Shazia Hussenbux is Dometic’s Global Head of Product Sustainability and Product Compliance, where she leads initiatives on climate, circularity, and regulatory alignment. She previously worked at AWS, Oatly and Husqvarna and now drives sustainable product strategy and cross-functional impact from Dometic’s Stockholm headquarters.
As the world accelerates toward a more sustainable future, artificial intelligence (AI) is emerging as both a powerful enabler and a potential risk. Ignoring its potential feels like a missed opportunity especially when it can address some of the most pressing challenges in ESG reporting, and decarbonization.
AI is already transforming how organizations approach sustainability. There is a variety of applications ranging from gathering, improving, automating data and data quality from diverse sources such as carbon emissions, assisting in drafting qualitative content for sustainability or ESG reports, tracking the evolving regulatory landscape and flagging compliance risks in real time, detecting patterns in climate data and offering insights on resilience planning and disaster preparedness, and extracting actionable insights from complex supply chains to enhance transparency. This trend reflects a broader shift toward sustainability solutions and services that are data-driven and scalable.
AI is not a panacea, but it can be a powerful lever in accelerating transformative change in sustainability.
Despite its promise, AI also introduces risks including ethical concerns such as data bias, high energy and water consumption linked to training of large language models, reliability concerns due to hallucinations, and propagation of false or misleading information leading to greenwashing. If left unaddressed, these risks can result in regulatory non-compliance, and reputational damage.
To unlock AI’s potential in sustainability, data integrity is nonnegotiable. The adage ‘garbage in, garbage out’ has never been more relevant. If AI models are trained on inaccurate or incomplete data such as flawed emissions metrics or unreliable supplier risk indicators, they may recommend actions that appear sustainable but increase environmental impact, fail to meet regulatory standards or shift impact elsewhere in the value chain or product lifecycle.
With new tools emerging daily, the temptation to adopting AI for novelty rather than impact is real. Without strategic alignment, the risk of diluting impact and wasting resources increases. Close collaboration between sustainability and IT functions is therefore crucial to identify right technology partners, deploy the relevant technology where it creates measurable value and build digital literacy across sustainability functions.
AI is not a panacea, but it can be a powerful lever in accelerating transformative change in sustainability. The challenge is not whether to use AI. Rather, how to use it responsibly and strategically. For sustainability leaders, understanding AI’s role is no longer optional, it’s imperative.
