{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"The Agentic Allocator","title":"Shaun Ng on the One Misdiagnosis That Explains Most LP AI Implementation Mistakes and How to Build an Investment Office That Thrives in the Post-AI World","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/dd7e85b3\"></iframe>","width":"100%","height":180,"duration":1379,"description":"Shaun Ng, founder of AI for Allocators and former Managing Director at the Cleveland Clinic Investment Office, joins The Agentic Allocator to share what three decades of capital allocation experience and over 50 newsletters on AI adoption have taught him about where LP organisations are going wrong and what they need to do differently.\nShaun's diagnosis is clear: the single biggest mistake allocators are making is misidentifying the AI challenge as a technology problem. It is not. It is the most consequential strategic transformation of their careers: a leadership challenge, a change management challenge, a cultural challenge. Every downstream mistake, from delegating AI to IT project managers to setting fixed start and end dates for implementation, flows from that one misdiagnosis.\nIn this episode, Shaun walks through the pre-AI pressures that were already straining investment offices: stakeholder demands, data complexity, talent, and explains how AI implementation maps onto each one. He makes the case for why CIOs who are not personally using AI are making a critical error, why creating the right environment matters more than choosing the right tools, and what the AI flywheel looks like when it is spinning properly. He also offers a vivid picture of what a genuinely AI native investment office looks like in four to five years. The edge will belong to organisations that start building that environment now.\nWhat You'll Learn:The three core pressures LP organisations were already facing before AI arrived: stakeholder demands, data complexity, and talentWhy every common AI mistake allocators make flows from one foundational misdiagnosis and what that misdiagnosis isWhy CIOs who encourage their teams to use AI without using it themselves are repeating a strategy that will not work this timeWhy starting with tools is the wrong first step, and what to focus on insteadWhat 'communicating your AI stance' means in practice How to build the AI flywheel: the combination of...","thumbnail_url":"https://img.transistorcdn.com/QigjduDJIqFTeeJFynBiyzGolv4eHG5zocOf8vY173o/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hZGY5/NTNkMjIzZGE0NWFj/YWEzYzY0ODU1ZTYx/NzE2Zi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}