus ushering in a new era of corporate responsibility.

Consider the supply chain, which is typically the most significant contributor to a company’s carbon footprint. In its vast, often complicated structure exist inefficiencies that are detrimental both environmentally and economically. With unparalleled data analysis capabilities, AI presents solutions that can untangle complex webs of information. According to recent research by PwC UK, using AI to better manage environmental reporting could reduce greenhouse gas emissions by 4%, boost global GDP by up to $5 trillion, and create up to 38.2 million new jobs across the global economy by 2030, offering more skilled occupations.

To streamline their ESG reporting procedures, many businesses currently use AI-powered ESG report automation. For instance, Microsoft employs AI to collect and evaluate data on its employees, customers, and supply chain, ensuring that its operations are ethically and socially responsible. The tech giant has used AI to gather and analyze information about corporate governance policies, social responsibility, and environmental effects. With AI, Microsoft has also successfully measured and monitored its own energy use throughout its supply chain to cut carbon emissions by 40%.

Similarly, global asset management company BlackRock has recently developed a cutting-edge tool that utilizes machine learning and natural language processing to help investors identify climate risks and opportunities in their portfolios. This AI-powered tool has added over 1,200 sustainability metrics and established data partnerships to help investors better understand ESG and physical climate risks and opportunities.

Nasdaq, a leading provider of trading, clearing, and exchange technology, has also launched an AI-powered ESG data platform. The platform helps both companies and investors track ESG metrics more accurately and reliably by leveraging AI and machine learning to analyze ESG data from a variety of sources, including company disclosures and news articles. Highlights include a commitment that 70% of Nasdaq’s suppliers by spend, covering purchased goods and services and capital goods, will set science-based targets by 2027. In 2022, 113 new diverse suppliers were added to the ESG-focused portfolio, which represents over $1.4 million in supplier revenues.


AI strengthens ESG investments

Even though more recently ESG has morphed into a debated theme among US conservatives, demand for sustainable investing continues to rise. Global ESG assets will exceed $40 trillion this year and grow to over $50 trillion by 2025, according to Bloomberg Intelligence. The adoption of AI in ESG reporting is on track to increase significantly by 2025, as investors demand more transparency and accuracy in ESG data. It is expected that 80% of senior leaders consider ESG to be an important part of their business strategy, while 86% of investors believe that a focus on ESG helps to drive long-term value.

By leveraging the power of AI to analyze vast amounts of data and identify key ESG trends, businesses can make more informed decisions, reduce their environmental footprint, and promote social responsibility. Additionally, AI-enabled ESG reporting helps investors better understand a company’s sustainability performance and make more informed investment decisions that align with their values and goals. As we move toward a more sustainable future, embracing AI in ESG reporting will be crucial for companies and investors to create a more equitable, resilient, and sustainable world.

Driven by the increasing commitments of governments, corporations, and asset managers to support the transition, there is evidence of ample AI support for the rapid augmentation of sustainable investing over the past two decades. For reference, the value in major financial markets of sustainable investment has grown by over 15% in the last few years alone. This growth has been experienced in the rising volume of assets managed by signatories to the Principles for Responsible Investment (PRI), a United Nations financial industry initiative that assists firms in integrating ESG criteria into their investment and ownership decisions. The roughly 4,000 signatories to the PRI represented more than $120 trillion of assets at the end of 2021, up from just $10 trillion in 2007.

AI-ESG business integration

AI continues to evolve at an unprecedented pace, pushing the boundaries of what was once thought possible. Among the branches of AI, two concepts have gained significant attention: generative AI and regenerative AI. Generative AI focuses on the generation of new content, such as images, by learning patterns and structures from existing data. Regenerative AI, on the other hand, takes the concept of generative AI a step further by not only creating new content but also actively participating in its refinement and evolution. It goes beyond imitation and aims to improve upon existing designs or systems by incorporating feedback loops and iterative processes.

In the realm of ESG reporting, regenerative AI has the enhanced potential to help optimize resource allocation and environmental sustainability. By analyzing real-time data on energy consumption, traffic patterns, and waste management, AI systems can suggest improvements and dynamically adjust parameters to achieve more efficient and eco-friendly outcomes. This approach can lead to ESG benefits such as smarter systems and reduced carbon footprints, in addition to a job market surge in positions focused on AI-ESG integration for roles dedicated to the continuous training and fine-tuning of these updated AI models.

For businesses that choose to treat ESG performance as a source of competitive advantage, regenerative AI presents a unique opportunity for enormous value creation and improved resilience. AI digital solutions for forecasting and planning, data fusion and reporting, and real-time stakeholder risk and opportunity analysis will continue to be a prominent factor in business success. We must continue to foster this lucrative AI-ESG collaboration for sustained global progress.