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CASE AI Coffee Chat: AI in Practice - Lessons from ...
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The document summarises lessons from LSE’s early experience with generative AI adoption and how these lessons are being applied within PAGE. It presents AI adoption as an ongoing learning process rather than a one-off technical rollout, emphasising experimentation, shared learning, and adaptation to local context.<br /><br />Key themes include the importance of responsible governance, with clear accountability, practical policies, and data protection designed to enable rather than block innovation. LSE’s approach encourages safe experimentation, iterative improvement, and feedback loops so that staff can test tools in low-risk settings and shape future policy and strategy.<br /><br />The Early Adopters Programme used a volunteer-led model, allowing motivated staff to explore AI in real work scenarios. This helped generate practical insights, build confidence through peer learning, and identify quick wins such as drafting, summarisation, and presentation support. Training is non-mandatory and tailored to different learning styles, with the aim of encouraging adoption rather than enforcing compliance.<br /><br />The document also outlines PAGE’s three-tier AI adoption model: <br />1. Immediate individual use of generative AI such as Copilot; <br />2. Medium-term team and systems-level adoption across PAGE tools and workflows; <br />3. Long-term strategic planning for people, technology, processes, and finances.<br /><br />Finally, it highlights both benefits and risks. AI can improve productivity and reduce manual work, but it may also increase workload pressure and affect wellbeing and team culture. The central message is that successful AI adoption depends on balancing efficiency with human judgement, sustainable practices, and thoughtful change management.
Keywords
generative AI
AI adoption
responsible governance
data protection
safe experimentation
peer learning
Early Adopters Programme
PAGE adoption model
productivity
change management
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