Sometimes we try to tackle problems head on without clearly examining their different parts. Seen as a whole they can seem overwhelming, unmanageable, and impossible to solve. But complicated problems almost always have solutions — even if it takes multiple steps to reach them. Complex problems, on the other hand, aren’t consistently replicable. There’s an aspect of the unknown to them that makes them harder to solve.
Building a brand-new car model is one example of a complicated problem. There are always numerous mechanical issues to resolve. But like all complicated problems, this one becomes easier to manage when it’s broken down into smaller parts. In this case, creating specifications for new models helps companies automate the manufacturing process. As for complex problems, the stock market — which is fast moving, fleeting, and unpredictable — provides a perfect example. It can move for countless reasons, from an event in the news cycle to the way a company’s CEO described its quarterly earnings in a recent report.
Why Does It Matter?
Why is it important to identify the type of problem we are dealing with?
It helps us manage the pace of change. When we see a space that’s rapidly evolving — AI is a great example — we can break down its challenges faster than ever.
It allows us to identify and reuse approaches we’ve used to address other problems to solve new ones.
It helps us break down problems into smaller ones that may be easier to solve.
How I Approach Solutions
When it comes to creating solutions I start by building a framework around the challenges early in the process. I aim to create predictability around all the moving parts. I start out by seeking familiar solutions. When it comes to AI, for example, I think of bringing it to automation, just like building a car. We may not have full instructions yet, but we still know quite a lot — a car has windows, air conditioning, radio, and so on.
Complex problems are unpredictable. Yet, by grounding ourselves in simple, known facts we can reduce their complexity, transforming them into complicated ones instead.
Tips for Getting to a Solution
Here are some tips that I’ve found helpful when trying to get to a solution:
Ask yourself if you’ve seen a similar type of problem before. How did you solve it? What patterns can you reuse?
Identify what’s in your control. Ask yourself what factors are wholly in your (or your company’s) control, and which problems are solvable right now.
Eat the elephant one bite at a time. Break complex problems down into smaller ones. By solving the simple ones you’ll eventually get to a bigger picture. In the case of our AI example, even if we assume that we’ll always get new models, we can still create frameworks for adoption lifecycle, regional needs, enablement, and more.
The next time you’re staring at a problem that feels impossible, stop and ask: is this complicated, or is it complex? That single question can change your entire approach.
