The conference had a clear throughline: L&D is moving beyond content. The job is no longer to produce content, it's to orchestrate the conditions where performance can happen. Less time building courses, more time understanding business problems, removing friction, and enabling people to do their best work.
But perhaps the most important shift of all was one of attention. The conference kept returning to one simple idea: spend time where it really matters. Understand the problem deeply before reaching for a solution. Get on the shop floor, ask questions, listen carefully, and keep asking the most important question of this moment: how can humans and AI work together, not just efficiently, but well?
The best learning experience is often a change in ritual or a better tool, not a course. Tracie Cantu illustrated this with a striking example: a pawn company had built seven different diamond valuation trainings, and employees had taken each of them multiple times, yet the problem persisted. When she asked not "what should we build?" but "what needs to change in the way people work?", the answer was clear: it was a management coaching problem, not a content problem. The solution wasn't another e-learning. It was changing the conditions of work.
From: Tracie Cantu — Rethinking the CLO Role
L&D roles are shifting toward data, engineering, and behavioral science. Maha Gad's team at Talabat is a vivid example: they moved away from the "skills" buzzword entirely and built Problem-Solving Squads, cross-functional teams including API and AI engineers connecting learning systems directly into business workflows, and behavioral scientists conducting deep UX research to understand why people aren't performing before designing any solution. Squads are organised around culture or business problems, not course categories. As Maha put it: "We don't talk about skills; we talk about solving problems. We keep the skills taxonomy in our pocket and lead with business."
From: Amanda Nolen, Maha Gad, Filip Lam, Rushton Bradshaw — From Framework to Flow
AI does the heavy lifting on content and administration, but L&D focuses on the culture, the context, the conditions. Think of AI as a powerful instrument in the orchestra. It can carry enormous weight and do things no single human could do alone. But without a conductor who understands the whole system, the people, the politics, the timing, it's just noise.
This signal came with an important nuance: experimentation matters. We are not at a point where anyone has the definitive answer to how humans and AI work best together. The organisations making progress are the ones willing to try things, learn from experience, and adjust, rather than waiting for a perfect strategy before moving.
From: Egle Vinauskaite & Donald H. Taylor — L&D in an AI World
Moving from broad taxonomies to micro-mapping specific business problems. Vodafone's story from the panel was instructive: they built a deep skills taxonomy mapped to job code level, but 75% of their people never used it. Breadth was the enemy. When they narrowed the focus to a specific business unit and mapped skills at a micro level appropriate to the actual role, they moved from a skills framework to a genuine strategic workforce plan. As Rushton Bradshaw put it plainly: precision beats completeness, every time.
From: Amanda Nolen, Rushton Bradshaw, Filip Lam & Maha Gad — From Framework to Flow
Moving from a central command centre to orchestrating learning in the flow of work, capability no longer lives in the L&D team – it lives across the whole organisation. Tracie described the shift in three stages: from Director (owning outputs and programmes), to Influencer (connecting people and problems across the business), to Catalyst (building trust and running experiments that prove the value of a new operating model). The goal is to stop managing all demand from the top down, and instead equip the organisation to meet L&D in the middle.