Linda Lorenzon (Junior Researcher G.E.O Environment)
Abstract
Not many years ago if someone had mentioned AI or ESG few would have known what they were and how they were used, but as of today, in 2025, they have become well-known concepts and everyday tools, especially for large companies. Indeed, artificial intelligence is transforming the reporting of corporate data related to sustainability and inclusion, so-called ESG (Environmental, Social, and Governance), by simplifying data collection, velocity, and real-time monitoring. However, while there are significant sustainability benefits for companies, the collection of huge masses of data by artificial intelligence also brings with it obstacles and concerns such as: job loss, environmental impact, and privacy. Therefore, it is necessary to balance risks and opportunities to enable AI to support responsible and sustainable ESG strategies and become a sustainable long-term tool.
Introduction
The world is changing. Artificial intelligence is no longer a futuristic concept, it is a current force that is transforming the way we live, work and interact. It is now an integral part of our lives. Whether it is Chat Gpt to write cover letters, search for information or even perform tasks or exercises (let's not lie we have all done it at least once!) or a tool that companies use to get the job done faster, AI is becoming increasingly important.
Numerous studies have already analysed the impact AI will have on jobs around the world in the coming years. A Goldman Sachs report from 2023 indicates that it could replace about 300 million full-time jobs, potentially impacting up to two-thirds of jobs in the United States and Europe (Goldman Sachs 2023). The jobs in demand will change and so will the very demand for “human” workers, indeed, it is estimated that 12 million people will need to change professions by 2030. These findings highlight the crucial importance of ensuring fair employment opportunities, fair working conditions and respect for workers' rights in the age of artificial intelligence.
Moreover, this rapid evolution of artificial intelligence is also redefining the way companies operate and account for their activities, and its influence on sustainability is no exception. In recent years, the growing focus on sustainability, ethical practices, and corporate transparency has made ESG - Environmental, Social, and Governance - an essential framework for assessing how companies operate in today's world. As the expectations of investors, consumers, and regulators continue to rise, organizations are under increasing pressure to demonstrate responsibility and long-term value beyond financial performance.
According to PwC's 2025, AI Business Predictions report, artificial intelligence will be an added value and a key driver for sustainability (Forbes, 2024). In fact, this technology can help companies address key investor priorities such as reducing carbon emissions, building resilient supply chains and adopting renewable energy, while helping to balance a growth strategy, meet regulatory demands and mitigate climate risks. In sum, the environmental, social, and governance landscape has seen dramatic changes in how data is collected, analysed, and reported in recent years.
But first, let's take a step back and see what ESG is.
What is ESG and why it matters more than ever
The term ESG refers to environmental, social, and corporate governance factors that are considered non-financial and relevant to stakeholders. ESG factors can highlight additional investment risks and opportunities for a company, not limited to purely investment factors, in fact the goal of ESG reporting is about using data to measure how a company's social-environmental initiatives are compared with industry benchmarks and goals. ESG is thus a framework used to assess an organization's business practices and performance on various sustainability, social and ethical issues (IBM, 2024). In simpler words, it is a set of standards that measure a company's impact on society, the environment, and its transparency and accountability.
In fact, one of the main differences between ESG and matters, such as sustainability or corporate social responsibility (CSR), is the notion of motivation versus outcome. Corporate social responsibility functions as a business model that motivates a company and its employees to act in the best interests of society as a whole. ESG reporting, on the other hand, is the result of these initiatives and provides stakeholders with the data they need to make decisions (IBM, 2024).
As of today, in the world of capital markets, companies are under close scrutiny by investors. Indeed, a company's reputation can have a direct impact on its profits. The investment community requires ESG metrics precisely to ensure that companies are solid investments and also in line with their values - for example, climate change, social responsibility, low carbon emissions. An example of how this framework has helped a company's reputation is the case of Microsoft. The tech giant in 2020 announced the goal to become carbon negative by 2030, increasingly investing in renewable energy and taking care of their environmental footprint, as well as committing to setting clear diversity and inclusion goals. It has become one of the most highly regarded tech companies globally for the credibility of its ESG commitment and the transparency of its reports (Microsoft, 2024).
However, there is no single ESG governing body or regulator to track and collect this data. Instead, there are numerous frameworks developed by international organizations, non-governmental organizations, and commercial data providers, each addressing the needs of different users and audiences. It is relevant to know that the adoption of ESG disclosures is growing more and more: a 2020 survey of the top 100 companies in 52 countries found that 80% of them report their sustainability performance. There are also efforts to improve the way ESG data is verified. As a matter of fact, more than half of investors surveyed in a 2020 BlackRock client survey, 53% of respondents cited poor quality or availability of ESG data as the top barrier to implementing sustainable investing (Government of Canada, 2023). And that is where AI comes into play.
How AI is transforming ESG Reporting and data collection
One of the biggest challenges in ESG reporting is the sheer amount of data required. Organizations need to capture incredibly detailed data on everything from carbon emissions, sustainability policies, employee diversity metrics, and inclusion in hiring processes, and this data often comes from different systems within the company. In this context, GenAI can serve as a critical tool to automate the collection and organization of complex data sets.
Through advanced algorithms, “AI can analyse internal databases, extract relevant ESG metrics, and classify them into standardized categories, while ensuring data integrity and accuracy” (SIA, 2024). In short, precise, accurate, and fast work that a human would be hard-pressed to replicate with the same amount of time and precision. For example, AI models can even continuously monitor environmental performance data, providing insights into real-time carbon footprint reductions or potential risks. (SIA, 2024) Instead of waiting for year-end assessments, companies can now use AI to provide continuous updates or quarterly insights on critical metrics such as carbon emissions, energy consumption and water use. As a result, this shift allows companies to proactively address sustainability issues, increase accountability and improve decision-making, demonstrating their responsibility and commitment to sustainability. Consumers are, indeed, increasingly favouring brands that are committed to corporate responsibility for sustainability (CSE, 2025). Thanks to a new and broader sensitivity towards the environment and climate (at least in some countries — any reference to the USA is purely coincidental —), companies that show real involvement in sustainability efforts are strengthening their image and buyer loyalty.
Furthermore, by constantly monitoring their carbon footprint, water consumption and waste management, companies can take immediate corrective measures and reduce both operating costs and environmental impact. These predictive capabilities enable better risk management and more data-driven decision making. For instance, using AI-based models, companies can predict how changes in supply chain sustainability will impact their long-term financial performance, allowing them to adjust operations before disruptions occur. (SIA, 2024).
The Digital Infrastructure Behind ESG: AI, IoT, and Blockchain
One of the most exciting features of AI is that it has the power to disrupt energy systems and transform the way energy is generated, distributed, and consumed. With artificial intelligence, companies can leverage the insights they gain to optimize the integration of renewables, improve grid performance, and reduce waste. In fact, “as sustainability regulations evolve, driven by frameworks such as the European Union’s Corporate Sustainability Reporting Directive, AI is emerging as a critical compliance tool”. (ESGDive, 2025) AI can generate content and recommend controls for risk management, in synergy with human supervision, can automate data collection, integrate different sources and help produce sustainability reports ready for auditing, significantly reducing compliance times and costs. (ESGDive, 2025)
But the real power of AI lies in its ability to integrate sustainability data into companies' strategic decision-making process, making sustainability data more accessible and " easier" for non-experts. To recap, keywords to describe the major benefits of AI: risk management, forecasting, accessibility.
But how are these AI-driven ESG innovations transforming sustainability reporting by enabling data collection, processing and analysis? Through these advanced technological tools:
- AI-Powered Analytics: Machine learning algorithms analyse data patterns, predict sustainability risks, and generate automated reports.
- IoT (Internet of Things) Sensors & Smart Meters: measure carbon emissions, energy consumption and water usage in real-time, ensuring precise tracking.
- Blockchain for Data Integrity: Increases transparency and provides verifiable reports for stakeholders.
- Automated Compliance Tracking: AI tools detect regulatory changes and notify companies of reporting obligations, reducing the risk of non-compliance.
Balancing AI risks and benefits
No innovation is free of charge, and these technological benefits also come at a cost. First, as mentioned above, artificial intelligence threatens to disrupt entire industries, causing large-scale job losses. According to the OECD 27% of jobs are in professions at high risk of automation, including highly skilled, non-routine roles in sectors such as finance, medicine, and law, among others. Whether this will lead to large-scale unemployment and how quickly this job loss could occur has been debated for years, so it is a risk that companies will have to take into account (CFA Institute, 2023).
Other important concerns include individuals' rights to non-discrimination, protection of personal data, and privacy; despite reassurances from large companies, the issue of privacy remains one of the great unknowns. Further complicating the issue is the fact that different AI applications have different carbon footprints depending on the computing power they require to run. As the volume of stored data grows exponentially, leading to increased energy consumption and e-waste, the environmental impact of AI is expected to increase. (CFA Institute, 2023). While there are many predictions about the productivity benefits of AI, there are widespread concerns about its environmental impact. The data centre industry, which powers AI applications, is estimated to contribute 2-3% of global greenhouse gas emissions. According to Goldman Sachs, a ChatGPT query needs nearly 10 times as much electricity to process as a Google search, and, following this pace, data centre power demand will grow 160% by 2030 (The Guardian, 2024).
Does it seem absurd that a technology which tracks the level of sustainability of companies, primarily climate/environmental sustainability, is itself contributing to massive CO2 emissions? It is, but it is also the reality that needs to be addressed when it comes to AI and ESG, and it requires an awareness that allows us to continue to use this tool in a way that is truly more “sustainable”.
Conclusion
By leveraging artificial intelligence and automation, companies around the world can ensure compliance, increase investor confidence, and improve operational efficiency and reporting of ESG metrics. Most importantly, moving to real-time ESG reporting can foster corporate responsibility and improve long-term sustainability strategies.
However, it is clear that AI has the power to revolutionize work and working conditions, creating obstacles such as job loss, while at the same time it has a large energy and environmental impact. Therefore, our approach to integrate this technology must be inclusive but thoughtful. To this end, it is imperative to pay attention and be aware of the inequalities and critical issues that occur. In addition, as ESG regulations continue to evolve globally, it is critical to keep AI tools up-to-date to reflect the latest standards. Ensuring that AI-driven reporting remains compliant with the growing number of regulatory frameworks will require continuous monitoring and algorithmic adjustments.
The challenges this path poses are considerable, and organizations must address these complexities seriously and with foresight to ensure that AI-driven ESG initiatives are right, transparent, and equitable. A fair and sustainable future depends not only on what AI can do, but on how consciously and equitably we choose to use it.
Riproduzione riservata®
Bibliography
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