London in late April. Two packed days. Thousands of people and conversations. And a field that is clearly in the middle of something significant.
A group of us from the community made the trip to Learning Technologies 2026 and we came back with full notebooks, buzzing heads, and a lot to bring back to you. Here are the signals that stuck.
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.
From: Tracie Cantu — Rethinking the CLO Role
L&D in an AI World with Egle Vinauskaite & Donald H. Taylor (Thursday)
Drawing on three years of industry research and findings from 20+ organisations, Egle and Donald proposed that there isn't one future for L&D, there are at least three. Their Transformation Triangle mapped the choices clearly: become a Skills Authority (rigour and validation that fuels the talent marketplace), an Enablement Partner (infrastructure for co-creation and scaling internal expertise), or an Adaptation Engine (internal consultants who fix friction in organisational systems). Each path requires different capabilities and a different idea of what L&D is actually there to do. Their provocation: "Success is no longer defined by how much content we produce, but by how much friction we remove from the system."
Rethinking the CLO Role with Tracie Cantu (Wednesday)
Tracie's session was one of the most practically useful of the conference. She reframed the CLO's job from ownership to orchestration, and introduced portfolio management as the operating model that makes it possible, balancing strategic bets, steady performers like compliance, and ruthless pruning of what no longer serves the business. Her "Yes, if..." framework for negotiating with stakeholders gave the room an immediately usable tool for building credibility without overcommitting.
From Framework to Flow with Amanda Nolen, Rushton Bradshaw, Filip Lam & Maha Gad (Wednesday)
The panel on making skills real in practice was one of the most honest conversations of the conference. Maha's Talabat case study showed what a genuinely redesigned L&D team looks like: multidisciplinary, problem-focused, and embedded in the business. Filip brought H&M's experience of replacing traditional leadership content for 7,000 managers with collaborative, AI-architected, human-delivered problem-solving. Together they made a compelling case that the skills conversation only gets interesting when you stop talking about frameworks and start talking about the actual work.
One theme that ran through the conference, and one that deserves more attention than it got, is the Social Imperative. As highlighted in the research presented by Egle Vinauskaite and Donald H. Taylor on the Future of L&D, we are moving into an era where "broadcasting" information is a zero-value activity. In an AI-saturated world where content is abundant, the meaning of information is found through human dialogue, not consumption.
The power of co-creation
We saw this principle in action through Filip Lam’s H&M case study. By discarding traditional leadership content in favor of peer coaching and collaborative problem-solving, H&M proved that social learning scales faster and more effectively than top-down curriculum. This approach builds the psychological safety and trust necessary for innovation, a point also championed by Maha Gad (Talabat), who argued that L&D’s role is to act as Behavioral Scientists, diagnosing the social rituals of a team before ever reaching for a digital tool.
Resilient networks vs. static libraries
Social learning networks are simply more resilient and adaptive to change than static training libraries. The conference brought a timely reminder about the human side of this shift: looking after ourselves as practitioners, learning from the business rather than just delivering to it, and being intentional about bringing human-centric approaches into our work even as we experiment with AI. As many speakers cautioned, the risk of this moment is that we get so focused on the technology that we forget the people, including ourselves.
The "how-to" gap
While the conference named the social imperative clearly, we missed more examples showing us the "how." We would have loved to see more concrete examples building on the radical structural shifts shared by Maha Gad and Filip Lam of how organizations deliberately design for social learning at scale. Specifically:
This is a conversation we would love to see more of next year. It’s the bridge between the "Capability Strategist" theory and the "Squad Model" reality, and it is a conversation we are committed to leading in this community.
It was a proud moment to see several of our community members, speakers and podcast guests shaping the conversation on the main stage.
Joy VerPlanck presented her session on psychological safety on Thursday, reframing it as an operational necessity and giving the room practical tools to build it intentionally, even in the hardest organisational moments. Her cases from Boeing and Pixar showed what it looks like when organisations get it right, and what it costs when they don't.
Megan Torrance led her data storytelling session on Thursday afternoon, making the case for analytics that actually change minds and decisions, not just fill dashboards. Her core argument: effective data storytelling starts not with charts, but with the right questions and trustworthy data.
Katy O'Donnell joined the Thirty Under 30 panel on scaling AI in learning on Wednesday morning, bringing a grounded and responsible perspective on what it really takes to move from AI experimentation to everyday practice and where human judgment must remain central.
Filip Lam joined the panel "From Framework to Flow" on Wednesday afternoon, sharing H&M's experience of replacing traditional leadership content for 7,000 managers with collaborative, AI-architected, human-delivered problem-solving.
Egle Vinauskaite, who recently joined us for a community webinar, was also on stage at Learning Technologies, sharing research on how AI is reshaping the foundations of the learning profession and what new futures for L&D are emerging.
Lavinia Mehedințu, who recently joined us for a community webinar on designing learning that drives real results, and it was a pleasure to continue the conversation in person with someone who is pushing experience-first, collaborative learning forward in such a meaningful way.
Anamaria Dorgo, who will soon guest on our podcast, was also part of the Learning Technologies conversation this year. We were so glad to finally connect in person with one of the most thoughtful and forward-looking voices in L&D and community-based learning.
Charles Jennings, who will join us for a webinar in June, was also part of the Learning Technologies conversation this year. Meeting him in person was a nice confirmation that our community programming is closely aligned with the voices shaping the field right now.
part of the whole trip.
The conversations that happen over a table, as well as at the floor in between the sessions, are where ideas really get tested. We talked about what L&D actually does on a Monday morning if it's not creating content, the future of our own roles and looking after ourselves in a profession that is being asked to change faster than ever, and about what it means to build a community of practice when the ground keeps shifting.
It was, in other words, a live demonstration of everything the conference said L&D needs to become: sense-making through dialogue, collective intelligence, and the kind of psychological safety that only comes from being with your people. The best part of any conference is never the slides. It's the conversations that happen around them.
We'd love to know what's still lingering for you. Bring it to the community, that's what we're here for.
Our community dinner at Learning Technologies
This article was written by Emilia Åström, Community Lead at L&D Leaders.
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