The real role of learning and development today

What is learning and development really responsible for in today’s organizations? And why do so many L&D initiatives struggle to create measurable business impact?

In this episode of Learning at Large, we’re joined by Peter Manniche Riber, Senior Consultant at PeopleStrat and former Head of Digital Learning at Novo Nordisk. Peter shares a candid view on the current state of L&D, why the function often defaults to content creation instead of solving real problems, and how teams can rethink their role in a fast-changing workplace.

While AI features in the discussion, this conversation goes deeper. Peter challenges L&D to focus less on tools and more on the craftsmanship of understanding business problems, creating meaningful interventions, and proving real impact.

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Top tips for redefining the role of L&D

Don’t have time to listen now? Here’s a quick summary of what you’ll learn in this episode:

• Stop optimizing for content delivery – Many L&D teams still focus on producing courses rather than solving real business problems.
• Fall in love with the problem, not the solution – Impact comes from understanding what needs to change before designing learning.
• Avoid generic AI training – Blanket courses about generative AI rarely help people apply it meaningfully in their roles.
• Focus on fewer, high-impact initiatives – Real change often comes from deep work on specific business challenges.
• Treat AI as a tool for redesigning processes – The biggest opportunity lies in rethinking work itself, not just adding AI features.

1. Stop optimizing for content delivery

Peter argues that many L&D teams are still structured around producing and distributing learning content. Even as organizations adopt new technologies and face rapidly changing skill requirements, the underlying model of learning delivery hasn’t evolved much. For L&D to stay relevant, Peter believes teams must shift their focus away from content production and toward measurable outcomes. Instead of asking what training to build, they should ask how learning can support real performance improvements.

“It hasn’t really changed a lot as I see it. Not even with AI coming in, it’s still content focus on content delivery, information delivery, stacking up stuff in an elearning or classroom where you haven’t thought about the impact or the business impact of the activity.”

2. Fall in love with the problem, not the solution

A common trap in learning design is jumping straight to solutions. When organizations identify a need – leadership development, project management, or new technology adoption – the immediate response is often to create training. But Peter believes that this mindset leads to generic solutions that fail to address the real issue. Without understanding the underlying business problem, even well-designed programs may have little practical impact.

Instead, learning teams should spend more time diagnosing the challenge they’re trying to solve. What behaviors need to change? What outcomes should improve? What barriers currently prevent success? Answering these questions first leads to far more effective learning strategies.

“We don’t really fall in love with the problem as much. We fall in love with the solution saying, okay, we need leadership training. Why do we need leadership training is the first great question, and it’s rarely asked because it’s self explanatory. What are we trying to fix here? What’s broken?”

3. Avoid generic AI training

AI has quickly become one of the most talked-about topics in workplace learning. Many organizations are racing to educate employees about generative AI tools, often through large-scale training programs. However, Peter warns that generic AI awareness training rarely helps people use the technology effectively in their jobs. Explaining how generative AI works does little to help someone solve real challenges in their role.

Instead, learning teams should focus on context. How could AI support specific workflows? What tasks could be improved or simplified? How might individuals experiment with AI in their own roles? By grounding learning in real work scenarios, L&D can make AI adoption far more meaningful.

“Most companies are struggling with the problem of how do we make AI a profitable and secure and good thing to use for the individual in their position. That question won’t be answered by a generic elearning about what Gen AI is.”

4. Focus on fewer, high-impact initiatives

Another challenge facing learning teams is scale. L&D is often asked to support thousands of employees across many topics with limited resources. The natural result is broad, standardized programs designed to reach everyone. But this approach spreads learning efforts too thinly. When solutions must work for everyone, they often fail to truly help anyone.

Peter suggests that learning teams should prioritize fewer initiatives where they can deeply understand the problem and design meaningful interventions. These focused projects may take more time and effort, but they are far more likely to drive measurable results.

“Learning and development just kind of dies in that threshold. How do we even navigate in that? So I think it’s much better for learning and development if someone’s brave out there… to focus in on detailed projects where you actually can make a difference.”

5. Use AI to rethink work, not just training

While AI is often discussed in terms of content generation or automation, Peter believes the most exciting opportunities lie in rethinking how work itself is structured. Rather than simply layering AI onto existing processes, organizations should explore where technology can genuinely improve the way tasks are performed. In some cases, AI may handle repetitive work. In others, it may augment human decision-making or provide support at the moment of need.

This shift requires organizations to map their processes and ask fundamental questions about where human expertise adds value and where technology can help.

“If we had to reimagine these processes without human intervention, just starting with AI… when do we actually need a human being? When is it necessary with a human being? Start testing that. That’s where you can really make a difference for the business, instead of just another chat bot or just another SharePoint site that becomes an information source that you can chat with.”

About Peter

Peter is a Senior Consultant in PeopleStrat at Implement Consulting Group. Previously, he worked in enterprise learning at Novo Nordisk, where he focused on digital learning and capability development. Today, he advises organizations on topics including AI in HR, leadership development, and people development, helping companies rethink how learning supports real business impact.

Connect with Peter on LinkedIn.

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