Two Days in Zurich: What Talent & Learning Leaders Are Really Talking About 

Last week, I had the pleasure of spending two days in Zurich at iVentiv’s Talent Management Executive Knowledge Exchange. Senior talent leaders from a variety of global companies came together to discuss new ideas, share expertise, and compare notes on all things talent and learning. 

The big topics of AI, skills, and culture were featured heavily, with a strong focus this year on practical application. Across the event, leaders shared recent success stories, openly acknowledged where progress has been uneven, and looked ahead to potential future challenges.  

Below is a summary of my conclusions from two days of deep discussion. As the event operates under Chatham House rules, there is no reference to specific individuals or organizations here. 

 

The AI Conversation Is All About ROI 

The conversation around AI is shifting rapidly from focusing on the capability and potential of the technology, to how organizations can best apply it meaningfully, and at scale. Following an era of experimentation, talent and learning leaders are now asking how we move from isolated pilots into something that’s embedded in the way work actually gets done.  

There’s a general recognition that not all experiments with AI tools have translated into value. After the Learning Technologies conference in April, I noted that the “AI-powered everything” phase is fading and the focus from vendors was shifting more toward how AI can meaningfully improve outcomes. That shift mirrors the growing need that learning and talent functions have to focus on intent and demonstrate real value for money. 

A consistent theme from the week was the importance of keeping humans in the loop. AI is highly effective at bringing together large volumes of data and identifying patterns that would be difficult to see otherwise. What it doesn’t do (yet) is create something genuinely new without human input. Creativity, judgment, and ownership of decisions remain firmly human responsibilities. If anything, they become more important as AI becomes more embedded in day-to-day work.  

One topic that came up repeatedly was the need to create more opportunities for collective learning. Not just access to content or tools, but spaces where people can share knowledge, challenge ideas, and build on each other’s thinking. If organizations rely too heavily on AI outputs without creating space for that collective exchange, there’s a risk that creativity will plateau.   

Those making the most progress in this space are using AI to accelerate access to information, while also putting emphasis on how people come together to interpret, challenge, and amplify that information. 

 

The Skills Agenda Is Being Tested in Practice 

Skills remain a central topic, with a growing and honest acknowledgment of why progress can be slow. The question “should we keep doing this?” came up several times, and although the group largely agreed that we probably should, challenges were raised.  

There’s a practical problem that often comes with becoming “skills-based.” When you start mapping skills properly, the workload scales very quickly. In some cases, you end up with hundreds of skills, multiple proficiency levels, and an ever-growing maintenance challenge. Many organizations still aren’t aligned on what a “skill” actually is, which makes things even more complicated! There’s a very real risk that the whole thing turns into an exercise in granular analysis rather than something that really helps leaders tackle broader business challenges.  

The question of “what problem are we really trying to solve?” keeps resurfacing. For some, the goal is workforce planning, so the discussion quickly moves to skills gaps versus critical roles, and the level of transparency you give people about where they stand. For others, the goal is mobility, so the focus becomes about what “good” looks like, and what you do with information once you have it.  

Underpinning all of this is a realization that focusing on skills alone may risk ignoring aspiration. Is this just another case of humans being humans, ruining an otherwise perfect-on-paper system? Surely any system that treats people as a set of attributes rather than individuals with direction and intent risks missing the mark?  

Skills appear to be here to stay, but the debate around how, what, and why rages on! 

 

Culture Is Moving from Principle to Proof 

Culture stood out as a priority topic in many of the conversations during the week. Notably, there’s less interest in defining values than in previous years, and more focus on making them visible in behavior and measurable in practice. That includes linking culture to performance, using data to demonstrate impact, and being more explicit about expectations and consequences. 

One of the more honest reflections was how difficult it is to scale culture change. Many organizations start with a relatively small leadership group and see some progress, but then struggle to translate that progress into consistent behavior across the wider organization. 

This has led to a sharper point of view. Culture shouldn’t be something that’s owned exclusively by HR, and it isn’t something that can just sit alongside the business. It needs to be embedded in leadership and in decision making, and it must be owned by everyone. 

 

For Talent & Learning Leaders, the Hardest Problem Is Execution 

I was energized by the week’s conversations, which made it very clear that Talent and Learning aren’t short of ideas. AI, skills, and culture are well understood as strategic priorities, and while everyone is at different stages of the journey, we all came away with ideas on what to do next. 

If there’s a single takeaway, it’s that the conversation has moved on from defining the future of talent to implementing it. Many of the concepts discussed aren’t new, but lessons have been learned and ambitions are maturing. What remains difficult is connecting these ambitions to the reality of how work gets done, and executing them at scale. 

Working in large organizations with multiple business units, different levels of maturity, and competing priorities often means spending more time figuring out things like operating models, governance, and ownership in order to get things done. Working out how to do all of this at the pace of change is where the real challenge for talent and learning leaders lies.