When we talk about sustainability, we actually refer to different dimensions of this concept. For most people, sustainability is about the environment: reducing waste, minimising carbon emissions and protecting natural resources. However, in the business world, it is often associated with long-term economic viability. This is in addition to social sustainability, which encompasses equity, working conditions and impact on communities. There are several models that integrate these three aspects, ecology, economy and society, such as the three-ring model, the three-legged stool or the nested model, but still add a fourth dimension: technology.
As in many other areas, artificial intelligence also plays a key role in sustainability. However, its implementation can be based on two main approaches:
- AI for sustainability refers to the use of artificial intelligence to optimise resources and achieve sustainable goals.
- Sustainable AI focuses on the development and application of AI by considering its environmental and social impact to begin with.
AI x Sustainability: Implementation Framework
Zechiel et al. (2024) mention several ways in which AI can be harnessed to design sustainable strategies, while stressing the importance of designing and implementing it with this very goal of sustainability in mind. These authors propose a two-stage theoretical framework, starting from a 2×2 quadrant that is divided between AI for sustainability vs. sustainable AI and the type of sustainability (ecological or social). To this, they add a dimension of internal and external resources for implementation. According to their proposal, in order to apply AI to sustainability strategies, it is necessary to:
- Define clear objectives that align with the company’s vision, such as reducing waste or improving energy efficiency.
- Assess opportunities where AI can add value, such as energy optimisation or predictive maintenance.
- Consider AI’s environmental impact, given its high resource consumption.
- Ensure ethics and inclusiveness to avoid bias and gain the trust of both consumers and investors.
- Measure and monitor results through key indicators.
- Foster collaboration with stakeholders and establish governance frameworks.
- Promote cultural change by integrating sustainability into organisational strategy.
In short, it is important to align AI with sustainability objectives, balancing its benefits and costs and focusing on ethics and inclusiveness, for which stakeholder participation and the development of governance frameworks are crucial. Furthermore, applying measuring tools and KPIs to monitor achievements is a must. The expected outcome of all this is to drive a cultural change that further promotes a sustainability mindset throughout the organisation.
Applications and Challenges
As pointed by Goel et al. (2024), AI is already transforming sustainability in various industries:
- Energy: smart grid management, renewable energy optimisation and consumption forecasting.
- Agriculture: precision agriculture, crop monitoring and sustainable supply chains.
- Manufacture: predictive maintenance, waste reduction and circular economy models.
- Transport: route optimisation, autonomous vehicles and emissions reduction.
- Social sustainability: improving access to education, health and financial services in underserved communities.
Despite its advantages, AI adoption in sustainability faces challenges such as high implementation costs, lack of experience in its application, data privacy and the risk of perpetuating biases, which combined with the fact that lack of regulation due to technological advancement’s rapid progress calls for the development of appropriate governance frameworks to ensure responsible use.
Conclusion
AI offers significant opportunities for sustainability, whether environmental, economic or social. It can not only help to optimise processes and reduce costs, but also strengthen a company’s reputation. However, its implementation requires a clear strategy that assesses its environmental impact and ensures its ethical and responsible use. Collaboration between businesses, governments and communities is key to realising its full potential.
References
Goel, A., Raut, G., Sharma, A., & Taneja, U. (2024). Artificial Intelligence and Sustainable Business: A Review. South Asian Journal of Business and Management Cases, 13(3), 340-365. https://doi.org/10.1177/22779779241302146
Zechiel, F., Blaurock, M., Weber, E., Büttgen, M., & Coussement, K. (2024). How tech companies advance sustainability through artificial intelligence: Developing and evaluating an AI x Sustainability strategy framework. Industrial Marketing Management, 119, 75-89. https://doi.org/10.1016/j.indmarman.2024.03.010
