The new learning ecosystem: creating your own learning content

And the strategic role of L&D teams

Learning has always been a social activity – knowledge is created, tested and shared. Yet for a long time, the way organizations shape learning has been top-down: Learning & Development or HR teams develop learning content, plan learning paths and populate the LMS. That works – to some extent – but it also creates a step between the practice and the learning content. That step explains why many trainings are perceived as too generic, too far from practice or too late.

With the rise of AI, logic is changing. Not because AI replaces human judgment, but because it lowers the threshold for capturing, structuring and sharing practical knowledge. It enables what can be summarily called the democratization of e-learning – a movement in which learning is no longer a product of a small team, but a task and a resource that takes place in the middle of the organization. Employees can create their own learning content. In this blog, we’ll take you through why that’s substantially different, what surprising implications it has, and how you can get practical about it as an organization.

From centralized production to a learning ecosystem

Traditional content creation is expensive and slow. A compact L&D team can deliver good educational designs, but rarely has the capacity to serve every niche demand within an organization quickly and relevantly. This creates gaps: outdated procedures, missed applicability and a gap between what employees need and what is available.

AI changes this by combining two simple functions: (1) automatically extracting and structuring key information from documents, notes or conversations, and (2) forming that information into a didactically useful shell – learning objectives, micro-modules, checks and short reflection assignments. This allows a specialist in the field to make his or her knowledge available to colleagues in a few steps, without that person having to be an instructional designer. This is no longer fiction, but increasingly already daily practice: organizations are increasingly using AI-driven authoring and personalization to dynamically generate and distribute learning content.

Why creating your own learning content is more than just efficiency

It seems easy to sell this as an efficiency gain – faster content, less work for L&D – but the effect reaches deeper. When employees themselves contribute to learning, the relationship to knowledge changes. It becomes not a passive product to be consumed, but an active, shared possession. This has three important consequences:

1. Relevance increases. Learning content originates from concrete situations: customer cases, troubleshooting, short how-tos that you need today and adapt tomorrow. This increases learning effectiveness because examples, language and problem statements match the context of the learner exactly.
2. Knowledge retention improves. Documentation and on-the-job expertise no longer become lost tactical knowledge when people leave; they are transformed into learnable building blocks that make new colleagues operational faster.
3. Culture of continuous learning. When learning is an ongoing process – embedded in the work – motivation shifts from “have to” to “want to. This is exactly what research and practice recommendations encourage: make learning part of the daily routine, not a separate project.

This shift is not just instrumental; it has roots in classical learning theories. Vygotsky’s sociocultural perspective and Etienne Wenger’s work on communities of practice have emphasized for decades that learning is social, context-specific and grown in practice – precisely the qualities that now make decentralized, AI-supported learning development scalable.

The (renewed) role of L&D: curator, coach, governance

A common fear is that L&D will become redundant when anyone can create learning content. The opposite is true: L&D teams will have a more strategic and valuable set of tasks. Instead of producing all content themselves, they focus on:

• quality assurance and didactic frameworks (what is good learning sequencing?);
• governance and metadata (who is allowed to publish, how do you guarantee timeliness?);
• tooling and empowerment (train employees in effectively generating learning content with AI tools);
• analysis and improvement (what content works, where are learning loop holes?).

In this way, L&D teams facilitate an ecosystem in which thousands of small-scale but well-made learning interventions combine to create a coherent learning landscape. Recent work by consulting firms also shows that organizations that use AI for in-the-moment learning and coaching achieve better outcomes in adaptivity and skills development.

Practical start with self-learning content creation

If you really want to embrace this movement, it helps not to change everything at once. Start with small, measurable steps:

1. Pilot within one domain. Choose a department where knowledge changes rapidly (for example, sales or operations). Set up an AI-authoring workflow that allows experts to create their own learning content in one or two sessions.
2. Rules and constraints. Clearly establish what quality checks are needed before learning content is shared widely – peer reviews, brief validation by a subject matter expert, and clear metadata (target audience, learning objective, skills).
3. Measure and learn. Use analytics within your LMS or authoring tool to see what modules are being used, what scores employees are achieving, and where follow-up content is needed. Iterate.

Collow has concrete tools and programs to accelerate this process.

Ethical and quality questions – stay critical

Democratization also means managing risk. Without clear frameworks, outdated or erroneous learning content can spread quickly. Managed versions, review loops and simple quality indicators (source citation, publication date, author tag) are not a luxury, but the backbone of reliable learning. AI also helps here: automatic source checks, deletion alerts and version control can be built into the process.

Creating your own learning content as a strategic choice

The new learning ecosystem in which employees create their own e-learnings is not a technological trick: it is a fundamental change in organizational learning. It makes learning more realistic, faster and better embedded in work. But it also requires vision: a place for governance, a role for L&D as curator, and practical tooling choices that actually help employees share their knowledge.

Want to see specifically what this looks like in practice? Check out Collow’s resources for AI authoring and implementation – from blog articles and white papers to hands-on programs that guide teams in setting up decentralized learning flows.