AI is reshaping not only individual tasks, but also organisational expectations, governance, infrastructure and wider social and environmental systems.
AI is increasingly becoming part of how organisations research, communicate, make decisions and deliver services. Its effects extend beyond the immediate task. Repeated use can change workflows, professional roles, management expectations and the standards by which work is judged.
Understanding AI in context therefore means looking beyond individual tools and outputs. It requires attention to the organisational, social and environmental conditions surrounding their use, including who benefits, who carries risk, what forms of judgement remain visible and how organisations learn from experience.
This page brings together selected reports and resources on organisational adoption, governance, workforce change, environmental impacts and responsible use. It complements the more practice-focused materials on research, evaluation, facilitation and professional judgement found elsewhere in the Learning for Sustainability generative AI section.
Governance and responsible use
Responsible AI involves more than managing technical risks. It also requires attention to the purposes AI serves, the values that guide its development, who has influence over key decisions, and how responsibility is shared across organisations, governments and wider society.
The turbulent AI era is here. The choices we make now are critical.
Bill Gates (2026) essay reflects on the social choices surrounding rapid AI development, including implications for work, education, inequality, human relationships and democratic decision-making. The essay is useful for its broad framing of AI as a societal rather than simply technological issue. It also raises questions about how responsibility should be shared between governments, institutions, communities and the companies developing increasingly powerful AI systems.
Magnifica Humanitas: On safeguarding the human person in the time of artificial intelligence
Pope Leo XIV’s 2026 encyclical considers AI in relation to human dignity, work, freedom, social justice, democracy and the common good. It argues that technological development is never neutral in practice, because its direction reflects the choices of those who design, finance, regulate and use it. The encyclical is especially useful for asking what purposes AI should serve, whose interests shape its development, and how societies might exercise greater collective responsibility over its direction.
Religious ethics in the age of AI: a comparative study of faith-based approaches to AI governance
Darren Winter’s 2026 paper compares Christian, Islamic and Buddhist ethical traditions with contemporary AI governance frameworks. It finds strong areas of convergence around human dignity and agency, justice, accountability, non-harm, compassion, sustainability and transparency. Particularly useful is its distinction between traditions that emphasise moral formation, intention and communal responsibility, and governance approaches that rely more heavily on procedures, documentation and audit. It offers a broader perspective on the values that can inform responsible AI governance.
AI governance and responsible use
Published by PwC in 2025, this resource examines emerging legislation, corporate governance, transparency and risk management around AI use, drawing on frameworks including those developed by the OECD. It provides a practical overview of how organisations can approach accountability, governance and reporting as expectations continue to develop. It is particularly useful for those responsible for connecting organisational AI initiatives with wider ethical, legal and risk-management requirements..
Organisational adoption and change
Understanding how AI is being adopted in practice helps distinguish widespread experimentation from changes that genuinely improve organisational performance, capability and ways of working.
The state of AI: How organizations are rewiring to capture value
This 2025 McKinsey survey offers a practical snapshot of how organisations are adapting to generative AI. It is useful reading for those working in strategy, systems change or organisational development, showing how firms are shifting workflows, investing in governance and responding to risks such as inaccuracy, intellectual property concerns and cybersecurity. It provides a grounded view of where AI is being applied and how roles, policies and organisational capabilities are evolving.
The GenAI divide: State of AI in business
The State of AI in Business 2025 Report, by Aditya Challapally and colleagues at MIT NANDA, examines how generative AI is being adopted in practice. Drawing on 300+ case reviews and 52 interviews, it highlights the “GenAI Divide”: while many firms experiment, only 5% report real transformation or ROI. For practitioners, it offers clear insights into adoption pitfalls, vendor strategies, and the conditions where AI can deliver meaningful impact.
Environmental impacts and infrastructure
AI use also depends on physical infrastructure, with consequences for energy, water, materials and the capacity of electricity systems to accommodate rapidly growing demand.
Environmental impact of AI: Balancing innovation with sustainability
Michael Vereb (2025) brings together research on AI’s environmental footprint, including energy use in data centres, hardware production and water consumption. The resource also considers ways AI may contribute to renewable energy, conservation, agriculture and climate modelling. It provides an accessible overview for sustainability and systems practitioners wanting to consider both the environmental costs of expanding AI infrastructure and its potential contribution to environmental problem-solving.
AI and energy security
This International Energy Agency analysis examines how rapid data-centre growth is placing pressure on electricity grids, particularly where large new loads are concentrated in particular regions. It discusses grid-connection delays, infrastructure bottlenecks and the risk that existing networks may struggle to accommodate planned capacity. Also see the IEA’s broader Energy and AI report for readers wanting the global figures and wider context.
Connecting the wider picture with practice
Structural and organisational conditions shape what is possible in everyday work, but they do not determine whether a particular use of AI is sound. This LfS essay connects wider questions about infrastructure, ownership, regulation and governance with practice-level questions about inquiry, participation, interpretation and responsibility.
This page forms part of the Learning for Sustainability resources on AI and professional practice. Explore the AI for professional practice, research and collaboration hub for related essays, organisational guidance and curated resources.
[* Image by Jintana / Adobe Stock]
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