In a fast growing company, everyone has less experience than they need for their roles, by definition. This will continue to be true as the company scales, one's role changing in a fundamental way every 3-6 months, especially when it continues to defy expectations for months and years. Ultimately, that’s all irrelevant. In Silicon Valley, we like to talk about visionary leaders making momentous decisions amid great uncertainty, but what really matters is the first derivative of understanding: how are you and your team learning from the experience as it unfolds? There are many considerations nested in this question – here are some of the most important:
How quickly are you learning? When you are operating within a tornado, speed counts for a great deal. It’s often been said that even the right decision is wrong when taken too late, and this begins with learning. If the second and third order effects of your original challenge are already on an irreversible course by the time you’ve grasped the nature of the challenge, it’s no longer the same challenge.
Are people taking the same things away from failures? In an ideal world, everyone would not only draw the same conclusions from the experience, but they would also be the correct ones. More often, the process is a lot messier, but that’s just reality – you learn together through give and take, not some mystical collective unconscious. The key is that you are unified about your next move.
Are you making meaningful abstractions, or just reacting to your immediate circumstances? Even when execution is everything, there is such a thing as being too tactical, and morale plummets when people can’t make abstractions (or they aren’t taken seriously). It’s a delicate line, because your abstractions have to be actionable and part of a continuous cycle of learning and responding.
When dissent occurs, is it productive? Just because you eventually arrive at the same takeaways doesn’t mean there is no room for disagreement. The question is whether it’s healthy and constructive, or pointed and personal. The “team of rivals” concept has gained many adherents in recent years, but it’s important to remember that it’s above all a team. Ideally, iron sharpens iron.
Three Two strikes and you’re out. In certain areas, such as distribution, you don’t get many chances to course-correct when one approach fails, so extracting the right lessons from the first failure is paramount. This is not to say that you should impose needless anxiety on these kinds of decisions, but be aware of what the stakes are.
Can you reform your model? Models can be extremely useful and necessary to consolidate your understanding of a complex world and plan accordingly. However, they can also be an especially insidious kind of blindfold. Adjusting your model, or abandoning it when necessary, can be incredibly difficult, because it requires you to first recognize and confront your inherent biases, and resist the tendency to rationalize away the model’s shortcomings.
In a hyper-growth environment, you will never have enough information, experience, or foresight. The first derivative will be the only thing that matters. We became the ultimate learning animals through many unforgiving eons of natural selection. This new evolutionary challenge of warp-speed learning and adaptation may feel significantly more abstract, but once again, it all comes down to survival.