Sort your outcomes into one of the following four categories of solution complexity.

Sort your outcomes into one of the following four categories of solution complexity.

Define Complexity

Define Complexity

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The solution is obvious and clear for everyone

An expert or some research can determine the "right" solution

No agreement on "right" solution - experimentation needed

Nobody knows what the "right" solution might be

The solution is obvious and clear for everyone

An expert or some research can determine the "right" solution

No agreement on "right" solution - experimentation needed

Nobody knows what the "right" solution might be

How to approach outcomes in each category

Different categories of complexity require appropriate approaches to ensure your outcomes are successful. The strategy we need to take when the solution to an outcome is obvious and clear should be different compared to when no obvious solution exists.

The solution is obvious and clear for everyone

In this category, there exist proven patterns and best practices on how to act.

The challenges with implementing these outcomes have little do with the outcomes themselves and more with the personal or organizational motivation to achieve them. For example, there could be a culture of having low expectations that the organization is capable of successfully implementing changes.

If issues around trust, or motivation exist that prevent these outcomes from being implemented successfully, the odds are against us attempting to achieve more complex outcomes.

An expert or some research can determine the "right" solution

No general best practice exists, but experts or methods (like the one you're reading) developed by experts exist for you to leverage to guide you towards the right outcomes.

No agreement on "right" solution - experimentation needed

It's only possible to know the right answer in hindsight after taking some action, which means the appropriate way to approach these outcomes is to try something and observe the results.

The appropriate strategy is to run multiple, safe-to-fail experiments in parallel, keeping feedback loops tight, and being open to innovation.

Nobody knows what the "right" solution might be

These outcomes might be entirely novel and require innovation to achieve. Planning, frameworks and models are premature as it's impossible to know enough about what's needed yet. Instead, acting quickly, and having real-time feedback needs will help us judge if we're on the path to achieving these outcomes.

Solutions are novel and temporary, and their value comes from revealing the shape of the problem we're dealing with, which before was impossible to know.