How AI and Digital Skills Can Support Climate and Business Resilience
The growing use of artificial intelligence (AI) presents organisations with both an opportunity and a responsibility. On the one hand, reliance on energy- and resource-intensive infrastructure creates carbon, water, and land impacts that vary depending on where and how systems are powered and used (Aczel et al., 2026). Organisations need to recognise and manage these impacts as part of responsible AI adoption. At the same time, when used with clear objectives, reliable data and appropriate human oversight, AI can help organisations interpret complex information, identify risks and patterns, and make better-informed decisions that support climate and business resilience. The challenge is to build the digital skills needed to use it responsibly and where it can deliver meaningful value.
That more balanced picture is emerging through Skillnet Climate Ready Academy’s new AI for Sustainability programme. Across six weeks of in-person and online sessions, participating Irish businesses explored the AI landscape, assessed their readiness, and developed action plans for viable uses that could support climate resilience and operational efficiency. Their experience points to a central lesson: the value of AI depends on an organisation’s ability to use it well.
The Importance of Data
Sustainability work is becoming increasingly data-driven. Teams may need to bring together emissions, energy, suppliers, operational, reporting, and climate-risk information from different systems. AI can help organise large datasets, identify gaps, review documents, and support initial analysis. Industry guidance identifies similar opportunities in sustainability reporting and supply-chain visibility, while warning that automation can amplify errors or misread incomplete information (World Economic Forum, 2025).
This is why human oversight remains essential. A polished AI response is not the same as reliable evidence. Sustainability professionals need to understand where data came from, what assumptions were applied, what may be missing, and whether an output is suitable for the decision being made. The NIST AI Risk Management Framework highlights validity, reliability, transparency, explainability, privacy, fairness, safety, and resilience as important characteristics of trustworthy AI (NIST, 2023), all of which can be controlled by a skilled workforce.
Building the Skills
AI literacy does not mean that every sustainability professional must become a data scientist. It means being able to recognise appropriate uses, ask informed questions, challenge outputs, protect information and know when technical, legal, or subject-matter expertise is required. The OECD notes that AI adoption is increasing demand for both specialist expertise and broader workforce literacy, with upskilling and reskilling essential to help employees work effectively with AI (OECD, 2025).
For sustainability teams, these capabilities have a practical and climate-resilient dimension. Building such capabilities – in areas such as data management to inform business and sustainability targets – will result in a workforce better positioned to improve a business’ operational performance, strengthen overall resilience, and respond to changing expectations in the wider community. Technology alone is unlikely to create meaningful value without a workforce that knows how to understand and interpret the output, thus requiring a hybrid approach to usage.
The AI for Sustainability Programme
Skills also need organisational support and a clear action plan. Employees cannot use AI responsibly without clear objectives, reliable data, approved tools, defined ownership and routes for validation or escalation.
To address these, the AI for Sustainability programme demonstrates a practical route from awareness to action. Businesses can assess their current readiness, identify proportionate opportunities, and build an implementation plan grounded in skills, governance, and measurable sustainability value.
This article begins a six-part series sharing insights from that journey. The next article will explore a crucial question: how ready are businesses to adopt AI responsibly?
References
Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., & Madani, K. (2026). Environmental cost of AI’s energy use: Carbon, water and land footprints. United Nations University Institute for Water, Environment and Health. https://doi.org/10.53328/INR26RMA002
National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1
Organisation for Economic Co-operation and Development. (2025). Bridging the AI skills gap: Is training keeping up? https://doi.org/10.1787/66d0702e-en
World Economic Forum. (2025, September 26). How AI can transform sustainability reporting. https://www.weforum.org/stories/climate-action/harnessing-ai-for-sustainability-reporting-path-forward/