Artificial Intelligence as a Driver of Sustainable Business

Introduction

Artificial Intelligence (AI) is increasingly becoming a cross-cutting technology with a significant impact on the competitiveness, efficiency, and sustainability of businesses. It can serve as a key enabler for sustainable business practices, as AI systems open up opportunities to make processes radically more efficient and conserve resources, thereby achieving positive ecological and economic outcomes. By improving forecasts, optimizing processes, and optimizing resource use, AI can create competitive advantages while simultaneously producing socially and environmentally beneficial results—for example, in energy management, logistics, agriculture, and industry. Sustainability, in the sense of ecologically and socially responsible business practices, can thus increasingly be reconciled with economic success.

At the same time, the increasing use of AI creates new conflicts of interest and regulatory questions. AI models are often very energy- and resource-intensive: The training phase of large AI models (such as language applications) requires enormous computing power and electricity—so much so that in some cases, training an AI model consumes more energy than the subsequent efficiency gains save. The global energy demand of the AI industry is growing rapidly and is expected to increase tenfold by 2026. Furthermore, AI raises questions of control, transparency, and accountability: for example, how to make algorithmic decisions transparent, avoid negative side effects (e.g., discrimination due to bias in data and models), and protect personal data. These challenges must be addressed in order to use AI responsibly as a lever for sustainable business—rather than merely as a tech buzzword without real impact.

The following sections examine these opportunities and challenges in greater detail. First, we address the role of AI in sustainable business: How and why can AI support companies in achieving environmental, social, and economic sustainability goals? This is followed by concrete real-world application examples—ranging from data analysis and process optimization to data governance and compliance. Finally, key legal frameworks in the EU and Switzerland are presented, particularly the EU AI Act and relevant Swiss approaches, with a focus on governance, transparency, risk management, and accountability in the use of AI. The conclusion summarizes the key findings: AI can significantly advance sustainable business practices —provided it is used responsibly and energy-efficiently.

The Role of AI in Sustainable Business

AI realizes its potential across all three dimensions of sustainability—environmental, social, and economic—and can thus holistically promote sustainable economic activity.

• Environmental sustainability: Intelligent algorithms can reduce resource consumption and lower emissions. Through better forecasting and data analysis, for example, energy supply and demand can be balanced more precisely, facilities can be controlled more efficiently, and climate risks can be identified early on. In agriculture and manufacturing, AI enables the meticulous optimization of input factors: This allows fertilizers to be applied more precisely according to need, thereby reducing consumption and minimizing environmental impact. AI can also improve material cycles—for example, through intelligent recycling concepts that optimize the recovery of recyclable materials. In the medium term, AI thus makes a significant contribution to decarbonization and the conservation of natural resources.
• Social and governance-related sustainability: AI systems can help achieve social goals, for example by improving workplace safety (e.g., automatic hazard detection in factories) or promoting fair HR decisions through bias detection. In global supply chains, AI can analyze data on working conditions or supplier certifications to uncover risks such as human rights violations or illegal raw material extraction—an important contribution to ethics and resilience in the supply chain. Due diligence requirements, such as those under the EU Supply Chain Directive (CSDDD), could be supported here through the use of AI. Furthermore, AI improves data quality and transparency, which provides a solid foundation for ESG reporting and governance. Overall, AI can support corporate leadership in systematically managing sustainability issues.
• Economic Sustainability & Resilience: AI-driven sustainability measures not only aim for the common good but also pay off financially. Efficiency gains (e.g., in energy or material use) reduce costs, improve margins, and boost competitiveness. AI makes it possible to decouple value creation from resource consumption—meaning companies can grow without emitting correspondingly higher levels of emissions. Additionally, AI promotes business resilience: it enables early warning systems for risks (e.g., extreme weather, market shifts, supply chain bottlenecks) and supports faster, well-informed decision-making in crisis scenarios . For example, automated analyses can increase the resilience of supply chains by enabling AI to screen new suppliers more quickly and identify potential issues (compliance, ESG risks) at an early stage. This contributes to crisis resilience and long-term value creation—especially in a world where sustainability performance is increasingly becoming a hallmark of reliable companies.

Where and how AI can be applied in practice

Where can AI be applied in practice? Below are some practical application areas where AI has already proven itself as a driving force for greater sustainability and efficiency:

• Data Analysis & Forecasting: Sustainability management generates immense amounts of data—from energy consumption and emissions to social metrics. AI can bring clarity here: Using machine learning algorithms, patterns in complex ESG data can be identified, risks forecasted, and actions prioritized. For example, companies are already using AI to calculate climate risks and scenarios (e.g., for TCFD analyses and stress tests), to make sustainability KPIs available internally in real time, or to monitor and evaluate supplier data globally in real time. AI improves the basis for decision-making for executives by providing precise forecasts (e.g., of energy prices, extreme weather events, or raw material availability). This increases predictability and enables companies to develop proactive sustainability strategies.
• Process Optimization & Resource Utilization: A key lever of AI in day-to-day operations is the automation and optimization of processes. AI can control production facilities more intelligently and cost-effectively—from rule-based machine learning to minimize material losses to predictive maintenance, which reduces unplanned downtime and extends the service life of equipment. In the manufacturing industry, companies use AI to maximize energy efficiency in real time and immediately sort out defective parts.
• Data Governance & Compliance: Clean—that is, accurate and structured—data and reliable processes form the foundation of sustainable business practices and robust compliance. AI can improve data quality and strengthen governance. For example, AI-powered tools enable the efficient creation and review of ESG reports: They can process unstructured information using natural language processing, making it easier to generate reports in accordance with new standards (e.g., the ESRS of the EU-CSRD)—ensuring high quality and reliability. Additionally, AI systems support the adherence to compliance regulations: They filter unstructured data sets (e.g., emails, documents) for indications of violations (e.g., environmental regulations or labor law violations) and alert those responsible. Furthermore, AI facilitates auditability—for instance, by having algorithms log decision-making processes and make them traceable, which enhances transparency and accountability in sustainability-related processes. Finally, AI itself serves as a verification tool against greenwashing: algorithms can compare publicly available corporate information (e.g., emissions data, sustainability pledges) with actual measurement data and uncover discrepancies.

Legal Framework in the EU and Switzerland

In light of the dynamic development of AI, legislators are also responding to the opportunities and risks described. Internationally, the regulation of AI is at various stages—ranging from soft law and sector-specific approaches to comprehensive legislation. In the EU, the Artificial Intelligence Act (AI Act) established the world’s first legal framework for AI in 2024, while Switzerland is currently relying on existing regulations (particularly the Data Protection Act) and a sector-specific approach. Both approaches emphasize governance, transparency, risk management, and accountability in the use of AI—albeit to varying degrees.

European Union: AI Act, GDPR, and ESG Considerations

In the EU, the AI Act (Regulation (EU) 2024/1689) has been in force since August 1, 2024—a regulatory approach designed to systematically identify and mitigate AI risks. At its core is a risk-based tiered approach: requirements are scaled according to the potential risk to safety and fundamental rights. High-risk AI applications, for example, must meet strict requirements, including robust risk management processes, data quality and governance obligations, comprehensive technical documentation of the systems, human oversight, and ongoing monitoring and compliance assessments. Less risky AI (e.g., chatbots) is primarily subject to transparency obligations: Users must be informed that they are interacting with AI or that content (text, images, audio) is artificially generated. Prohibited systems (such as social scoring or public real-time facial recognition for surveillance purposes) are completely banned, in line with corresponding prohibited uses of AI under the GDPR and EU fundamental rights protection. On May 6, the EU agreed on an “AI Omnibus Package” to simplify the AI Regulation. Key planned changes include deadline extensions for full compliance and a new ban on AI-generated nude images (so-called “nudification”).

A key focus of the AI Act is energy efficiency and resource conservation in the AI sector—an indirect contribution to sustainable economic activity. Indeed, the regulation emphasizes in its objectives the need to ensure environmental protection alongside innovation. Providers of large-scale AI models (so-called generative or general-purpose AI) will be required to produce technical documentation, including a breakdown of their models’ energy consumption. Excessive power consumption could even lead to a system being classified as a “system posing systemic risk,” triggering additional testing and due diligence obligations. Furthermore, the European Commission is to develop standards for energy-efficient AI and promote voluntary codes of conduct for sustainable, energy-efficient AI. Additionally, AI is embedded within the context of existing sustainability regulations: AI providers and users must ensure that applicable data protection laws (GDPR) continue to be complied with. The EU is also building bridges to ESG regulation: AI is recognized as a tool for sustainability goals, for example by enabling AI-based solutions to improve the measurability and controllability of environmental and social factors or to strengthen corporate risk management (CSDDD).

Switzerland: Data Protection Act (DPA) and Soft Law

Switzerland does not yet have a specific AI law. However, the revised Federal Act on Data Protection (FADP), which has been in effect since September 1, 2023, includes a provision on AI-related automated decisions for the first time. Art. 21 DSG adopts the concept from Art. 22 GDPR and stipulates that data subjects must be informed about fully automated individual decisions and have the option to request a human review of such decisions. The DSG is, moreover, technology-neutral and therefore fully applicable to AI-supported data processing. The Federal Data Protection and Information Commissioner (FDPIC) emphasizes that manufacturers and users of AI systems must already ensure transparency regarding the purpose, functioning, and data sources of their AI. In particular, it must be clearly evident when users are interacting with an AI (e.g., a chatbot instead of a human) and whether entered data is being used for further purposes (such as training). High-risk AI applications are also permitted under Swiss law, but require additional safeguards—such as the obligation to conduct a data protection impact assessment (DPIA) prior to deployment. Certain extreme uses of AI—such as widespread public facial recognition or social scoring—are indirectly prohibited by existing law (e.g., as violations of fundamental rights or data protection).

In parallel with the application of data protection law, Switzerland relies on soft law and international cooperation regarding AI. In February 2025, the Federal Council decided to ratify the Council of Europe’s “Convention on Artificial Intelligence.” This convention—in the drafting of which Switzerland participated—defines ethical s and minimum legal standards for AI, particularly regarding transparency, non-discrimination, the protection of fundamental rights, and oversight. Through ratification, Switzerland is positioning itself internationally as a partner for trustworthy AI. To implement the convention, necessary legal amendments (e.g., to existing laws) are to be drafted by the end of 2026. In doing so, the Federal Council favors a sector-specific approach rather than a comprehensive AI law: Where necessary, industry-specific rules for AI are to be created (e.g., in healthcare, the financial sector) and existing regulations, such as the DPA, are to be supplemented in a targeted manner. This pragmatic approach is intended to avoid stifling innovation while simultaneously safeguarding core values such as data protection and accountability (see also: Obstacles to the Use of AI Tools in Companies).

A comparison between the EU and Switzerland reveals a stark contrast: With the AI Act, the EU is pursuing a comprehensive and binding regulatory framework that imposes strict requirements on market participants (including energy transparency and environmental considerations), but also promises legal certainty and a level playing field for innovation. Switzerland, on the other hand, is currently relying on more flexible instruments, existing law (especially data protection), and a wait-and-see approach to international developments to maintain a balance between promoting innovation and minimizing risk. However, companies with cross-border activities—such as many corporations with a presence in Switzerland—must already be mindful of key AI obligations from the EU here as well (extraterritorial effect), for example in the context of ESG reporting (CSRD) or supply chain AI (CSDDD).

Conclusion

AI touches on law, technology, and practice in equal measure and has the potential to fundamentally transform sustainable business practices. However, this transformation will only be successful if AI is used responsibly, energy-efficiently, and purposefully. Legal frameworks such as the EU AI Act or the Swiss Data Protection Act (DSG) promote appropriate governance, transparency, and accountability by establishing clear guidelines for data quality, risk analyses, control mechanisms, and user rights. This encourages companies to align their use of AI with their sustainability and compliance goals. If successful, AI can lead to significant environmental improvements and efficiency gains in practice, reduce risks, and strengthen the resilience of organizations. What is crucial is a strategic use of AI that truly generates impact, rather than mere “tech or sustainability labeling.” AI is not an end in itself, but a tool—and as such, it will contribute to sustainable value creation if its use is carefully planned and its potential is harnessed specifically for sustainability goals. Understood in this way, artificial intelligence can become a true driver of sustainable business—as an innovator, efficiency enhancer, and problem-solver that benefits the environment and society just as much as economic performance.

Because ultimately, sustainable business is becoming smarter—and smart technologies are becoming more sustainable.

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