AI and ESG: How AI is changing the way sustainability is managed

Authors of the article: JUDr. Pavel Staněk, MBA, Mgr. Vendula Korčáková, ARROWS (office@arws.cz, +420 245 007 740)

In recent years, Artificial Intelligence (AI) has become one of the most significant technology trends that is permeating various industries and changing the way modern companies operate. One of the areas where AI is finding increasing application is in sustainability management and ESG (Environmental, Social, Governance) strategies. How is AI improving the achievement of corporate sustainability goals and what are the implications for investors and society?

AI as a key tool for environmental sustainability

One of AI's greatest strengths is its ability to quickly process and analyse large volumes of data. This allows companies to more accurately monitor their environmental impacts, such as greenhouse gas emissions, energy consumption or natural resource use. AI systems can optimise energy consumption in office buildings, leading to significant cost and carbon footprint reductions.

AI also plays a key role in predictive maintenance and supply chain optimization. By analyzing large volumes of data, AI can predict machine and equipment failures, minimizing unplanned downtime and reducing losses. In logistics, AI helps analyze transportation efficiency, optimize routes and minimize empty trips, reducing emissions and operating costs. At the same time, it enables better planning of storage capacity and reduction of raw material wastage. AI systems also identify high carbon footprint areas in the supply chain, helping companies choose more sustainable suppliers.

AI to automate ESG reporting

One key area where AI can play an important role is in ESG reporting. Collecting and analyzing data for ESG reporting can be a time-consuming and complex process. AI simplifies and streamlines this process through, for example, natural language processing (NLP), which enables the analysis of large volumes of unstructured data, including media reports. This improves the accuracy and timeliness of ESG reports and allows companies to better focus on strategic ESG topics.

Social dimension: improving conditions through AI

But AI is not just about conservation. It can also improve conditions for employees and promote social sustainability. Companies are already using AI to explore issues such as diversity or inclusion. For example, AI can help identify gender inequalities and suggest measures to help create a more equitable environment.

Other uses include analysing employee feedback to improve the working environment, or monitoring safety standards in real time to prevent risks early on. These technologies are particularly useful in manufacturing companies, where they contribute to improved safety and better human resource management.

AI is also increasingly being used in employee training. Through personalised training programmes, employees can develop sustainability-relevant skills and improve their ESG awareness. Another area is combating misinformation about climate and sustainability that is spread on social media - AI tools can monitor and refute false information, contributing to a more objective public discourse.

Governance: AI as a guarantor of transparency

Corporate governance is another area where AI plays a crucial role. With advanced analytics tools, companies can better monitor regulatory compliance and ensure a higher level of transparency. For example, in finance, AI systems can detect irregularities and potential fraud in financial data. This approach is already being used by companies to increase credibility with clients and investors.

Challenges and ethical issues

Although AI offers a wide range of benefits, its implementation is not without its challenges. Privacy is one of the most important aspects, where it must be ensured that data processed by AI is secure and not misused. Another challenge is the transparency of algorithms, which often act as a "black box" and make responsible use difficult. For example, when deploying AI in logistics, it is important to ensure that optimisation algorithms do not disadvantage certain suppliers or regions.

Companies looking to use AI for ESG must also consider its energy intensity. Operating AI systems requires a large amount of computing power, which can lead to high energy consumption and greenhouse gas emissions. One solution is to use energy-efficient AI models and switch to renewable energy sources.

Another risk is the bias of AI algorithms. If models are trained on incomplete or biased data, they may exhibit systematic errors that affect ESG decision-making. In corporate governance, for example, AI may unintentionally favour certain groups of investors or stakeholders.

Last but not least, there is the issue of accountability. Who is responsible for the decisions made by AI? This is still an open question that requires not only technological but also legislative solutions.

The future of AI in ESG

Going forward, AI can be expected to be increasingly integrated into ESG strategies. Autonomous systems can monitor the use of natural resources or predict risks associated with climate change. AI will also play an important role in investment with respect to ESG factors and help companies develop new sustainable products and services.

Companies that can use AI effectively will not only gain a competitive advantage, but also the trust of investors and the respect of wider society. However, the key will be to ensure that AI is developed in a responsible way - with an emphasis on transparency, ethics and sustainability.