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 AI section.
Organisational adoption and change
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 in real time. It’s 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 like inaccuracy, IP concerns, and cybersecurity. With insights on where AI is being applied—and how roles, policies, and capabilities are evolving—it provides a grounded view of what mainstream AI adoption looks like in practice.
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.
Governance and responsible use
AI governance and responsible use
Published by PwC (2025) and supported by frameworks from OECD, this resource examines emerging legislation, corporate governance, and transparency in AI use. It provides essential guidance for governance, risk management, and compliance professionals responsible for aligning AI initiatives with ethical standards and evolving legal expectations. Practitioners gain insights into accountability measures and reporting frameworks critical for responsible AI governance in complex organizational contexts.
Environmental impacts and infrastructure
Environmental impact of AI: Balancing innovation with sustainability
Michael Vereb (2025) compiles research detailing AI’s substantial environmental footprint, including intensive energy use in data centers, hardware production, and water consumption. Alongside these challenges, the resource highlights AI’s potential to optimize renewable energy, conservation, agriculture, and climate modeling—helping reduce emissions by up to 4%. Sustainability and systems design practitioners will find this content useful for navigating the urgency of greener AI infrastructure and adopting practical, responsible AI practices.
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. Questions about infrastructure, ownership, regulation and governance sit alongside practice-level questions about inquiry, participation, interpretation and responsibility. This page provides a fuller discussion of these connected levels.
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. This includes links to the related resource page Using AI in research and practice and the accompanying posts: AI as a thought partner: reflections on collaborative practice and systems work and AI prompts for shared thinking: a light framework for purposeful prompting.
[* Image by Jintana / Adobe Stock]
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