Session Abstract

Many organisations are experimenting with AI, but struggle to turn early ideas into deployed, adopted solutions that deliver real value. This session explores the gap between proof of concept and production, focusing on the practical challenges of scaling, adoption, and trust. Drawing on experience across industries, we will examine why initiatives stall, how unmet expectations arise, and what foundational capabilities are needed to enable successful rollout. The session will also explore behavioural change, helping organisations move beyond giving prompts towards enabling people to think, experiment, and innovate with AI. We will look at how to build trust in AI outputs, address scepticism, and navigate the tension between enthusiasm and concern about its impact. Taking organisational, process, and individual perspectives, this session will provide practical insights to help move from experimentation to measurable impact. Learning outcomes - Understand why AI initiatives fail to scale beyond proof of concept - Identify the foundations required for deployment and adoption - Explore approaches to driving behavioural change and experimentation

Al Eardley

Al is an enterprise architect at the Microsoft Innovation Hub in London where he helps Microsoft's largest and most complex clients to understand how they can deploy and adopt technology solutions. Al focuses on applying and adopting technologies to deliver business solutions including AI-driven transformation, collaborating to get sh*t done, communicating effectively, automating the mundane.