When One Person Becomes The Learning System
A customer success team has one person everyone turns to when a client situation becomes complicated. New hires ask her how to handle unusual requests. Managers check with her before making exceptions. Colleagues forward difficult emails because they trust her judgment more than any documented process. As long as she is available, the system appears to work.
The company has onboarding materials, process documentation, and an internal knowledge base. None of them explain how experienced employees make difficult decisions when the situation falls outside the standard workflow. The expertise never became part of the organization's learning system. It remained inside one high performer.
Expertise Doesn't Automatically Become Organizational Knowledge
Organizations often assume that expertise spreads naturally through experience. In practice, much of what makes high performers effective never reaches the rest of the team. It stays embedded in judgment calls, pattern recognition, and the contextual decisions experienced employees make every day.
This distinction sits at the center of organizational learning research. Ikujiro Nonaka and Hirotaka Takeuchi describe two forms of knowledge. Explicit knowledge can be documented, explained, and taught through manuals, courses, or procedures. Tacit knowledge develops through experience. It includes the judgment, intuition, and contextual understanding people rely on when a situation falls outside the written process.
The challenge for L&D begins when most critical expertise remains tacit. Employees may complete onboarding, follow documented procedures, and still depend on a handful of experienced colleagues whenever work becomes less predictable. The organization has information. It has not yet built shared capability.
For learning teams, the question is no longer simply: What should people know? It becomes: Which expertise still exists only inside individual employees?
High Performance Can Become A Learning Bottleneck
High performance becomes a learning bottleneck when one person's judgment consistently fills the gaps left by formal training. The organization may have onboarding, documentation, and learning resources in place, yet employees continue relying on the same colleague whenever they face an unfamiliar situation.
The pattern often reveals itself through everyday work rather than performance metrics. Employees may complete the same training and follow the same documented process, yet arrive at very different decisions when circumstances change. The difference often comes from access to experienced judgment rather than access to information.
For L&D teams, the most useful question is: How many people can make this decision confidently without asking for help?
A simple framework can help identify a knowledge bottleneck before it becomes an organizational risk. Three signals usually appear together when expertise has become a learning bottleneck:
- Employees regularly depend on the same person to handle exceptions.
- Performance slows whenever that person is unavailable.
- Training explains the process, but experienced colleagues still supply the judgment.
When these signals appear together, the challenge is rarely individual capability. It is a learning system that has documented the workflow without capturing the expertise that makes the workflow effective.
The Best Time To Capture Expertise Is Before You Need It
Many organizations begin documenting expertise only after a key employee announces a promotion, retirement, or resignation. By then, the focus has already shifted to replacing knowledge under time pressure instead of understanding how that expertise developed in the first place.
A more effective approach is to capture judgment while experienced employees are still doing the work. The most valuable insights rarely appear in formal presentations or process documents. They surface in everyday decisions: why an exception was approved, how a difficult customer conversation unfolded, or which signals led someone to choose one solution over another. When working with high performers, pay attention to:
- The signals they noticed first.
- The options they considered but rejected.
- The trade-offs they weighed before deciding.
- The situations they treated as exceptions.
- The questions they asked before reaching a conclusion.
Consider a financial services company that redesigned its onboarding program. Instead of asking senior specialists to write longer documentation, the L&D team recorded short reviews of real decisions. New employees learned not only what happened, but also how experienced colleagues weighed trade-offs, recognized patterns, and reached a conclusion.
The result was more than better documentation. It gave new employees access to the reasoning behind experienced decisions before they had to make similar ones themselves.
Designing Learning Around Knowledge Transfer
When expertise is concentrated in a few individuals, adding more courses rarely solves the problem. The priority shifts from creating new content to making experienced thinking visible and transferable. Three questions can help guide that shift:
- Where does critical judgment still depend on one person?
Look for decisions that consistently require the same expert, even when documented processes already exist. - How can that reasoning become visible?
Capture real decisions, conversations, and examples that explain not only what happened, but why experienced employees chose a particular course of action. - How will others practice using that knowledge?
Documentation alone rarely develops expertise. Scenarios, coaching discussions, and decision exercises give learners opportunities to apply the same reasoning before they face similar situations in their own work.
Knowledge transfer is most effective when it becomes part of everyday learning rather than a one-time documentation project. Each captured decision strengthens the learning system by making expertise available to people who have not yet accumulated years of experience and helps cicumvent learning bottlenecks.
Conclusion
Learning systems are often evaluated by how efficiently they deliver knowledge. Their greater challenge is ensuring that valuable expertise does not remain trapped inside a few experienced employees.
The organizations that scale learning most effectively are rarely the ones with the largest course libraries. They are the ones that consistently turn individual judgment into something the entire organization can learn from.
References:
- Argote, L. 2013. Organizational Learning: Creating, Retaining and Transferring Knowledge (2nd ed.). Springer.
- Nonaka, I., and H. Takeuchi. 1995. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford University Press.
