People instinctively recoil when something goes wrong, and a common reaction is to contain the visibility of the failure as much as possible. While this is natural, it is very limiting for learning and continual improvement. Still, this reaction repeats itself daily in organizations. There is a better way. Google's experimentation with its self-driving car illustrates just such a way--networked learning. Chris Urmson, the Director of Self-Driving Cars at Google, provides a great example of networked learning in action when he recently discussed a situation where one of their cars encountered a woman in a wheelchair who was chasing a duck with a broom. This is certainly an extreme situation, but nonetheless one that his team could analyze and program for a proper response should it happen again. What is so powerful about this effect is that once the learning occurs it can be "pushed" to every car via the new code. In other words, the entire network learns from a situation simultaneously, thereby improving performance. Compare this to a regular driver. If an accident or near accident arises the driver may relay the incident to a few friends, but then it is lost; other than the person who experienced the situation, there is essentially no other learning that transpires. This is problematic in the workplace. Think about the power of how this could unfold for a moment. Imagine an environment where only one person learns from something, versus a workplace where everyone can harness the lessons learned. While the latter is clearly preferable, it is harder to attain. When you do, though, many positive outcomes transpire:
- A more efficient workplace
- A faster pace of learning and development
- A feeling of safety since failure is not universally frowned upon
- A new baseline is established with every collective learning, resulting in higher standards
- A willingness to innovate and take risks because there is always an upside--learning
Leaders often hold up powerful examples of success, but less frequently find ways to spread the learning from other critical incidents. Without the latter, networked learning is impossible. Next time someone sheepishly shares something that did not go as planned, consider if there are any positive lessons within the situation and how you can network that learning to drive a higher standard of performance.