Training Hybrid Teams: What AI Means for L&D

Corporate learning and development (L&D) has long defined its purpose as building human capability. We design programs, run workshops, roll out digital modules — all with the goal of helping people acquire new skills and knowledge. But a recent paper by Vegard Kolbjørnsrud (BI Norwegian Business School) makes a case that should give L&D leaders pause.

The central argument of the paper – Designing the Intelligent Organization: Six Principles for Human-AI Collaboration – is that organizational success is no longer about efficiency alone. It is about intelligence: the ability of a collective — human and digital — to sense, adapt, and solve complex problems. In this framing, AI is not simply a tool for speeding up processes. It is a new kind of actor in the organization. Which means learning is no longer only about people. It is about hybrid teams of humans and machines working together.

Kolbjørnsrud sets out six principles for building intelligent organizations: addition, relevance, substitution, diversity, collaboration, and explanation. Read through an L&D lens, each principle becomes a challenge to rethink what we do:

• Addition: Intelligence grows when you add new actors, human or digital. For L&D, this means developing not only the skills of employees but also their ability to bring AI systems into their work in meaningful ways.

• Relevance: The type of intelligence must match the problem. Training should help people learn when to rely on machines, and when to lean on uniquely human strengths such as judgment, empathy, and ethical reasoning.

• Substitution: Automation frees people from routine work, but the real learning challenge is what happens next. Are employees equipped to use their freed-up capacity for higher-order, creative tasks?

• Diversity: Just as diverse human teams solve harder problems, so do hybrid teams of humans and machines with complementary capabilities. L&D needs to help employees build “fusion skills” — the ability to integrate machine insights with human perspective.

• Collaboration: Humans and AI must learn to “speak” to each other. That calls for AI literacy across the workforce, and for managers who are bilingual — equally comfortable working with people and with digital colleagues.

• Explanation: Trust in AI depends on people understanding how its outputs are generated. But explanation matters for another reason too — it helps employees learn how to use AI effectively, knowing when to rely on it and when to apply their own judgment. This is not just a technical issue. It’s a cultural one, requiring organizations to value transparency, reflection, and ethical awareness.

Put together, these principles point to a shift in how we think about learning at work. The job of L&D is no longer just skill delivery. It is capability orchestration — shaping the conditions for human and digital actors to work together intelligently. That means broad-based AI literacy, yes, but also culture-building: fostering psychological safety, trust, and a shared sense of purpose.

The uncomfortable truth is that most current L&D practices are still geared to efficiency and compliance. But if intelligence is the new frontier, then the measure of success is different. Not how many people completed a course, but whether the organization is making better judgments, adapting faster, and using more of its human potential.