AI’s environmental impact needs a place in workplace learning
A new guide puts the energy used to generate AI outputs alongside judgement and data handling in workplace skills development.
Understanding the environmental impact of everyday AI use should form part of workplace AI training, according to a new guide for L&D and HR teams.
Sustained Futures’ Future Skills A-Z brings the energy used to generate AI outputs into a broader account of the capabilities employees need to use the technology responsibly.
Its entry on inference, the process of a trained model producing an output from new input, asks users to improve a prompt before rerunning an answer that has missed the mark. It links that everyday decision to the resources consumed each time a model runs.
Katie Neck, founder of Sustained Futures, said: ‘Teams need to understand bias, their data and the energy behind every query, as well as how to write a prompt.’
The energy involved varies considerably. The International Energy Agency’s 2026 analysis says efficiency improvements have reduced energy use per AI task, while video generation, reasoning and agentic applications can use hundreds or thousands of times more energy per query than simple text generation.
That variation matters for workplace learning: awareness of AI’s energy use needs to take account of the task being performed, rather than treating every request as equivalent.
The free guide contains 78 terms across climate fluency, AI skills and what Sustained Futures calls ‘future mindset’. Each entry includes a definition, an explanation of its relevance and an action readers can try.
Alongside inference, the AI entries cover bias, hallucination, provenance, safety and users’ own data. Climate entries include adaptation, emissions, greenwashing and physical risk. The mindset section includes curiosity, listening and questioning.
The guide draws on existing reports and guidance, including material from the World Economic Forum, LinkedIn and sustainability and regulatory bodies. It is a practical introductory resource rather than a new study of workforce skills or AI’s environmental footprint.
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